diff --git a/docs/_static/freq-vs-bayes-thumbnail.jpg b/docs/_static/freq-vs-bayes-thumbnail.jpg deleted file mode 100644 index 112beba1..00000000 Binary files a/docs/_static/freq-vs-bayes-thumbnail.jpg and /dev/null differ diff --git a/docs/_static/sobo-vs-mobo-thumbnail.png b/docs/_static/sobo-vs-mobo-thumbnail.png deleted file mode 100644 index 2c5e9b69..00000000 Binary files a/docs/_static/sobo-vs-mobo-thumbnail.png and /dev/null differ diff --git a/docs/_static/thumbnails/BatchBO_concept_thumbnail.png b/docs/_static/thumbnails/BatchBO_concept_thumbnail.png new file mode 100644 index 00000000..73e98b3e Binary files /dev/null and b/docs/_static/thumbnails/BatchBO_concept_thumbnail.png differ diff --git a/docs/_static/thumbnails/FullyBayesian_concept_thumbnail.png b/docs/_static/thumbnails/FullyBayesian_concept_thumbnail.png new file mode 100644 index 00000000..f9d3eb56 Binary files /dev/null and b/docs/_static/thumbnails/FullyBayesian_concept_thumbnail.png differ diff --git a/docs/_static/thumbnails/SOBOMOBO_concept_thumbnail.png b/docs/_static/thumbnails/SOBOMOBO_concept_thumbnail.png new file mode 100644 index 00000000..50b755cc Binary files /dev/null and b/docs/_static/thumbnails/SOBOMOBO_concept_thumbnail.png differ diff --git a/docs/_static/thumbnails/batch_tutorial_thumbnail.png b/docs/_static/thumbnails/batch_tutorial_thumbnail.png new file mode 100644 index 00000000..7c341776 Binary files /dev/null and b/docs/_static/thumbnails/batch_tutorial_thumbnail.png differ diff --git a/docs/_static/mobo-tutorial-thumbnail.jpg b/docs/_static/thumbnails/mobo-tutorial-thumbnail.jpg similarity index 100% rename from docs/_static/mobo-tutorial-thumbnail.jpg rename to docs/_static/thumbnails/mobo-tutorial-thumbnail.jpg diff --git a/docs/_static/sobo-tutorial-thumbnail.jpg b/docs/_static/thumbnails/sobo-tutorial-thumbnail.jpg similarity index 100% rename from docs/_static/sobo-tutorial-thumbnail.jpg rename to docs/_static/thumbnails/sobo-tutorial-thumbnail.jpg diff --git a/docs/concepts.md b/docs/concepts.md index b490c673..bfaec555 100644 --- a/docs/concepts.md +++ b/docs/concepts.md @@ -3,6 +3,7 @@ ```{nbgallery} :maxdepth: 1 -tutorials/sobo-vs-mobo/sobo-vs-mobo.md -tutorials/freq-vs-bayes/freq-vs-bayes.md +curriculum/concepts/sobo-vs-mobo/sobo-vs-mobo.md +curriculum/concepts/freq-vs-bayes/freq-vs-bayes.md +curriculum/concepts/batch/SingleVsBatch_concept.md ``` diff --git a/docs/conf.py b/docs/conf.py index e2e3283c..ed75ff8d 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -218,10 +218,12 @@ html_static_path = ["_static"] nbsphinx_thumbnails = { - "tutorials/sobo-tutorial": "_static/sobo-tutorial-thumbnail.jpg", - "tutorials/mobo-tutorial": "_static/mobo-tutorial-thumbnail.jpg", - "tutorials/sobo-vs-mobo/sobo-vs-mobo": "_static/sobo-vs-mobo-thumbnail.png", - "tutorials/freq-vs-bayes/freq-vs-bayes": "_static/freq-vs-bayes-thumbnail.jpg", + "curriculum/tutorials/sobo/sobo-tutorial": "_static/thumbnails/sobo-tutorial-thumbnail.jpg", + "curriculum/tutorials/mobo/mobo-tutorial": "_static/thumbnails/mobo-tutorial-thumbnail.jpg", + "curriculum/tutorials/batch/Batch_BO_tutorial": "_static/thumbnails/batch_tutorial_thumbnail.png", + "curriculum/concepts/sobo-vs-mobo/sobo-vs-mobo": "_static/thumbnails/SOBOMOBO_concept_thumbnail.png", + "curriculum/concepts/freq-vs-bayes/freq-vs-bayes": "_static/thumbnails/FullyBayesian_concept_thumbnail.png", + "curriculum/concepts/batch/SingleVsBatch_concept": "_static/thumbnails/BatchBO_concept_thumbnail.png", } # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, diff --git a/docs/tutorials/README.md b/docs/curriculum/README.md similarity index 100% rename from docs/tutorials/README.md rename to docs/curriculum/README.md diff --git a/docs/curriculum/concepts/batch/SingleVsBatch_concept.md b/docs/curriculum/concepts/batch/SingleVsBatch_concept.md new file mode 100644 index 00000000..bfc54f1f --- /dev/null +++ b/docs/curriculum/concepts/batch/SingleVsBatch_concept.md @@ -0,0 +1,23 @@ +# Single vs. Batch Optimization + +Many optimization tasks permit experiments to be run in parallel such that observations for several parameter combinations can be collected within one iteration. For example, microplate arrays allow chemists to analyze tens to hundreds of potential compositions simultaneously. In such scenarios, it is desirable to retrieve several potential experimental designs at each optimization iteration to increase resource efficiency. In sequential optimization, the next experiment is simply the maximum of the acquisition function. However, identifying multiple candidates that are likely to be optimal for batch optimization is more challenging both conceptually and computationally. Consider the gaussian process model and calculated acquisition function (expected improvement) in the figure below. After selecting the most optimal acquisition function value $x_1$, the choice of a second point, $x_2$, to test isn't immediately obvious. Testing a point near the first may produce a similarly good value, but it is unlikely to provide useful information that will improve the model. Moving further away may improve the surrogate model, but risks using resources on poor performing candidates. + +![](batch-choices.png) + +Ideally, a set of *q* points is selected such that their joint expected improvement is maximized. This is denoted mathematically in the equation below: + +$$qEI(X) = E\left[\textrm{max}(\textrm{max}(f(x_1), f(x_2),..., f(x_q)) - f(x^*), 0)\right]$$ + +Finding the optimal joint expected improvement is computationally difficult and typically requires the use of Monte Carlo estimation methods. This estimation has become easier through the development of several notable algorithms, and trivial to utilize thanks to the inclusion of efficient versions of these algorithms in state of the art libraries like `Ax` and `Botorch`. That said, a variety alternative approaches have emerged within the literature that are less computationally demanding. These typically rely on "*fantasy models*," which utilize simulated outcomes derived from the current surrogate model predictions to preemptively update and refine the model at each selection of a batch point. Put another way, for each requested point in the batch, the model assumes an observation value at the optimal acquisition function value and refits the model before selecting the next point. Common assumption strategies include the 'Kriging believer,' which takes an optimistic view by assuming the function's mean at the point of interest, and the 'constant liar,' which assumes values pessimistically to safeguard against overestimation in pursuit of the optimization goal. Other approaches propose seeking iteratively lower modes of the acquisition function, penalize the acquisition function near already observed points, or maximize exploration beyond the optimal point. While more computationally efficient, these approaches show weaker empirical performance relative to joint expected improvement estimation. + +In estimating the optimal joint expected improvement for three points in function shown at the start of this article, the following points would have been sampled. + +![](examples_1.png) + +This is both sensical and likely what many practitioners would apply under naive assumptions as well. However, it is worth noting that batch optimization approaches can, at times, behave in unexpected ways due to implicit penalties in the computation of joint expected improvement. Consider the batch point selection for the function below. The chosen points $x_1$ and $x_2$ lie to either side of the sharp acquisition function peak rather than at the center, which reflects a balance between the maximum value and the maximum joint probability. Thus, in batch optimization, the optimal acquisition function value won't always be selected. + +![](examples_2.png) + +## Which approach is right for your problem? + +For tasks that allow experiments to be performed in parallel, batch optimization is generally preferred as it is more time and resource efficient compared with sequential optimization. That said, in the absence of per-observation model updates, it is likely some batch points will show relatively poor objective function performance. Poor performing observations may, however, improve model representation, resulting in better subsequent predictions. Sequential optimization allows the model to be updated with each observation, which potentially allows greater per trial improvement in model predictions. These advantages and disadvantages are often situation dependent, and the parallelizability of a given task is often a better selection criteria for single vs. batch optimization. diff --git a/docs/curriculum/concepts/batch/batch-choices.png b/docs/curriculum/concepts/batch/batch-choices.png new file mode 100644 index 00000000..0a17dc7f Binary files /dev/null and b/docs/curriculum/concepts/batch/batch-choices.png differ diff --git a/docs/curriculum/concepts/batch/examples_1.png b/docs/curriculum/concepts/batch/examples_1.png new file mode 100644 index 00000000..5f0effd6 Binary files /dev/null and b/docs/curriculum/concepts/batch/examples_1.png differ diff --git a/docs/curriculum/concepts/batch/examples_2.png b/docs/curriculum/concepts/batch/examples_2.png new file mode 100644 index 00000000..0b1f638f Binary files /dev/null and b/docs/curriculum/concepts/batch/examples_2.png differ diff --git a/docs/tutorials/freq-vs-bayes/Comparison.jpg b/docs/curriculum/concepts/freq-vs-bayes/Comparison.jpg similarity index 100% rename from docs/tutorials/freq-vs-bayes/Comparison.jpg rename to docs/curriculum/concepts/freq-vs-bayes/Comparison.jpg diff --git a/docs/tutorials/freq-vs-bayes/FREQvsBAYES.pdf b/docs/curriculum/concepts/freq-vs-bayes/FREQvsBAYES.pdf similarity index 100% rename from docs/tutorials/freq-vs-bayes/FREQvsBAYES.pdf rename to docs/curriculum/concepts/freq-vs-bayes/FREQvsBAYES.pdf diff --git a/docs/tutorials/freq-vs-bayes/Frequentist.jpg b/docs/curriculum/concepts/freq-vs-bayes/Frequentist.jpg similarity index 100% rename from docs/tutorials/freq-vs-bayes/Frequentist.jpg rename to docs/curriculum/concepts/freq-vs-bayes/Frequentist.jpg diff --git a/docs/tutorials/freq-vs-bayes/FullyBayesian.jpg b/docs/curriculum/concepts/freq-vs-bayes/FullyBayesian.jpg similarity index 100% rename from docs/tutorials/freq-vs-bayes/FullyBayesian.jpg rename to docs/curriculum/concepts/freq-vs-bayes/FullyBayesian.jpg diff --git a/docs/tutorials/freq-vs-bayes/LengthScale.jpg b/docs/curriculum/concepts/freq-vs-bayes/LengthScale.jpg similarity index 100% rename from docs/tutorials/freq-vs-bayes/LengthScale.jpg rename to docs/curriculum/concepts/freq-vs-bayes/LengthScale.jpg diff --git a/docs/tutorials/freq-vs-bayes/freq-vs-bayes.md b/docs/curriculum/concepts/freq-vs-bayes/freq-vs-bayes.md similarity index 100% rename from docs/tutorials/freq-vs-bayes/freq-vs-bayes.md rename to docs/curriculum/concepts/freq-vs-bayes/freq-vs-bayes.md diff --git a/docs/tutorials/sobo-vs-mobo/HVI.jpg b/docs/curriculum/concepts/sobo-vs-mobo/HVI.jpg similarity index 100% rename from docs/tutorials/sobo-vs-mobo/HVI.jpg rename to docs/curriculum/concepts/sobo-vs-mobo/HVI.jpg diff --git a/docs/tutorials/sobo-vs-mobo/MOBO.jpg b/docs/curriculum/concepts/sobo-vs-mobo/MOBO.jpg similarity index 100% rename from docs/tutorials/sobo-vs-mobo/MOBO.jpg rename to docs/curriculum/concepts/sobo-vs-mobo/MOBO.jpg diff --git a/docs/tutorials/sobo-vs-mobo/SOBO.jpg b/docs/curriculum/concepts/sobo-vs-mobo/SOBO.jpg similarity index 100% rename from docs/tutorials/sobo-vs-mobo/SOBO.jpg rename to docs/curriculum/concepts/sobo-vs-mobo/SOBO.jpg diff --git a/docs/tutorials/sobo-vs-mobo/sobo-vs-mobo.md b/docs/curriculum/concepts/sobo-vs-mobo/sobo-vs-mobo.md similarity index 100% rename from docs/tutorials/sobo-vs-mobo/sobo-vs-mobo.md rename to docs/curriculum/concepts/sobo-vs-mobo/sobo-vs-mobo.md diff --git a/docs/curriculum/tutorials/batch/Batch_BO_tutorial.ipynb b/docs/curriculum/tutorials/batch/Batch_BO_tutorial.ipynb new file mode 100644 index 00000000..3aecc5b7 --- /dev/null +++ b/docs/curriculum/tutorials/batch/Batch_BO_tutorial.ipynb @@ -0,0 +1,280 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Noisy Batch Optimization\n", + "\n", + "Many high performing materials suffer from poor corrosion resistance, which limits their use in real world applications. Anti-corrosion coatings are a common solution that can be adapted to protect a given material in a number of harsh environments. You have the task of designing an anti-corrosion coating for a new material that needs to withstand a relatively high temperature corrosive environment. You decided to simulate the corrosive environment in the lab and test a number of different coatings to see which one performs the best. Based on the lab space, you find that you can test up to six coatings in a single test, allowing three replicates of two coating compositions to be tested simultaneously.\n", + "\n", + "You believe Bayesian optimization can help you in this task and decide to put together an optimization script using Honegumi to help solve this problem.\n", + "\n", + "You identify the following tunable parameters for this problem:\n", + "\n", + "| | **Parameter Name** | **Bounds** |\n", + "|------|--------------------|-------------|\n", + "| x1 | Resin Fraction | [0, 1] |\n", + "| x2 | Inhibitor Fraction | [0, 1] |\n", + "| x3 | Insulator Fraction | [0, 1] |\n", + "| x4 | Stabilizer Fraction | [0, 0.1] |\n", + "| x5 | Coating Thickness | [0.1, 10] |\n", + "\n", + "Looking through the literature, you identify constraints on the relative fractions of each component that should reduce the size of the search space and make the optimization task easier. Notably, you find that the best materials keep the `Resin Fraction` greater than the `Inhibitor Fraction` and the `Insulator Fraction` and decide to incorporate this as a constraint.\n", + "\n", + "A dummy objective function that emulates the results of the corrosion experiment has been constructed in the code cell below. To simulate sample variability, random noise is added to the output of the function on call. Although we can easily find optimal values using the equations, we will pretend that the objective function is unknown and use a Bayesian optimization approach to find the optimal set of input parameters instead." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "def simulate_corrosion(x1, x2, x3, x4, x5):\n", + " \"\"\"\n", + " Calculate the corrosion damage based on the input parameters.\n", + "\n", + " Args:\n", + " x1 (float): the fraction of resin used in the coating formulation\n", + " x2 (float): the fraction of inhibitor used in the coating formulation\n", + " x3 (float): the fraction of insulator used in the coating formulation\n", + " x4 (float): the fraction of stabilizer used in the coating formulation\n", + " x5 (float): the coating thickness\n", + "\n", + " Returns:\n", + " dict: the measured corrosion damage and uncertainty\n", + " \"\"\"\n", + " score = float(\n", + " 1/(np.exp(-40*(x1-0.42)**2) + np.exp(-24*(x1-0.75)**2) + 0.1) +\n", + " 1/(np.exp(-30*(x2-0.22)**2) + np.exp(-1000*(x2-0.22)**2) + np.exp(-800*(x2-0.42)**2) + 0.15) +\n", + " 1/(np.exp(-25*(x3-0.27)**2) + np.exp(-1000*(x3-0.27)**2) + np.exp(-150*(x3-0.37)**2) +1) +\n", + " 1/(np.exp(-400*(x4-0.08)**2) +1) + 0.5*x4 +\n", + " 1/(np.exp(-(x5-8)**2) + 0.05*x5 + 0.3)\n", + " ) - 3.18\n", + " \n", + " return (score, abs(np.random.normal(0.0, 0.1)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Applying Honegumi\n", + "\n", + "We will now use the [Honegumi website](https://honegumi.readthedocs.io/en/latest/) to generate a script that will help us optimize the coating parameters. From the description, we observe that our problem is a **single objective** optimization problem with a **constraint on the fractional sum of coating components** and an **ordering constraint** on the relative fractions of each component. As there is room for several samples to be tested in parallel, **batch optimization** could make the approach more efficient. Lastly, it is expected that the reults will be noisy, so you decide to use a **Fully Bayesian** model to make the optimization process more robust.\n", + "\n", + "![Honegumi Selections for The Problem Statement](batch-honegumi-selection.jpg)\n", + "\n", + "The Honegumi generated optimization script will provide a framework for our optimization campaign that we can modify to suit our specific problem needs. In the code sections below, we will make several modifications to this generated script to make it compatible with our problem." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Modifying the Code for Our Problem\n", + "\n", + "We can modify this code to suit our problem with a few simple modifications. Wherever a modification has been made to the code, a comment starting with `# CHANGE:` has been added along with a brief description of the change.\n", + "\n", + "NOTE: This problem uses a fully Bayesian GP model, which is more computationally demanding than the standard GP model. As such, the number of trials has been limited for demonstration purposes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from ax.service.ax_client import AxClient, ObjectiveProperties\n", + "\n", + "from ax.modelbridge.factory import Models\n", + "from ax.modelbridge.generation_strategy import GenerationStep, GenerationStrategy\n", + "\n", + "\n", + "obj1_name = \"corrosion_score\" # CHANGE: update objective name\n", + "\n", + "# CHANGE: remove the branin dummy objective function, we will use the above function\n", + "\n", + "total = 1.0 # CHANGE: update total component fraction\n", + "\n", + "gs = GenerationStrategy(\n", + " steps=[\n", + " GenerationStep(\n", + " model=Models.SOBOL,\n", + " num_trials=6,\n", + " min_trials_observed=3,\n", + " max_parallelism=5,\n", + " model_kwargs={\"seed\": 999},\n", + " model_gen_kwargs={},\n", + " ),\n", + " GenerationStep(\n", + " model=Models.FULLYBAYESIAN,\n", + " num_trials=-1,\n", + " max_parallelism=3,\n", + " should_deduplicate=True, # CHANGE: reduce duplicate suggestions\n", + " model_kwargs={\"num_samples\": 1024, \"warmup_steps\": 1024},\n", + " ),\n", + " ]\n", + ")\n", + "\n", + "ax_client = AxClient(generation_strategy=gs, random_seed=42) # CHANGE: add random seed for reproducibility\n", + "\n", + "ax_client.create_experiment(\n", + " parameters=[\n", + " {\"name\": \"x1\", \"type\": \"range\", \"bounds\": [0.0, total]}, # CHANGE: update parameter\n", + " {\"name\": \"x2\", \"type\": \"range\", \"bounds\": [0.0, total]}, # CHANGE: update parameter\n", + " {\"name\": \"x3\", \"type\": \"range\", \"bounds\": [0.0, total]}, # CHANGE: add new parameter\n", + " {\"name\": \"x5\", \"type\": \"range\", \"bounds\": [0.1, 10.0]}, # CHANGE: add new parameter\n", + " ],\n", + " objectives={\n", + " obj1_name: ObjectiveProperties(minimize=True),\n", + " },\n", + " parameter_constraints=[\n", + " f\"x1 + x2 + x3 <= {total}\", # CHANGE: update composition constraint\n", + " \"x1 >= x2\", # CHANGE: update order constraint\n", + " \"x1 >= x3\", # CHANGE: add order constraint\n", + " ],\n", + ")\n", + "\n", + "batch_size = 2\n", + "\n", + "for _ in range(10): # CHANGE: decrease number of iterations\n", + "\n", + " parameterizations, optimization_complete = ax_client.get_next_trials(batch_size)\n", + " for trial_index, parameterization in list(parameterizations.items()):\n", + " \n", + " # CHANGE: pull all added parameters from the parameterization\n", + " x1 = parameterization[\"x1\"]\n", + " x2 = parameterization[\"x2\"]\n", + " x3 = parameterization[\"x3\"]\n", + " x4 = total - (x1 + x2 + x3) # CHANGE: update composition constraint\n", + " x5 = parameterization[\"x5\"]\n", + "\n", + " results = simulate_corrosion(x1, x2, x3, x4, x5)\n", + " ax_client.complete_trial(trial_index=trial_index, raw_data=results)\n", + "\n", + "best_parameters, metrics = ax_client.get_best_parameters()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Show the Best Parameters\n", + "\n", + "After our optimization loop has completed, we can use the model to find the best parameters and their corresponding strength value. These will be our optimial set of parameters that we use in the 3D printer going forward." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 05-08 17:02:39] ax.modelbridge.base: Leaving out out-of-design observations for arms: 16_0\n", + "Sample: 100%|██████████| 2048/2048 [00:28, 72.86it/s, step size=3.60e-01, acc. prob=0.903]\n" + ] + }, + { + "data": { + "text/plain": [ + "(19,\n", + " {'x1': 0.444310612880253,\n", + " 'x2': 0.23909903307319696,\n", + " 'x3': 0.2647932814508338,\n", + " 'x5': 10.0},\n", + " ({'corrosion_score': 0.3585147123389083},\n", + " {'corrosion_score': {'corrosion_score': 0.00032350561483642887}}))" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ax_client.get_best_trial()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plotting Optimization Performance\n", + "\n", + "We can plot the performance of our optmization loop to see how the optimization task progressed as a function of iteration count." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[WARNING 05-08 17:24:10] ax.service.utils.report_utils: Column reason missing for all trials. Not appending column.\n" + ] + }, + { + "data": { + "image/png": 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+Ph7Xr1+Hv79/ubd6pVIp/P39ce3aNcTHx9dwhEREpNYzgStXrsTKlSsBFCeA3t7eMDAwUKmTm5uLjIwMvPjii9qLkojqlaysLACAg4NDhfXs7e1V6hMRUc1RKwm0srJCly5dAACpqamwt7dH8+bNVeoYGBjAycmJU8QQNWLm5uYAgJSUlArrXbhwQaU+ERHVHImo6OG+Cnh6euKLL75Ax44dtR1TnZGUlARHR0ckJiYqk18iejqFQgGZTAZ9fX0kJyeXeUtYoVCgXbt2UCgUuHTpEkcIExFVkbp5i8bPBE6bNg0tWrQoc19WVha2bNmi6amJqJ6TSqUICAhAWloa/P39Sw38UCgU8PPzQ1paGgICApgAEhHVAo17AnV1dXHy5En07Nmz1L5ffvkFPXv2RGFhYZUDrE3sCSTSnEKhgI+PDw4dOgSZTAZ/f3/Y29sjJSUF69evR1paGry8vDhPIBGRlqibt2g8WXRFuePDhw85TyBRIyeVShEVFYWIiAjI5XIEBwcr99nY2CAsLAyBgYFMAImIaolaSWBaWhpSU1OV27/99hsePnyoUic3Nxdr166FTCbTSoBEVH9JpVIsWrRIudY41w4mIqo71EoCN27ciJCQEEgkEkgkEgQEBJSqU9JDWDKVDBGRVCqFh4dHbYdBRESPUSsJfOmll+Do6AghBF566SUsX74czz77rEodAwMDODo6ok2bNtqMk4iIiIi0SK0ksFOnTujUqROA4l7BkSNHwsLColoCIyIiIqLqo/HAkClTpij/ff78edy6dQsuLi4wNjbWSmBEREREVH2qtHbwli1b0Lp1a3Tu3Bn9+vXD+fPnARTfNl63bp3a5/v999/h5eUFmUwGQ0NDmJubw83NDdu2bavU8RkZGZg6dSosLS1hZGQENzc3xMTEqB0HERERUUOncRK4e/duTJ06Fd26dcOqVatUpozp1q0bvv76a7XPmZ2djWeeeQbLly9HdHQ0tmzZgjZt2mDy5MlYtmxZhcfm5eVh4MCBiImJwcqVK7F//360bNkSw4YNww8//KB2LEREREQNmcaTRXfr1g3PPfcc1q9fj8LCQkilUpw9exbdunXD/v37ERAQgPT0dK0E6erqiqtXryItLa3cOnK5HLNmzcKJEyfg5uYGACgoKICzszNMTEyQkJCgdrucLJqIiIjqixpbNu7vv//G+PHjy9xnbm6OzMxMTU9diqWlJfT0Kn58MSoqCh06dFAmgACgp6eHSZMm4fTp01pLSImIiIgaAo0HhhgZGeHOnTtl7ktPT4eZmZnGQRUVFaGoqAi3b9/G7t278d1332HVqlUVHpOYmIi+ffuWKu/atSuA4uzY1ta23OMzMjJw8+ZNlbLk5GQNoiciIiKq+zROAt3d3bFq1SqMHTu21L5NmzZVaWLYgIAArFmzBgCgr6+Pzz77DK+99lqFx2RmZsLc3LxUeUnZ03om5XI5QkJCNIyYiIiIqH7ROAkMDg5Gnz590LNnT0yYMAESiQR79+7FkiVL8OOPP+L06dMaB7VgwQL4+/sjIyMDBw4cwBtvvIGcnBy8++67FR4nkUg02gcUJ57jxo1TKUtOToa3t3el4yYiIiKqLzROArt3747Dhw8jICAA77zzDgAoVxCJjo6Go6OjxkHJZDLl2sMjRowAALz33nuYMmUKWrRoUeYxFhYWZfb2ZWVlAUCZvYSPs7KygpWVlcYxExEREdUnGieBAODp6Ym///4bKSkpuHHjBiwtLdG+fXttxabUs2dPfPnll7hw4UK5SaCTkxPOnTtXqrykrCpJKREREVFDU6XJoks4ODigd+/e1ZIAAkBsbCx0dHRgb29fbh0fHx/8888/KlPBFBQUYNu2bejVqxdatWpVLbERERER1Uca9QQmJydDKpXCzs4OQPEt148++gh//PEHWrZsiYCAADz//PNqn3fGjBkwNTVFz5490bJlS9y6dQu7d+/Grl27MG/ePGUvoJ+fHzZv3oyUlBRlDNOnT8fq1asxbtw4hIeHw8rKCnK5HOfPn8exY8c0uUwiIiKiBkutJDAzMxPDhw/HL7/8AgDo06cPdu/erbwtXGLnzp04deqUcnqWynJzc8PGjRuxefNmZGdnw8TEBM7Ozti6dSsmTZqkrFdYWIjCwkKVVUoMDAwQExOD+fPnY/bs2Xjw4AFcXFxw+PBh9O/fX604iIiIiBo6tVYMeeedd7BmzRq88847MDU1xcqVK2Fra4uMjAzs2LEDXbt2xW+//YaJEyeiV69e+Oqrr6oz9mrHFUOIiIiovlA3b1GrJ/Dbb79FSEiIcjRwt27dMHDgQGzYsAG9evUCAPTu3Rvvvfce/u///k+D8ImIiIioJqg1MOTKlSvo3r27crtnz54AgI4dO6rU69y5M5dpIyIiIqrD1EoC8/LyYGhoqNxu0qQJgOJVPR4nlUpRWFiohfCIiIiIqDqoPUVMWStvPG01DiIiIiKqW9SeIuadd95B8+bNAUA5Onfu3Llo1qyZsk52drZWgiMiIiKi6qFWEiiTyXD58mVcvnxZWWZnZ4e0tLQy6xIRERFR3aRWEpiamlpNYRARERFRTdLKsnFEREREVL8wCSQiIiJqhJgEEhERETVCTAKJiIiIGiG1p4ih+kmhUCA+Ph5ZWVkwNzeHu7s7pFJpbYdFREREtYRJYAOnUCgQHh4OuVyO69evK8ttbGwwc+ZMBAUFMRkkIiJqhLSaBCYkJOC3335D//790alTJ22emjSgUCjg7e2N6OhoyGQyhIaGwsHBASkpKYiMjERwcDASEhIQFRXFRJCIiKiR0TgJ9Pf3R0FBATZt2gQA+OqrrzBx4kQIIaCvr4/Y2Fi4ublpK07SQHh4OKKjo+Hr64vIyEiVRC8oKAj+/v7YsmULIiIisGjRolqMlIiIiGqaxgNDYmNjMWDAAOX2+++/j6FDh+L3339H7969sXz5cq0ESJpRKBSQy+WQyWSlEkAAkEqliIyMhEwmg1wuh0KhqKVIiYiIqDZonARev34ddnZ2AICrV68iKSkJ7733Hrp27Yo5c+bg7NmzWguS1BcfH4/r16/D39+/3Fu9UqkU/v7+uHbtGuLj42s4QiKihkehUCAuLg579+5FXFwcv2BTnabx7WCpVIqHDx8CKE44mjRpAldXVwCAmZkZsrOztRIgaSYrKwsA4ODgUGE9e3t7lfpERKQ+DsKj+kjjnsCOHTti69atuHfvHtavX68y5ciVK1fQokULrQVJ6jM3NwcApKSkVFjvwoULKvWJiEg9JYPwgoODoa+vj9DQUGzfvh2hoaGQSqUIDg6Gj48PewWpztE4CXznnXewc+dONG/eHEePHsWbb76p3BcTE4OuXbtqJUDSjLu7O6ytrREZGVnuB49CoUBkZCRsbGzg7u5ewxESETUMjw/CS05OxuLFizFhwgQsXrwYycnJ8PX1xaFDhxAREVHboRKp0DgJHDduHOLj4/HBBx8gLi4Oo0aNUu5r3bo1AgICtBIgaUYqlSIgIABpaWnw9/cvlQgqFAr4+fkhLS0NAQEBvE1BRKQBDsKj+qxK8wS6uroqnwN8XEhISFVOS1oSFBSEhIQEbNmyBXFxcfD394e9vT1SUlKwfv16pKWlwcvLC4GBgbUdKhFRvVQyCK/k1m9ZSgbhBQcHIz4+Hh4eHjUbJFE5uGJIAyaVShEVFYWIiAjI5XIEBwcr99nY2CAsLAyBgYHsBSQi0hAH4VF9plYSaG9vj6ioKDg7O6Nt27aQSCTl1pVIJE8dlEDVTyqVYtGiRQgMDOTawUREWsZBeFSfqZUE9u/fH6ampsp/V5QEUt0ilUp5C4KISMseH4RX3jQwHIRHdZVaSeDGjRuV/y5ZLo6IiKixKhmEFxwcDH9//1KDQx4fhBcWFsY7MFSn8JlAIiKiKuAgPKqvmAQSERFVAQfhUX3FJJCIiKiKOAiP6iMmgURERFrCQXhUn2i8YggRERER1V8aJYEPHz7EggUL8Msvv2g7HiIiIiKqARolgU2aNMEnn3yCnJwcbcdDRERERDVA49vBnTp1wsWLF7UZCxERERHVEI2TwMWLF2PZsmVaXRru+PHjmD59Ojp27AhjY2PY2tpi9OjRlbrtvGnTJkgkkjJf169f11qMRERERA2BxqODN27ciAcPHqBTp07o2rUrbGxsVJaRk0gk2L9/v1rn/OKLL5CZmYk5c+agc+fOuHnzJj766CO4urriu+++w4ABAyoVV8eOHVXKLCws1IqDiIiIqKHTOAn8888/oa+vD1tbW2RmZiIzM1NlvybrCq9evRpWVlYqZcOGDUO7du2wfPnySiWBjo6O6N69u9ptExERETUmGieBqampWgyj2JMJIACYmJigc+fOuHz5stbbIyIiImqs6vxk0Xfu3MGvv/5aqV5AABg5ciRu3ryJZs2awcPDA6GhoXB0dHzqcRkZGbh586ZKWXJyskYxExEREdV1VUoC8/LysGnTJsTFxeHWrVuQy+V49tlnsX//fjg5OcHe3r7KAc6aNQs5OTlYuHBhhfWsra2xcOFCuLq6wtTUFOfOnUN4eDhcXV0RHx8PZ2fnCo+Xy+UICQmpcrxERERE9YFECCE0OfDWrVvw9PREUlISrK2tcePGDZw5cwbdunXDtGnTYGhoCLlcXqXgSkYgf/7553jjjTfUPj41NRVOTk4YMGDAUweplNcT6O3tjcTERHTp0kXt9omIiIhqSlJSEhwdHSudt2jcEzh//nxkZ2fj7Nmz6Nq1K/T19ZX7PD09ERERoempAQAhISFYtmwZ3n//fY0SQABo06YN+vTpg1OnTj21rpWVVZnPJBIRERE1RBongQcPHkRERAS6deuGwsJClX2tW7fGlStXNA4qJCQES5cuxdKlS7FgwQKNzwMAQgjo6HCJZCIiIqLHaZwd3b17F3Z2dmXuUygUKCgo0Oi8YWFhWLp0KRYtWoQlS5ZoGh4A4OLFi4iPj4erq2uVzkNERETU0GjcE9i2bVucPHmyzFG7p0+fRocOHdQ+50cffYTg4GAMGzYMXl5epW7jliRzfn5+2Lx5M1JSUpSJ6KBBg9CvXz907dpVOTBkxYoVkEgkCAsL0+AKiYiIiBoujZPAiRMnIiIiAo6OjvDy8gJQPEH0mTNnsHLlyqeO5i3LgQMHAABHjhzBkSNHSu0vGcNSWFiIwsJCPD6mxcnJCbt27cKHH36I3NxcWFlZYcCAAVi8eDHat2+vySUSERERNVgajw5WKBR44YUX8N1338HMzAy3b9+GpaUlMjMzMWzYMBw4cKDeP4un7igbIiIiotpSY6ODpVIpoqOjsWvXLhw6dAg3btyApaUlRo4cifHjx9f7BJCIiIioIavSZNESiQTjx4/H+PHjtRUPEREREdUAjbvr5s2bh++//x65ubnajIeIiIiIaoDGSWBkZCSGDx8OMzMzeHp64v3330dCQgKKioq0GR8RERERVQONk8CsrCycOnUKwcHB0NHRwbJly+Dm5gYLCwv4+PhUeck4IiIiIqo+GieBEokEPXr0wIIFCxATE4Pbt2/jyJEj6NWrF/bv34/Zs2drM04iIiIi0qIqDQwBgCtXruDo0aM4duwYYmJikJGRgbZt22LQoEHaiI+IiIiIqoHGSeDs2bNx9OhR/Pfff7CwsMCAAQMQFhaGwYMHo02bNloMkYiIiIi0TeMkcPXq1TAyMkJgYCDefvttWFpaajMuIiIiIqpGGj8T+MEHH6Bfv374/PPPYW1trXw+MDY2Fvn5+dqMkYiIiIi0TOMk8J133kF0dDSysrJw7NgxDBs2DMePH8fQoUNhbm6O4cOHazNOIiIiItKiKg8MkUql8PDwwDPPPANbW1s0bdoUMTEx+P7777URHxERERFVA42TwFu3biEmJgbHjh3DsWPHkJaWBiEEOnfujNmzZ3N0MBEREVEdpnES2LJlSwCAra0tBg4ciEGDBmHgwIGwtrbWWnBEREREVD00TgI///xzDBo0CO3bt9dmPERERERUAzROAgMCArQZBxERERHVoCoNDMnKysInn3yCmJgYZGZmwtLSEoMGDcLcuXNhZmamrRipnlEoFIiPj0dWVhbMzc3h7u4OqVRa22ERERHRYzSeIiY9PR3dunXD+++/jzt37kAmkyE7OxthYWHo1q0brl69qs04qR5QKBQICwuDTCaDp6cnxo4dC09PT9jZ2SEsLAwKhaK2QyQiIqL/0TgJXLBgAXJzc5GQkICkpCQcPXoUSUlJSEhIQG5uLhYsWKDNOKmOUygU8Pb2RnBwMPT19REaGort27cjNDQUUqkUwcHB8PHxYSJIRERUR2icBB45cgTLli1Djx49VMp79OiB0NBQHD58uMrBUf0RHh6O6Oho+Pr6Ijk5GYsXL8aECROwePFiJCcnw9fXF4cOHUJERERth0pERESoQhJ4584dtGnTpsx9bdu2xZ07dzQ9NdUzCoUCcrkcMpkMkZGRpZ7/k0qliIyMhEwmg1wuZ28gERFRHaBxEti2bVscOnSozH2HDx9G27ZtNQ6K6pf4+Hhcv34d/v7+5Q4AkUql8Pf3x7Vr1xAfH1/DERIREdGTNB4dPG3aNAQFBaGoqAhTpkyBjY0Nrl27hm3btuHzzz9HeHi4NuOkOiwrKwsA4ODgUGE9e3t7lfpERERUezROAufNm4eUlBSsWrUKq1evVpYLITBjxgy88847WgmQ6j5zc3MAQEpKSoX1Lly4oFKfiIiIao9ECCGqcoLz588jNjYWmZmZsLCwwIABAxrMKiJJSUlwdHREYmIiunTpUtvh1FkKhQIymQz6+vpITk4u85awQqFAu3btoFAocOnSJc4bSEREpGXq5i0aPxNYokOHDnj99dexcOFCvP7662jfvj1+/PFHDBgwoKqnpnpCKpUiICAAaWlp8Pf3LzXwQ6FQwM/PD2lpaQgICGACSEREVAdUacWQ8ty8eRM//PBDdZya6qigoCAkJCRgy5YtiIuLg7+/P+zt7ZGSkoL169cjLS0NXl5eCAwMrO1QiYiICNWUBFLjI5VKERUVhYiICMjlcgQHByv32djYICwsDIGBgewFJCIiqiOYBJLWSKVSLFq0CIGBgVw7mIiIqI5jEkhaJ5VK4eHhUdthEBERUQWqPDCEiIiIiOoftXoCu3btWql6d+/e1SgYIiIiIqoZaiWB5ubmkEgkT61nYWHBZeOIiIiI6jC1ksC4uLhqCqPY8ePHsW3bNpw4cQKXL19G8+bN0b17dwQHB+P5559/6vEZGRmYP38+Dh48iAcPHsDZ2RnLli3DwIEDqzVuIiIiovqmTj0T+MUXXyA1NRVz5sxBdHQ0Vq5ciYyMDLi6uuL48eMVHpuXl4eBAwciJiYGK1euxP79+9GyZUsMGzaMcxYSERERPUGtnsDLly/jmWeeUbuR9PR02NraPrXe6tWrYWVlpVI2bNgwtGvXDsuXL69wFZL169cjMTERJ06cgJubGwDA09MTzs7OmD9/PhISEtSOm4iIiKihUqsn8Nlnn8WcOXOQnJz81LoKhQK7d++Gi4sLNmzYUKnzP5kAAoCJiQk6d+6My5cvV3hsVFQUOnTooEwAAUBPTw+TJk3C6dOnkZ6eXqkYiIiIiBoDtXoCjx49irfeegurVq1Cjx494OnpiW7dusHKygpNmjRBVlYWUlJScOrUKRw5cgQ5OTmYM2cO3nrrLY0DvHPnDn799denrkWcmJiIvn37liovGdGclJRUYW9kRkYGbt68qVJWmWSXiIiIqD5SKwns27cvzp49i8OHD+PLL7/EZ599htzcXOWIYSEEAMDe3h6zZs3C66+/DhsbmyoFOGvWLOTk5GDhwoUV1svMzIS5uXmp8pKyzMzMCo+Xy+UICQnRPFAiIiKiekSjFUOGDx+O4cOHQ6FQ4Pfff8fVq1eRm5sLS0tLdOrUqVLP/1XG4sWLsX37dnz++eeVGh1c0fQ1T5vaJiAgAOPGjVMpS05Ohre3d6ViJSIiIqpPqrRsnFQqRY8ePbQVi4qQkBAsW7YM77//Pt54442n1rewsCizty8rKwsAyuwlfJyVlVWZzyQSERERNUR1aoqYEiEhIVi6dCmWLl2KBQsWVOoYJycnnDt3rlR5SZmjo6NWYyQiIiKqz+pcEhgWFoalS5di0aJFWLJkSaWP8/HxwT///KMyFUxBQQG2bduGXr16oVWrVtURLhEREVG9VKeSwI8++gjBwcEYNmwYvLy8cOrUKZVXCT8/P+jp6eHSpUvKsunTp6NLly4YN24cduzYgWPHjuGll17C+fPnERERURuXQ0RERFRnVemZQG07cOAAAODIkSM4cuRIqf0lo48LCwtRWFio3AYAAwMDxMTEYP78+Zg9ezYePHgAFxcXHD58GP3796+ZCyAiIiKqJyTi8UyKVCQlJcHR0RGJiYno0qVLbYdDREREVC518xat3A7Ozc1Feno6CgoKtHE6IiIiIqpmVUoCY2Nj4ebmhqZNm8LOzg5//vkngOIJnvfu3auVAImIiIhI+zROAo8fP44hQ4bg4cOHePfdd1FUVKTcZ2lpiU2bNmkjPiIiIiKqBhongcHBwRgxYgR+++03LFu2TGWfs7Mzfv/996rGRkRERETVROPRwb/99ht2794NoPSSbC1atEBGRkbVIiMiIiKiaqNxT6Cenh4UCkWZ+zIyMtC0aVONgyIiIiKi6qVxEtijRw9s3bq1zH3ffPMN3NzcNA6KiIiIiKqXxreDg4KCMHToUPj4+MDX1xcSiQQJCQnYsGEDvvnmG8TGxmozTiIiIiLSIo2TwEGDBmHz5s2YO3cu9u/fD6B4apjmzZtj06ZN6NOnj9aCJCIiIiLt0igJLCwsREpKCkaOHImxY8fixIkTuHHjBiwtLeHu7g5jY2Ntx0lEREREWqRREiiEQOfOnXHgwAEMHz4cAwcO1HZcRERERFSNNBoYoqenB2tra5UJoomIiIio/tB4dPD48eOxZcsWbcZCRERERDVE44EhLi4u2LVrFwYMGIAxY8bAxsam1KTRY8aMqXKARERERKR9GieBvr6+AID09HTExcWV2i+RSFBYWKhxYERERERUfTROAjkPIBEREVH9pXES2L9/f23GQUREREQ1SOMksMS9e/dw8uRJZGZmwtLSEq6urlw3mIiIiKiOq1IS+OGHHyIkJAQPHjyAEAIAYGxsjJCQELz99ttaCZCIiIiItE/jJHDLli2YP38+hg8fjqlTp6JVq1a4evUqNm/ejHnz5qFFixaYPHmyNmMlIiIiIi3ROAn85JNPMGHCBGzbtk2lfNy4cZg0aRI++eQTJoFEREREdZTGk0X/888/mDRpUpn7Jk2ahL///lvjoIiIiIioemmcBBoaGiIrK6vMfVlZWTA0NNQ4KCIiIiKqXhongX379sXSpUtx9epVlfLr168jNDQU/fr1q3JwRERERFQ9NH4mcPny5ejduzfatWuHgQMHwsbGBteuXcPx48chlUqxd+9ebcZJRERERFqkcU9gly5dcObMGYwePRpnzpzBxo0bcebMGXh7e+P06dPo3LmzNuMkIiIiIi2q0jyB7du3x86dO7UVCxERERHVEI17AhUKBXJycsrcl5OTA4VCoXFQRERERFS9NE4CX331Vfj7+5e5b8aMGZg5c6bGQRERERFR9dI4CYyNjcULL7xQ5r5Ro0YhJiZG46CIiIiIqHppnATeuHEDNjY2Ze6ztrbG9evXNQ6KiIiIiKqXxklg8+bNkZycXOa+5ORkNG3aVOOgiIiIiKh6aZwEenp64v/+7/9KrRqSlZWF8PBwDBgwoMrBEREREVH10DgJXLp0KW7evIlnn30WAQEBeP/99zFz5ky0b98eN2/eREhIiEbnvXfvHubPn48hQ4agRYsWkEgkWLp0aaWO3bRpEyQSSZkv3p4mIiIiekTjeQI7dOiAn376CW+//TbWrVuHwsJC6Orqon///vj444/RoUMHjc6bmZmJtWvXwtnZGd7e3oiMjFT7HBs3bkTHjh1VyiwsLDSKh4iIiKghqtJk0c7OzoiJiUFubi5u374Nc3NzNGnSpEoB2dnZ4fbt25BIJLh165ZGSaCjoyO6d+9epTiIiIiIGrIqJYElDA0NYWhoiFu3bkFPTw96epqfViKRaCMkIiIiIqqAWs8EJiYmYtu2baXKd+3aBWtra7Rs2RJmZmYIDQ3VWoCaGDlyJHR1dWFubo4xY8YgMTHxqcdkZGQgKSlJ5VXe6GciIiKi+k6tLrsVK1YgNTUVkyZNUpadO3cOkydPhr6+PkaPHo1///0XISEhePbZZ/HKK69oPeCKWFtbY+HChXB1dYWpqSnOnTuH8PBwuLq6Ij4+Hs7OzuUeK5fLNR7MQkRERFTfqJUEnjlzBq+99ppK2RdffIHCwkIcOXIEffr0QVFREQYPHox169bVeBI4bNgwDBs2TLndr18/eHl5wcnJCcHBwdi/f3+5xwYEBGDcuHEqZcnJyfD29q6ucImIiIhqjVpJ4LVr19C+fXuVsu+++w5OTk7o06cPAEBHRwf+/v6YPXu29qKsgjZt2qBPnz44depUhfWsrKxgZWVVQ1ERERER1S61ngksKCiAoaGhcjsrKwsXL15E7969Veo988wzuHfvnnYi1AIhBHR0NJ4SkYiIiKjBUSszsrOzw59//qnc/umnnwAAvXr1Uql3584dmJmZaSG8qrt48SLi4+Ph6upa26EQERER1Rlq3Q5+4YUXsGLFCjz33HOwtrbG8uXLYWBggBEjRqjUO3PmDOzs7DQO6vDhw8jJyVH2Jv7111/45ptvAAAjRoyAkZER/Pz8sHnzZqSkpCjbGjRoEPr164euXbsqB4asWLECEokEYWFhGsdDRERE1NColQTOmzcP33zzDTw9PQEU32YNCwtDixYtlHWEENixY0eVBlTMnDkTly5dUm7v3r0bu3fvBlDcs9emTRsUFhaisLAQQghlPScnJ+zatQsffvghcnNzYWVlhQEDBmDx4sWlnmUkIqLapVAoEB8fj6ysLJibm8Pd3R1SqbS2wyJqNCTi8SyqEnJycvD1118jKysLbm5upZ4HzMjIwNatWzFy5EiNl46rK5KSkuDo6IjExER06dKltsMhImoQFAoFwsPDIZfLVdZ1t7GxwcyZMxEUFMRkkEgD6uYtai/tYWxsjGnTppW738rKCu+88466pyUiokZAoVDA29sb0dHRkMlkCA0NhYODA1JSUhAZGYng4GAkJCQgKiqKiSBRNdPKsnFERESVER4ejujoaPj6+iIyMlIl0QsKCoK/vz+2bNmCiIgILFq0qBYjJWr4OG8KERHVCIVCAblcDplMVioBBACpVIrIyEjIZDLI5XIoFIpaipSocWASSERENSI+Ph7Xr1+Hv79/ubd6pVIp/P39ce3aNcTHx9dwhESNC5NAIiKqEVlZWQAABweHCuvZ29ur1Cei6sEkkIiIaoS5uTkAICUlpcJ6Fy5cUKlPRNWDSSAREdUId3d3WFtbIzIystzn/RQKBSIjI2FjYwN3d/cajpCocanS6OCff/4ZO3bswKVLl5Cbm6uyTyKRICYmpkrBERFRwyGVShEQEIDg4GD4+/uXGhyiUCjg5+eHtLQ0hIWFcYoYomqmcRK4ceNG+Pn5wdzcHO3bt4eBgYHKfjXnoCYiokYgKCgICQkJ2LJlC+Li4uDv7w97e3ukpKRg/fr1SEtLg5eXFwIDA2s7VKIGT+MkcMWKFXjppZewefPmUgkgERFRWaRSKaKiohAREQG5XI7g4GDlPhsbG4SFhSEwMJC9gEQ1QOMk8NKlS/j888+ZABIRkVqkUikWLVqEwMBArh1MVIs0TgI7deqEGzduaDMWIiJqRKRSKTw8PGo7DKJGS+PRwcuXL0d4eDjS09O1GQ8RERER1QCNewJXr16NO3fuoH379nBxcYGFhYXKfolEgv3791c5QCIiIiLSPo2TwD///BO6urqwsrLC1atXcfXqVZX9EomkysERERERUfXQOAlMTU3VYhhEREREVJO4YggRERFRI1SlFUMUCgW2bNmCmJgYZGZmwtLSEoMGDcKkSZM4zL8SFAoFp0egUvi+ICKimqBxEnjnzh0MHDgQv/76K4yNjWFtbY0TJ05g586dkMvliImJgampqTZjbTAUCgXCw8Mhl8tx/fp1ZbmNjQ1mzpyJoKAg/tFvhPi+ICKimqTx7eCFCxfi/Pnz2LVrF+7du4f//vsP9+7dw9dff43z589j4cKF2oyzwVAoFPD29kZwcDD09fURGhqK7du3IzQ0FFKpFMHBwfDx8Sl3cXVqmPi+ICKiGic0ZGtrKz7++OMy93300UfC1tZW01PXGYmJiQKASExM1No5Q0NDBQDh6+sr8vPzVfbl5+cLX19fAUCEhYVprU2q+/i+ICJ15efni9jYWLFnzx4RGxtb6rODGh918xaNbwffvHkTXbt2LXOfs7Mzbt26pempGyyFQgG5XA6ZTIbIyEjE/yHFkZOP15DCqtsGmJ7phfCtRbjTtBC6Orq1FS5VQCL53+vxf5dX9pQ6RUWF+GBLIZp3CIXTwAVYvVtXua+YFC5DNuBAQht8sDkPxm0LoavL90VdYSAFRvYFbK1qOxJqLPjoCGmLxkmgra0tfv75ZwwcOLDUvvj4eLRq1apKgTVE8fHxuH79uvIW36lEIGLzk7V0AaMAAMCHW2s8RKoVukCzpQCAeZ9VUMckBADw9ic1EhSpoeVa4PRmQGZd25FQQ1fy6Eh0dDRkMhlCQ0Ph4OCAlJQUREZGIjg4GAkJCYiKimIiSE+l8TOBL7/8MpYvX46PP/4YmZmZAIDMzEysXLkSy5cvx/jx47UWZEORlZUFAHBwcKjlSIhIm25kAmPnAw/zajsSaujCw8MRHR0NX19fJCcnY/HixZgwYQIWL16M5ORk+Pr64tChQ4iIiKjtUKkekAghhCYH5uXlYfTo0fj+++8hkUigp6eHgoICCCEwdOhQ7N+/H/r6+tqOt0YlJSXB0dERiYmJ6NKlS5XPFxcXB09PT4SGhmLx4sXY+C2w6uvS9a5du4ZrV6/i2fbt0bRp0yq3S9olxP9ej/9bnbInth8+fIiMjJswNW0Gk6amyvIn3b9/D/fv34e5uTn09Q1q9qKpTA/zgex7j7anjgI2BD9+K59IexQKBWQyGfT19ZGcnFxmT59CoUC7du2gUChw6dIl9gY2MurmLRrfDjYwMMCRI0fw3XffITY2FpmZmbCwsMDAgQMxePBgTU/boLm7u8Pa2hqRkZEICgrCtBekmPaCap3iX2BX2CgUSPrmEvj72/ApFLqQyXpC/87TPtgd0VShwKWzfF/UFQ8eAr2nA3/8W7y96QDQozMQMK5246KG6clHisoilUrh7++P4OBgxMfHw8PDo2aDpHqlyiuGDB06FOHh4Vi3bh3Cw8OZAFZAKpUiICAAaWlp8Pf3LzXdh0KhgJ+fH9LS0hAQEMBvcI0E3xf1l1ETIOoDwLzZo7I5HwI//15rIVEDVtlHiuzt7VXqE5WnSiuGkPqCgoKQkJCALVu2IC4uDv7+/rC3t0dKSgrWr1+PtLQ0eHl5ITAwsLZDpRrE90X91dYW+Op9YNibQFERUFAIvBgI/LKVI4ZJu8zNzQEAKSkpFda7cOGCSn2i8qj1TKC9vT2ioqLg7OyMtm3bQlLBgy8SieSpb9S6TtvPBJZQKBSIiIiAXC7HtWvXlOU2NjYICAhAYGAge3saIb4v6rcPtgDzHxvd3csR+GEtYFC/H42mOoTPBNLTVOszgf3791cuBde/f/8Kk0Aqn1QqxaJFixAYGMg1YkmJ74v67d3JwNm/ga+PFm8nJAJvfgCs4eJJpCUlj44EBwfD398fkZGRKp8Njz86EhYWxs8NeiqNRwc3BtXVE0hEDdP9B4DbNCDxsZsgaxYAM8bUXkzUsCgUCvj4+ODQoUOQyWTlPjrCeQIbpxobHUxERKpMjICoD4Eevo+mjnljBeDUDnAre4ElIrVIpVJERUUpHx0JDg5W7rOxsUFYWJjWHx1RKBQ1dneiJtuiKowOPn78OHbv3q3cvnHjBkaMGAFra2v4+vri4cOHWgmQiKg+afcMsGPZo7kCFQXFE0lf40qapCUlj45cunQJsbGx2LNnD2JjY3Hp0iUsWrRIa0mTQqFAWFgYZDIZPD09MXbsWHh6esLOzg5hYWGlZjKoL23RIxongcHBwfjrr7+U2/Pnz8dPP/2E3r1745tvvsEHH3yg0Xnv3buH+fPnY8iQIWjRogUkEgmWLl1a6eMzMjIwdepUWFpawsjICG5uboiJidEoFiIiTQx3B5bNfLR97RYwLhDI598x0iKpVAoPDw+MGTMGHh4eWu/98/b2RnBwMPT19REaGort27cr5ygMDg6Gj4+PVpKzmmyLniA01KJFC/Htt98KIYRQKBSiadOmYvXq1UIIIT744APRqVMnjc578eJF0axZM9GvXz/h7+8vAIglS5ZU6tiHDx8KR0dH0bp1a7Ft2zbx/fffi9GjRws9PT0RFxendiyJiYkCgEhMTFT7WCJq3IqKhPB5Vwg8/+g1K7y2oyKqnNDQUAFA+Pr6ivz8fJV9+fn5wtfXVwAQYWFh9aqthk7dvEXjJNDAwED8+OOPQgghTp06JXR0dMTly5eFEEL88MMPwtjYWKPzFhUViaKiIiGEEDdv3lQrCVy9erUAIE6cOKEsUygUonPnzqJnz55qx8IkkIiq4u59ITq9qJoIbthf21ERVSw/P19YW1sLmUxWKil7vI5MJhM2Njbl1qlrbTUG6uYtGt8OtrKywn///QcAOHbsGOzs7NC6dWsAxbd0Ne2WlkgkGk89ExUVhQ4dOsDNzU1Zpqenh0mTJuH06dNIT0/X6LxERJpoaly8ooip8aOymeHAmaTai4noaUqWp/P393/q8nTXrl1DfHx8vWiLStN4dPCwYcOwYMECJCUlYdOmTZgyZYpy3z///IM2bdpoIz61JCYmom/fvqXKu3YtHpaXlJQEW1vbMo/NyMjAzZs3VcqSk5O1HyQRNSod2gDbwoAX3i7ezssHxswvXlHEigs6UB1Uk8vTcSm82qVxErh8+XKkpaVh3bp16NmzJxYtWqTct2PHDvTu3VsrAaojMzOzzGVySsoyMzPLPVYulyMkJKTaYiOixmtUP2DpDGDp2uLtKzeAl4KAo3JAyom6qI6pyeXpuBRe7dL4drClpSWOHDmCu3fv4tixYyr/MbGxsfjkk0+0EqC6nraUXXkCAgKQmJio8tq3b181REhEjdFif2DUYzcqfvgVmL+y9uIhKo+7uzusra0RGRlZ7ohchUKByMhI2NjYwN3dvV60RaVpnAQ+7uHDh7h27ZpybkBTU1Po69f8gpkWFhZl9vaVdB9X9A3CysoKXbp0UXm1a9eu2mIlosZFRwfYGga0lz0q+3QnsC269mIiKkvJ8nRpaWnw9/cvlZw9vjxdQEBAlaamqcm2qLQq3Yg4ceIEAgMDcerUKRQVFUFHRwe9e/dGeHi4yuCMmuLk5IRz586VKi8pc3R0rOmQiIiUmpkUryjSa2rxEnMA8Or7QBd74LmOtRoakYqgoCAkJCRgy5YtiIuLK3d5usDAwHrVFj1B02HIJ0+eFAYGBqJly5YiICBALFu2TMycOVO0bNlSNGnSRJw6dUrTUyupO0WMXC4XAFTaVigUokuXLqJXr15qt88pYoioOuyJUZ02xm6kEDdv13ZURKry8/NFWFiYsLGxEQCULxsbGxEWFqbV6Vpqsq2GTN28RSKEEJokj0OGDEF2djZiY2NhbPxo/oOcnBx4enrCzMwM3333nUaJ6eHDh5GTk4N79+5h+vTpGDduHF566SUAwIgRI2BkZAQ/Pz9s3rwZKSkpsLOzAwDk5eXh+eefx927dxEeHg4rKyvI5XIcOHAAx44dQ//+/dWKQ92FmImIKmuRHHh/w6PtgT2BI58BehwoQnUM1w6uP9TNWzT+uDl16hQ2bNigkgACgLGxMebNmwc/Pz9NT42ZM2fi0qVLyu3du3cr1ym+ePEi2rRpg8LCQhQWFuLxHNbAwAAxMTGYP38+Zs+ejQcPHsDFxQWHDx9WOwEkIqpOIa8Bv/wDHDlRvB1zGliwGlgxp3bjInpSyfJ0Da0tqkISWFhYCAMDgzL3NWnSBIWFhRoHlZqa+tQ6mzZtwqZNm0qVt2zZEps3b9a4bSKimqCrC+xYBnSfDFz43zz2H2wFnu8EvDykdmMjosZB49HBzs7O+OKLL8rct2bNGjg7O2scFBFRY2BmCuz7CDBq8qhseijw53+1FxMRNR4a9wQGBQXB29sbzz33HCZNmgQbGxtcu3YNO3bswO+//8459oiIKsGpHbBxCfDye8XbDx4CPu8CZ7YA5s1qNzYiatg0TgJfeOEFbNu2DfPnz8e8efOU5ba2tti2bRtGjRqllQCJiBq6lwYDv/wNrNhSvH0hHZiwCDj0afFtYyKi6qBREpifn4+4uDj06dMHly9fxvnz55GZmQkLCwt06NChwpU5iIiotPcDgF//AY6dLt7+7iQQ/CXw/qzajYuIGi6NngnU09PDyJEj8d9//0EikaBjx45wd3dHx44dmQASEWlATw/4ajnQptWjsuUbgT0xtRcTETVsGvUE6ujooHXr1rh796624yEiarQsmgNRHwC9pwO5ecVlU5YCDq0VyL7GedqISLs0Hh3s5+eH1atXV2kqGCIiUuXSAYhc/Gg7Jxfo/nIaPAd5Y+zYsfD09ISdnR3CwsJKrbNaFQqFAmFhYZDJZPD09KzWtoiobtB4YIi+vj7Onz+PTp064YUXXoCNjY3KrWCJRIK33npLK0ESETUmE4YBpxMLsfKr4lEhhXoOaD/0NBZPOIuLF1IQGRmJ4OBgJCQkICoqqso9dQqFAt7e3oiOjoZMJkNoaCgcHByQkqL9toioDtF0fTqJRFLhS0dHR9NT1xlcO5iIasuSpWEC7Y+rrDG85Mviffn5+cLX11cAEGFhYVVuKzQ0VAAQvr6+pdZo1XZbRFR9amzt4MeXdStPyZq+9RXXDiai2qBQKCCTyaBrYAOdLr/g8o1Hd1lsrQCJBJBA4OrVdEAIPPNMa0h0JJDgf/tKXgB0dFS3VfZLAAiBpKRESCSAk1MX6OroPNr3P0IU4ffff4cQAs899xwkEo2fJCpFqgeMHwLMeklrpyRqtGpk7eDc3Fy89957CAgIQJ8+fTQ5BRERlSM+Ph7Xr19HaGgAho+VoI8/kJdfvC89o6SWBNBtDQBIvVaV1iSAvhOA4ilqyqYDSLsBAE4nVaWtsv38O9CqBeDjqf1zE1H5NPo6Z2hoiP3796OoqEjb8RARNXpZWVkAAAcHB3TvDKxZUMsB1YCAcOA2J5wgqlEaDwxxcXFBYmIi+vXrp814iIgaPXNzcwBASkoKAGDKSKBd6+JeOCEAAaCoCDh67BiOfn8Ur854DW3b2hfvE4/qlPy76PHyJ/ZfupSGnTt3wr1PX7i59S6uX8b3+1MJp3DyxAm8OG4cnmn9jFau8+ZtYNvh4n9fzwTe/RRYH6yVUxNRJWj8TOBPP/2EyZMnY/Pmzejfv7+246oT+EwgEdWGkmcC9fX1kZycXOaIXIVCgXbt2kGhUODSpUsaj9qtybaeJATgNQc4fOJR2dHVwKBeWjk9UaOjbt6i8dO9AQEBuH//PgYMGABLS0s4OTmha9euypezs7OmpyYiatSkUikCAgKQlpYGf3//UnP0KRQK+Pn5IS0tDQEBAVVKymqyrSdJJMCXCwATo0dlr74P3H+gtSaIqAIa3w62sLCApaWlNmMhIqL/CQoKQkJCArZs2YK4uDj4+/vD3t4eKSkpWL9+PdLS0uDl5YXAwMB61daTZNZAxGxgVkTxdupVYNEXwKfvaL0pInqCxreDGwPeDiai2qRQKBAREQG5XI5r1x4NAbaxsUFAQAACAwO11jNXk209qagI8HgN+Om34m2JBIhfD7h1rZbmiBosdfMWJoEVYBJIRHVBTa7nW1trB/97Cej6yqOpcDq1BX7bDhjoV3vTRA1GjcwTWCIrKwuffPIJYmJikJmZCUtLSwwaNAhz586FmZlZVU5NRET/I5VK4eHh0eDaelx7OyBkBhC0qnj774vAsvVA2MwaD4Wo0dB4YEh6ejq6deuG999/H3fu3IFMJkN2djbCwsLQrVs3XL16VZtxEhFRA/fOJKBbx0fb4ZuAP/6ttXCIGjyNk8AFCxYgNzcXCQkJSEpKwtGjR5GUlISEhATk5uZiwYJGMLspERFpjZ4esCEY0NMt3i4oBKaHAgUFtRsXUUOlcRJ45MgRLFu2DD169FAp79GjB0JDQ3H48OEqB0dERI2Lc3sgcMqj7V//AT7eXnvxEDVkGieBd+7cQZs2bcrc17ZtW9y5c0fTUxMRUSO22B/o2ObR9pK1xQNHiEi7NE4C27Zti0OHDpW57/Dhw2jbtq3GQRERUeNloA+sX1w8VQwAPMwD/JeVvZwdEWlO49HB06ZNQ1BQEIqKijBlyhTY2Njg2rVr2LZtGz7//HOEh4drM846SwiBe/fu4e7du1AoFOCMO1RdJBIJpFIpTE1N0bRpU0hK/kISNUC9nYHZLwOffVW8/dNvwJq9wMwXazcuooZE4yRw3rx5SElJwapVq7B69WpluRACM2bMwLvvvquVAOuygoICpKen48GD4jWO9PT0oKOjwz/OpHVCCBQWFuLhw4e4d+8ejIyMYGtrCz29Ks3yRFSnvR8AfPtj8SoiADD/M8CrT/EqI0RUdRr/BZFIJFizZg3efvttxMbGIjMzExYWFhgwYADat2+vzRjrrNu3b+PBgwdo1qwZrKys+AeZql1BQQEyMjJw584d3L59Gy1atKjtkIiqjYkRsHYBMOSN4u37D4DXlwOHVj66VUxEmqty1tKhQwd06NBBG7HUO/fv34euri5sbGzY+0c1Qk9PDzY2Nrh//z7u37/PJJAavMGuwPQXgA3fFm8fPgFsiwYme9VuXEQNgVoDQ27fvo2xY8fi4MGD5dY5ePAgxo4di8zMzCoHV9cJIaCnp8cEkGqURCKBrq4unz+lRuPDuYC1xaPtuR8DNxr+nxiiaqdWEhgZGYk//vgDw4YNK7fOsGHDcO7cOZXnBIlIu/jFgxoTM1NAHvRoO+sO8OaHtRcPUUOhVhL41Vdf4dVXX63w2Tc9PT28+uqr+Pbbb6scHBEREQD4eALjBj3a/voosC+u1sIhahDUSgL//fdfdO/e/an1unXrhn//5YKPRESkPZ/PK+4VLDHz/4Dbd2svHqL6Tq0ksKCgAFKp9Kn1pFIpFAqFxkERoFAoEBcXh7179yIuLq5Wf56nTp3CuHHjYGNjA319fVhbW+PFF1/EyZMnVeotXboUEokEt27dqqVIa0+bNm0wderU2g6DqEFraQF8+vaj7euZwLyVtRcPUX2nVhJoY2ODv/7666n1kpKSYG2t2URO9+/fx9y5c9GqVSs0adIELi4u+Oqrr5563KZNmyCRSMp8Xb9+XaNYaoNCoUBYWBhkMhk8PT0xduxYeHp6ws7ODmFhYTWeDH7++edwd3fHlStXsGLFChw7dgwffvgh0tPT0adPH6xatapG4yGixm2yFzCs96Pt9fuBYwm1Fw9RfabWFDH9+/eHXC6Hn59fuT2CCoUCX3zxBTw9PTUKaMyYMThz5gzCw8PRvn177NixA6+88gqKioowYcKEpx6/ceNGdOzYUaXMwsKinNp1i0KhgLe3N6KjoyGTyRAaGgoHBwekpKQgMjISwcHBSEhIQFRUVKV6ZKsqPj4ec+fOxYgRIxAVFaXyLOj48ePh4+ODOXPm4LnnnoO7u3u1x6OO3NxcGBoa1nYYRKRlEgnw5XuA48vF8wYCwKvvA4m7AGP+yhOpR6jhjz/+EFKpVHh5eYn09PRS+9PT08WIESOEvr6++PPPP9U5tRBCiEOHDgkAYseOHSrlgwcPFq1atRIFBQXlHrtx40YBQJw5c0btdsuTmJgoAIjExMQy96ekpIiUlBSttRcaGioACF9fX5Gfn6+yLz8/X/j6+goAIiwsTGttVsTLy0vo6uqKy5cvl7k/LS1N6OrqipEjRwohhFiyZIkAIH799Vfh4+MjmjZtKkxNTcXEiRNFRkaGyrExMTGif//+wtzcXDRp0kQ888wzYsyYMSInJ0dZJy8vT4SFhYkOHToIfX19YWlpKaZOnVrqXHZ2dsLLy0vs2bNHuLi4CAMDAxEYGChcXFxEnz59SsVdUFAgWrVqJXx8fNRuKz8/X8ybN0+0bNlSGBoaCnd3d5GQkCDs7OzElClT1Pr5VoW233tE9c2qXULg+UevOR/WdkRU3+Tn54vY2FixZ88eERsbW+rvbn30tLzlSWolgUIIsXbtWqGnpyf09fWFm5ubmDBhgpgwYYJwc3MT+vr6Qk9PT6xbt07twIUQwt/fX5iYmAiFQqFSvmPHDgFAxMfHl3tsfU8C8/PzhbW1tZDJZOW+EfPz84VMJhM2NjbV/mYtKCgQRkZGolevXhXW69mzpzAyMhIFBQXKJNDOzk7MmzdPfPfdd+Ljjz8WxsbG4rnnnlPGfPHiRdGkSRMxePBgsW/fPhEXFye2b98uJk+eLG7fvi2EEKKwsFAMGzZMGBsbi5CQEHH06FERGRkpbG1tRefOncWDBw+UMdjZ2QkbGxthb28vNmzYIGJjY8Xp06fFypUrBQDx77//qsQcHR0tAIhvv/1W7bamTJkiJBKJmDdvnvj+++/Fxx9/LGxtbYWpqSmTQKIaVFgoRB+/R0mgpLsQJ/6o7aioPsjPzxehoaHC2tpaAFC+bGxsRGhoaL1OBqs9CRRCiBMnToiRI0cKY2NjIZFIhEQiEcbGxuKFF14QJ0+e1OSUQgghXF1dRY8ePUqVl1zUmjVryj22JAls2bKl0NHREWZmZsLHx0ecO3euUm3fuHFDJCYmqrz27dtXY0lgbGysACBCQ0MrrFfSWxgbG6uVdstz/fp1AUCMHz++wnovv/yyACBu3LihTALfeustlTrbt28XAMS2bduEEEJ88803AoD4/fffyz3vzp07BQCxZ88elfIzZ84IAEIulyvL7OzshK6urjh//rxK3Vu3bgl9fX2xYMEClfKXXnpJtGzZUvllo7Jt/f333xVeH5NAopr1z0UhDNweJYKdXhTiYV5tR0V1WX5+vhgxYoQAIGQymQgNDRXbt28XoaGhQiaTCQDCy8ur3iaC6iaBag0MKeHm5oYDBw7g7t27uH79Oq5fv467d+9i//79cHV11eSUAIDMzEyYm5uXKi8pq2gVEmtrayxcuBCRkZGIjY1FWFgYzpw5A1dXV/zxxx9PbVsul8PR0VHl5e3trfG1qCsrKwsA4ODgUGE9e3t7lfq1Tfxv1YrHJy+eOHGiSp2XXnoJenp6iI2NBQC4uLhAX18fM2bMwObNm3HhwoVS5z148CCaN2+OUaNGoaCgQPlycXGBtbU14uLiVOp37dq11JrVFhYWGDVqFDZv3oyioiIAxave7N+/H76+vspnHCvbVkn85V0fEdWsDm2ApTMebf99EVi2vtbCoXogPDwc0dHR8PX1RXJyMhYvXowJEyZg8eLFSE5Ohq+vLw4dOoSIiIjaDrVGaJQEKg/W0YGVlRWsrKygo1OlUylVtBJCRfuGDRuGZcuWYeTIkejXrx9mzZqFn376CRKJBMHBwU9tNyAgAImJiSqvffv2aXIJGilJdFNSUiqsV5IwlZUsa5OlpSWMjIxw8eLFCuulpqbCyMhIJZ4nR4br6enBwsJCmcQ7ODjg2LFjsLKywqxZs+Dg4AAHBwesXPlorocbN24gOzsb+vr6kEqlKq/r16+XmobGxsamzPimT5+O9PR0HD16FACwc+dO5OXlqUznUtm2SuIv7/qIqOa9Owno9thYwPBNwB+cppbKoFAoIJfLIZPJEBkZWWqApVQqRWRkJGQyGeRyeaOY6q5OdV88nig8rqTXS93Ep02bNujTpw9OnTr11LolyWxtcXd3h7W1NSIjIxEUFFTm6F+FQoHIyEjY2NhU+2hcXV1deHp64siRI7hy5Qpat25dqs6VK1fwyy+/YPjw4dDV1VWWX79+Hba2tsrtgoICZGZmqiRKffv2Rd++fVFYWIizZ8/i888/x9y5c9GyZUuMHz8elpaWsLCwwJEjR8qMr2nTpirb5X1BGDp0KFq1aoWNGzdi6NCh2LhxI3r16oXOnTsr61S2rZL4y7s+Iqp5enrA+sVAd1+gsBAoKAT8woBTG4v3EZWIj4/H9evXERoaWu4MG1KpFP7+/ggODkZ8fDw8PDxqNsgapp3uOy1xcnLC33//jYKCApXyc+fOAQAcHR3VPqcQQmu9lNVJKpUiICAAaWlp8Pf3L/UNRKFQwM/PD2lpaQgICKiRKWLee+89CCEQEBCAwsJClX2FhYWYOXMmhBB47733VPZt375dZfvrr79GQUFBmb9Murq66NWrl3Kt6V9//RUAMHLkSGRmZqKwsBDdu3cv9erQoUOlrkFXVxeTJ0/Gvn378NNPP+Hs2bOYPn26Sp3KtlUSf3nXR0S1w6UDEOj7aPuXv4GPt5dfnxqn+vrYVXWqU9+TfHx8sG7dOuzZswcvv/yysnzz5s1o1aoVevXqpdb5Ll68iPj4eAwaNOjpleuAoKAgJCQkYMuWLYiLi4O/vz/s7e2RkpKC9evXIy0tDV5eXggMDKyReNzd3fHpp59i7ty56NOnD9544w3IZDKkpaVh9erVSEhIwKefforevXurHLd3717o6elh8ODBSEpKwuLFi+Hs7IyXXnoJAPDll1/i+PHj8PLygkwmw8OHD7FhwwYAUP5fjR8/Htu3b8eIESMwZ84c9OzZE1KpFFeuXEFsbCxGjx4NHx+fSl3H9OnTERERgQkTJsDQ0FDlvaVOW506dcKkSZPw6aefQiqVYtCgQUhMTMSHH34IU1PTclonopqw2B/YGwv8k1q8vWQt4O0BtLerzaioLqlrj13VCdU4SEUjgwcPFmZmZmLt2rXi+PHj4tVXX1UZWSqEENOnTxe6uroiNTVVWTZw4EAREhIioqKiRExMjPj0009Fq1atRNOmTSs9QvhJNT1PoBDFI5fCwsKEjY1NqaHrYWFhtTJi6eTJk+LFF18ULVu2FHp6esLKykqMGTNGnDhxQqVeyejgX375RYwaNUqYmJiIpk2bildeeUXcuHFD5Xw+Pj7Czs5OGBgYCAsLC9G/f3/llC0lFAqF+PDDD4Wzs7No0qSJMDExER07dhSvvfaa+O+//5T1SuYJrEjv3r0FADFx4sQy91e2rby8PPHOO+8IKysr0aRJE+Hq6ipOnjzJeQKJ6oCffyueKqZktHBf/+KpZIiEqHtTsVWHGpkipjrdu3dPvPnmm8La2lro6+uLrl27ip07d6rUmTJligAgLl68qCybO3eu6Ny5s2jatKnQ09MTrVq1EpMmTSo1bYg6aiMJLNEQJ7Ek7WESSFS22StUJ5GW767tiKguedqiDJMnT67RRRm0Td0kUCLE/+b4oFKSkpLg6OiIxMREdOnSpdT+ki7jkucHiGoK33tEZbv/oHhJuUvXirdNjICkrwGZZsvZUwOjUCjg4+ODQ4cOQSaTlfvYVU0tz6ptT8tbnlSnngkkIiKqChMjYO1CYOgbxdv3HwCvvV+E+WN+xO3bWTA3N4e7u3u9/ANPVSeVShEVFYWIiAjI5XKVKeRsbGwQFhaGwMDARvP+YBJIREQNyhBXYNooYOOB4u0jJ3VwZEckkFU8ZNjGxgYzZ84sdzouatikUikWLVqEwMBAxMfHIyur8X45YBJIREQNTvgbCuw4dA95RcUjPA3br8VHfj64de0vREZGIjg4GAkJCfX2th9VnVQqbfDzAD5N3Z9Aj4iISE1r5OHI+89fuZ2rMEJcythGuzwYUVnYE0hERA2KcnkwU3109yzC3tji/o6vjwLZ9wADqRRFZhth6DgJy74SOHWrCDoS9olQzVizELCxrO0oijEJJCKiBuXx5cFenamD2F+A23eL932vXEVUBzAYDAA49HOthEmN1CcPazuCR/jVh4iIGpTHlweztgQ+fbuWAyKqo9gTSEREDcqTy4P5jgQkEuDYaaCo6FG9c+f+xB9//IHBgwejZUtOJEg1w8SwtiN4hEkgERE1KO7u7rC2tkZkZKRyGpjJXsBkr0d1FAoF2rUbBRuFAodWjwcHCFNjxNvBRETUoEilUgQEBCAtLQ3+/v5QKBQq+xUKBfz8/JCWloaAgABOEUONFpNAKtemTZsgkUhUXi1atICHhwcOHjxYbe0+ePAAS5cuRVxcXKXqX716FUuXLsXvv/+utRg8PDxKXXtZr6VLl5Z5/NKlSyGRSDRquyrHElGxoKAgeHl5YcuWLWjXrh3CwsKwfft2hIaGol27dti6dSu8vLwQGBhY26ES1RreDqan2rhxIzp27AghBK5fv45Vq1Zh1KhR+PbbbzFq1Citt/fgwQOEhIQAQKUm8rx69SpCQkLQpk0buLi4aCUGuVyOu3fvKrcPHTqEZcuWKX8WJVq3bl3m8f7+/hg2bJhWYiEi9XF5MKKnYxJIT+Xo6Iju3bsrt4cNGwYzMzPs3LmzWpLAuqBz584q2//88w+A0j+LJz148ABGRkZo3bp1uQkiEdUMLg9GVDHeDq4GD/OApJS69XqYp73ra9KkCfT19Ut9iObn52PZsmXo2LEjDAwM0KJFC0ybNg03b95UqXf8+HF4eHjAwsIChoaGkMlkGDt2LB48eIDU1FS0aNECABASEqK87Tp16tQyY4mLi0OPHj0AANOmTSvzNu23334LNzc3GBkZoWnTphg8eDBOnjxZ5Z9DyW3bX3/9FS+++CLMzMzg4OCgsu9xu3btwpAhQ2BjYwNDQ0N06tQJQUFByMnJqXIsRFS+kuXBxowZAw8PDyaARP/DnsBqkHIFcHy5tqNQlbgL6OKg2bGFhYUoKCiAEAI3btzABx98gJycHEyYMEFZp6ioCKNHj8ZPP/2E+fPno3fv3rh06RKWLFkCDw8PnD17FoaGhkhNTYWXlxf69u2LDRs2oHnz5khPT8eRI0eQn58PGxsbHDlyBMOGDYOfnx/8/YuXfSpJDJ/UrVs3bNy4EdOmTcOiRYvg5VU8/K+kF27Hjh2YOHEihgwZgp07dyIvLw8rVqyAh4cHYmJi0KdPH81+KI8ZM2YMxo8fj9dff73ChO6///7DiBEjMHfuXBgbG+Off/5BREQETp8+jePHj1c5DiIiInUwCaSncnV1Vdk2MDDAqlWrMHToUGXZ119/jSNHjmDPnj0YM2aMstzZ2Rk9evTApk2bMHPmTPzyyy94+PAhPvjgAzg7OyvrPZ5QPv/88wCKE7kn236SqakpHB0dARRPDPt4/aKiIsybNw9OTk44fPgwdHSKO75HjBgBBwcH5S2iqpoyZYryGcaKLFq0SPlvIQTc3d3RqVMn9O/fH3/++Se6du1a5ViIiIgqi7eD6am2bNmCM2fO4MyZMzh8+DCmTJmCWbNmYdWqVco6Bw8eRPPmzTFq1CgUFBQoXy4uLrC2tlaO9HVxcYG+vj5mzJiBzZs348KFC9UW9/nz53H16lVMnjxZmQACgImJCcaOHYtTp07hwYMHVW5n7Nixlap34cIFTJgwAdbW1tDV1YVUKkX//v0BAH///XeV4yAiIlIHewKrgUPr4tuvdYlDFcYodOrUqdTAkEuXLmH+/PmYNGkSmjdvjhs3biA7Oxv6+vplnuPWrVvFcTg44NixY1ixYgVmzZqFnJwc2Nvb480338ScOXM0D7IMmZmZAIpHAj6pVatWKCoqwu3bt2FkZFSldso6/5Pu37+Pvn37okmTJli2bBnat28PIyMjXL58GWPGjEFubm6VYiAiIlIXk8Bq0MRA8+fv6ouuXbviu+++w7///ouePXvC0tISFhYWOHLkSJn1mzZtqvx337590bdvXxQWFuLs2bP4/PPPMXfuXLRs2RLjx4/XWowWFhYAgGvXrpXad/XqVejo6MDMzKzK7VRmTr/jx4/j6tWriIuLU/b+AUB2dnaV2yciItIEbweTRkomZi4ZsDFy5EhkZmaisLAQ3bt3L/Xq0KFDqXPo6uqiV69eWL16NQDg119/BVD8zCGASveOlVe/Q4cOsLW1xY4dOyCEUJbn5ORgz549yhHDNaEkUSyJtcSaNWtqpH0iIqInsSeQnioxMREFBQUAim+x7t27F0ePHoWPjw/atm0LABg/fjy2b9+OESNGYM6cOejZsyekUimuXLmC2NhYjB49Gj4+Pvjyyy9x/PhxeHl5QSaT4eHDh9iwYQMAYNCgQQCKew3t7Oywf/9+DBw4EObm5rC0tESbNm3KjM/BwQGGhobYvn07OnXqBBMTE7Rq1QqtWrXCihUrMHHiRIwcORKvvfYa8vLy8MEHHyA7Oxvh4eHV/8P7n969e8PMzAyvv/46lixZAqlUiu3bt+OPP/6osRiIiIgex55Aeqpp06bBzc0Nbm5umDhxIn799Vd8/PHH2Llzp7KOrq4uvv32WyxYsAB79+6Fj48PvL29ER4ejiZNmsDJyQlA8cCQgoICLFmyBMOHD8fkyZNx8+ZNfPvttxgyZIjyfOvXr4eRkRFeeOEF9OjRo9zl2QDAyMgIGzZsQGZmJoYMGYIePXpg7dq1AIpHHe/btw+ZmZl4+eWXMW3aNJiamiI2NlYr08NUloWFBQ4dOgQjIyNMmjQJ06dPh4mJCXbtqmMPjxIRUaMhEY/fJyMVSUlJcHR0RGJiIrp06VJqf8nIVnt7+5oOjRo5vveIiOhJT8tbnsSeQCIiIqJGiEkgERERUSPEJJCIiIioEWISSFQP8VFeIiKqKiaBVSCRSFBQUMA/yFSjhBAoLCys1CTVRERE5WESWAUmJiYoLCzEtWvXlPPoEVWngoICXLt2DYWFhTAxMantcIiIqB7jZNFVYGZmhgcPHuDOnTu4c+cO9PT0oKOjwx4a0johBIqKipRfNoyMjLSy5B0RETVeTAKrQE9PDzKZDPfu3cPdu3ehUCh4a5iqhUQigZ6eHgwNDWFqaoqmTZvyywYREVUJk8AqkkgkMDU1hampaW2HQkRERFRpfCaQiIiIqBFiEkhERETUCNW5JPD+/fuYO3cuWrVqhSZNmsDFxQVfffVVpY7NyMjA1KlTYWlpCSMjI7i5uSEmJqaaIyYiIiKqf+rcM4FjxozBmTNnEB4ejvbt22PHjh145ZVXUFRUhAkTJpR7XF5eHgYOHIjs7GysXLkSVlZWWL16NYYNG4Zjx46hf//+NXgVRERERHVbnUoCo6OjcfToUWXiBwCenp64dOkS5s2bh5dffhm6urplHrt+/XokJibixIkTcHNzUx7r7OyM+fPnIyEhocaug4iIiKiuq1O3g6OiomBiYoJx48aplE+bNg1Xr16tMJGLiopChw4dlAkgUDyFy6RJk3D69Gmkp6dXW9xERERE9U2d6glMTExEp06doKenGlbXrl2V+3v37l3usX379i1VXnJsUlISbG1ty207IyMDN2/eVCn766+/AADJycmVvwgiIiKiWlCSr+Tl5VWqfp1KAjMzM2Fvb1+q3NzcXLm/omNL6ql7LADI5XKEhISUuc/b27vCY4mIiIjqisuXL6Nbt25PrVenkkAAFa6C8LQVEqpybEBAQKnb0Hfv3sW///4LJycnGBgYVHi8ppKTk+Ht7Y19+/ahXbt21dJGQ26rIV5TTbbVEK+pJttqiNfUUNtqiNdUk201xGuqybZqqp28vDxcvny50oNh61QSaGFhUWaPXVZWFgCU2dOnjWMBwMrKClZWVqXKH3/GsDq1a9cOXbp0YVt1vJ2G2lZDvKaabKshXlNDbashXlNNttUQr6km26qJdirTA1iiTg0McXJywt9//42CggKV8nPnzgEAHB0dKzy2pJ66xxIRERE1NnUqCfTx8cH9+/exZ88elfLNmzejVatW6NWrV4XH/vPPPyojiAsKCrBt2zb06tULrVq1qra4iYiIiOqbOnU7ePjw4Rg8eDBmzpyJu3fvol27dti5cyeOHDmCbdu2KecI9PPzw+bNm5GSkgI7OzsAwPTp07F69WqMGzcO4eHhsLKyglwux/nz53Hs2LHavCwiIiKiOqdOJYEAsHfvXixcuBDBwcHIyspCx44dsXPnTowfP15Zp7CwEIWFhRBCKMsMDAwQExOD+fPnY/bs2Xjw4AFcXFxw+PDhOr1aSIsWLbBkyRK0aNGCbdXhdhpqWw3xmmqyrYZ4TQ21rYZ4TTXZVkO8pppsqyavSR0S8XgmRURERESNQp16JpCIiIiIagaTQCIiIqJGiEkgERERUSPEJJCIiIioEWISWEvu37+PuXPnolWrVmjSpAlcXFzw1Vdfab2de/fuYf78+RgyZAhatGgBiUSCpUuXar0dADh+/DimT5+Ojh07wtjYGLa2thg9ejR++eUXrbbz+++/w8vLCzKZDIaGhjA3N4ebmxu2bdum1XbKExkZCYlEAhMTE62eNy4uDhKJpMzXqVOntNoWAPz8888YMWIEzMzMYGhoiGeffRZhYWFabWPq1KnlXlN1XNdvv/0Gb29vtGrVCkZGRujYsSNCQ0Px4MEDrbZz+vRpDB06FE2bNoWJiQk8PT0RHx9fpXOq87v666+/YtCgQTAxMUHz5s0xZswYXLhwQett/fzzz/D398fzzz8PAwMDSCQSpKamav26CgsL8fHHH2PYsGFo3bo1jIyM0KlTJwQFBSE7O1vr1/XZZ5/B1dUVlpaWMDAwgEwmw/jx45GUlKTVdh4nhEC/fv0gkUjwxhtvaP2ayvtd69ixo9bbAgCFQoGPP/4YTk5OMDQ0RPPmzdG7d2+cOHFCa+1U9NlR2euqbFtCCKxbtw7PP/88TE1NYWFhgf79++PQoUOVakfdtj777DN07NgRBgYGsLGxwcyZM3H79u1Kt6UtTAJryZgxY7B582YsWbIEhw8fRo8ePfDKK69gx44dWm0nMzMTa9euRV5eHry9vbV67id98cUXSE1NxZw5cxAdHY2VK1ciIyMDrq6uOH78uNbayc7OxjPPPIPly5cjOjoaW7ZsQZs2bTB58mQsW7ZMa+2UJT09He+++261Tj6+fPlynDx5UuWl7RVvduzYgf79+6NZs2bYsmULoqOjERgYCG1PFrB48eJS13Ly5ElYWlrC1tYWPXr00Fpbf/31F3r37o3U1FR8+umnOHjwIMaPH4/Q0FC88sorWmvnzJkz6NevH3Jzc7F161Zs3boVDx8+xMCBA3Hy5EmNz1vZ39V//vkHHh4eyM/Px9dff40NGzbg33//Rd++fXHz5k2tthUTE4Njx45BJpOhd+/eal5R5dvKzc3F0qVLYWdnh08//RTR0dF49dVXsXbtWri7uyM3N1drbZXUGz58OCIjI/H9998jJCQEv/32G3r16oXz589rrZ3HrV69GsnJyZWqq2lbhoaGpX7Xdu3apfW2CgsL4ePjo/zdOnz4MLZv345hw4YhJydHa+2U9dnx6aefAiheIEKb17RkyRLMmDEDPXv2xJ49e7Bp0yYYGBhg5MiR2Lt3r1bbevfdd/HWW29h9OjROHjwIIKCgrBjxw4MHjwYCoWiUm1pjaAad+jQIQFA7NixQ6V88ODBolWrVqKgoEBrbRUVFYmioiIhhBA3b94UAMSSJUu0dv7H3bhxo1TZvXv3RMuWLcXAgQOrpc3H9erVSzzzzDPV2sbIkSPFqFGjxJQpU4SxsbFWzx0bGysAiN27d2v1vE+6cuWKMDY2FjNnzqzWdsoTFxcnAIhFixZp9bwLFy4UAERycrJK+YwZMwQAkZWVpZV2hg4dKlq2bClycnKUZXfv3hWWlpaid+/eGp+3sr+r48aNE5aWluLOnTvKstTUVCGVSsX8+fO12lZhYaHy3x988IEAIC5evFj5i6pkWwUFBeLWrVuljt29e7cAILZu3aq1tsrz119/CQBi8eLFWm/n4sWLwsTEROzdu1cAELNmzapUTOq0pY3PpMq29cknnwgdHR1x8uTJam2nLFOnThUSiUT8999/Wm3L1tZW9OnTR6UsNzdXNGvWTLzwwgtaa+vKlStCV1dXzJ49W6V8x44dAoBYu3ZtpdrSFvYE1oKoqCiYmJhg3LhxKuXTpk3D1atXVZa+q6qSrvOaYGVlVarMxMQEnTt3xuXLl6u9fUtLS+jpVd/859u2bcMPP/wAuVxebW3UhMjISOTk5CAwMLBW2l+/fj0kEgmmT5+u1fNKpVIAQLNmzVTKmzdvDh0dHejr62ulnfj4eHh4eMDIyEhZ1rRpU/Tr1w8nTpzAtWvXNDpvZX5XCwoKcPDgQYwdOxampqbKcjs7O3h6eiIqKkprbQGAjk7V/0RUpi1dXV1YWFiUKu/ZsycAVPrzoyqfdyWT+FbmM0TddmbMmIHBgwdXuveqKm1VRWXbWrlyJfr16wdXV9dqbedJ9+7dw+7du9G/f3+0a9dOq21JpdJSnx1NmjRRvrTV1qlTp1BYWIgRI0aolI8cORIASi2bW92YBNaCxMREdOrUqdSHTdeuXZX7G4o7d+7g119/RZcuXbR+7qKiIhQUFODmzZuQy+X47rvvqi2xycjIwNy5cxEeHo7WrVtXSxslZs2aBT09PZiammLo0KH4+eeftXr+H3/8Eebm5vjnn3/g4uICPT09WFlZ4fXXX8fdu3e12taT7ty5g2+++QYDBw5E27ZttXruKVOmoHnz5pg5cyYuXLiAe/fu4eDBg1izZg1mzZoFY2NjrbSTn58PAwODUuUlZefOndNKO2VJSUlBbm6u8rPicV27dkVycjIePnxYbe3XtJLHSKrj8wMovq2Zl5eHf/75B/7+/rCyssK0adO02kZkZCROnz6NVatWafW8ZcnNzYW1tTV0dXXRunVrvPHGG8jKytJqG5cvX0ZqaiqcnJywYMECtGzZEnp6eujSpQs2b96s1bae9NVXXyEnJwf+/v5aP/ecOXNw5MgRrF+/Hrdv38a1a9fw9ttv486dO3jzzTe11k5+fj4AlPoMkUqlkEgk+PPPP7XWVmXUuWXjGoPMzEzY29uXKjc3N1fubyhmzZqFnJwcLFy4UOvnDggIwJo1awAA+vr6+Oyzz/Daa69pvZ2Stjp06ICZM2dWy/mB4h6sOXPmwMPDAxYWFkhOTsYHH3wADw8PHDp0CEOHDtVKO+np6Xjw4AHGjRuH9957D59++inOnDmDJUuWIDExET/99FO19Tzs3LkTubm58PPz0/q527Rpg5MnT8LHxwcODg7K8jfffFP5HJE2dO7cGadOnUJRUZGyp6ygoEDZg1+dv78l5y75rHicubk5hBC4ffs2bGxsqi2GmpKeno6goCB0795d2UuibcbGxsjLywMAtG/fHnFxcXjmmWe0dv6SZ4hXrFhRrc8RA4CzszOcnZ2Vzw//8MMP+OSTTxATE4MzZ85obSBbeno6AGDz5s1o3bo1Vq1ahWbNmmHdunWYOnUq8vPz8eqrr2qlrSetX78ezZs3x9ixY7V+7rlz58LQ0BCzZs1SJpnm5uY4cOAA3N3dtdZO586dARTfUfD09FSWnzhxAkKIGv/7zySwllT0R7amuv6r2+LFi7F9+3Z8/vnneP7557V+/gULFsDf3x8ZGRk4cOAA3njjDeTk5ODdd9/Vajt79uzBgQMH8Ntvv1Xr/81zzz2H5557Trndt29f+Pj4wMnJCfPnz9daElhUVISHDx9iyZIlCAoKAgB4eHhAX18fc+fORUxMDAYNGqSVtp60fv16WFhYaHRb7GlSU1MxatQotGzZEt988w1atGiBhIQELFu2DPfv38f69eu10s7s2bPh5+eHN954AwsXLkRRURFCQkJw6dIlANq5hfo0Df3zIysrCyNGjIAQArt27aq2n+mJEyeQn5+PlJQUfPLJJ/D09ERMTIzWeh5ff/11ODs7V1tS9Li33npLZXvw4MF47rnn8OKLL2LdunWl9muqqKgIAPDw4UNER0fDzs5O2V737t0RGhpaLdeblJSEhIQEzJo1q9K3Z9WxceNGzJkzB2+88QaGDx+O/Px8bNmyBaNHj8bevXu19vnr7OyMfv364YMPPkCHDh0wePBg/PXXX3j99dehq6tbI58fj+Pt4FpgYWFRZrZf0m1f1rf8+iYkJATLli3D+++/r9Z0COqQyWTo3r07RowYgS+++AIzZszAe++9V+kRkpVx//59zJo1C7Nnz0arVq2QnZ2N7OxsZZd+dnZ2pUbDaap58+YYOXIk/vzzz0qPkHyakmevnvxQGz58OIDi6Ueqw59//omzZ89i0qRJZd5OraqgoCDcvXsX3333HcaOHYt+/fph3rx5+PTTT7Fhwwb88MMPWmln+vTpCA8Px9atW9G6dWvIZDL89ddfyi8ftra2WmmnLCX/d+V9fkgkEjRv3rza2q8Jt2/fxuDBg5Geno6jR4+WeddEW7p16wZXV1dMnDgRsbGxEEJgwYIFWjn3N998gyNHjmDFihW4c+eO8rMDKL4lmJ2dXe0jQX18fGBsbKzVqZhK3oMdO3ZUJoBA8ZePoUOH4sqVK8jIyNBaeyVKvsRVx63g27dvK3sAP/zwQwwcOBDDhw/Hzp070aNHD7z++utabW/37t1wd3fHSy+9BDMzM3h6emLMmDFwcXGp1s+PsjAJrAVOTk74+++/UVBQoFJe8iyRtqcDqWkhISFYunQpli5dqrUP1Mro2bMnCgoK1Jov7Wlu3bqFGzdu4KOPPoKZmZnytXPnTuTk5MDMzAwTJ07UWntlEf+btkVbPTxlPU/2eDvV9U20Oj/EgeL5Izt37lzq2b+SaWi0+axtYGAgbt26hXPnziE1NRUnTpzA7du3YWxsXC293iUcHBxgaGhY5nOH586dQ7t27aqll6Sm3L59G4MGDcLFixdx9OjRct+r1aFp06bo2LEj/v33X62cLzExEQUFBXB1dVX57ACAdevWwczMTK056DQlhNDq77SDg4PKoKgn2wK0/xmSn5+PrVu34vnnn4eLi4tWzw0A58+fR25ubplTVnXv3h2pqam4f/++1tqzsrJCdHQ0bty4gT/++AMZGRkIDQ3Fv//+i379+mmtncpgElgLfHx8cP/+/VKjgDZv3oxWrVqhV69etRRZ1YWFhWHp0qVYtGgRlixZUqNtx8bGQkdHR6s9B9bW1oiNjS31Gjp0KJo0aYLY2NhqnZvw9u3bOHjwIFxcXLT2x73keZrDhw+rlEdHRwOAxiP+KpKXl4dt27ahZ8+e1fYlp1WrVkhKSir1YV0yd5+2B/QYGBjA0dERdnZ2SEtLw65du/Dqq6/C0NBQq+08Tk9PD6NGjcLevXtx7949ZXlaWhpiY2MxZsyYamu7upUkgBcuXMD333+v8mhETShJ6is76vRppk6dWuZnBwB4e3sjNjYWffr00Upb5fnmm2/w4MEDrf5O6+npYfTo0fj7779VJg4XQuDIkSNwcHCApaWl1toDgG+//Ra3bt2qlmeJASif13yyx1QIgVOnTsHMzExrA8seZ2Vlha5du6JZs2b48ssvkZOTU213zsrDZwJrwfDhwzF48GDMnDkTd+/eRbt27bBz504cOXIE27Ztg66urlbbO3z4MHJycpR/NP766y988803AIARI0aU+61OXR999BGCg4MxbNgweHl5lfqF0tYH0YwZM2BqaoqePXuiZcuWuHXrFnbv3o1du3Zh3rx5yqketKFJkybw8PAoVb5p0ybo6uqWuU9TEyZMUN7itrS0xH///YePPvoIN27cwKZNm7TWzpAhQzBq1CiEhoaiqKgIrq6uOHv2LEJCQjBy5Mhq+cO0b98+ZGVlVVsvIFD8YLe3tzcGDx6Mt956C5aWljh16hT+7//+D507d1be7q6qxMRE7NmzB927d4eBgQH++OMPhIeHa2XFlcr8roaEhKBHjx4YOXIkgoKC8PDhQwQHB8PS0hLvvPOOVtu6efOm8jZ6Se/j4cOH0aJFC7Ro0QL9+/fXSlsltxJ/++03fPrppygoKFD5/GjRooXKYJ+qtKVQKDB48GBMmDABzz77LAwNDfHvv/9i5cqVyMvLq/SX16e106ZNG7Rp06bMY21tbdX67HhaWzdv3sSECRMwfvx4tGvXDhKJBD/88AM+/fRTdOnSRa3fu8q8L8LCwnD48GEMGzYMS5cuhampKSIjI/HHH3/g66+/1lo7JdavXw9DQ0NMmDCh0tehTlsymQxjxozB2rVrYWBggBEjRiAvLw+bN29GfHw8wsLCKn0npjLXtW7dOgDFvarZ2dk4fPgw1q9fj+XLl6Nbt24aXaPGanRWQlK6d++eePPNN4W1tbXQ19cXXbt2FTt37qyWtuzs7ASAMl/qTvxakf79+5fbjjbfahs2bBB9+/YVlpaWQk9PTzRv3lz079+/0hPKakN1TBb9f//3f8LFxUU0a9ZM6OrqihYtWggfHx9x+vRprbYjhBAPHjwQgYGB4plnnhF6enpCJpOJ9957Tzx8+FDrbQlRPBG6sbGxuHv3brWcv8Tx48fFkCFDhLW1tTA0NBTt27cX77zzTpkTEWvq/Pnzol+/fsLc3Fzo6+uLdu3aiUWLFon79+9X+dyV/V09e/asGDhwoDAyMhKmpqbC29u71CTZ2mirZALzsl79+/fXWlsXL16s8LNjypQpWmvr4cOHwt/fX3Tq1EmYmJgIPT090bp1azFp0iSRlJSk1Z9fWaDmZNGVaSsrK0v4+PiINm3aCENDQ6Gvry+effZZMX/+fJGdna3VtkqcO3dOeHl5iaZNm4omTZoIV1dXceDAAa23k5aWJnR0dISvr69a16FuW7m5ueKDDz4QXbt2FU2bNhXm5ubC1dVVbNu2TTkBtLbaWrNmjejUqZMwMjISJiYmom/fvmLfvn0aX19VSITQ8jpRRERERFTn8ZlAIiIiokaISSARERFRI8QkkIiIiKgRYhJIRERE1AgxCSQiIiJqhJgEEhERETVCTAKJiIiIGiEmgURERESNEJNAIiIiokaISSAR1XkSiaRSr7i4uDKP9/Dw0Hid58oe6+HhAYlEgmHDhpXal5qaColEgg8//FCjGKpq6tSpMDExqZW2iaju0qvtAIiInubkyZMq22FhYYiNjcXx48dVyjt37lzm8XK5vNpie9J3332H48ePY8CAATXWJhGRJpgEElGd5+rqqrLdokUL6OjolCp/0oMHD2BkZFRucqht7du3R0FBAebPn48zZ85AIpHUSLt1RcnPm4jqB94OJqIGwcPDA46Ojvjxxx/Ru3dvGBkZYfr06cp9T97SDQkJQa9evWBubg5TU1N069YN69evhxBC4xikUinef/99/PLLL9i1a1eFdZcuXVpmkrhp0yZIJBKkpqYqy9q0aYORI0fi4MGDeO6552BoaIhOnTrh4MGDymM6deoEY2Nj9OzZE2fPni2zzaSkJAwcOBDGxsZo0aIF3njjDTx48ECljhACcrkcLi4uMDQ0hJmZGV588UVcuHBBpV5FP28iqh+YBBJRg3Ht2jVMmjQJEyZMQHR0NAICAsqtm5qaitdeew1ff/019u7dizFjxmD27NkICwurUgwvv/wynn/+eSxatAgKhaJK53rcH3/8gffeew+BgYHYu3cvmjVrhjFjxmDJkiWIjIzE8uXLsX37dty5cwcjR45Ebm6uyvEKhQIjRozAwIEDsW/fPrzxxhtYs2YNXn75ZZV6r732GubOnYtBgwZh3759kMvlSEpKQu/evXHjxg2Vuur8vImo7uHtYCJqMLKysrB79+5KPY+3ceNG5b+Liorg4eEBIQRWrlyJxYsXa3wrVyKRICIiAoMGDcKaNWvwxhtvaHSeJ2VmZuLUqVOwtbUFALRq1QouLi5Yt24dkpOTlbdhJRIJvL29cezYMYwaNUp5fH5+Pt555x28+eabAIDBgwdDKpVi4cKFiI+Ph7u7O06dOoV169bho48+wttvv608tm/fvmjfvj0+/vhjREREKMvV+XkTUd3DnkAiajDMzMwqnZAcP34cgwYNQrNmzaCrqwupVIrg4GBkZmYiIyOjSnEMHDgQQ4YMQWhoKO7du1elc5VwcXFRJoAA0KlTJwDFt2Uffw6vpPzSpUulzjFx4kSV7QkTJgAAYmNjAQAHDx6ERCLBpEmTUFBQoHxZW1vD2dm51OhrdX7eRFT3MAkkogbDxsamUvVOnz6NIUOGAADWrVuH+Ph4nDlzBgsXLgSAUrdSNREREYFbt25pbVoYc3NzlW19ff0Kyx8+fKhSrqenBwsLC5Uya2trAMW9jABw48YNCCHQsmVLSKVSldepU6dw69YtleMr+/MmorqJt4OJqMGo7C3cr776ClKpFAcPHkSTJk2U5fv27dNaLC4uLnjllVfw8ccfY8SIEaX2l7Sbl5cHAwMDZfmTiZa2FBQUIDMzUyURvH79OgAoyywtLSGRSPDTTz+pxFTiybLGNvqZqKFhTyARNToSiQR6enrQ1dVVluXm5mLr1q1abWfZsmXIz89HSEhIqX1t2rQBAPz5558q5QcOHNBqDI/bvn27yvaOHTsAQDlyeuTIkRBCID09Hd27dy/1cnJyqrbYiKjmsSeQiBodLy8vfPzxx5gwYQJmzJiBzMxMfPjhh2X2flVF27ZtMXPmTKxcubLUvhEjRsDc3Bx+fn4IDQ2Fnp4eNm3ahMuXL2s1hhL6+vr46KOPcP/+ffTo0QMnTpzAsmXLMHz4cPTp0wcA4O7ujhkzZmDatGk4e/Ys+vXrB2NjY1y7dg0///wznJycMHPmzGqJj4hqHnsCiajRGTBgADZs2IBz585h1KhRWLhwIV588UUEBQVpva1FixbB1NS0VLmpqSmOHDmCpk2bYtKkSXj99dfh6OiofC5R20pufx89ehSjR4/GZ599hldffRW7d+9WqbdmzRqsWrUKP/74I8aPHw8vLy8EBwcjJycHPXv2rJbYiKh2SERVZkYlIiIionqJPYFEREREjRCTQCIiIqJGiEkgERERUSPEJJCIiIioEWISSERERNQIMQkkIiIiaoSYBBIRERE1QkwCiYiIiBohJoFEREREjRCTQCIiIqJGiEkgERERUSPEJJCIiIioEWISSERERNQI/T8dVHc9RRjVCgAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "df = ax_client.get_trials_data_frame()\n", + "\n", + "fig, ax = plt.subplots(figsize=(6,4), dpi=120)\n", + "\n", + "ax.plot(df.corrosion_score, ls='None', marker='o', mfc='None', mec='k', label='Observed')\n", + "\n", + "best_to_trial = np.minimum.accumulate(df.corrosion_score.values)\n", + "ax.plot(best_to_trial, color='#0033FF', lw=2, label='Best to Trial')\n", + "\n", + "plt.xticks(range(len(df)))\n", + "plt.xlabel('Trial Number')\n", + "plt.ylabel('Corrosion Score (Lower is Better)')\n", + "plt.ylim(0, 4)\n", + "plt.legend()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "ax_env", + "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": "3.9.19" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/tutorials/batch-honegumi-selection.jpg b/docs/curriculum/tutorials/batch/batch-honegumi-selection.jpg similarity index 100% rename from docs/tutorials/batch-honegumi-selection.jpg rename to docs/curriculum/tutorials/batch/batch-honegumi-selection.jpg diff --git a/docs/tutorials/freq-vs-bayes-hg-selection.jpeg b/docs/curriculum/tutorials/mobo/freq-vs-bayes-hg-selection.jpeg similarity index 100% rename from docs/tutorials/freq-vs-bayes-hg-selection.jpeg rename to docs/curriculum/tutorials/mobo/freq-vs-bayes-hg-selection.jpeg diff --git a/docs/curriculum/tutorials/mobo/mobo-tutorial.ipynb b/docs/curriculum/tutorials/mobo/mobo-tutorial.ipynb new file mode 100644 index 00000000..59745d57 --- /dev/null +++ b/docs/curriculum/tutorials/mobo/mobo-tutorial.ipynb @@ -0,0 +1,590 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Optimizing a Polymer Compound for Strength and Density\n", + "\n", + "Imagine you work at a custom materials solutions company that specializes in creating polymer compounds for various applications. A customer has requested a polymer formulation with high strength and a high biodegradability score. The customer is unsure of the tradeoff between the two properties but knows that the target application will require a strength of at least 70 MPa. As the customer is concerned about the toxicitiy and biodegradability of the polymer, they have limited you to a set of **five** thermoplastic monomers that can be used in the formulation.\n", + "\n", + "You believe Bayesian optimization can help you in this task and decide to put together an optimization script using Honegumi to help solve this problem.\n", + "\n", + "Taking note of available composition and process parameters you decide to restrict your design space to the following:\n", + "\n", + "| | **Parameter Name** | **Bounds** |\n", + "|------|--------------------|-------------|\n", + "| x1 | Monomer A | [0, 1] |\n", + "| x2 | Monomer B | [0, 1] |\n", + "| x3 | Monomer C | [0, 1] |\n", + "| x4 | Monomer D | [0, 1] |\n", + "| x5 | Monomer E | [0, 1] |\n", + "| x6 | Extrusion Rate | [0.01, 0.1] |\n", + "| x7 | Temperatrue | [120, 200] |\n", + "\n", + "To help find a solution quickly, you dig up some data on these polymer systems in the literature and decide to use them to help improve the surrogate model. While none of these meet the customer requirement, you think they might at least help tell your model where NOT to look. The collected data is as follows:\n", + "\n", + "| **x1** | **x2** | **x3** | **x4** | **x5** | **x6** | **x7** | **Strength** | **BioDeg** |\n", + "|--------|--------|--------|--------|--------|--------|--------|--------------|------------|\n", + "| 0.3 | 0.2 | 0.1 | 0.0 | 0.4 | 0.05 | 150 | 43.73 | 1.81 |\n", + "| 0.0 | 0.0 | 0.3 | 0.7 | 0.0 | 0.1 | 160 | 25.79 | 3.83 |\n", + "| 0.2 | 0.2 | 0.2 | 0.2 | 0.2 | 0.09 | 184 | 41.37 | 2.29 |\n", + "\n", + "A dummy objective function that returns outputs for each property has been constructed in the code cell below. This functions aims to emulate the results of experimental trials under different inputs. Although we can easily find optimal values using the equations, we will pretend that the objective function is unknown and use a Bayesian optimization approach to find the optimal set of input parameters instead." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "def polymer_properties(x1, x2, x3, x4, x5, x6, x7):\n", + " \"\"\"\n", + " Calculates the strength and biodegradability properties of a polymer based \n", + " on a set of given input parameters.\n", + "\n", + " Parameters:\n", + " x1 (float): volume fraction of monomer 1. Range: [0.0, 1.0].\n", + " x2 (float): volume fraction of monomer 2: [0.0, 1.0].\n", + " x3 (float): volume fraction of monomer 3: [0.0, 1.0].\n", + " x4 (float): volume fraction of monomer 4: [0.0, 1.0].\n", + " x5 (float): volume fraction of monomer 5: [0.0, 1.0].\n", + " x6 (float): the polymer extrusion rate. Range: [0.01, 0.1].\n", + " x7 (float): the processsing temperature. Range: [120.0, 200.0].\n", + "\n", + " Returns:\n", + " dict: calculated strength and biodegradability properties of polymer in form:\n", + " {\n", + " \"strength\": float,\n", + " \"biodegradability\": float\n", + " }\n", + " \"\"\"\n", + " strength = float(\n", + " np.exp(-(50*(x1-0.5)**2)) +\n", + " np.exp(-(5*(x2-0.4)**2)) -\n", + " 0.8*x3 +\n", + " np.exp(-(300*(x4-0.1)**2)) -\n", + " 0.3*x5**2 +\n", + " np.exp(-(2000*(x6-0.025)**2)) +\n", + " 1/(1+np.exp(-(x7-137)/15))\n", + " )\n", + "\n", + " biodegradability = float(\n", + " -1/(1+np.exp(-(x1-0.1)/0.1)) + 1 +\n", + " -1/(1+np.exp(-(x2-0.3)/0.1)) + 1 +\n", + " x3**2 +\n", + " x4 +\n", + " 1/(1+np.exp(-(x5-0.7)/0.075)) +\n", + " 10*x6 +\n", + " -(x7/200)**2+1\n", + " )\n", + "\n", + " return {\"strength\" : strength*25, \"biodegradability\" : biodegradability*5}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Applying Honegumi\n", + "\n", + "We will now use the [Honegumi website](https://honegumi.readthedocs.io/en/latest/) to generate a script that will help us optimize the polymer parameters. From the description, we observe that our problem is a **multi objective** optimization problem with a **constraint on the fractional sum of monomer components** and a **custom threshold** on the optimized strength. Additionally, we would like to include some **historical data** in our model training. To create an optimization script for this problem, we select the following options:\n", + "\n", + "![Selection.jpg](freq-vs-bayes-hg-selection.jpeg)\n", + "\n", + "The Honegumi generated optimization script will provide a framework for our optimization campaign that we can modify to suit our specific problem needs. In the code sections below, we will make several modifications to this generated script to make it compatible with our problem." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Modifying the Code for Our Problem\n", + "\n", + "We can modify this code to suit our problem with a few simple modifications. Wherever a modification has been made to the code, a comment starting with `# CHANGE:` has been added along with a brief description of the change." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 04-17 12:10:39] ax.service.ax_client: Starting optimization with verbose logging. To disable logging, set the `verbose_logging` argument to `False`. Note that float values in the logs are rounded to 6 decimal points.\n", + "[WARNING 04-17 12:10:39] ax.service.ax_client: Random seed set to 12345. Note that this setting only affects the Sobol quasi-random generator and BoTorch-powered Bayesian optimization models. For the latter models, setting random seed to the same number for two optimizations will make the generated trials similar, but not exactly the same, and over time the trials will diverge more.\n", + "[INFO 04-17 12:10:39] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicitly specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.\n", + "[INFO 04-17 12:10:39] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicitly specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.\n", + "[INFO 04-17 12:10:39] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x3. If that is not the expected value type, you can explicitly specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.\n", + "[INFO 04-17 12:10:39] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x4. If that is not the expected value type, you can explicitly specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.\n", + "[INFO 04-17 12:10:39] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x5. If that is not the expected value type, you can explicitly specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.\n", + "[INFO 04-17 12:10:39] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x6. If that is not the expected value type, you can explicitly specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.\n", + "[INFO 04-17 12:10:39] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x7. If that is not the expected value type, you can explicitly specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.\n", + "[INFO 04-17 12:10:39] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x3', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x4', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x5', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x6', parameter_type=FLOAT, range=[0.01, 0.1]), RangeParameter(name='x7', parameter_type=FLOAT, range=[120.0, 200.0])], parameter_constraints=[ParameterConstraint(1.0*x1 + 1.0*x2 + 1.0*x3 + 1.0*x4 <= 1.0)]).\n", + "[INFO 04-17 12:10:39] ax.modelbridge.dispatch_utils: Using Models.BOTORCH_MODULAR since there is at least one ordered parameter and there are no unordered categorical parameters.\n", + "[INFO 04-17 12:10:39] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=7 num_trials=None use_batch_trials=False\n", + "[INFO 04-17 12:10:39] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=14\n", + "[INFO 04-17 12:10:39] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=14\n", + "[INFO 04-17 12:10:39] ax.modelbridge.dispatch_utils: `verbose`, `disable_progbar`, and `jit_compile` are not yet supported when using `choose_generation_strategy` with ModularBoTorchModel, dropping these arguments.\n", + "[INFO 04-17 12:10:39] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+BoTorch', steps=[Sobol for 14 trials, BoTorch for subsequent trials]). Iterations after 14 will take longer to generate due to model-fitting.\n", + "[INFO 04-17 12:10:39] ax.core.experiment: Attached custom parameterizations [{'x1': 0.3, 'x2': 0.2, 'x3': 0.1, 'x4': 0.0, 'x5': 0.4, 'x6': 0.05, 'x7': 150.0}] as trial 0.\n", + "[INFO 04-17 12:10:39] ax.service.ax_client: Completed trial 0 with data: {'strength': (46.660238, None), 'biodegradability': (9.078739, None)}.\n", + "[INFO 04-17 12:10:39] ax.core.experiment: Attached custom parameterizations [{'x1': 0.0, 'x2': 0.0, 'x3': 0.3, 'x4': 0.7, 'x5': 0.0, 'x6': 0.1, 'x7': 160.0}] as trial 1.\n", + "[INFO 04-17 12:10:39] ax.service.ax_client: Completed trial 1 with data: {'strength': (25.79598, None), 'biodegradability': (19.168606, None)}.\n", + "[INFO 04-17 12:10:39] ax.core.experiment: Attached custom parameterizations [{'x1': 0.2, 'x2': 0.2, 'x3': 0.2, 'x4': 0.2, 'x5': 0.2, 'x6': 0.09, 'x7': 184.0}] as trial 2.\n", + "[INFO 04-17 12:10:39] ax.service.ax_client: Completed trial 2 with data: {'strength': (41.652192, None), 'biodegradability': (11.474355, None)}.\n", + "[INFO 04-17 12:10:39] ax.service.ax_client: Generated new trial 3 with parameters {'x1': 0.222494, 'x2': 0.114947, 'x3': 0.5139, 'x4': 0.020708, 'x5': 0.829686, 'x6': 0.021624, 'x7': 181.654276} using model Sobol.\n", + "[INFO 04-17 12:10:39] ax.service.ax_client: Completed trial 3 with data: {'strength': (58.799907, None), 'biodegradability': (8.839131, None)}.\n", + "[INFO 04-17 12:10:39] ax.service.ax_client: Generated new trial 4 with parameters {'x1': 0.154027, 'x2': 0.242956, 'x3': 0.386497, 'x4': 0.173289, 'x5': 0.617998, 'x6': 0.026884, 'x7': 162.506085} using model Sobol.\n", + "[INFO 04-17 12:10:39] ax.service.ax_client: Completed trial 4 with data: {'strength': (65.371678, None), 'biodegradability': (9.692252, None)}.\n", + "[INFO 04-17 12:10:39] ax.service.ax_client: Generated new trial 5 with parameters {'x1': 0.179133, 'x2': 0.005902, 'x3': 0.101222, 'x4': 0.461255, 'x5': 0.171468, 'x6': 0.092874, 'x7': 195.245879} using model Sobol.\n", + "[INFO 04-17 12:10:39] ax.service.ax_client: Completed trial 5 with data: {'strength': (33.640468, None), 'biodegradability': (13.557441, None)}.\n", + "[INFO 04-17 12:10:39] ax.service.ax_client: Generated new trial 6 with parameters {'x1': 0.088445, 'x2': 0.425975, 'x3': 0.134375, 'x4': 0.143051, 'x5': 0.807816, 'x6': 0.059075, 'x7': 186.147521} using model Sobol.\n", + "[INFO 04-17 12:10:39] ax.service.ax_client: Completed trial 6 with data: {'strength': (62.787792, None), 'biodegradability': (8.18435, None)}.\n", + "[INFO 04-17 12:10:39] ax.service.ax_client: Generated new trial 7 with parameters {'x1': 0.026292, 'x2': 0.082697, 'x3': 0.27327, 'x4': 0.60958, 'x5': 0.650338, 'x6': 0.02124, 'x7': 155.349769} using model Sobol.\n", + "[INFO 04-17 12:10:39] ax.service.ax_client: Completed trial 7 with data: {'strength': (53.265418, None), 'biodegradability': (14.337877, None)}.\n", + "[INFO 04-17 12:10:39] ax.service.ax_client: Generated new trial 8 with parameters {'x1': 0.412713, 'x2': 0.228674, 'x3': 0.047212, 'x4': 0.091807, 'x5': 0.307525, 'x6': 0.033846, 'x7': 150.470045} using model Sobol.\n", + "[INFO 04-17 12:10:39] ax.service.ax_client: Completed trial 8 with data: {'strength': (101.00477, None), 'biodegradability': (7.90621, None)}.\n", + "[INFO 04-17 12:10:39] ax.service.ax_client: Generated new trial 9 with parameters {'x1': 0.117707, 'x2': 0.10513, 'x3': 0.039212, 'x4': 0.253231, 'x5': 0.552579, 'x6': 0.072357, 'x7': 146.750515} using model Sobol.\n", + "[INFO 04-17 12:10:39] ax.service.ax_client: Completed trial 9 with data: {'strength': (30.385234, None), 'biodegradability': (14.123691, None)}.\n", + "[INFO 04-17 12:10:39] ax.service.ax_client: Generated new trial 10 with parameters {'x1': 0.353, 'x2': 0.132346, 'x3': 0.124854, 'x4': 0.13, 'x5': 0.906883, 'x6': 0.018858, 'x7': 181.506837} using model Sobol.\n", + "[INFO 04-17 12:10:39] ax.service.ax_client: Completed trial 10 with data: {'strength': (89.00033, None), 'biodegradability': (7.147958, None)}.\n", + "[INFO 04-17 12:10:39] ax.service.ax_client: Generated new trial 11 with parameters {'x1': 0.145964, 'x2': 0.46252, 'x3': 0.193453, 'x4': 0.071685, 'x5': 0.407677, 'x6': 0.083634, 'x7': 157.186764} using model Sobol.\n", + "[INFO 04-17 12:10:39] ax.service.ax_client: Completed trial 11 with data: {'strength': (60.091989, None), 'biodegradability': (9.398967, None)}.\n", + "[INFO 04-17 12:10:39] ax.service.ax_client: Generated new trial 12 with parameters {'x1': 0.043166, 'x2': 0.023531, 'x3': 0.564187, 'x4': 0.201326, 'x5': 0.449078, 'x6': 0.05358, 'x7': 150.02964} using model Sobol.\n", + "[INFO 04-17 12:10:39] ax.service.ax_client: Completed trial 12 with data: {'strength': (24.454797, None), 'biodegradability': (15.363259, None)}.\n", + "[INFO 04-17 12:10:39] ax.service.ax_client: Generated new trial 13 with parameters {'x1': 0.080444, 'x2': 0.145896, 'x3': 0.441666, 'x4': 0.103938, 'x5': 0.221277, 'x6': 0.047577, 'x7': 134.169413} using model Sobol.\n", + "[INFO 04-17 12:10:39] ax.service.ax_client: Completed trial 13 with data: {'strength': (54.110101, None), 'biodegradability': (13.494714, None)}.\n", + "[INFO 04-17 12:10:39] ax.service.ax_client: Generated new trial 14 with parameters {'x1': 0.008678, 'x2': 0.049031, 'x3': 0.155088, 'x4': 0.11705, 'x5': 0.87693, 'x6': 0.043737, 'x7': 176.119587} using model Sobol.\n", + "[INFO 04-17 12:10:39] ax.service.ax_client: Completed trial 14 with data: {'strength': (65.61865, None), 'biodegradability': (14.216473, None)}.\n", + "[INFO 04-17 12:10:39] ax.service.ax_client: Generated new trial 15 with parameters {'x1': 0.033407, 'x2': 0.200164, 'x3': 0.361986, 'x4': 0.280265, 'x5': 0.334687, 'x6': 0.056508, 'x7': 183.545813} using model Sobol.\n", + "[INFO 04-17 12:10:39] ax.service.ax_client: Completed trial 15 with data: {'strength': (40.479783, None), 'biodegradability': (12.629707, None)}.\n", + "[INFO 04-17 12:10:39] ax.service.ax_client: Generated new trial 16 with parameters {'x1': 0.30056, 'x2': 0.032063, 'x3': 0.286449, 'x4': 0.149094, 'x5': 0.191202, 'x6': 0.025519, 'x7': 148.145571} using model Sobol.\n", + "[INFO 04-17 12:10:39] ax.service.ax_client: Completed trial 16 with data: {'strength': (64.053814, None), 'biodegradability': (9.970079, None)}.\n", + "[INFO 04-17 12:10:42] ax.service.ax_client: Generated new trial 17 with parameters {'x1': 0.623365, 'x2': 0.264021, 'x3': 0.0, 'x4': 0.112022, 'x5': 0.346625, 'x6': 0.02824, 'x7': 151.606778} using model BoTorch.\n", + "[INFO 04-17 12:10:42] ax.service.ax_client: Completed trial 17 with data: {'strength': (101.039555, None), 'biodegradability': (7.070961, None)}.\n", + "[INFO 04-17 12:10:44] ax.service.ax_client: Generated new trial 18 with parameters {'x1': 0.036143, 'x2': 0.083187, 'x3': 0.0, 'x4': 0.123627, 'x5': 0.741901, 'x6': 0.024407, 'x7': 169.50347} using model BoTorch.\n", + "[INFO 04-17 12:10:44] ax.service.ax_client: Completed trial 18 with data: {'strength': (79.395803, None), 'biodegradability': (14.41337, None)}.\n", + "[INFO 04-17 12:10:47] ax.service.ax_client: Generated new trial 19 with parameters {'x1': 0.185593, 'x2': 0.230212, 'x3': 0.0, 'x4': 0.120535, 'x5': 0.341583, 'x6': 0.027248, 'x7': 150.241196} using model BoTorch.\n", + "[INFO 04-17 12:10:47] ax.service.ax_client: Completed trial 19 with data: {'strength': (84.672987, None), 'biodegradability': (9.178258, None)}.\n", + "[INFO 04-17 12:10:50] ax.service.ax_client: Generated new trial 20 with parameters {'x1': 0.030186, 'x2': 0.05624, 'x3': 0.0, 'x4': 0.145763, 'x5': 1.0, 'x6': 0.021301, 'x7': 180.761653} using model BoTorch.\n", + "[INFO 04-17 12:10:50] ax.service.ax_client: Completed trial 20 with data: {'strength': (70.805624, None), 'biodegradability': (14.205617, None)}.\n", + "[INFO 04-17 12:10:53] ax.service.ax_client: Generated new trial 21 with parameters {'x1': 0.483281, 'x2': 0.223379, 'x3': 0.003256, 'x4': 0.093533, 'x5': 0.438249, 'x6': 0.029267, 'x7': 159.783298} using model BoTorch.\n", + "[INFO 04-17 12:10:53] ax.service.ax_client: Completed trial 21 with data: {'strength': (114.991376, None), 'biodegradability': (7.265252, None)}.\n", + "[INFO 04-17 12:10:56] ax.service.ax_client: Generated new trial 22 with parameters {'x1': 0.171795, 'x2': 0.080973, 'x3': 0.0, 'x4': 0.113554, 'x5': 0.655177, 'x6': 0.027367, 'x7': 168.605868} using model BoTorch.\n", + "[INFO 04-17 12:10:56] ax.service.ax_client: Completed trial 22 with data: {'strength': (82.802476, None), 'biodegradability': (10.980162, None)}.\n", + "[INFO 04-17 12:10:59] ax.service.ax_client: Generated new trial 23 with parameters {'x1': 0.0, 'x2': 0.094322, 'x3': 0.0, 'x4': 0.0, 'x5': 0.837995, 'x6': 0.019695, 'x7': 151.13371} using model BoTorch.\n", + "[INFO 04-17 12:10:59] ax.service.ax_client: Completed trial 23 with data: {'strength': (52.382159, None), 'biodegradability': (15.915414, None)}.\n", + "[INFO 04-17 12:11:01] ax.service.ax_client: Generated new trial 24 with parameters {'x1': 0.0, 'x2': 0.15087, 'x3': 0.0, 'x4': 0.198818, 'x5': 0.63805, 'x6': 0.026114, 'x7': 181.20332} using model BoTorch.\n", + "[INFO 04-17 12:11:01] ax.service.ax_client: Completed trial 24 with data: {'strength': (65.184941, None), 'biodegradability': (12.632999, None)}.\n", + "[INFO 04-17 12:11:04] ax.service.ax_client: Generated new trial 25 with parameters {'x1': 0.036207, 'x2': 0.0, 'x3': 0.0, 'x4': 0.250386, 'x5': 0.908954, 'x6': 0.029536, 'x7': 163.175483} using model BoTorch.\n", + "[INFO 04-17 12:11:04] ax.service.ax_client: Completed trial 25 with data: {'strength': (52.719962, None), 'biodegradability': (15.157616, None)}.\n", + "[INFO 04-17 12:11:07] ax.service.ax_client: Generated new trial 26 with parameters {'x1': 0.076866, 'x2': 0.173221, 'x3': 0.0, 'x4': 0.11133, 'x5': 0.797933, 'x6': 0.013231, 'x7': 160.373806} using model BoTorch.\n", + "[INFO 04-17 12:11:07] ax.service.ax_client: Completed trial 26 with data: {'strength': (79.935322, None), 'biodegradability': (11.222913, None)}.\n", + "[INFO 04-17 12:11:11] ax.service.ax_client: Generated new trial 27 with parameters {'x1': 0.479459, 'x2': 0.346647, 'x3': 0.0, 'x4': 0.097935, 'x5': 0.484028, 'x6': 0.026186, 'x7': 167.117139} using model BoTorch.\n", + "[INFO 04-17 12:11:11] ax.service.ax_client: Completed trial 27 with data: {'strength': (121.019677, None), 'biodegradability': (5.346441, None)}.\n", + "[INFO 04-17 12:11:14] ax.service.ax_client: Generated new trial 28 with parameters {'x1': 0.580115, 'x2': 0.041994, 'x3': 0.0, 'x4': 0.096781, 'x5': 0.463182, 'x6': 0.025367, 'x7': 169.884797} using model BoTorch.\n", + "[INFO 04-17 12:11:14] ax.service.ax_client: Completed trial 28 with data: {'strength': (103.120131, None), 'biodegradability': (7.851948, None)}.\n", + "[INFO 04-17 12:11:17] ax.service.ax_client: Generated new trial 29 with parameters {'x1': 0.0, 'x2': 0.008422, 'x3': 0.0, 'x4': 0.114974, 'x5': 0.570566, 'x6': 0.01, 'x7': 172.607485} using model BoTorch.\n", + "[INFO 04-17 12:11:17] ax.service.ax_client: Completed trial 29 with data: {'strength': (68.035485, None), 'biodegradability': (15.315641, None)}.\n", + "[INFO 04-17 12:11:20] ax.service.ax_client: Generated new trial 30 with parameters {'x1': 0.0, 'x2': 0.049395, 'x3': 0.0, 'x4': 0.127239, 'x5': 0.817694, 'x6': 0.025151, 'x7': 152.918907} using model BoTorch.\n", + "[INFO 04-17 12:11:20] ax.service.ax_client: Completed trial 30 with data: {'strength': (72.020162, None), 'biodegradability': (16.439821, None)}.\n", + "[INFO 04-17 12:11:24] ax.service.ax_client: Generated new trial 31 with parameters {'x1': 0.257347, 'x2': 0.645363, 'x3': 0.0, 'x4': 0.09729, 'x5': 0.480215, 'x6': 0.022957, 'x7': 166.427894} using model BoTorch.\n", + "[INFO 04-17 12:11:24] ax.service.ax_client: Completed trial 31 with data: {'strength': (91.473496, None), 'biodegradability': (4.18437, None)}.\n", + "[INFO 04-17 12:11:27] ax.service.ax_client: Generated new trial 32 with parameters {'x1': 0.413376, 'x2': 0.072098, 'x3': 0.0, 'x4': 0.099215, 'x5': 0.457643, 'x6': 0.031045, 'x7': 154.361334} using model BoTorch.\n", + "[INFO 04-17 12:11:27] ax.service.ax_client: Completed trial 32 with data: {'strength': (97.744146, None), 'biodegradability': (8.924053, None)}.\n", + "[INFO 04-17 12:11:31] ax.service.ax_client: Generated new trial 33 with parameters {'x1': 0.50416, 'x2': 0.273746, 'x3': 0.0, 'x4': 0.094571, 'x5': 0.503533, 'x6': 0.022394, 'x7': 167.617223} using model BoTorch.\n", + "[INFO 04-17 12:11:31] ax.service.ax_client: Completed trial 33 with data: {'strength': (119.509999, None), 'biodegradability': (5.995635, None)}.\n", + "[INFO 04-17 12:11:36] ax.service.ax_client: Generated new trial 34 with parameters {'x1': 0.0, 'x2': 0.058864, 'x3': 0.0, 'x4': 0.121596, 'x5': 0.762225, 'x6': 0.02506, 'x7': 165.611183} using model BoTorch.\n", + "[INFO 04-17 12:11:36] ax.service.ax_client: Completed trial 34 with data: {'strength': (77.437982, None), 'biodegradability': (15.832145, None)}.\n", + "[INFO 04-17 12:11:40] ax.service.ax_client: Generated new trial 35 with parameters {'x1': 0.005073, 'x2': 0.182147, 'x3': 0.0, 'x4': 0.112601, 'x5': 0.727606, 'x6': 0.02698, 'x7': 167.409672} using model BoTorch.\n", + "[INFO 04-17 12:11:40] ax.service.ax_client: Completed trial 35 with data: {'strength': (86.774828, None), 'biodegradability': (13.339966, None)}.\n", + "[INFO 04-17 12:11:44] ax.service.ax_client: Generated new trial 36 with parameters {'x1': 0.472891, 'x2': 0.132407, 'x3': 0.0, 'x4': 0.095867, 'x5': 0.440817, 'x6': 0.032109, 'x7': 160.997823} using model BoTorch.\n", + "[INFO 04-17 12:11:44] ax.service.ax_client: Completed trial 36 with data: {'strength': (109.173001, None), 'biodegradability': (8.197528, None)}.\n", + "[INFO 04-17 12:11:48] ax.service.ax_client: Generated new trial 37 with parameters {'x1': 0.005355, 'x2': 0.259832, 'x3': 0.0, 'x4': 0.107173, 'x5': 0.701674, 'x6': 0.028271, 'x7': 168.553791} using model BoTorch.\n", + "[INFO 04-17 12:11:48] ax.service.ax_client: Completed trial 37 with data: {'strength': (91.075536, None), 'biodegradability': (11.375168, None)}.\n", + "/Users/andrewf/miniconda3/envs/ax_env/lib/python3.9/site-packages/ax/modelbridge/modelbridge_utils.py:878: UserWarning: FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", + " frontier_observations, f, obj_w, obj_t = get_pareto_frontier_and_configs(\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "from ax.service.ax_client import AxClient, ObjectiveProperties\n", + "\n", + "\n", + "import pandas as pd\n", + "\n", + "obj1_name = \"strength\" # CHANGE: add name of first objective\n", + "obj2_name = \"biodegradability\" # CHANGE: add name of first objective\n", + "\n", + "\n", + "# CHANGE: remove the moo_branin dummy objective function, we will use the above function\n", + "\n", + "# CHANGE: update the total quantity for the composition constraint\n", + "total = 1.0\n", + "\n", + "# CHANGE: add the historical data that was pulled from the literature\n", + "X_train = pd.DataFrame(\n", + " [\n", + " {\"x1\": 0.3, \"x2\": 0.2, \"x3\": 0.1, \"x4\": 0.0, \"x5\": 0.4, \"x6\": 0.05, \"x7\": 150.0},\n", + " {\"x1\": 0.0, \"x2\": 0.0, \"x3\": 0.3, \"x4\": 0.7, \"x5\": 0.0, \"x6\": 0.1, \"x7\": 160.0},\n", + " {\"x1\": 0.2, \"x2\": 0.2, \"x3\": 0.2, \"x4\": 0.2, \"x5\": 0.2, \"x6\": 0.09, \"x7\": 184.0},\n", + " ]\n", + ")\n", + "\n", + "# CHANGE: calculate the y_train values using the polymer_properties function\n", + "y_train = [polymer_properties(**row[1]) for row in X_train.iterrows()]\n", + "\n", + "# Define the number of training examples\n", + "n_train = len(X_train)\n", + "\n", + "ax_client = AxClient(random_seed=12345) # CHANGE: add random seed for reproducibility\n", + "\n", + "ax_client.create_experiment(\n", + " parameters=[\n", + " {\"name\": \"x1\", \"type\": \"range\", \"bounds\": [0.0, 1.0]}, # CHANGE: update parameter\n", + " {\"name\": \"x2\", \"type\": \"range\", \"bounds\": [0.0, 1.0]}, # CHANGE: update parameter\n", + " {\"name\": \"x3\", \"type\": \"range\", \"bounds\": [0.0, 1.0]}, # CHANGE: add new parameter\n", + " {\"name\": \"x4\", \"type\": \"range\", \"bounds\": [0.0, 1.0]}, # CHANGE: add new parameter\n", + " {\"name\": \"x5\", \"type\": \"range\", \"bounds\": [0.0, 1.0]}, # CHANGE: add new parameter\n", + " {\"name\": \"x6\", \"type\": \"range\", \"bounds\": [0.01, 0.1]}, # CHANGE: add new parameter\n", + " {\"name\": \"x7\", \"type\": \"range\", \"bounds\": [120.0, 200.0]}, # CHANGE: add new parameter\n", + " ],\n", + " objectives={\n", + " obj1_name: ObjectiveProperties(minimize=False, threshold=70.0), # CHANGE: set minimize to False and change threshold\n", + " obj2_name: ObjectiveProperties(minimize=False, threshold=0.0), # CHANGE: set minimize to False and change threshold\n", + " },\n", + " parameter_constraints=[\n", + " f\"x1 + x2 + x3 + x4 <= {total}\", # CHANGE: update composition constraint\n", + " ],\n", + ")\n", + "\n", + "# Add existing data to the AxClient\n", + "for i in range(n_train):\n", + " parameterization = X_train.iloc[i].to_dict()\n", + "\n", + " ax_client.attach_trial(parameterization)\n", + " ax_client.complete_trial(trial_index=i, raw_data=y_train[i])\n", + "\n", + "\n", + "for _ in range(35): # CHANGE: increase number of trials\n", + "\n", + " parameterization, trial_index = ax_client.get_next_trial()\n", + "\n", + " # CHANGE: pull all added parameters from the parameterization\n", + " x1 = parameterization[\"x1\"]\n", + " x2 = parameterization[\"x2\"]\n", + " x3 = parameterization[\"x3\"]\n", + " x4 = parameterization[\"x4\"]\n", + " x5 = total - (x1 + x2 + x3 + x4) # CHANGE: update composition constraint\n", + " x6 = parameterization[\"x6\"]\n", + " x7 = parameterization[\"x7\"]\n", + "\n", + " results = polymer_properties(x1, x2, x3, x4, x5, x6, x7) # CHANGE: switch to polymer function\n", + " ax_client.complete_trial(trial_index=trial_index, raw_data=results)\n", + "\n", + "pareto_results = ax_client.get_pareto_optimal_parameters()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Show the Pareto Optimal Parameters\n", + "\n", + "After the optimization loop has completed, we can view the set of parameter combinations that are found to be Pareto optimal. This will help us understand the tradeoff between the two objectives of interest." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 04-17 12:11:49] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.\n", + "/Users/andrewf/miniconda3/envs/ax_env/lib/python3.9/site-packages/ax/modelbridge/modelbridge_utils.py:878: UserWarning: FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", + " frontier_observations, f, obj_w, obj_t = get_pareto_frontier_and_configs(\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " x1 x2 x3 x4 x5 x6 x7 strength biodegradability\n", + "21 0.48 0.22 0.0 0.09 0.44 0.03 159.78 114.99 7.26\n", + "36 0.47 0.13 0.0 0.10 0.44 0.03 161.00 109.16 8.20\n", + "33 0.50 0.27 0.0 0.09 0.50 0.02 167.62 119.51 6.00\n", + "27 0.48 0.35 0.0 0.10 0.48 0.03 167.12 121.00 5.35\n", + "32 0.41 0.07 0.0 0.10 0.46 0.03 154.36 97.75 8.92\n", + "37 0.01 0.26 0.0 0.11 0.70 0.03 168.55 91.05 11.38\n", + "35 0.01 0.18 0.0 0.11 0.73 0.03 167.41 86.79 13.33\n", + "18 0.04 0.08 0.0 0.12 0.74 0.02 169.50 79.40 14.41\n", + "34 0.00 0.06 0.0 0.12 0.76 0.03 165.61 77.41 15.84\n", + "30 0.00 0.05 0.0 0.13 0.82 0.03 152.92 72.03 16.44" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "p_op = ax_client.get_pareto_optimal_parameters()\n", + "\n", + "# parse p_op values to get parameters and values\n", + "p_op_index = list(p_op.keys())\n", + "p_op_params = [p_op[i][0] for i in p_op_index]\n", + "p_op_values = [p_op[i][1][0] for i in p_op_index]\n", + "\n", + "# organize the results into a dataframe\n", + "pareto_results = pd.DataFrame(p_op_params, columns=[\"x1\", \"x2\", \"x3\", \"x4\", \"x5\", \"x6\", \"x7\"])\n", + "pareto_results[\"strength\"] = [v[\"strength\"] for v in p_op_values]\n", + "pareto_results[\"biodegradability\"] = [v[\"biodegradability\"] for v in p_op_values]\n", + "pareto_results.index = p_op_index\n", + "display(pareto_results.round(2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot the Optimal Values Found During Optimization\n", + "\n", + "We can visualize the set of pareto optimial soltuions relative to the entire dataset by plotting them.\n", + "\n", + "We observe that our historical data was indeed of poor quality, but that our model was able to find many candidates with signficantly higher strength and biodegradability scores. Additionally, we can now see a clear tradeoff between the two properties." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[WARNING 04-17 12:11:49] ax.service.utils.report_utils: Column reason missing for all trials. Not appending column.\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.style.use(\"ggplot\")\n", + "\n", + "fig, ax = plt.subplots(figsize=(4, 4), dpi=100)\n", + "\n", + "all_trials = ax_client.get_trials_data_frame()\n", + "ax.scatter(\n", + " all_trials[\"strength\"], \n", + " all_trials[\"biodegradability\"], \n", + " color='#818180', \n", + " facecolor='none', \n", + " s=25,\n", + " label='All Trials'\n", + ")\n", + "ax.scatter(pareto_results[\"strength\"], pareto_results[\"biodegradability\"], color='#0041FF', label='Pareto Optimal')\n", + "historical = np.array([[d['strength'][0], d['biodegradability'][0]] for d in y_train])\n", + "ax.scatter(historical[:,0], historical[:,1], color='#FF9A00', label='Historical data')\n", + "ax.axvline(70, ls=':', color='k')\n", + "ax.set_xlabel(\"Strength\")\n", + "ax.set_ylabel(\"Biodegradability\")\n", + "ax.legend(facecolor='w', fontsize=8, loc='center left', bbox_to_anchor=(1, 0.1))\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "ax_env", + "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": "3.9.19" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/tutorials/sobo-vs-mobo-hg-selection.jpeg b/docs/curriculum/tutorials/mobo/sobo-vs-mobo-hg-selection.jpeg similarity index 100% rename from docs/tutorials/sobo-vs-mobo-hg-selection.jpeg rename to docs/curriculum/tutorials/mobo/sobo-vs-mobo-hg-selection.jpeg diff --git a/docs/tutorials/sobo-tutorial.ipynb b/docs/curriculum/tutorials/sobo/sobo-tutorial.ipynb similarity index 100% rename from docs/tutorials/sobo-tutorial.ipynb rename to docs/curriculum/tutorials/sobo/sobo-tutorial.ipynb diff --git a/docs/tutorials.md b/docs/tutorials.md index b5fb0f1c..df478a51 100644 --- a/docs/tutorials.md +++ b/docs/tutorials.md @@ -3,6 +3,7 @@ ```{nbgallery} :maxdepth: 1 -tutorials/sobo-tutorial.ipynb -tutorials/mobo-tutorial.ipynb +curriculum/tutorials/sobo/sobo-tutorial.ipynb +curriculum/tutorials/mobo/mobo-tutorial.ipynb +curriculum/tutorials/batch/Batch_BO_tutorial.ipynb ``` diff --git a/docs/tutorials/batch-bo-tutorial.ipynb b/docs/tutorials/batch-bo-tutorial.ipynb deleted file mode 100644 index bb6f3d12..00000000 --- a/docs/tutorials/batch-bo-tutorial.ipynb +++ /dev/null @@ -1,1544 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Noisy Batch Optimization\n", - "\n", - "Many high performing materials suffer from poor corrosion resistance. Anti-corrosion coatings are a common solution that can be adapted to protect a given material in a number of harsh environments. You have the task of designing an anti-corrosion coating for a new material that needs to withstand a relatively high temperature corrosive environment. You decided to simulate the corrosive environment in the lab and test a number of different coatings to see which one performs the best. Based on the lab space, you see that you can specify up to nine coatings in a single test, allowing three replicates of three coating designs to be tested at once.\n", - "\n", - "You believe Bayesian optimization can help you in this task and decide to put together an optimization script using Honegumi to help solve this problem.\n", - "\n", - "Looking through the literature you identify the following tunable parameters for this problem:\n", - "\n", - "| | **Parameter Name** | **Bounds** |\n", - "|------|--------------------|-------------|\n", - "| x1 | Resin Fraction | [0, 1] |\n", - "| x2 | Inhibitor Fraction | [0, 1] |\n", - "| x3 | Insulator Fraction | [0, 1] |\n", - "| x4 | Stabilizer Fraction | [0, 0.1] |\n", - "| x5 | Coating Thickness | [0.1, 10] |\n", - "\n", - "Additionally, you identify several constraints on the relative fractions of the different components based on prior work in the literature. Notably, you observe that the best materials keep `Resin Fraction` > `Inhibitor Fraction` > `Insulator Fraction` > `Stabilizer Fraction`.\n", - "\n", - "A dummy objective function that emulates the results of the corrosion experiment has been constructed in the code cell below. To simulate sample variability, random noise is added to the output of the function on call. Although we can easily find optimal values using the equations, we will pretend that the objective function is unknown and use a Bayesian optimization approach to find the optimal set of input parameters instead." - ] - }, - { - "cell_type": "code", - "execution_count": 317, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "def simulate_corrosion(x1, x2, x3, x4, x5):\n", - " \"\"\"\n", - " Calculate the corrosion damage based on the input parameters.\n", - "\n", - " Args:\n", - " x1 (float): the fraction of resin used in the coating formulation\n", - " x2 (float): the fraction of inhibitor used in the coating formulation\n", - " x3 (float): the fraction of insulator used in the coating formulation\n", - " x4 (float): the fraction of stabilizer used in the coating formulation\n", - " x5 (float): the coating thickness\n", - "\n", - " Returns:\n", - " dict: the measured corrosion damage and uncertainty\n", - " \"\"\"\n", - " score = float(\n", - " 1/(np.exp(-40*(x1-0.42)**2) + np.exp(-24*(x1-0.75)**2) + 0.1) +\n", - " 1/(np.exp(-30*(x2-0.22)**2) + np.exp(-1000*(x2-0.22)**2) + np.exp(-800*(x2-0.42)**2) + 0.15) +\n", - " 1/(np.exp(-25*(x3-0.27)**2) + np.exp(-1000*(x3-0.27)**2) + np.exp(-150*(x3-0.37)**2) +1) +\n", - " 1/(np.exp(-400*(x4-0.08)**2) +1) + 0.5*x4 +\n", - " 1/(np.exp(-(x5-8)**2) + 0.05*x5 + 0.3)\n", - " ) - 3.18\n", - " \n", - " return (abs(score), np.random.uniform(0.01, 0.1))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Applying Honegumi\n", - "\n", - "We will now use the [Honegumi website](https://honegumi.readthedocs.io/en/latest/) to generate a script that will help us optimize the coating parameters. From the description, we observe that our problem is a **single objective** optimization problem with a **constraint on the fractional sum of coating components** and an **ordering constraint** on the relative fractions of each component. As there is room for several samples to be tested in parallel, **batch optimization** could make the approach more efficient. Lastly, it is expected that the reults will be noisy, so you decide to use a **Fully Bayesian** model to make the optimization process more robust.\n", - "\n", - "![Honegumi Selections for The Problem Statement](Selection-1.jpg)\n", - "\n", - "The Honegumi generated optimization script will provide a framework for our optimization campaign that we can modify to suit our specific problem needs. In the code sections below, we will make several modifications to this generated script to make it compatible with our problem." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Modifying the Code for Our Problem\n", - "\n", - "We can modify this code to suit our problem with a few simple modifications. Wherever a modification has been made to the code, a comment starting with `# CHANGE:` has been added along with a brief description of the change." - ] - }, - { - "cell_type": "code", - "execution_count": 337, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[INFO 05-01 08:47:30] ax.service.ax_client: Starting optimization with verbose logging. To disable logging, set the `verbose_logging` argument to `False`. Note that float values in the logs are rounded to 6 decimal points.\n", - "[WARNING 05-01 08:47:30] ax.service.ax_client: Random seed set to 42. Note that this setting only affects the Sobol quasi-random generator and BoTorch-powered Bayesian optimization models. For the latter models, setting random seed to the same number for two optimizations will make the generated trials similar, but not exactly the same, and over time the trials will diverge more.\n", - "[INFO 05-01 08:47:30] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicitly specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.\n", - "[INFO 05-01 08:47:30] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicitly specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.\n", - "[INFO 05-01 08:47:30] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x3. If that is not the expected value type, you can explicitly specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.\n", - "[INFO 05-01 08:47:30] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x5. If that is not the expected value type, you can explicitly specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.\n", - "[INFO 05-01 08:47:30] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x3', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x5', parameter_type=FLOAT, range=[0.1, 10.0])], parameter_constraints=[ParameterConstraint(1.0*x1 + 1.0*x2 + 1.0*x3 <= 1.0), OrderConstraint(x2 <= x1), OrderConstraint(x3 <= x1)]).\n", - "[INFO 05-01 08:47:30] ax.service.ax_client: Generated new trial 0 with parameters {'x1': 0.462035, 'x2': 0.140554, 'x3': 0.120071, 'x5': 7.491386} using model Sobol.\n", - "[INFO 05-01 08:47:30] ax.service.ax_client: Generated new trial 1 with parameters {'x1': 0.681195, 'x2': 0.083172, 'x3': 0.147011, 'x5': 2.201755} using model Sobol.\n", - "[INFO 05-01 08:47:30] ax.service.ax_client: Completed trial 0 with data: {'corrosion_score': (1.163775, 0.049476)}.\n", - "[INFO 05-01 08:47:30] ax.service.ax_client: Completed trial 1 with data: {'corrosion_score': (2.737126, 0.048125)}.\n", - "[INFO 05-01 08:47:30] ax.service.ax_client: Generated new trial 2 with parameters {'x1': 0.540494, 'x2': 0.28639, 'x3': 0.106513, 'x5': 3.150732} using model Sobol.\n", - "/Users/andrewf/miniconda3/envs/ax_env/lib/python3.9/site-packages/ax/core/data.py:284: FutureWarning:\n", - "\n", - "The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", - "\n", - "[INFO 05-01 08:47:30] ax.service.ax_client: Generated new trial 3 with parameters {'x1': 0.17495, 'x2': 0.00773, 'x3': 0.057119, 'x5': 9.218317} using model Sobol.\n", - "[INFO 05-01 08:47:30] ax.service.ax_client: Completed trial 2 with data: {'corrosion_score': (2.172886, 0.019982)}.\n", - "[INFO 05-01 08:47:30] ax.service.ax_client: Completed trial 3 with data: {'corrosion_score': (7.653779, 0.056008)}.\n", - "[INFO 05-01 08:47:30] ax.service.ax_client: Generated new trial 4 with parameters {'x1': 0.282052, 'x2': 0.122099, 'x3': 0.234689, 'x5': 3.671898} using model Sobol.\n", - "/Users/andrewf/miniconda3/envs/ax_env/lib/python3.9/site-packages/ax/core/data.py:284: FutureWarning:\n", - "\n", - "The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", - "\n", - "[INFO 05-01 08:47:30] ax.service.ax_client: Generated new trial 5 with parameters {'x1': 0.369966, 'x2': 0.357531, 'x3': 0.268685, 'x5': 7.204235} using model Sobol.\n", - "[INFO 05-01 08:47:30] ax.service.ax_client: Completed trial 4 with data: {'corrosion_score': (3.35743, 0.092051)}.\n", - "[INFO 05-01 08:47:30] ax.service.ax_client: Completed trial 5 with data: {'corrosion_score': (1.162666, 0.012073)}.\n", - "Sample: 100%|██████████| 2048/2048 [00:29, 69.31it/s, step size=3.53e-01, acc. prob=0.912]\n", - "[INFO 05-01 08:48:03] ax.service.ax_client: Generated new trial 6 with parameters {'x1': 1.0, 'x2': 0.0, 'x3': 0.0, 'x5': 6.657872} using model FullyBayesian.\n", - "/Users/andrewf/miniconda3/envs/ax_env/lib/python3.9/site-packages/ax/core/data.py:284: FutureWarning:\n", - "\n", - "The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", - "\n", - "[INFO 05-01 08:48:03] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.\n", - "Warmup: 9%|▊ | 178/2048 [03:56, 1.33s/it, step size=4.18e-01, acc. prob=0.778]\n", - "Warmup: 5%|▌ | 108/2048 [01:52, 1.05s/it, step size=8.54e-01, acc. prob=0.781]\n", - "[INFO 05-01 08:48:07] ax.service.ax_client: Generated new trial 7 with parameters {'x1': 1.0, 'x2': 0.0, 'x3': 0.0, 'x5': 6.658105} using model FullyBayesian.\n", - "[INFO 05-01 08:48:07] ax.service.ax_client: Completed trial 6 with data: {'corrosion_score': (5.56047, 0.016152)}.\n", - "[INFO 05-01 08:48:07] ax.service.ax_client: Completed trial 7 with data: {'corrosion_score': (5.56029, 0.082192)}.\n", - "Sample: 100%|██████████| 2048/2048 [00:30, 66.32it/s, step size=3.76e-01, acc. prob=0.868]\n", - "[INFO 05-01 08:48:41] ax.service.ax_client: Generated new trial 8 with parameters {'x1': 0.5, 'x2': 0.0, 'x3': 0.5, 'x5': 10.0} using model FullyBayesian.\n", - "/Users/andrewf/miniconda3/envs/ax_env/lib/python3.9/site-packages/ax/core/data.py:284: FutureWarning:\n", - "\n", - "The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", - "\n", - "[INFO 05-01 08:48:41] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.\n", - "[INFO 05-01 08:48:45] ax.service.ax_client: Generated new trial 9 with parameters {'x1': 0.5, 'x2': 0.0, 'x3': 0.5, 'x5': 7.195933} using model FullyBayesian.\n", - "[INFO 05-01 08:48:45] ax.service.ax_client: Completed trial 8 with data: {'corrosion_score': (3.228185, 0.015832)}.\n", - "[INFO 05-01 08:48:45] ax.service.ax_client: Completed trial 9 with data: {'corrosion_score': (2.850999, 0.079142)}.\n", - "Sample: 100%|██████████| 2048/2048 [00:26, 76.71it/s, step size=3.86e-01, acc. prob=0.895]\n", - "[INFO 05-01 08:49:16] ax.service.ax_client: Generated new trial 10 with parameters {'x1': 0.424484, 'x2': 0.424484, 'x3': 0.0, 'x5': 7.339948} using model FullyBayesian.\n", - "/Users/andrewf/miniconda3/envs/ax_env/lib/python3.9/site-packages/ax/core/data.py:284: FutureWarning:\n", - "\n", - "The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", - "\n", - "[INFO 05-01 08:49:16] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.\n", - "[INFO 05-01 08:49:20] ax.service.ax_client: Generated new trial 11 with parameters {'x1': 0.424514, 'x2': 0.424514, 'x3': 0.0, 'x5': 7.345266} using model FullyBayesian.\n", - "[INFO 05-01 08:49:20] ax.service.ax_client: Completed trial 10 with data: {'corrosion_score': (0.953817, 0.019769)}.\n", - "[INFO 05-01 08:49:20] ax.service.ax_client: Completed trial 11 with data: {'corrosion_score': (0.950801, 0.022215)}.\n", - "Sample: 100%|██████████| 2048/2048 [00:22, 89.76it/s, step size=5.13e-01, acc. prob=0.835] \n", - "[INFO 05-01 08:49:46] ax.service.ax_client: Generated new trial 12 with parameters {'x1': 0.426213, 'x2': 0.0, 'x3': 0.0, 'x5': 0.1} using model FullyBayesian.\n", - "/Users/andrewf/miniconda3/envs/ax_env/lib/python3.9/site-packages/ax/core/data.py:284: FutureWarning:\n", - "\n", - "The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", - "\n", - "[INFO 05-01 08:49:46] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.\n", - "[INFO 05-01 08:49:52] ax.service.ax_client: Generated new trial 13 with parameters {'x1': 0.5, 'x2': 0.5, 'x3': 0.0, 'x5': 10.0} using model FullyBayesian.\n", - "[INFO 05-01 08:49:52] ax.service.ax_client: Completed trial 12 with data: {'corrosion_score': (5.697937, 0.069679)}.\n", - "[INFO 05-01 08:49:52] ax.service.ax_client: Completed trial 13 with data: {'corrosion_score': (4.724092, 0.051947)}.\n", - "Sample: 100%|██████████| 2048/2048 [00:32, 62.65it/s, step size=3.01e-01, acc. prob=0.918]\n", - "[INFO 05-01 08:50:29] ax.service.ax_client: Generated new trial 14 with parameters {'x1': 0.420853, 'x2': 0.344086, 'x3': 0.0, 'x5': 5.92896} using model FullyBayesian.\n", - "/Users/andrewf/miniconda3/envs/ax_env/lib/python3.9/site-packages/ax/core/data.py:284: FutureWarning:\n", - "\n", - "The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", - "\n", - "[INFO 05-01 08:50:29] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.\n", - "[INFO 05-01 08:50:36] ax.service.ax_client: Generated new trial 15 with parameters {'x1': 0.619151, 'x2': 0.230517, 'x3': 0.150332, 'x5': 7.898638} using model FullyBayesian.\n", - "[INFO 05-01 08:50:36] ax.service.ax_client: Completed trial 14 with data: {'corrosion_score': (2.554639, 0.05664)}.\n", - "[INFO 05-01 08:50:36] ax.service.ax_client: Completed trial 15 with data: {'corrosion_score': (0.45323, 0.083762)}.\n", - "Sample: 100%|██████████| 2048/2048 [00:40, 50.65it/s, step size=2.53e-01, acc. prob=0.903]\n", - "[INFO 05-01 08:51:21] ax.service.ax_client: Generated new trial 16 with parameters {'x1': 0.81943, 'x2': 0.18057, 'x3': 0.0, 'x5': 8.956439} using model FullyBayesian.\n", - "/Users/andrewf/miniconda3/envs/ax_env/lib/python3.9/site-packages/ax/core/data.py:284: FutureWarning:\n", - "\n", - "The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", - "\n", - "[INFO 05-01 08:51:21] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.\n", - "[INFO 05-01 08:51:26] ax.service.ax_client: Generated new trial 17 with parameters {'x1': 0.819333, 'x2': 0.180667, 'x3': 0.0, 'x5': 8.951648} using model FullyBayesian.\n", - "[INFO 05-01 08:51:26] ax.service.ax_client: Completed trial 16 with data: {'corrosion_score': (1.247548, 0.044192)}.\n", - "[INFO 05-01 08:51:26] ax.service.ax_client: Completed trial 17 with data: {'corrosion_score': (1.243592, 0.038841)}.\n", - "Sample: 100%|██████████| 2048/2048 [00:33, 61.85it/s, step size=3.20e-01, acc. prob=0.874]\n", - "[INFO 05-01 08:52:04] ax.service.ax_client: Generated new trial 18 with parameters {'x1': 0.553224, 'x2': 0.184695, 'x3': 0.262081, 'x5': 8.289913} using model FullyBayesian.\n", - "/Users/andrewf/miniconda3/envs/ax_env/lib/python3.9/site-packages/ax/core/data.py:284: FutureWarning:\n", - "\n", - "The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", - "\n", - "[INFO 05-01 08:52:04] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.\n", - "[INFO 05-01 08:52:09] ax.service.ax_client: Generated new trial 19 with parameters {'x1': 0.553114, 'x2': 0.184659, 'x3': 0.262227, 'x5': 8.29011} using model FullyBayesian.\n", - "[INFO 05-01 08:52:09] ax.service.ax_client: Completed trial 18 with data: {'corrosion_score': (0.409177, 0.034298)}.\n", - "[INFO 05-01 08:52:09] ax.service.ax_client: Completed trial 19 with data: {'corrosion_score': (0.409134, 0.061954)}.\n", - "Sample: 100%|██████████| 2048/2048 [00:39, 51.86it/s, step size=2.33e-01, acc. prob=0.921]\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "from ax.service.ax_client import AxClient, ObjectiveProperties\n", - "\n", - "from ax.modelbridge.factory import Models\n", - "from ax.modelbridge.generation_strategy import GenerationStep, GenerationStrategy\n", - "\n", - "\n", - "obj1_name = \"corrosion_score\" # CHANGE: update objective name\n", - "\n", - "# CHANGE: remove the branin dummy objective function, we will use the above function\n", - "\n", - "total = 1.0 # CHANGE: update total component fraction\n", - "\n", - "\n", - "gs = GenerationStrategy(\n", - " steps=[\n", - " GenerationStep(\n", - " model=Models.SOBOL,\n", - " num_trials=6,\n", - " min_trials_observed=3,\n", - " max_parallelism=5,\n", - " model_kwargs={\"seed\": 999},\n", - " model_gen_kwargs={},\n", - " ),\n", - " GenerationStep(\n", - " model=Models.FULLYBAYESIAN,\n", - " num_trials=-1,\n", - " max_parallelism=3,\n", - " should_deduplicate=True, # CHANGE: reduce duplicate suggestions\n", - " model_kwargs={\"num_samples\": 1024, \"warmup_steps\": 1024}, # CHANGE: increase\n", - " ),\n", - " ]\n", - ")\n", - "\n", - "ax_client = AxClient(generation_strategy=gs, random_seed=42) # CHANGE: add random seed for reproducibility\n", - "\n", - "ax_client.create_experiment(\n", - " parameters=[\n", - " {\"name\": \"x1\", \"type\": \"range\", \"bounds\": [0.0, total]}, # CHANGE: update parameter\n", - " {\"name\": \"x2\", \"type\": \"range\", \"bounds\": [0.0, total]}, # CHANGE: update parameter\n", - " {\"name\": \"x3\", \"type\": \"range\", \"bounds\": [0.0, total]}, # CHANGE: add new parameter\n", - " {\"name\": \"x5\", \"type\": \"range\", \"bounds\": [0.1, 10.0]}, # CHANGE: add new parameter\n", - " ],\n", - " objectives={\n", - " obj1_name: ObjectiveProperties(minimize=True),\n", - " },\n", - " parameter_constraints=[\n", - " f\"x1 + x2 + x3 <= {total}\", # CHANGE: update composition constraint\n", - " \"x1 >= x2\", # CHANGE: update order constraint\n", - " \"x1 >= x3\", # CHANGE: add order constraint\n", - " ],\n", - ")\n", - "\n", - "batch_size = 2\n", - "\n", - "for _ in range(10): # CHANGE: decrease number of iterations\n", - "\n", - " parameterizations, optimization_complete = ax_client.get_next_trials(batch_size)\n", - " for trial_index, parameterization in list(parameterizations.items()):\n", - " \n", - " # CHANGE: pull all added parameters from the parameterization\n", - " x1 = parameterization[\"x1\"]\n", - " x2 = parameterization[\"x2\"]\n", - " x3 = parameterization[\"x3\"]\n", - " x4 = total - (x1 + x2 + x3) # CHANGE: update composition constraint\n", - " x5 = parameterization[\"x5\"]\n", - "\n", - " results = simulate_corrosion(x1, x2, x3, x4, x5)\n", - " ax_client.complete_trial(trial_index=trial_index, raw_data=results)\n", - "\n", - "best_parameters, metrics = ax_client.get_best_parameters()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Show the Best Parameters\n", - "\n", - "After our optimization loop has completed, we can use the model to find the best parameters and their corresponding strength value. These will be our optimial set of parameters that we use in the 3D printer going forward." - ] - }, - { - "cell_type": "code", - "execution_count": 338, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Sample: 100%|██████████| 2048/2048 [00:42, 47.73it/s, step size=2.50e-01, acc. prob=0.877]\n" - ] - }, - { - "data": { - "text/plain": [ - "(19,\n", - " {'x1': 0.5531141417704178,\n", - " 'x2': 0.1846591406143514,\n", - " 'x3': 0.26222671761513805,\n", - " 'x5': 8.2901097602077},\n", - " ({'corrosion_score': 0.41040928033269086},\n", - " {'corrosion_score': {'corrosion_score': 0.0008997393689987544}}))" - ] - }, - "execution_count": 338, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ax_client.get_best_trial()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plotting Optimization Performance\n", - "\n", - "We can plot the performance of our optmization loop to see how the optimization task progressed as a function of iteration count." - ] - }, - { - "cell_type": "code", - "execution_count": 339, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[WARNING 05-01 08:58:34] ax.service.utils.report_utils: Column reason missing for all trials. Not appending column.\n" - ] - }, - { - "data": { - "text/plain": [ - "(0.0, 2.0)" - ] - }, - "execution_count": 339, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "h_blue = '#0033FF'\n", - "\n", - "df = ax_client.get_trials_data_frame()\n", - "fig, ax = plt.subplots(figsize=(6,4), dpi=120)\n", - "ax.plot(df.corrosion_score.values, ls='None', marker='o', mfc='none', mec='black')\n", - "ax.plot(np.minimum.accumulate(df.corrosion_score.values), color=h_blue)\n", - "plt.xlabel('Trial')\n", - "plt.ylabel('Corrosion Score [lower is better]')\n", - "plt.ylim(0, 2)" - ] - }, - { - "cell_type": "code", - "execution_count": 340, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "linkText": "Export to plot.ly", - "plotlyServerURL": "https://plot.ly", - "showLink": false - }, - "data": [ - { - "hoverinfo": "none", - "line": { - "color": "black", - "dash": "dot", - "width": 2 - }, - "mode": "lines", - "showlegend": false, - "type": "scatter", - "visible": true, - "x": [ - -3.952154604781554, - 10.903313786058435 - ], - "y": [ - -3.952154604781554, - 10.903313786058435 - ] - }, - { - "error_x": { - "array": [ - 0.09697307193758328, - 0.09432580615956097, - 0.03916477554641934, - 0.10977525367163038, - 0.18041947262022728, - 0.023662859070073566, - 0.0316572302731216, - 0.16109585493320808, - 0.03103124681089616, - 0.15511805238701318, - 0.03874663888694734, - 0.04354174301098419, - 0.13657119941037701, - 0.1018157587494321, - 0.1110139250248606, - 0.1641737784542783, - 0.08661683840337824, - 0.07612889196206886 - ], - "color": "rgba(128,177,211,0.4)", - "type": "data" - }, - "error_y": { - "array": [ - 2.7256679784174875, - 4.199633838796122, - 3.599793582557131, - 5.2253166045607005, - 4.516845300842414, - 3.0953335998152287, - 0.16104538196927914, - 0.03166602726203759, - 4.657165685333809, - 3.574701887771811, - 0.045083000885533506, - 0.040628188869374526, - 6.458009708196848, - 5.374882199652275, - 3.9081294574205843, - 2.977922279320509, - 0.07742037877804649, - 0.08754464854448725 - ], - "color": "rgba(128,177,211,0.4)", - "type": "data" - }, - "hoverinfo": "text", - "marker": { - "color": "rgba(128,177,211,1)" - }, - "mode": "markers", - "name": "In-sample", - "showlegend": true, - "text": [ - "Arm 0_0

Actual Outcome: 1.164 [1.067, 1.261]
Predicted Outcome: 2.102 [-0.624, 4.828]

Parameterization:
x1: 0.46203490160405636
x2: 0.1405540555715561
x3: 0.12007084302604198
x5: 7.49138588104397", - "Arm 1_0

Actual Outcome: 2.737 [2.643, 2.831]
Predicted Outcome: 3.082 [-1.118, 7.281]

Parameterization:
x1: 0.6811950104311109
x2: 0.08317248616367579
x3: 0.14701138995587826
x5: 2.201754858437926", - "Arm 2_0

Actual Outcome: 2.173 [2.134, 2.212]
Predicted Outcome: 3.007 [-0.593, 6.607]

Parameterization:
x1: 0.5404942771419883
x2: 0.28639009688049555
x3: 0.10651267971843481
x5: 3.1507322339341046", - "Arm 3_0

Actual Outcome: 7.654 [7.544, 7.764]
Predicted Outcome: 3.416 [-1.809, 8.641]

Parameterization:
x1: 0.17495027277618647
x2: 0.007729803211987019
x3: 0.0571193927899003
x5: 9.218316878192127", - "Arm 4_0

Actual Outcome: 3.357 [3.177, 3.538]
Predicted Outcome: 2.838 [-1.679, 7.354]

Parameterization:
x1: 0.2820521341636777
x2: 0.12209852878004313
x3: 0.23468931019306183
x5: 3.671897889301181", - "Arm 5_0

Actual Outcome: 1.163 [1.139, 1.186]
Predicted Outcome: 1.707 [-1.388, 4.803]

Parameterization:
x1: 0.36996550764888525
x2: 0.35753058083355427
x3: 0.2686848407611251
x5: 7.204235409293323", - "Arm 6_0

Actual Outcome: 5.56 [5.529, 5.592]
Predicted Outcome: 5.557 [5.396, 5.718]

Parameterization:
x1: 0.9999999999999886
x2: 0.0
x3: 1.2886717519318773e-14
x5: 6.657871762887141", - "Arm 7_0

Actual Outcome: 5.56 [5.399, 5.721]
Predicted Outcome: 5.56 [5.529, 5.592]

Parameterization:
x1: 1.0
x2: 5.879023101716506e-16
x3: 8.221970846891384e-16
x5: 6.658104954751326", - "Arm 8_0

Actual Outcome: 3.228 [3.197, 3.259]
Predicted Outcome: 4.24 [-0.417, 8.897]

Parameterization:
x1: 0.5000000000004156
x2: 0.0
x3: 0.5000000000021749
x5: 9.999999999999234", - "Arm 9_0

Actual Outcome: 2.851 [2.696, 3.006]
Predicted Outcome: 2.316 [-1.259, 5.891]

Parameterization:
x1: 0.49999999999999983
x2: 2.9442422767142952e-15
x3: 0.5000000000000115
x5: 7.195932963944851", - "Arm 10_0

Actual Outcome: 0.954 [0.915, 0.993]
Predicted Outcome: 0.952 [0.907, 0.997]

Parameterization:
x1: 0.4244838630578679
x2: 0.42448386306381597
x3: 2.1289729987226923e-11
x5: 7.339947666832862", - "Arm 11_0

Actual Outcome: 0.951 [0.907, 0.994]
Predicted Outcome: 0.953 [0.912, 0.994]

Parameterization:
x1: 0.4245136618061755
x2: 0.42451366180615974
x3: 0.0
x5: 7.345265540626473", - "Arm 12_0

Actual Outcome: 5.698 [5.561, 5.835]
Predicted Outcome: 3.77 [-2.688, 10.228]

Parameterization:
x1: 0.4262126487368945
x2: 8.147112918011196e-16
x3: 4.376823862696568e-15
x5: 0.1000000000000202", - "Arm 13_0

Actual Outcome: 4.724 [4.622, 4.826]
Predicted Outcome: 2.098 [-3.277, 7.473]

Parameterization:
x1: 0.5000000000012181
x2: 0.5000000000106196
x3: 0.0
x5: 10.0", - "Arm 14_0

Actual Outcome: 2.555 [2.444, 2.666]
Predicted Outcome: 1.663 [-2.245, 5.571]

Parameterization:
x1: 0.42085294074668167
x2: 0.3440857934253744
x3: 0.0
x5: 5.928959908522031", - "Arm 15_0

Actual Outcome: 0.453 [0.289, 0.617]
Predicted Outcome: 1.004 [-1.974, 3.981]

Parameterization:
x1: 0.6191508786933483
x2: 0.23051694149204408
x3: 0.1503321798146068
x5: 7.898638178540372", - "Arm 16_0

Actual Outcome: 1.248 [1.161, 1.334]
Predicted Outcome: 1.246 [1.169, 1.324]

Parameterization:
x1: 0.8194301997175271
x2: 0.18056980028249606
x3: 2.299616162607646e-15
x5: 8.956439436443388", - "Arm 17_0

Actual Outcome: 1.244 [1.167, 1.320]
Predicted Outcome: 1.245 [1.158, 1.333]

Parameterization:
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] - } - ], - "label": "corrosion_score", - "method": "update" - } - ], - "x": 0, - "xanchor": "left", - "y": 1.125, - "yanchor": "top" - }, - { - "buttons": [ - { - "args": [ - { - "error_x.thickness": 2, - "error_x.width": 4, - "error_y.thickness": 2, - "error_y.width": 4 - } - ], - "label": "Yes", - "method": "restyle" - }, - { - "args": [ - { - "error_x.thickness": 0, - "error_x.width": 0, - "error_y.thickness": 0, - "error_y.width": 0 - } - ], - "label": "No", - "method": "restyle" - } - ], - "x": 1.125, - "xanchor": "left", - "y": 0.8, - "yanchor": "middle" - } - ], - "width": 530, - "xaxis": { - "linecolor": "black", - "linewidth": 0.5, - "mirror": true, - "range": [ - -3.952154604781554, - 10.903313786058435 - ], - "title": { - "text": "Actual Outcome" - }, - "zeroline": false - }, - "yaxis": { - "linecolor": "black", - "linewidth": 0.5, - "mirror": true, - "range": [ - -3.952154604781554, - 10.903313786058435 - ], - "title": { - "text": "Predicted Outcome" - }, - "zeroline": false - } - } - } - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from ax.modelbridge.cross_validation import cross_validate\n", - "from ax.plot.diagnostic import interact_cross_validation\n", - "\n", - "model = ax_client.generation_strategy.model\n", - "cv_results = cross_validate(model)\n", - "render(interact_cross_validation(cv_results))" - ] - }, - { - "cell_type": "code", - "execution_count": 325, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[CVResult(observed=, predicted=ObservationData(metric_names=['corrosion_score'], means=[3.33038581], covariance=[[0.0086991]])),\n", - " CVResult(observed=, predicted=ObservationData(metric_names=['corrosion_score'], means=[3.38045204], covariance=[[1.14749778]])),\n", - " CVResult(observed=, predicted=ObservationData(metric_names=['corrosion_score'], means=[1.57425067], covariance=[[0.00012101]])),\n", - " CVResult(observed=, predicted=ObservationData(metric_names=['corrosion_score'], means=[3.74291177], covariance=[[0.00775312]])),\n", - 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" CVResult(observed=, predicted=ObservationData(metric_names=['corrosion_score'], means=[2.22755944], covariance=[[1.44631305]])),\n", - " CVResult(observed=, predicted=ObservationData(metric_names=['corrosion_score'], means=[1.10185481], covariance=[[0.3645476]])),\n", - " CVResult(observed=, predicted=ObservationData(metric_names=['corrosion_score'], means=[0.52667369], covariance=[[0.06076658]])),\n", - " CVResult(observed=, predicted=ObservationData(metric_names=['corrosion_score'], means=[0.81682378], covariance=[[0.00202599]])),\n", - " CVResult(observed=, predicted=ObservationData(metric_names=['corrosion_score'], means=[0.30203122], covariance=[[0.06494224]])),\n", - " CVResult(observed=, predicted=ObservationData(metric_names=['corrosion_score'], means=[2.92546491], covariance=[[2.91704295]])),\n", - " CVResult(observed=, predicted=ObservationData(metric_names=['corrosion_score'], means=[5.5097873], covariance=[[7.68646695]])),\n", - " CVResult(observed=, predicted=ObservationData(metric_names=['corrosion_score'], means=[0.81779914], covariance=[[0.00204636]])),\n", - " CVResult(observed=, predicted=ObservationData(metric_names=['corrosion_score'], means=[5.52898111], covariance=[[0.00526972]])),\n", - " CVResult(observed=, predicted=ObservationData(metric_names=['corrosion_score'], means=[1.73056953], covariance=[[0.32395525]]))]" - ] - }, - "execution_count": 325, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cv_results" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "ax_env", - "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": "3.9.19" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/tutorials/batch-tutorial.ipynb b/docs/tutorials/batch-tutorial.ipynb deleted file mode 100644 index 20e320f5..00000000 --- a/docs/tutorials/batch-tutorial.ipynb +++ /dev/null @@ -1,1546 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Noisy Batch Optimization\n", - "\n", - "Many high performing materials suffer from poor corrosion resistance. Anti-corrosion coatings are a common solution that can be adapted to protect a given material in a number of harsh environments. You have the task of designing an anti-corrosion coating for a new material that needs to withstand a relatively high temperature corrosive environment. You decided to simulate the corrosive environment in the lab and test a number of different coatings to see which one performs the best. Based on the lab space, you see that you can specify up to nine coatings in a single test, allowing three replicates of three coating designs to be tested at once.\n", - "\n", - "You believe Bayesian optimization can help you in this task and decide to put together an optimization script using Honegumi to help solve this problem.\n", - "\n", - "Looking through the literature you identify the following tunable parameters for this problem:\n", - "\n", - "| | **Parameter Name** | **Bounds** |\n", - "|------|--------------------|-------------|\n", - "| x1 | Resin Fraction | [0, 1] |\n", - "| x2 | Inhibitor Fraction | [0, 1] |\n", - "| x3 | Insulator Fraction | [0, 1] |\n", - "| x4 | Stabilizer Fraction | [0, 0.1] |\n", - "| x5 | Coating Thickness | [0.1, 10] |\n", - "\n", - "Additionally, you identify several constraints on the relative fractions of the different components based on prior work in the literature. Notably, you observe that the best materials keep `Resin Fraction` > `Inhibitor Fraction` > `Insulator Fraction` > `Stabilizer Fraction`.\n", - "\n", - "A dummy objective function that emulates the results of the corrosion experiment has been constructed in the code cell below. To simulate sample variability, random noise is added to the output of the function on call. Although we can easily find optimal values using the equations, we will pretend that the objective function is unknown and use a Bayesian optimization approach to find the optimal set of input parameters instead." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "def simulate_corrosion(x1, x2, x3, x4, x5):\n", - " \"\"\"\n", - " Calculate the corrosion damage based on the input parameters.\n", - "\n", - " Args:\n", - " x1 (float): the fraction of resin used in the coating formulation\n", - " x2 (float): the fraction of inhibitor used in the coating formulation\n", - " x3 (float): the fraction of insulator used in the coating formulation\n", - " x4 (float): the fraction of stabilizer used in the coating formulation\n", - " x5 (float): the coating thickness\n", - "\n", - " Returns:\n", - " dict: the measured corrosion damage and uncertainty\n", - " \"\"\"\n", - " score = float(\n", - " 1/(np.exp(-40*(x1-0.42)**2) + np.exp(-24*(x1-0.75)**2) + 0.1) +\n", - " 1/(np.exp(-30*(x2-0.22)**2) + np.exp(-1000*(x2-0.22)**2) + np.exp(-800*(x2-0.42)**2) + 0.15) +\n", - " 1/(np.exp(-25*(x3-0.27)**2) + np.exp(-1000*(x3-0.27)**2) + np.exp(-150*(x3-0.37)**2) +1) +\n", - " 1/(np.exp(-400*(x4-0.08)**2) +1) + 0.5*x4 +\n", - " 1/(np.exp(-(x5-8)**2) + 0.05*x5 + 0.3)\n", - " ) - 3.18\n", - "\n", - " # simulate 3 replicate measurements\n", - " scores = [abs(score + np.random.normal(0, 0.1)) for _ in range(3)]\n", - "\n", - " # report the uncertainty to allow the model to learn from it\n", - " return (np.mean(scores), np.std(scores))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Applying Honegumi\n", - "\n", - "We will now use the [Honegumi website](https://honegumi.readthedocs.io/en/latest/) to generate a script that will help us optimize the coating parameters. From the description, we observe that our problem is a **single objective** optimization problem with a **constraint on the fractional sum of coating components** and an **ordering constraint** on the relative fractions of each component. As there is room for several samples to be tested in parallel, **batch optimization** could make the approach more efficient. Lastly, it is expected that the reults will be noisy, so you decide to use a **Fully Bayesian** model to make the optimization process more robust.\n", - "\n", - "![Honegumi Selections for The Problem Statement](batch-honegumi-selection.jpg)\n", - "\n", - "The Honegumi generated optimization script will provide a framework for our optimization campaign that we can modify to suit our specific problem needs. In the code sections below, we will make several modifications to this generated script to make it compatible with our problem." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Modifying the Code for Our Problem\n", - "\n", - "We can modify this code to suit our problem with a few simple modifications. Wherever a modification has been made to the code, a comment starting with `# CHANGE:` has been added along with a brief description of the change." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[INFO 05-01 16:33:32] ax.service.ax_client: Starting optimization with verbose logging. To disable logging, set the `verbose_logging` argument to `False`. Note that float values in the logs are rounded to 6 decimal points.\n", - "[WARNING 05-01 16:33:32] ax.service.ax_client: Random seed set to 42. Note that this setting only affects the Sobol quasi-random generator and BoTorch-powered Bayesian optimization models. For the latter models, setting random seed to the same number for two optimizations will make the generated trials similar, but not exactly the same, and over time the trials will diverge more.\n", - "[INFO 05-01 16:33:32] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicitly specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.\n", - "[INFO 05-01 16:33:32] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicitly specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.\n", - "[INFO 05-01 16:33:32] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x3. If that is not the expected value type, you can explicitly specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.\n", - "[INFO 05-01 16:33:32] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x5. If that is not the expected value type, you can explicitly specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.\n", - "[INFO 05-01 16:33:32] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x3', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x5', parameter_type=FLOAT, range=[0.1, 10.0])], parameter_constraints=[ParameterConstraint(1.0*x1 + 1.0*x2 + 1.0*x3 <= 1.0), OrderConstraint(x2 <= x1), OrderConstraint(x3 <= x2), ParameterConstraint(-1.0*x1 + -1.0*x2 + -2.0*x3 <= -1.0)]).\n", - "[WARNING 05-01 16:33:32] ax.service.ax_client: Random seed set to 42. Note that this setting only affects the Sobol quasi-random generator and BoTorch-powered Bayesian optimization models. For the latter models, setting random seed to the same number for two optimizations will make the generated trials similar, but not exactly the same, and over time the trials will diverge more.\n", - "[INFO 05-01 16:33:32] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicitly specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.\n", - "[INFO 05-01 16:33:32] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicitly specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.\n", - "[INFO 05-01 16:33:32] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x3. If that is not the expected value type, you can explicitly specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.\n", - "[INFO 05-01 16:33:32] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x5. If that is not the expected value type, you can explicitly specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.\n", - "[INFO 05-01 16:33:32] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x3', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x5', parameter_type=FLOAT, range=[0.1, 10.0])], parameter_constraints=[ParameterConstraint(1.0*x1 + 1.0*x2 + 1.0*x3 <= 1.0), OrderConstraint(x2 <= x1), OrderConstraint(x3 <= x2), ParameterConstraint(-1.0*x1 + -1.0*x2 + -2.0*x3 <= -1.0)]).\n", - "[INFO 05-01 16:33:32] ax.service.ax_client: Generated new trial 0 with parameters {'x1': 0.540494, 'x2': 0.28639, 'x3': 0.106513, 'x5': 3.150732} using model Sobol.\n", - "[INFO 05-01 16:33:32] ax.service.ax_client: Generated new trial 1 with parameters {'x1': 0.369966, 'x2': 0.357531, 'x3': 0.268685, 'x5': 7.204235} using model Sobol.\n", - "[INFO 05-01 16:33:32] ax.service.ax_client: Completed trial 0 with data: {'corrosion_score': (2.171497, 0.055504)}.\n", - "[INFO 05-01 16:33:32] ax.service.ax_client: Completed trial 1 with data: {'corrosion_score': (1.180876, 0.036845)}.\n", - "[INFO 05-01 16:33:32] ax.service.ax_client: Generated new trial 2 with parameters {'x1': 0.881586, 'x2': 0.096711, 'x3': 0.01376, 'x5': 6.697699} using model Sobol.\n", - "c:\\Users\\sterg\\miniforge3\\envs\\honegumi\\Lib\\site-packages\\ax\\core\\data.py:284: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", - " return cls(df=pd.concat(dfs, axis=0, sort=True))\n", - "[INFO 05-01 16:33:32] ax.service.ax_client: Generated new trial 3 with parameters {'x1': 0.509931, 'x2': 0.485848, 'x3': 0.002866, 'x5': 9.512716} using model Sobol.\n", - "[INFO 05-01 16:33:32] ax.service.ax_client: Completed trial 2 with data: {'corrosion_score': (2.240028, 0.043211)}.\n", - "[INFO 05-01 16:33:32] ax.service.ax_client: Completed trial 3 with data: {'corrosion_score': (3.998066, 0.055256)}.\n", - "[INFO 05-01 16:33:32] ax.service.ax_client: Generated new trial 4 with parameters {'x1': 0.354248, 'x2': 0.292253, 'x3': 0.232996, 'x5': 4.822508} using model Sobol.\n", - "c:\\Users\\sterg\\miniforge3\\envs\\honegumi\\Lib\\site-packages\\ax\\core\\data.py:284: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", - " return cls(df=pd.concat(dfs, axis=0, sort=True))\n", - "[INFO 05-01 16:33:32] ax.service.ax_client: Generated new trial 5 with parameters {'x1': 0.421201, 'x2': 0.223665, 'x3': 0.200311, 'x5': 7.933213} using model Sobol.\n", - "[INFO 05-01 16:33:32] ax.service.ax_client: Completed trial 4 with data: {'corrosion_score': (1.889654, 0.063915)}.\n", - "[INFO 05-01 16:33:32] ax.service.ax_client: Completed trial 5 with data: {'corrosion_score': (0.312144, 0.108107)}.\n", - "Sample: 100%|██████████| 2048/2048 [01:02, 32.59it/s, step size=4.12e-01, acc. prob=0.905]\n", - "[INFO 05-01 16:34:39] ax.service.ax_client: Generated new trial 6 with parameters {'x1': 0.58821, 'x2': 0.205895, 'x3': 0.205895, 'x5': 10.0} using model FullyBayesian.\n", - "c:\\Users\\sterg\\miniforge3\\envs\\honegumi\\Lib\\site-packages\\ax\\core\\data.py:284: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", - " return cls(df=pd.concat(dfs, axis=0, sort=True))\n", - "[INFO 05-01 16:34:39] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.\n", - "[INFO 05-01 16:34:45] ax.service.ax_client: Generated new trial 7 with parameters {'x1': 1.0, 'x2': 0.0, 'x3': 0.0, 'x5': 0.1} using model FullyBayesian.\n", - "[INFO 05-01 16:34:45] ax.service.ax_client: Completed trial 6 with data: {'corrosion_score': (1.052019, 0.141698)}.\n", - "[INFO 05-01 16:34:45] ax.service.ax_client: Completed trial 7 with data: {'corrosion_score': (7.553051, 0.057187)}.\n", - "Sample: 100%|██████████| 2048/2048 [00:53, 38.06it/s, step size=5.12e-01, acc. prob=0.855]\n", - "[INFO 05-01 16:35:45] ax.service.ax_client: Generated new trial 8 with parameters {'x1': 0.25, 'x2': 0.25, 'x3': 0.25, 'x5': 10.0} using model FullyBayesian.\n", - "c:\\Users\\sterg\\miniforge3\\envs\\honegumi\\Lib\\site-packages\\ax\\core\\data.py:284: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", - " return cls(df=pd.concat(dfs, axis=0, sort=True))\n", - "[INFO 05-01 16:35:45] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.\n", - "[INFO 05-01 16:35:51] ax.service.ax_client: Generated new trial 9 with parameters {'x1': 0.25, 'x2': 0.25, 'x3': 0.25, 'x5': 10.0} using model FullyBayesian.\n", - "[INFO 05-01 16:35:51] ax.service.ax_client: Completed trial 8 with data: {'corrosion_score': (2.644857, 0.151135)}.\n", - "[INFO 05-01 16:35:51] ax.service.ax_client: Completed trial 9 with data: {'corrosion_score': (2.528284, 0.04713)}.\n", - "[INFO 05-01 16:35:51] ax.modelbridge.base: Leaving out out-of-design observations for arms: 9_0\n", - "Sample: 100%|██████████| 2048/2048 [01:02, 32.76it/s, step size=4.35e-01, acc. prob=0.880]\n", - "[INFO 05-01 16:37:01] ax.service.ax_client: Generated new trial 10 with parameters {'x1': 0.615542, 'x2': 0.192229, 'x3': 0.192229, 'x5': 7.587642} using model FullyBayesian.\n", - "c:\\Users\\sterg\\miniforge3\\envs\\honegumi\\Lib\\site-packages\\ax\\core\\data.py:284: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", - " return cls(df=pd.concat(dfs, axis=0, sort=True))\n", - "[INFO 05-01 16:37:01] ax.modelbridge.base: Leaving out out-of-design observations for arms: 9_0\n", - "[INFO 05-01 16:37:01] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.\n", - "[INFO 05-01 16:37:08] ax.service.ax_client: Generated new trial 11 with parameters {'x1': 0.615612, 'x2': 0.192194, 'x3': 0.192194, 'x5': 7.586841} using model FullyBayesian.\n", - "[INFO 05-01 16:37:08] ax.service.ax_client: Completed trial 10 with data: {'corrosion_score': (0.55539, 0.077393)}.\n", - "[INFO 05-01 16:37:08] ax.service.ax_client: Completed trial 11 with data: {'corrosion_score': (0.761784, 0.067236)}.\n", - "[INFO 05-01 16:37:08] ax.modelbridge.base: Leaving out out-of-design observations for arms: 9_0\n", - "Sample: 100%|██████████| 2048/2048 [00:54, 37.74it/s, step size=5.73e-01, acc. prob=0.778]\n", - "[INFO 05-01 16:38:08] ax.service.ax_client: Generated new trial 12 with parameters {'x1': 1.0, 'x2': 0.0, 'x3': 0.0, 'x5': 10.0} using model FullyBayesian.\n", - "c:\\Users\\sterg\\miniforge3\\envs\\honegumi\\Lib\\site-packages\\ax\\core\\data.py:284: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", - " return cls(df=pd.concat(dfs, axis=0, sort=True))\n", - "[INFO 05-01 16:38:08] ax.modelbridge.base: Leaving out out-of-design observations for arms: 9_0\n", - "[INFO 05-01 16:38:08] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.\n", - "[INFO 05-01 16:38:15] ax.service.ax_client: Generated new trial 13 with parameters {'x1': 1.0, 'x2': 0.0, 'x3': 0.0, 'x5': 10.0} using model FullyBayesian.\n", - "[INFO 05-01 16:38:15] ax.service.ax_client: Completed trial 12 with data: {'corrosion_score': (5.661515, 0.075103)}.\n", - "[INFO 05-01 16:38:15] ax.service.ax_client: Completed trial 13 with data: {'corrosion_score': (5.415819, 0.045722)}.\n", - "[INFO 05-01 16:38:15] ax.modelbridge.base: Leaving out out-of-design observations for arms: 9_0\n", - "Sample: 100%|██████████| 2048/2048 [01:09, 29.27it/s, step size=3.20e-01, acc. prob=0.940]\n", - "[INFO 05-01 16:39:31] ax.service.ax_client: Generated new trial 14 with parameters {'x1': 0.481826, 'x2': 0.172725, 'x3': 0.172725, 'x5': 7.767653} using model FullyBayesian.\n", - "c:\\Users\\sterg\\miniforge3\\envs\\honegumi\\Lib\\site-packages\\ax\\core\\data.py:284: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", - " return cls(df=pd.concat(dfs, axis=0, sort=True))\n", - "[INFO 05-01 16:39:31] ax.modelbridge.base: Leaving out out-of-design observations for arms: 9_0\n", - "[INFO 05-01 16:39:31] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.\n", - "[INFO 05-01 16:39:39] ax.service.ax_client: Generated new trial 15 with parameters {'x1': 0.481717, 'x2': 0.172761, 'x3': 0.172761, 'x5': 7.768358} using model FullyBayesian.\n", - "[INFO 05-01 16:39:39] ax.service.ax_client: Completed trial 14 with data: {'corrosion_score': (0.681955, 0.01967)}.\n", - "[INFO 05-01 16:39:39] ax.service.ax_client: Completed trial 15 with data: {'corrosion_score': (0.802229, 0.073447)}.\n", - "[INFO 05-01 16:39:39] ax.modelbridge.base: Leaving out out-of-design observations for arms: 15_0, 9_0\n", - "Sample: 100%|██████████| 2048/2048 [01:01, 33.48it/s, step size=3.64e-01, acc. prob=0.899]\n", - "[INFO 05-01 16:40:49] ax.service.ax_client: Generated new trial 16 with parameters {'x1': 0.71564, 'x2': 0.242649, 'x3': 0.041711, 'x5': 7.952869} using model FullyBayesian.\n", - "c:\\Users\\sterg\\miniforge3\\envs\\honegumi\\Lib\\site-packages\\ax\\core\\data.py:284: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", - " return cls(df=pd.concat(dfs, axis=0, sort=True))\n", - "[INFO 05-01 16:40:49] ax.modelbridge.base: Leaving out out-of-design observations for arms: 15_0, 9_0\n", - "[INFO 05-01 16:40:49] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.\n", - "[INFO 05-01 16:40:57] ax.service.ax_client: Generated new trial 17 with parameters {'x1': 0.470028, 'x2': 0.264986, 'x3': 0.264986, 'x5': 8.34103} using model FullyBayesian.\n", - "[INFO 05-01 16:40:57] ax.service.ax_client: Completed trial 16 with data: {'corrosion_score': (0.607151, 0.094466)}.\n", - "[INFO 05-01 16:40:57] ax.service.ax_client: Completed trial 17 with data: {'corrosion_score': (0.344838, 0.043934)}.\n", - "[INFO 05-01 16:40:57] ax.modelbridge.base: Leaving out out-of-design observations for arms: 15_0, 9_0\n", - "Sample: 100%|██████████| 2048/2048 [01:01, 33.31it/s, step size=3.33e-01, acc. prob=0.871]\n", - "[INFO 05-01 16:42:05] ax.service.ax_client: Generated new trial 18 with parameters {'x1': 0.5, 'x2': 0.5, 'x3': 0.0, 'x5': 0.1} using model FullyBayesian.\n", - "c:\\Users\\sterg\\miniforge3\\envs\\honegumi\\Lib\\site-packages\\ax\\core\\data.py:284: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", - " return cls(df=pd.concat(dfs, axis=0, sort=True))\n", - "[INFO 05-01 16:42:05] ax.modelbridge.base: Leaving out out-of-design observations for arms: 15_0, 9_0\n", - "[INFO 05-01 16:42:05] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.\n", - "[INFO 05-01 16:42:11] ax.service.ax_client: Generated new trial 19 with parameters {'x1': 0.5, 'x2': 0.5, 'x3': 0.0, 'x5': 0.1} using model FullyBayesian.\n", - "[INFO 05-01 16:42:11] ax.service.ax_client: Completed trial 18 with data: {'corrosion_score': (6.691786, 0.072006)}.\n", - "[INFO 05-01 16:42:11] ax.service.ax_client: Completed trial 19 with data: {'corrosion_score': (6.70339, 0.036864)}.\n", - "[INFO 05-01 16:42:11] ax.modelbridge.base: Leaving out out-of-design observations for arms: 19_0, 15_0, 9_0\n", - "Sample: 100%|██████████| 2048/2048 [00:44, 45.67it/s, step size=4.54e-01, acc. prob=0.914]\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "from ax.service.ax_client import AxClient, ObjectiveProperties\n", - "\n", - "from ax.modelbridge.factory import Models\n", - "from ax.modelbridge.generation_strategy import GenerationStep, GenerationStrategy\n", - "\n", - "\n", - "obj1_name = \"corrosion_score\" # CHANGE: update objective name\n", - "\n", - "# CHANGE: remove the branin dummy objective function, we will use the above function\n", - "\n", - "total = 1.0 # CHANGE: update total component fraction\n", - "\n", - "\n", - "gs = GenerationStrategy(\n", - " steps=[\n", - " GenerationStep(\n", - " model=Models.SOBOL,\n", - " num_trials=6,\n", - " min_trials_observed=3,\n", - " max_parallelism=5,\n", - " model_kwargs={\"seed\": 999},\n", - " model_gen_kwargs={},\n", - " ),\n", - " GenerationStep(\n", - " model=Models.FULLYBAYESIAN,\n", - " num_trials=-1,\n", - " max_parallelism=3,\n", - " should_deduplicate=True, # CHANGE: reduce duplicate suggestions\n", - " model_kwargs={\"num_samples\": 1024, \"warmup_steps\": 1024}, # CHANGE: increase\n", - " ),\n", - " ]\n", - ")\n", - "\n", - "ax_client = AxClient(generation_strategy=gs, random_seed=42) # CHANGE: add random seed for reproducibility\n", - "\n", - "ax_client.create_experiment(\n", - " parameters=[\n", - " {\"name\": \"x1\", \"type\": \"range\", \"bounds\": [0.0, total]}, # CHANGE: update parameter\n", - " {\"name\": \"x2\", \"type\": \"range\", \"bounds\": [0.0, total]}, # CHANGE: update parameter\n", - " {\"name\": \"x3\", \"type\": \"range\", \"bounds\": [0.0, total]}, # CHANGE: add new parameter\n", - " {\"name\": \"x5\", \"type\": \"range\", \"bounds\": [0.1, 10.0]}, # CHANGE: add new parameter\n", - " ],\n", - " objectives={\n", - " obj1_name: ObjectiveProperties(minimize=True),\n", - " },\n", - " parameter_constraints=[\n", - " f\"x1 + x2 + x3 <= {total}\", # CHANGE: update composition constraint\n", - " \"x1 >= x2\", # CHANGE: update order constraint\n", - " \"x2 >= x3\", # CHANGE: add order constraint\n", - " \"2.0*x3 + x1 + x2 >= 1.0\", # CHANGE: add order constraint based on `x3 >= x4` and `x4 = 1 - (x1 + x2 + x3)`\n", - " ],\n", - ")\n", - "\n", - "batch_size = 2\n", - "\n", - "for _ in range(10): # CHANGE: decrease number of iterations\n", - "\n", - " parameterizations, optimization_complete = ax_client.get_next_trials(batch_size)\n", - " for trial_index, parameterization in list(parameterizations.items()):\n", - " \n", - " # CHANGE: pull all added parameters from the parameterization\n", - " x1 = parameterization[\"x1\"]\n", - " x2 = parameterization[\"x2\"]\n", - " x3 = parameterization[\"x3\"]\n", - " x4 = total - (x1 + x2 + x3) # CHANGE: update composition constraint\n", - " x5 = parameterization[\"x5\"]\n", - "\n", - " results = simulate_corrosion(x1, x2, x3, x4, x5)\n", - " ax_client.complete_trial(trial_index=trial_index, raw_data=results)\n", - "\n", - "best_parameters, metrics = ax_client.get_best_parameters()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Show the Best Parameters\n", - "\n", - "After our optimization loop has completed, we can use the model to find the best parameters and their corresponding strength value. These will be our optimial set of parameters that we use in the 3D printer going forward." - ] - }, - { - "cell_type": "code", - "execution_count": 338, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Sample: 100%|██████████| 2048/2048 [00:42, 47.73it/s, step size=2.50e-01, acc. prob=0.877]\n" - ] - }, - { - "data": { - "text/plain": [ - "(19,\n", - " {'x1': 0.5531141417704178,\n", - " 'x2': 0.1846591406143514,\n", - " 'x3': 0.26222671761513805,\n", - " 'x5': 8.2901097602077},\n", - " ({'corrosion_score': 0.41040928033269086},\n", - " {'corrosion_score': {'corrosion_score': 0.0008997393689987544}}))" - ] - }, - "execution_count": 338, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ax_client.get_best_trial()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plotting Optimization Performance\n", - "\n", - "We can plot the performance of our optmization loop to see how the optimization task progressed as a function of iteration count." - ] - }, - { - "cell_type": "code", - "execution_count": 339, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[WARNING 05-01 08:58:34] ax.service.utils.report_utils: Column reason missing for all trials. Not appending column.\n" - ] - }, - { - "data": { - "text/plain": [ - "(0.0, 2.0)" - ] - }, - "execution_count": 339, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "h_blue = '#0033FF'\n", - "\n", - "df = ax_client.get_trials_data_frame()\n", - "fig, ax = plt.subplots(figsize=(6,4), dpi=120)\n", - "ax.plot(df.corrosion_score.values, ls='None', marker='o', mfc='none', mec='black')\n", - "ax.plot(np.minimum.accumulate(df.corrosion_score.values), color=h_blue)\n", - "plt.xlabel('Trial')\n", - "plt.ylabel('Corrosion Score [lower is better]')\n", - "plt.ylim(0, 2)" - ] - }, - { - "cell_type": "code", - "execution_count": 340, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "linkText": "Export to plot.ly", - "plotlyServerURL": "https://plot.ly", - "showLink": false - }, - "data": [ - { - "hoverinfo": "none", - "line": { - "color": "black", - "dash": "dot", - "width": 2 - }, - "mode": "lines", - "showlegend": false, - "type": "scatter", - "visible": true, - "x": [ - -3.952154604781554, - 10.903313786058435 - ], - "y": [ - -3.952154604781554, - 10.903313786058435 - ] - }, - { - "error_x": { - "array": [ - 0.09697307193758328, - 0.09432580615956097, - 0.03916477554641934, - 0.10977525367163038, - 0.18041947262022728, - 0.023662859070073566, - 0.0316572302731216, - 0.16109585493320808, - 0.03103124681089616, - 0.15511805238701318, - 0.03874663888694734, - 0.04354174301098419, - 0.13657119941037701, - 0.1018157587494321, - 0.1110139250248606, - 0.1641737784542783, - 0.08661683840337824, - 0.07612889196206886 - ], - "color": "rgba(128,177,211,0.4)", - "type": "data" - }, - "error_y": { - "array": [ - 2.7256679784174875, - 4.199633838796122, - 3.599793582557131, - 5.2253166045607005, - 4.516845300842414, - 3.0953335998152287, - 0.16104538196927914, - 0.03166602726203759, - 4.657165685333809, - 3.574701887771811, - 0.045083000885533506, - 0.040628188869374526, - 6.458009708196848, - 5.374882199652275, - 3.9081294574205843, - 2.977922279320509, - 0.07742037877804649, - 0.08754464854448725 - ], - "color": "rgba(128,177,211,0.4)", - "type": "data" - }, - "hoverinfo": "text", - "marker": { - "color": "rgba(128,177,211,1)" - }, - "mode": "markers", - "name": "In-sample", - "showlegend": true, - "text": [ - "Arm 0_0

Actual Outcome: 1.164 [1.067, 1.261]
Predicted Outcome: 2.102 [-0.624, 4.828]

Parameterization:
x1: 0.46203490160405636
x2: 0.1405540555715561
x3: 0.12007084302604198
x5: 7.49138588104397", - "Arm 1_0

Actual Outcome: 2.737 [2.643, 2.831]
Predicted Outcome: 3.082 [-1.118, 7.281]

Parameterization:
x1: 0.6811950104311109
x2: 0.08317248616367579
x3: 0.14701138995587826
x5: 2.201754858437926", - "Arm 2_0

Actual Outcome: 2.173 [2.134, 2.212]
Predicted Outcome: 3.007 [-0.593, 6.607]

Parameterization:
x1: 0.5404942771419883
x2: 0.28639009688049555
x3: 0.10651267971843481
x5: 3.1507322339341046", - "Arm 3_0

Actual Outcome: 7.654 [7.544, 7.764]
Predicted Outcome: 3.416 [-1.809, 8.641]

Parameterization:
x1: 0.17495027277618647
x2: 0.007729803211987019
x3: 0.0571193927899003
x5: 9.218316878192127", - "Arm 4_0

Actual Outcome: 3.357 [3.177, 3.538]
Predicted Outcome: 2.838 [-1.679, 7.354]

Parameterization:
x1: 0.2820521341636777
x2: 0.12209852878004313
x3: 0.23468931019306183
x5: 3.671897889301181", - "Arm 5_0

Actual Outcome: 1.163 [1.139, 1.186]
Predicted Outcome: 1.707 [-1.388, 4.803]

Parameterization:
x1: 0.36996550764888525
x2: 0.35753058083355427
x3: 0.2686848407611251
x5: 7.204235409293323", - "Arm 6_0

Actual Outcome: 5.56 [5.529, 5.592]
Predicted Outcome: 5.557 [5.396, 5.718]

Parameterization:
x1: 0.9999999999999886
x2: 0.0
x3: 1.2886717519318773e-14
x5: 6.657871762887141", - "Arm 7_0

Actual Outcome: 5.56 [5.399, 5.721]
Predicted Outcome: 5.56 [5.529, 5.592]

Parameterization:
x1: 1.0
x2: 5.879023101716506e-16
x3: 8.221970846891384e-16
x5: 6.658104954751326", - "Arm 8_0

Actual Outcome: 3.228 [3.197, 3.259]
Predicted Outcome: 4.24 [-0.417, 8.897]

Parameterization:
x1: 0.5000000000004156
x2: 0.0
x3: 0.5000000000021749
x5: 9.999999999999234", - "Arm 9_0

Actual Outcome: 2.851 [2.696, 3.006]
Predicted Outcome: 2.316 [-1.259, 5.891]

Parameterization:
x1: 0.49999999999999983
x2: 2.9442422767142952e-15
x3: 0.5000000000000115
x5: 7.195932963944851", - "Arm 10_0

Actual Outcome: 0.954 [0.915, 0.993]
Predicted Outcome: 0.952 [0.907, 0.997]

Parameterization:
x1: 0.4244838630578679
x2: 0.42448386306381597
x3: 2.1289729987226923e-11
x5: 7.339947666832862", - "Arm 11_0

Actual Outcome: 0.951 [0.907, 0.994]
Predicted Outcome: 0.953 [0.912, 0.994]

Parameterization:
x1: 0.4245136618061755
x2: 0.42451366180615974
x3: 0.0
x5: 7.345265540626473", - "Arm 12_0

Actual Outcome: 5.698 [5.561, 5.835]
Predicted Outcome: 3.77 [-2.688, 10.228]

Parameterization:
x1: 0.4262126487368945
x2: 8.147112918011196e-16
x3: 4.376823862696568e-15
x5: 0.1000000000000202", - "Arm 13_0

Actual Outcome: 4.724 [4.622, 4.826]
Predicted Outcome: 2.098 [-3.277, 7.473]

Parameterization:
x1: 0.5000000000012181
x2: 0.5000000000106196
x3: 0.0
x5: 10.0", - "Arm 14_0

Actual Outcome: 2.555 [2.444, 2.666]
Predicted Outcome: 1.663 [-2.245, 5.571]

Parameterization:
x1: 0.42085294074668167
x2: 0.3440857934253744
x3: 0.0
x5: 5.928959908522031", - "Arm 15_0

Actual Outcome: 0.453 [0.289, 0.617]
Predicted Outcome: 1.004 [-1.974, 3.981]

Parameterization:
x1: 0.6191508786933483
x2: 0.23051694149204408
x3: 0.1503321798146068
x5: 7.898638178540372", - "Arm 16_0

Actual Outcome: 1.248 [1.161, 1.334]
Predicted Outcome: 1.246 [1.169, 1.324]

Parameterization:
x1: 0.8194301997175271
x2: 0.18056980028249606
x3: 2.299616162607646e-15
x5: 8.956439436443388", - "Arm 17_0

Actual Outcome: 1.244 [1.167, 1.320]
Predicted Outcome: 1.245 [1.158, 1.333]

Parameterization:
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] - } - ], - "label": "corrosion_score", - "method": "update" - } - ], - "x": 0, - "xanchor": "left", - "y": 1.125, - "yanchor": "top" - }, - { - "buttons": [ - { - "args": [ - { - "error_x.thickness": 2, - "error_x.width": 4, - "error_y.thickness": 2, - "error_y.width": 4 - } - ], - "label": "Yes", - "method": "restyle" - }, - { - "args": [ - { - "error_x.thickness": 0, - "error_x.width": 0, - "error_y.thickness": 0, - "error_y.width": 0 - } - ], - "label": "No", - "method": "restyle" - } - ], - "x": 1.125, - "xanchor": "left", - "y": 0.8, - "yanchor": "middle" - } - ], - "width": 530, - "xaxis": { - "linecolor": "black", - "linewidth": 0.5, - "mirror": true, - "range": [ - -3.952154604781554, - 10.903313786058435 - ], - "title": { - "text": "Actual Outcome" - }, - "zeroline": false - }, - "yaxis": { - "linecolor": "black", - "linewidth": 0.5, - "mirror": true, - "range": [ - -3.952154604781554, - 10.903313786058435 - ], - "title": { - "text": "Predicted Outcome" - }, - "zeroline": false - } - } - } - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from ax.modelbridge.cross_validation import cross_validate\n", - "from ax.plot.diagnostic import interact_cross_validation\n", - "\n", - "model = ax_client.generation_strategy.model\n", - "cv_results = cross_validate(model)\n", - "render(interact_cross_validation(cv_results))" - ] - }, - { - "cell_type": "code", - "execution_count": 325, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[CVResult(observed=, predicted=ObservationData(metric_names=['corrosion_score'], means=[3.33038581], covariance=[[0.0086991]])),\n", - " CVResult(observed=, predicted=ObservationData(metric_names=['corrosion_score'], means=[3.38045204], covariance=[[1.14749778]])),\n", - " CVResult(observed=, predicted=ObservationData(metric_names=['corrosion_score'], means=[1.57425067], covariance=[[0.00012101]])),\n", - " CVResult(observed=, predicted=ObservationData(metric_names=['corrosion_score'], means=[3.74291177], covariance=[[0.00775312]])),\n", - 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" CVResult(observed=, predicted=ObservationData(metric_names=['corrosion_score'], means=[2.22755944], covariance=[[1.44631305]])),\n", - " CVResult(observed=, predicted=ObservationData(metric_names=['corrosion_score'], means=[1.10185481], covariance=[[0.3645476]])),\n", - " CVResult(observed=, predicted=ObservationData(metric_names=['corrosion_score'], means=[0.52667369], covariance=[[0.06076658]])),\n", - " CVResult(observed=, predicted=ObservationData(metric_names=['corrosion_score'], means=[0.81682378], covariance=[[0.00202599]])),\n", - " CVResult(observed=, predicted=ObservationData(metric_names=['corrosion_score'], means=[0.30203122], covariance=[[0.06494224]])),\n", - " CVResult(observed=, predicted=ObservationData(metric_names=['corrosion_score'], means=[2.92546491], covariance=[[2.91704295]])),\n", - " CVResult(observed=, predicted=ObservationData(metric_names=['corrosion_score'], means=[5.5097873], covariance=[[7.68646695]])),\n", - " CVResult(observed=, predicted=ObservationData(metric_names=['corrosion_score'], means=[0.81779914], covariance=[[0.00204636]])),\n", - " CVResult(observed=, predicted=ObservationData(metric_names=['corrosion_score'], means=[5.52898111], covariance=[[0.00526972]])),\n", - " CVResult(observed=, predicted=ObservationData(metric_names=['corrosion_score'], means=[1.73056953], covariance=[[0.32395525]]))]" - ] - }, - "execution_count": 325, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cv_results" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "ax_env", - "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": "3.11.8" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/tutorials/mobo-tutorial.ipynb b/docs/tutorials/mobo-tutorial.ipynb deleted file mode 100644 index 01dc6b10..00000000 --- a/docs/tutorials/mobo-tutorial.ipynb +++ /dev/null @@ -1,595 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Optimizing a Polymer Compound for Strength and Density\n", - "\n", - "Imagine you work at a custom materials solutions company that specializes in creating polymer compounds for various applications. A customer has requested a polymer formulation with high strength and a high biodegradability score. The customer is unsure of the tradeoff between the two properties but knows that the target application will require a strength of at least 70 MPa. As the customer is concerned about the toxicitiy and biodegradability of the polymer, they have limited you to a set of **five** thermoplastic monomers that can be used in the formulation.\n", - "\n", - "You believe Bayesian optimization can help you in this task and decide to put together an optimization script using Honegumi to help solve this problem.\n", - "\n", - "Taking note of available composition and process parameters you decide to restrict your design space to the following:\n", - "\n", - "| | **Parameter Name** | **Bounds** |\n", - "|------|--------------------|-------------|\n", - "| x1 | Monomer A | [0, 1] |\n", - "| x2 | Monomer B | [0, 1] |\n", - "| x3 | Monomer C | [0, 1] |\n", - "| x4 | Monomer D | [0, 1] |\n", - "| x5 | Monomer E | [0, 1] |\n", - "| x6 | Extrusion Rate | [0.01, 0.1] |\n", - "| x7 | Temperatrue | [120, 200] |\n", - "\n", - "To help find a solution quickly, you dig up some data on these polymer systems in the literature and decide to use them to help improve the surrogate model. While none of these meet the customer requirement, you think they might at least help tell your model where NOT to look. The collected data is as follows:\n", - "\n", - "| **x1** | **x2** | **x3** | **x4** | **x5** | **x6** | **x7** | **Strength** | **BioDeg** |\n", - "|--------|--------|--------|--------|--------|--------|--------|--------------|------------|\n", - "| 0.3 | 0.2 | 0.1 | 0.0 | 0.4 | 0.05 | 150 | 43.73 | 1.81 |\n", - "| 0.0 | 0.0 | 0.3 | 0.7 | 0.0 | 0.1 | 160 | 25.79 | 3.83 |\n", - "| 0.2 | 0.2 | 0.2 | 0.2 | 0.2 | 0.09 | 184 | 41.37 | 2.29 |\n", - "\n", - "A dummy objective function that returns outputs for each property has been constructed in the code cell below. This functions aims to emulate the results of experimental trials under different inputs. Although we can easily find optimal values using the equations, we will pretend that the objective function is unknown and use a Bayesian optimization approach to find the optimal set of input parameters instead." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "def polymer_properties(x1, x2, x3, x4, x5, x6, x7):\n", - " \"\"\"\n", - " Calculates the strength and biodegradability properties of a polymer based \n", - " on a set of given input parameters.\n", - "\n", - " Parameters:\n", - " x1 (float): volume fraction of monomer 1. Range: [0.0, 1.0].\n", - " x2 (float): volume fraction of monomer 2: [0.0, 1.0].\n", - " x3 (float): volume fraction of monomer 3: [0.0, 1.0].\n", - " x4 (float): volume fraction of monomer 4: [0.0, 1.0].\n", - " x5 (float): volume fraction of monomer 5: [0.0, 1.0].\n", - " x6 (float): the polymer extrusion rate. Range: [0.01, 0.1].\n", - " x7 (float): the processsing temperature. Range: [120.0, 200.0].\n", - "\n", - " Returns:\n", - " dict: calculated strength and biodegradability properties of polymer in form:\n", - " {\n", - " \"strength\": float,\n", - " \"biodegradability\": float\n", - " }\n", - " \"\"\"\n", - " strength = float(\n", - " np.exp(-(50*(x1-0.5)**2)) +\n", - " np.exp(-(5*(x2-0.4)**2)) -\n", - " 0.8*x3 +\n", - " np.exp(-(300*(x4-0.1)**2)) -\n", - " 0.3*x5**2 +\n", - " np.exp(-(2000*(x6-0.025)**2)) +\n", - " 1/(1+np.exp(-(x7-137)/15))\n", - " )\n", - "\n", - " biodegradability = float(\n", - " -1/(1+np.exp(-(x1-0.1)/0.1)) + 1 +\n", - " -1/(1+np.exp(-(x2-0.3)/0.1)) + 1 +\n", - " x3**2 +\n", - " x4 +\n", - " 1/(1+np.exp(-(x5-0.7)/0.075)) +\n", - " 10*x6 +\n", - " -(x7/200)**2+1\n", - " )\n", - "\n", - " return {\"strength\" : strength*25, \"biodegradability\" : biodegradability*5}" - ] - }, - { - "attachments": { - "Selection.jpg": { - "image/jpeg": 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0vXiVVVdp3Qweth7CzDPDjDv3SKpu2DZls2odWaZOKmc5b1Mi08pyI573QE+qOTpN6maDPQ9dDGFsl08l7vr6Evu6+ufLhmYdcyGxapmJWP8AfeYXAjMheuXev4kz3KtdNEtmG6cJyqTFJfq78KbAlRZSI1SnEueqDI6RhmQ4rokknQbRl0Rbg7yZTcn07LPgVe+Elaxhu3F+v309ds2p3xeK33pMeXc1AfobTUOI++8pqFT0OIf6EnNXnErM9DWItkuibb70z2dc+W8i7d/gnlfbxum4+MbN7IJVshlFxfplXxGgYgvYn3NTK4y5TqNIpDdLYpsVyMiO8Uh55T7ru/vKUk0oLQtOeoi7LZZ2XLknx4lMkpkSj1MuPnGR4qnl80Up3C7TAn6uSp4p9QSC7zAZ8bdyfNfoVbqewvbv5gTV/vO8/laxIDVbw+IAareHqv1Kd1PYAsv8y+SL+43U9gDVd/N6fcbqez4wFl/m9EG4ns9Z/OAs7+b0QEhJd/lAKjl/tW7k+43U9nxgLO/m9EJ3Ul1fL8YDVXe5fDIbqewvj+MC25r52+GQ0LsL0EBNX+87zG6XYQDV/vO8/sNC7C9ADV/vO8xoXYXoIBq/3neY0LsL0ANXm5fH6WJ005ANVF2/FfqRoRciIBqpvuveqkgNVvAANVOlUANVvD1X6jQuwBqpzVOF8kI0LXXTiBdVLW2J3qSBNVNnzUafMA1U5919i8e8p3E9gCztzvNE9VG4n29vo7gFnW2+SdfXmRuFrzAzrOTK2+yKu/d6943C7TA1d/BE7/sNwu8Cfr5bel6z5E7iQH6+Sc8vv6AkkR6+xAWzlyVcuSZr38PAqAuSckL6S0L4wCZ58fh1t5+BfSnTygS2tZV2bk+anHllq0fnL1GfyGAVEvw2KnC6Lb1uhnB2fhaZbbfL/wCeS8f+EM0ZYt07lsndkaO7I0AfHgfIwBqRZzLkf2UW3Zwszv3JFcp2VDaB2VRsBsY7kZStqj2bitRTiQberdbdI+iZQ84UecuRINCFtVea5vGUN3R111x5g20afPh1SFEqNOlMTYM6MxLhy4ziXo8mLJbS8xIYdQZocZeaWlxpxJmlaFEojMjDZmoMSe3pcW1sg896m/tvqMKT5lXTbpHxPu1BFvmgMKvgXKFpyIZj1nrurzGp3dS4Hu2dTSPQ+4+feYi3ui2v8s8/T4eY3ITMiIzM9CIRVXcl02XT493jt3oDUY2qF8L2pW1FylbK/B+UmvYZ5eL2puY3OBctLUcukUVVsPx5lFs6oyGTOOc5lTLDbkVxZOFLlyGCLfbMXv8AHh5dbdqg23KfBiUyBDpsFhEaFAix4cSO2W62xFisoYjsoT1JbZbQgi6iSRA5L77Z3BdWj0dXd7e3ETlv9FTh3/DOyWuDxr7fP27/AKergHXy8/7O1c7KCilpJMt7/ST1/dEMrtS+5zbLy/dPHbsB5wcgAAAAANNbwhudBpO0h2e1Zx+hSKhlJp9FsyZcsJTEuZBmwoGORycwMVEBlqQcyQ7h19T5FQiQo65c2IuFGLVxcc0fcn8M6XmJz2W/aWkdG8eHLaZpmersClR0iQoExAjzGj5IWjaKsw98PsIbMT/0ldLR48RIMvGbHi/wtiIvVrTg9kLHmAItcY1+F2LAfHa5HOa5GVeCtbSIxWxGxIfuP5er4bWK+IzXhra7FTblwqv/AApxKsS3bnwVuyx7yw5kU2I1bVWw7q9GrFrJpjLKGokOnO0J52BEahsoTGKnoJlUHo/FVsMqaNtPxcxfhvGOFcRVSk48o+IKHiiFNxnVWSxNJT0lV1m3xHPjR5plRYyZjPjvcsVZl2ukxrdq2JER6PXszITkhPykCZpkzKzclEhsWXjScWFFl3QtVNTs3wVczVRtkREXJEtZLWMam182ZsbaPYI23b9luWFamO1g3ZTKnY2I95oqsSPCtac4cW9rVqFRt+j1qsP0uqw1R6rEp509+P7v0amL6WA27Lkn2p9if2rY3su4+qlSrzcR1jR5iOjTcpiHC1CdKRosery7e1oFYlpWpT0hIw5yTjpEk48ykxDi/ls9Nt1Jh7IMJPAdJuj+Fj2jQZWCspK1eTmYcWRqMy2J/UwXua2cgOdBa6I6FHhIjuysrFjQoL11dXWT6o5f+I+zr2d9gVbFy3bszPYiYD2Lhvh7clOwbi1KuXNfjqK5RrDp9XozNYhMVWeuDSJcCrV2RPiplKagVOW646ojfc8QbhzC/tOe01iSTwXU6NolwzpExDinEtLmscxpWnUnDrHU+exFMyU/EkY8STlmzE7BmZOnwpaKsFHzEpBY1iLqN/WdOTuC8FykWowJ7Ec7R5KnyUwykwXzE7UHpEgSXbwYMRUe9yNe2Yjq5dZGMivVXWz1ItmDtGPrXs3eejF/61/H/Fz6u111ire8nDa3fdS68PPHMTrwufxK9YfQr8RlMe7PuQ8ndRu1KDLa0+BoX2f9rT2Yf8LWhT2e8E/4W9G+C/8AB5R5GS/P8VVT3OjYm7DCdEpHb0GPrp7xCie4++sW7rysxBff9R1m0dY6/IcU46qX9GsR1T86qD5j3OmSPvE3Tr1Gox+yn4WsnYxE947JUuv9bCiN3G2bnX2mGH2SNeVtF9YbXlcy80lxO23Qios+iU4rNlsnZCXXrm92Ho5nHZO9oxP+Jkt1r3Pl7zfwmx8adAvsp4l0+N0uuw7imh0puiKmNqtQ/MJefmlrsCJ+fqxlK9xhxUSLE/IIvZpHVrH+8wbPycdlsVY5kMJuw82ek5uOuIp5khLrAWCiS0WIsuiPme2iQ7Q29umsrNZyaq/oUxoY0+Ex5dMOcwFTwhw1wKv/AB0s+gXU7aFRxQtO7aHS2q/U406PTZL+G1ryKTUFXnTXJip0akT5twWwzXXY0V+mG9S6lGqRdrcBfhS6T8UaOJTG2KtIWHNHtbqNHbW5XCNZotQm302Uiy0WahQ8U1eFOyyUKabASXizsvAptWiU5kWNDm0ZNykWVX11WvaAw9Ta1GpMjSKhWYUtNLKRZ6VjQIcONGZEZDie4QXo502xH9oyG9z4DY72NWEroMVsYzP5u89OXvI7hFGxczD3JJtNmrsON2pYMViJVsR7zrzMNiXIti2bfhT3Is+owDkxmKvU11Ri1qIuRHfq9ww4cmNJe6L6FfZ80l+0BjWLgrRnSoVZiSURrqziOLEjSeF6FTnx4kGHVqtUpiXZFl5WY7KLEkpRJR9Xn0hRIclTY8eFFhM9pYoxlQ8HUttUrsw6WSK1UlZJrWxJ+bjoxHOlpeAx6tfEZrNbFiLEbLQVc1YsdjHNcuET/mkiY9CPECDs4cyUrANttx17F/3ZdKE00g1qN02mcO5FmdGlhp11xasR0JQbay1NCVOF37/+NaQIcf8Ao3Me1Fosg6RnOaxmCfcWLHe91kRmu/E0Ku6yxHMY1qYXcrtZFsjlRi+oP8Pr3M9+ZgGvuoqIqrVO1XUREzVbJIOlLI1FVVWfS1uGaZtslOe/Lpn4wxXiVgDdblQVSVQIl8WLXmGqXfuHVYqLL70SlXbQ0SJTbKZiYk33KrVLmVO3a2UGd7kVaY5Antxeg+nn2d9J/s5YsbhXSRRmyyTiTMbD+IadEfOYcxPJSr4bI05Rag6HBc9YCxoHvkhOQJSpyCzEv77JQGzEu+N7ewjjSg41py1CiTKvWF2bZySjNSHOyEWIjlbCmoKOcia+q/s40J8SBG1H9lFerHo3r5m/2qmEeTLNVgPlkxNtCrpYxsolLuSTiu9cdCollYeUKddFbtudVbpTUyTJODRm6FKq059h1CChmSUmTiFa+ytCfsf4006aH9ImljCdbkliYCqE3S4WDmUuoT9exNUYFJkKpLydIWUvCSYnnVCDJy8OIxzljpdf0qh+HinSTS8J4kouHqjKxdWrwYcw6prHgwZSRgvmY0u+LMpE/VqQkguivVq21MtqGNfEvwlixYVcuCbl9yX42484O2pUHYVcxlk1eXh9QegjO9G9VYVNZw+vo41PfQpuRT0XZVLSqjjLzRVGmUp9SmUdp8K/hWYhjyFNl9JOnbAOjvG9YlmTEhgaFJwcS1HtIrNZknHmn4kw92s1Dcjocy6jSlZk2RGP92m5yGiRF8AqHtASTI0d9DwnV61SpZ6sjVZ0V0jB1WrZ0VkNJGd1YapZzEmYkrEVqp2kOEuRlB2de1Sy57R+h3E3hg3cNk4m2TGaqF54T3q1FKv06iyZSYUO5qNVaa7IpFyW2/LcbhOzIjseo0uc4zHrNIpyJ9JeqXUr2nPY/wBJ/suVCluxa+mV/CdfivlqFjKgvjfls1PwoKx49KnpOaZDnaXVIcFr47IEZkSVm5dsSLIzs06XnIcr7FwJpIoOPYMwlOSPJ1CTaj5umTaN7eHBc5GMmIUSGroUxLueqMVzFSJDeqNjQoaPhLE7IZu84uA+R/CGo40Y/wB1KoFtR5TdKodGpjDdTu+9rjkNOvRLYs2gqkxFVesyGWH5LnSyYdNpsGPJqdZqNNpkWTMa9XaFdCGkTT/jWVwJo3o6VGqxYLpyoT03EfKUWg0uE9kONVq7UUhRkkpGFEiQ4TdSFHmpqYiQpSRlZqbiwoD/AN7FGKqLg+lxKtW5lYEu1yQoMKG1Ik1OTDkVWy8pBVze1jORrnLdzIcNjXRIsSHDa56YG6V4SqiqNzb7Z2duYqTl9gPyUSMYqVXiqjcZiO+8g3ZsFNgx7JhvpYYeckx14nqRGfZeY8ZcQ0qQX0RnPwrXSj5fDz/ab0YQtJMxDhLCwPOU5ZR8WJEhsVGS8wuI4tejw3RIjGwojcJI6LDeyJ2TXPSEemIftAJER86mBa86hsc5HVWHH7RGtaqpd7EkmybHarVVzVqKo1UVusqIrjunhttz8sGNuarL1lnwXt25sQY+YS2oFcp+I0apUqlw7Fq0iDeEyp2Ze9pzEqrtNumgnaLjVRhpcXHebqlNqVMkzqPLh1CX6IxT+HzpawFoe0l6Vsd1OlYai6NKrM0+ZwvFlZybj4gk4UxRIErXaBWYCpT5qkVFK018rHVrYjHSk1KzUKXnYMeWg+W0/THh2sYloeHqTAmJ5tcl2RmT7YkKGyTiuZNPiSk5LOvGhzMD3VUiMvqqkSHEhufCcyI/D34S7nD9+6I2TP6jOItC+o5jRh/f31aqnH3cN7093cEKxUve/bknxNHSVaB9ULxaUjxx7R63auXRp3NG+7n4VGhH8gdG05/06wxUP6cYFxJhz+gcpFvimhfl2PpGV/MqpC7d2rJzP9Gu1gu7Bl2VOS/Uutd3qv2gcVe+auFPymfg/lNWkZ383iN/1BN9tR4sTsJd2ol4rPftVya65wIuWWWYzZD7Qv66qxrTwJ+t0xewm+ofl7wwj+/6/InQWtffuHSLfs7pLXd9z4vSon9D7sxT6d/WnLQr4Wu+Oj3tq+zR/gfxBWNIf+E/BWMv8IGkrFsT+jmHY3aVfD35hO1KuatXZ7zG1HS+v7jF/q4f+qmuTL+E9raL8c/0kk5ajfkNUpn5PQ6c332dZqy072MKBK3ll1G3R9u1b+pf6tU7zXg2R+ffL9s/MR9pNihjrWp/TVu47UpdhWFbUVqo3tiHXYV94tyJVNt2BIkQ4TManMyI0itVyrzqdRaQxIiokzDnz6XAn/TT2z/Z00k+0lhb2WMJaPZCW1JCl1ebxHiKqxnytBwzTo+HcGQ4U3U5iHDjx3xZmJDiw5CnyUvMz87EhxXQoCS8tNzEt6L0Y41oeCKhj+o1mM+8aYloclJS7UiTc9GZOVRzocBjnNYjWI5qxY0V7IMJHNRz9d8Nj8mi/CbaFbNxUKTipkGx2w8wluV1tyh4gSbsjP12sUxRKNyoUe069YlpW9VzbSW/0NPxGeaWglmUrVJErqg38KCo1amVCFg/2jdHmJsZ0pjm1DDcGjxYdPkppLI2XnazTsQ1mpyWsq6uvM4XhuRytRYVlunsH/GFgy8eC6pYKrMjTJhUWDPOmmujRYe1XwpaNJSsCLZM7Mn1S1/1cdj/AAOxuwyzHYT2Rjbg7c8W8MOMQqOitW1XYrb8c3mSfehzYU6FKbZmUyr0ipRZlJrNKnMszaXVYUyBLZbkR3EF8utIGAcV6L8Y1/AWOKTGomKMNTzpCq06M6HE7OIsOHHgR5ePBc+BNyU7KxoE7IzkvEiS83Jx4ExBe6HEaq+/KPV6fXqZJ1elTDZqQnoSRpeM1FbdLq17HsciPhxYURroUWE9EfDiscx6I5qofVx4cfpAAaQ+ylxwt3ZR7R3NtlczlPR7ARivU4FJgY0Xk9HiUuHOtKsXVcdkV6rVx1LcaHZWLlt3i5V13EbzcOJW41tx6vHiNnUJNH++/tiaP6n7Yvsu6GNLmg1kTEi4NlJmdmcC0OHFjTkeXrMjR6XX6dJU9ivix69guqUNsk2majo8aQi1SJJRIzkloU71A0a1iBo0x7ifDmLFbI/mcRkJlWm3NbDY+WizMxJxosZbNZKVSXm1irH1ka2M2XbFa1Nd0L2urXy1tdtungHi3lUoledwRyovYRzr1xgfokuhU+p29g/iFcuJDtcnOPJ8bioxArFQXYVi02qMRa9Vqa2VRmUmDTYVY9y/xpPD7/Yr/D20jYL0w1CnMx/pjZjSBQcEw5+DUZmUqeNsNUrC7KfLtYvYxVw3IyrcR4hmpSJGp0nNvWWgTsxNTEl75/VFnE0oaZKJVMNQY60fDS0t83VHQXQGRIFKnpifWM9V/W336K9ZKSZEa2PEhoj3wocNkVYe6aPhCdtQAOlO0l/neueL/Ym5gf8Aisuge+fZZ/3S3s//APjl0bf9L6QeI4//ANg2MP8AwZrf/NswYe/Ba/8AKI41f7La7/8AidwQHdz8Xb/dD4D/APExRP8Apxj89Wezl/sMq/8A4TzX/NVHNjG+KFZdzWdc9AxGpVt1ywqrQ6lDvGk3jEps+1J9uORXPdZi4YlYQ5SnqQcMnVTk1BBxEx0rW9ohJmXzBw/UK7Sq5SajhecqtPxHJ1CVjUOcocaal6xL1RsZvuUSmRpFzZxk726sSXWWckZYitbDu5URffE5BlJiUmIE/Cl40lFgxGzcKbZDfLPl1avapHZFRYawtS+vrpq6t1XI05fB3aQdP2lOfFvBORPmZW6TamIVIpE9C1ro8uMWOtLTgQ9JNt55hdWkWHAvl+lOqdkEUD3dJt1XS76vuF+JrO+8+yv7Ozsew5eBpdnKxhqdnZZWtSdgxv8AB7NrpEhwteGyI2ShYimMPw5tqNh3mfy9XsTU1U6q6CoWpj/GiUhz34chS09ChPuvZOb+cw/yVXWVUWK6SZOLDVVX9HbWXO5ne2mm0nwhyA3Bl5ouKOB1WxikY3zL5i25Kpj1sNJtVdoTbAiT3JBXFFkrUVSXelOcb8QNsyKludNqZsbvzw9lH2WMa+0hTNJlQwjpAk8EQsAQMPxqpBmmVZ61htal8Rx5dsL8sjQmosq2hTLXe8ayKs23UsiRL+5tIWkCl4Ij0ODUaPFqrqw6cbLuhrLp7ssq+Sa9Xdu1y/1izcNU1Lf62t9xlEu27bYsK17hva9a/SbVtC06NUbhua5K7NYptGoVDpMV2bUqpU58lbceJChRGXX333VpShtBmZ8iHUii0WrYjq9MoFBp05WK3WZ6WplKpVPl4k1PVCoTsZkCVk5SXhNdEjR48Z7IcOGxqq5zkPY01NS0lLR5ycjwpaVlYUSPMTEZ7YcKDBhNV8SJEe5Ua1jGoquVVsiIa6eInhH2GVRv2u2Pk9yj465v27fWlMi5rcTU7PpdTjkpw3qpR6RCsbEC8U0jomXlRZVxWzbkmSth/egsMNpkufTrDP4XWLJXDlPxBpt00aPdCb6k1Vh0mqrKVublIiozUlJ6dj4hw3Q1ndZ7EjQaZVapChJEh2mIkRywm+iJ7T3Tok7Gk8K4XrOKUgKl5iX7SVhxG560SFCZJz032VkXVdHl4DnKjv0NaiOXslko27GV/NliexgNfNqXrlfxxqNRiUW3bKxZXEOlXXcMxZMtWrRLnYZp6o11OyFNMQaFdFEtuVWn5MOHQjqlTke56PVmnr8PTS1oawlE0iYfrFB0taP5WWjT1Tr2DUje+UemQGq99YqFJiPmUi0dkNHxJio0mfqsGQhwo0eopKSkP3l37+EdMuHcTVFtFnJacw7WYkRsGBJ1PV7KZjvWyS0GYajNWZVbIyDMwZd0VzmMgrEiO1EzS16vUS1aHWbnuar0237ctylVCu1+vVmbHptHolFpER6fVKtVajMcZiQKdToMd+ZOmynWo8WMy6+84htClF0Sp1On6xUJGk0qSmqlVKpOS1PptOkYEWanZ+fnYzJaUk5OWgtfGmJqamIkOBLwITHxIsV7IbGuc5EX21HjwZaDFmJiLDgS8CG+NHjRnthwoMGE1XxIsWI9UayHDY1XPe5Ua1qKqqiIa4WIPhIOHNQxLrNhZRsoGOObel23IcbrF4W3JnWtGmQY0h5EqvW3btKsbEO5JlBVGYckQJtzQLOkS9x0noUJlBSV/UTDX4XOKJbCkjiPTRpr0f6F5uqQmOkaJVYUvV4sGYiw4boVOqlTnMQYZpcCopFiNhTEClTFbhwdZqsjx4jlhJ6EntPkg+oRZLC+FqxieHLuVIs1LufLNcxrlR0aXgQ5OemHwdVFcx8wyVV2d2sT9S4pdtBtOsBtoRlCwbp9iU27MNsWsN8w8xOIeC2I0NiBedvw3LBuaGzXYa4rjsOs0Bc81U1+S14rU6TUkoi12j0o51JXUe4nsJ+ybpF9mrTZjiZxDNUbFODMU6M4K4Zx3hePEmaFUo7cSUmO+nx2xmMjyNSbLIk1DhP7aUnJVXRqfOziS842W9a6WdIdFxxhalQ5OHM0+qSFdd79SJ9rWTcBiyUwxIzNVVbFgK/+rc5NWJCiWbGhQ9eEsTdIy7KJOXvAtSjJKU4NYYqUpRkRJIrIohmZmfAiIuJmfAiHwj0moq6StIKIiqq45xYiImaqq1+fsiJvVTtrQv8A2R0f/vVT/wDzSCYXMxHhCeX2wsVKngrlbwZxNztX3SEvsypeEjxRrImVOM8ceXT7fr0Gh3hXrnZgvdGiVXaDaE+23TfYOk1iqoWpSO92jL8NLSViPB8pjzS5jrCegTDs8sN8GDjRixa/AlIrO0gzNSp0xUKJTqS+YZrOg0+o1uXqjEhxEnZKTc1Gu9SV3TlQ5KpRKRhyk1DF87CRUc6mLqybojVVr4cCMyDNR5hGLZHRoEq+XXWTsosRL28llk8IDy+YrYsUzAvMlhDiTksxMra4sOlN4uOlIs1ytzJBRYtEqtxTKLadYtmRPf3m6dUrltKlW+84y9HmViBKOLHl/wAmlj8NzSVg7Bs3pC0W41wrp3wpIJGjzjsFM7OuNkIELto1Qk6ZAn6zJVWHLw7OmZSlVmcqTGvZEgyUxBSNEg8mHtN1DqdTh0av0uoYSqMZWshJVF1pRYz3arYMWO+DKxZdz1yhxJiVhQFVFa6Kx2q12fVSiSRqUZJSkjUpSjIiSRFqZmZ8CIi4mZ8CIfOREVVRERVVVsiJmqquxETeqnuw13cxHhEeCdkYuVPBPKjgBiXnVvGhTJlOqVTw7qnuJZ1RnwXW40pqy6lSLXxDuO8YcSWpyLIrUK0otBkuEw/QqpW4Epmaf010Z/hlY9r+C5THumPSRhTQNQ6hAgTMrK4nlPf63Ky8wx8WC6vSs7V8M0uiR40FGxoUhHrUaowmrEh1CUkJiC+Aeiq7p1o8nU4lIw1RKhi6agucyJEkYnYykR7FRrklIkKWno801jrtdGZKtgOWywYkZjkeWqH4RxlsRhfi7W8TcF8VcHcc8JqfblTLLzfciBSrhv5mt3PQbdqEWxrjqUKltyKvbUetncVVodwUG36xIt6DMqdIhVGLDqrtL1UPwvtKa4twVIYUx3g/HGj7Gc1VJT/CZh2HMzlMw5EkKTUanKxcQUuVmJt8OSqsSQSmSdQptRqUlCqUxBlJ2PKxo8mybkHT1QEp1UjVCkVKlVmmMl4n5FOOZDjzrY0zAl4jZOPEZDR0WXbGWYiwY8GBFdAY6JCbEayKsPLVgRm9tzHbJlQs5lJs+t0G2a7hjeGJrNl1GfBk11iDZ53EUmluVCMkqeqXM97z3QPJT0LfjDXSF8BY6ZaQ9ClU0eadKjoLnK3IVGrU/FlEwm+uy0vMQqfEmK3+WdlNtloqrMpBgfmcPtGKuu7sn6u1D2dRsUQKzhODiuFKxoMvGp01UUlIj2OjNZK9vrQ1e39Gs/sFsqJZNZL7FMOdY8JXyz07LNDxxjYP3pLxDr+KF1Yc2tgS9d9usXBKgWja9pXFUMQbkuOJBqUa17NkS7vhW5THlUaq1Wq1iJUk0umzYtKrD9M7wSP4Vmlaa0rR9H8XG1Bg4ap2EqPiir6Q2USpxKbBma1V6zTJbDVLpcaYlY1WrkKDRY9Um2JPScnJyMaVWbmoEWckoc16qi+0Bh6Hh5tYbS5t09GqMzIS1FWagNjuZKy0rHfPTEdrIjZaUc6aZAhr2USJFitidnDe2HFWHkC2d+1FsTPjl5xSzE1jDSuZerQwaq9Spd+Vm9roolcsaM1RLZZvC4KjS70ZjUGRIh2xb8mFULldq9sUFqnM1CAuO7PS5IOL1u9pr2R8RezvpMwjoxksVU/SXW8cyUpN4dkaBSahIYgivqFWfRKbKzlCiRKjDhx6tUoUeWpTJKrVF80+WmEiMllbCSN5vgXSNJYzoVSrsWnxqFK0mLEhzsWbmIMaTakGXSajxIc2jYLnNl4DmvmFiy8FIaPYrVfd2r0Eu3wjbDW5bpuW1soGTjMjm0Vbb3RKrtuUuXbNGqTajcSxUosSj2ziJd0CkTFNOeJv3BatFqDyW3DcpbJo3T7G0b8L/FVKpFKq+mzTjos0MpVWa7adVJyDVp+VciNWJKxo07VsM0WZnYCPb28OmViflmK5qNm4l7nhM1p5p8xMzEthbClfxOsutu2gQ3S8KIi3RsRrYUvPzTIT7LqLHloMRbLeGh5zL54RbgJf2L1DwSzJ4DYq5Rbwr86BRWJ98SPfHbdHr9WejMUin3S45b1o3TbsOpuymm2K5MtFVJhk43LrEil0vp6jH/g0lfhiaRsOYJqGPtFmkXB+mmiU2XmZ+JL4fhLS6rO02SZFiTszR2NqVapFTjyjIT3RKfArSTkbVdBkYc3N9nLROah6eKJO1SDSK/RalheajvZBR8473iXhR4qtbCZMqsCVmYDIiuREjOlezZdHRVhw9aI3YemTIdOhy6hUJcaBAgRn5k6dMfaiw4cOK0p+TLlyX1IZjxo7KFvPvvLQ0y0hTjikoSZl8zIECPNR4MtLQYsxMzEWHAl5eBDfGjx48Z6Q4UGDCho6JFixYjmshw2Nc973I1qK5UQ96PeyGx0SI5rGMa573vcjWMY1Fc5znOVEa1qIqucqoiIiqq2NcrGLwjTCWDitVcKMoeWDF3ObOoEuVEqdyWTOkW7QaoUGUUeZUbKjUizcRbluWiNfDJmtTbft6BPcShynuS6bIjVN36gYI/DAxnMYPk8Y6atLWCtBcvUoMKNKUuvy8Kp1GU94g9rBlq9Fna5hilUuoO/T2kjL1KpzEu1XNmWwZqHFlGehqrp5pjKnEpmF8O1TFj4DnNiTEm90CDE1Har3ybYUpPzExBTO0Z8CAx6oiw1dDc2KuMXa+bVnAnP5kHp+H1Etm9sGcecPc0WHdVu/BHFCG1Duhmgt4cYsw5Fx0KUxuNVijwalVIFMqiJMWkVuly5cJVQoseFUabMmdsfYo9jzSH7OPtGTGJahVaBjrR3iXRJieTouPsJR3x6Q+ouxRg6PCpdQgxLvkZ6YlZOYm5R0KNO0+bgwY7ZafizErNQIHrzSjpKo2NsFMkYMvN0mtSOI5CJNUeotRkykBJCptWYguSyRYTIkRkOIjmwo0JzmdpBayJDe/a42frjbWQTJI66tDbTeT/La4444okNttowXsxS1rWoyShCEkalKUZEkiMzMiIfHb2kmuf7R2nxjGue9+m3Sm1rWornOc7HddRrWtS6uc5VREREVVVbIdlMDqiYJwgqqiImFcPqqrkiIlIlLqq7kQxUYu+ER5f6XiXcGE2VbADHDOXc9urlsPVbC+nph2fV3oMk4kt6250OnXZdNaozEncaK4WbMbo07pWpNIl1OC/GlvdwsFfhlaSJvClNxlpf0kYA0GUmptgxGSeLplY9bkocxCSNBZVJeNNUekSE9Ehaz1pj666el9R8KdgysxDiwYfraqadaJDqEemYbolYxXMQFc1YtOh6krFVjtVyy72w5mZjQmuy7dJRIT7o6E6IxzXr86sPwk3C6lYg0LD/NzlGxyymyKu62idWK49Mu+PbkV9x1hqs1igVGysPb0eoqJDK2pUih2zWJzRod8XpsxbS0F5NiL8LLF05hqoYk0L6adH2mWHJMcsCRp7INEi1ONDayI+RkqjLV7EtCZPuhPa+DCqFWkZd+sztZqC17XL/DJafqdDnoMjijC9ZwysVU14sZXzTYDVVWpFiwHycjNrBRyKjnQZeK9LLqw3qioZ4MeKzSbjyu4y3DQajEq9Dr2AeIlZo1Wp76JMCqUmqYeVidTqjCktmpuREmw32ZMZ9tRodZdQ4gzSojHzw0eSM5S9LeBqZUZaNJVCnaRsMyM9JzMN0KYlJyUxNJS81LR4T0R0ONAjw3wosNyI5j2OaqIqKe56zGhTGHarHgRGxYMeiz0aDFYqOZEhRJGK+HEY5MnNexyOaqZKioppl7GHae4D7PfKDjJTb3pd14l4v4j5hoX1OsFMO4jcy77hhpsO14D1cmvyNIdFoSJxe50Z9xMyqVapmqJQ6NU/E6q7Tvuh7dfsmaQ/aV014Gm6BOUfCmCcLaM4/9J8eYnjOgUSmR3Yiq0xDp8CHCvHnqg6X/ANVRYbVgSklK6saoT0p28mya6naJtIlFwNharQ5yHM1CqT9dZ7hSJFqOmo7Pcpdixnud+iDBR/8AVtVdeJFiXbBhRNSIsPLbht4RthfHxIt7DzN1lNxxyfNXTNJmmXZeCptx0WnU2QoigV+46bUrMsG6olHdJyMUyZQLfudEA5KHVG9CQ7MR0xxT+F/i6Jhap4m0LaZNH+m59IgK+bo1DSXpc/MzUJF94p1LmpWu4io8aeYrIvYQalUqS6Z7JzESHHVkB3s+n6eac2oQJHFGGaxhVJl9oc1Na8xChw3fwR48OJKSUy2Et267oECYRmsi5sRXGxXR6/RLkt+l3TblVptw25XqPCr9BrlGmxqlSK3RKpCaqNLqtKqMRx6HPp1SgvsS4M2K67GlRnmn2XFtLSo/mLPU2fpdSnKRVJOaptUp09MU2o0+egRZWdkJ+UjvlZuTnJWM1kaWmpWYhxIMxAisZFgxYb4cRrXtVE97wo8GPAhzMCJDjy8aEyPBjQntiQo0GIxIkOJCiNVWPhxGKjmPaqtc1UciqimLXZabSXCLaI0vGmo4UYH1bBZrCioWHCrjNUeth07hcvKPdr9PdZ97cWMlJUxNtTEOeOEtRnOR0G6RO69uva79lnGvsyTeA5XGOP5PHb8Yy2Ipinvk2VZiUxtDi0WHMsifmkaKqrNrVYCt7HVRPd17S66lvXGjjH9Lx3Dq0SmUeLSUpj5JkZIiy69us22aVip7u1v+t+7vRda/8aW3mGvKz/8AdO2Z/wD1FxL/AOLaxB3l0vf+0mtEn/d+FP8ApTiI9UYc/wB0PiL/ALVUP+b5MzI58NpPhDk4zE5acDb/AMDqtiZcmPs2kxLXuyA9bDcez11K+aZaDLklNaivz1ExPqDVSP3PcbPcZMk6Pbqh0c9nf2WMa6cdGWlXSBhvSBJ4UpejiBORqtR5hlWdFrbZXD83W3tgrIRocuixJeWfKp7y1yaz0v8A1d0Pa2NNIFLwpXcP0eeo8WoTFadCbLzLFl0bKrEnIcqiu7Zqv/S96RP0KmScToF4RvnD95OXW+8mX1GcRa59WGxMKcQPq10yPvYb2Z7iY802oe964pPia+jq876nvi8VHjrOrtxUgujVvaOdkPwvtCP5/pOw7p0/p1hin/0IxFjDDf8AQKbi2xTXfzDR1Ny35nTIXbt1pKX/AKS9rFd2D7Mpc7+pNW7fCdPOKvdKDO4T/KZ+N+ayVMnvzeG28hKdjWob+wmHai2iv9x1WprpnHhZcfUNhXtFZ1TwPy+ZIqXlhxgkVO0sP8XKjTsY5JMwMLrnnUqtX3iBEpMGsSKb4vEdqsiW3azUp+Y4zHqyXVupUhtTY/a/EI9mKXlMf6StPs3pawTDlK1iXBkrNYHg68zi6ky85I4ew3GnZiShTXaRmScOC6rvgw4DXxZNWNYqOcjz+XQ3jt8Sj0PCEPDtVdElZKqRIdVdZlOmHw407PNhMiuh6rViOcksjleqNioqrkljJ/s39qvhjtGKvjLalt4bXjhBfGCztvnXrPvqo0qZVahArkqt02ROhIpqWlNHQavRF0uuRpLSXYMmoUslnrLJKepXtR+x7iz2YJLA1YqmKaHjbD+PGVJKdW8PSs5Ak5aZp8KQmocvHWaV6P8AzGSqDZunxYT1ZMQpabVv+sqq+xcA6SqdjyLVpaXp83S5ykrA7aVnXwnRYjIzo0Nz2JDsqdhFgrDjNciKx0SHf+KyZMbuuqgWLalz3vdVRYo9r2bb1auq5KvJM0xqXQLepsmr1ioyFFqZMQadDkSXTItSbaUY6p0Wj1HENYpNAo8rEnqvXKnIUelyUJLxZyo1OahSUjKwk3xJiZjwoTE3ueh7BmpmBJS0xOTMRIUtKQI0zMRXfww4ECG6LFiO5Mhsc5eSGPXZsbSCg7SWzMTsRLLwavbC+y8PLspdlxKzeNYotTK67glUtyt1iFTmaMg0xHLdpcq3JNRJ990lnckJDBq6F1Z9lvan9lyo+yzXcJ4Zr2OaDi2u4mo03Xo0jRJGflPyemwptshJR5p8868ZtTm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E6kdXNFH+3FpJ/wC24l/6UwDb2HxSO0YAAAAB6xAvey6pWpVt0y7rYqNxQTWU2gwK/Splahm1vE4Uqlx5bk6Obe4vf6ZhG5uq3tN09P1pigV2TkYNUm6LVpWmTGr7vUZinTkCRj69tTsZuLBbLxda6aupEdrXS17ofzsnJSJGdLw5qXiR2X14DI8J0Zlr31obXK9trLe7UtZTzdQqNPpMKRUqrOh0ynw2zelz6hJZhworRGRG7IlSVtsMNkZkRrcWlJGZFrxIfwS0tMzkeFKycvHmpmM7Ugy8tCiR48V6oqoyFBhNdEiOsirqsaq2RcjlfEZCY6JFeyHDYl3Pe5GManFznKiInNVQ4dDuGgXPAbqtt1yj3DS3TNLVSodThVaA4ZaGZNzID8iOsyIyMyS4ZlqXaOeoUypUmZdJ1Wnz1Mm2IivlahKR5OZai7FdAmYcOK1FstlVqbDMGPAmGJFl40KPDXZEgxGRWL3PYrmr5nmB/Ccp4auXHb1sxE1C5K7RregLebjpm1yqQqTEVIeUSGWEyZ78dk3nVmSG2iWa3FGSUpMz0H91PpdSq0ZZalU6eqUwjHRVl6fKTE5GSGxFc+IsKXhxHoxjUVXPVuq1EVVVEOKNHgS7deYjQoDFVGo+NEZCarl2N1nq1LruS913HlWnWn2m32HW3mXkIdaeaWlxp1taSUhxtxBmhaFpMlIWkzSpJkZGZGP43sfDe6HEa5kRjlY9j2q17HNWzmua5EVrmqioqKiKipZUORFRURUVFRURUVFuiouaKipkqKmxS4MlPEVq4KDbcRE+4q3SKDBcksQm5taqUOlxHJkpW5GiIkTnmGVyZC/gMMJWbryvgtoUfAf2yFNqNUjOl6ZITtRmGwokd0CQlY83GbAgprRYzoUvDiPSFCb+qJEVuoxM3KiHFFjwZdqPjxoUFiuRiOixGQ2q52TWo56oiucuSJe67kPKpUlaUrQpK0LSSkqSZKSpKi1SpKi1JSVEZGRkZkZHqQ/jVFaqtcitc1VRzVRUVFRbKiouaKi5Ki5opyot80zRc0VN5UIDS4zX1LHTbPbWO/8AIHR8V6thXlfy41C74lzUmmKfkxZKcJ6nSrav69anb7EyNTrvvSp4jVJi2LPVXH0U60qE9HmxYyJ3u+3Xvu3ocldH3sK+xthv2j53BsnjDS1pQlqJGpU7NpDhRoS4ylJyq4coMpUokGNM0WhSmGJWJVq2lPhrNVqosiQI0VYH5c6ndS8SxKxpZ0mT2CYVTi03DtBfNMmIUPWc135ZEhy87ORIDXNhzU3En4iS8qsZdSVgq1zWo/t0jZKGfBlNnS3b5Uh6v5kH6r0KELuo8SbXbq/TJa3FvohIw797qekc/VeiXRXEpPRBHuEZH1Xifive086pLOsp2i6HJ9o5zaP/AEWq75LUV+s2G6O7E35m7Vb+jXbPsVUu5U1s09gJ7PWA0gdksevuiWRFmfzCWSLe1lVGJIdgl1zssFbbL2MSFLj48+D6bRLCzC5jFiu4l5NMfqhRZlQplW6SHTKlZ9cr0S17mrMy1ymu0ajYr4YTFRaimv0NyK1c9DTS2Ji4FPr8+g0nuhORNHX4lHsyYvxfEwbTsK6c9G8tPwZabktWNNytbp9OjVelSMCr+7tnp/B2LYCRpVabUGxn0moLNxIDZiZp0vUZ31hDbWtB+O6bTm1ONUMJ1t8Fz4cW7YcSVjRmy8xFdLa6woNTpz9WIkeCrUmIPZo/Uhx3wYe23nyxMvXBjJbmmxYw4q5W/f2HmBWJN3WfXfEafUzo9xUW2KhNpVSTT6tFnUyYuFLaakIj1CHLhurbSmRHeaNTavjF7O2FKDjrTvohwbiiS/MsOYm0hYWotbp/vEzKe/Uyfq0tAnJVZmTjS83AbHgvfCdFlo8GOxrlWFFY+zk7OY0qM3ScJYjqchF7CdkaNPzUrG1IcTso8KXe+HE1IrXw3qxyI5GvY5iqn6mqmRjp8HxxLxCxgyFVTEXFO9bmxCvq5cwmK02u3Zd9ZnV2uVN841qJR4xPqDz7/QR2iRHhxG1IiwYrbUSGyxGZaaR2c/EqwphrBPtFymGMIUGk4aw9StGuDoFOo1FkZenU+Vh9pWFd2ctLMhw+0ivV0WPGcjo0xGc+NHiRIr3vd4JoOqE9VMFRJ+pTcxPTkxXKm+NMzUV8aNEdqy1tZ71VbNT9LWpZrGojWojUREzkj5+nuI8PDuK36jU6lRKfXaPOrNHNsqvSIdThSanSjeQhxoqlAZfXKgm6hxtbfjLTW+hxCk6kpJn/AHR6ZUpaUlZ+Zp89LyM9re5TseUjwpSc1Fc1/uszEhtgzGo5rkd2T36qtci2VFOJkeBEiRILI0J8aFbtYTIjHRId0RU7RiKrmXRUVNZEuipxPMD+E5TwVIui2bgeqEag3FQq3IpMjxWqsUir0+pPUyVpr4tUGoUh5cORpx6GSltzTjuj9CdpFVpsOWi1GmVCQhTsPtpOJOyUzKw5uDs7WWfHhw2x4d/7cJXt5nDCmJeOr2wI8GM6E7ViJCisiLDd/K9GOVWO5Osp50fnnMAB643eNovV5y1Wbptx26GWzeettuuUxdeaaSRGp1yjplHUENpJRGa1RySRGRmfEh+o+h1qHTmVh9IqjKTEcjGVR9Pm20571VURrJ1YSSznKqKiNSKqqqKljgSalVjrLJMwFmUS6y6Roax0TisJHdoiZpmrbZnsY/LOcpWtDaFOOKShtCVLWtaiShCEkalKUpRklKUpIzUozIiIjMz0Fa1znI1qK5zlRrWtRVc5yrZEREzVVXJETNVyQKqIiqq2RM1VckRE2qqng6HdNsXOU07buOg3CVNkeJ1E6HV6fVigS93e8VmnAkSCiyN34XQP7ju7x3dB+hUKRVqSsBKrS6jTVmofbSyVCSmZNZiDe3awPeYUPtod8u0h6zb5XOGDMy8xr+7zEGP2btWJ2MWHF1Hfyv1HO1Xclsp54fnHMawW1EzMY7ULbM7NjLDQcTLmoOBVermWfEi5MPaHLTSKVdN21jMXflvSpd0v09uPUbjp7dMs6gJhUGszJlBgTIaqlEprVRfelL+tHskaKdHlR9hj2p9LVRwpSqjpCp0hpWwtSsS1CCs7OUejSWjDDtTgwaPDmXRJWlzL5uuVJZioyMCBUZmBHSUjTT5WGyC3rtpFxDWoOljAGHYFQmIFGjRsPT8xIwX9lCmZmLXp2A50yrEa+YYkOVgIyDFe+CxzO0bDSIquXKtthP52TnL/ANZ6o/8A1Xo46fexH/ur9Bn/AIbyv/mU6eytKf8Ate4s/wC9UT/4rCOqng6rjbOy3w0ddcQ001iJjI4444pKG220XxUVLWtajJKEJSRqUpRkSSIzMyIh7h/E6a5/tdYrYxrnvfhnAzWtaiuc5zsPyqNa1qXVznKqIiIiqqrZDxrQQqJo5p6qqIiT1WVVXJERJyJdVXciGbOgXZa11syJFrXLQLlYiPHHlvUCs06sMxZBKWk2JDlOkyUMvEptxJtOKSslNrLd1SrToTUqNV6O+FDq9KqVKiR2drBh1KRmpF8aHZq9pCbNQoTojLPYuuxFbZzVvZyX9uwJmWmUc6WmIEw1q6rlgRYcVGuzycsNzkRclyXPJTz4/NOcADXKzu7UfPps982eKEjEPKVOxhyFzJ1mP4d4k0OmzaFUqBDlWJabd0MOYiUJq4rYYcYvk7rXFtzEWgUev1JTRN06uxaH4m+j6gaAvZG9nX2ldDWEYWGdM8vgj2ioEvXYeJ8K1CagVCVqUeDiGsupERuGag+mVZ7YmH0o7Y1UwxUZ6nSjX601To1Q7eG70NjDSNjTA2Jqk6eww+q4Lc+UWQqEGG+DEgNdJSqTLVn4KR5dFSc95VsCfgQo8RUtDjNg6jk735QtsZkMzle5dGsXF2Hh/iTU+haRhNjGUOwb0enPaEin0N+XOk2neMxxe/0UOzrlrs/omzdkQ4xHoOvGmv2H/aK0G+9z2IcFR8SYWldd7sZYHWPiOgsl2X1pmfhwZeFWaJAamrrRq5SqdL67kZCjxVzPM8L6VcF4r7OFJ1RkjPxLIlMqupJTavXZDgq97paaeudmykxGfZLua0yjDqMexgAPh+ZbA+lZlcAcXsAa5XKhbNHxdsSv2JUbgpUeNLqVHiV+GuG9PhRphlFfkx0rNbbb5k0pRaK4Dz/RVj+c0V6R8FaR6fT5aqzuCsRU7EUrTZyLFgys7Gp0dsdkvMRYF40OFEVuq90P9aJszPx8QUeHiCiVSiRoz5eFVJKNJRI8JrXxITY7Var2Nd+lXNRbojsuJrg/8yqZf/6azGL8jLK/Oj6i/wDx4PST/wC8ewP/AMe17/RPQn+LbRP98tV/+FJT6mH/AGq+ytywbPOkYd2TY2PeK2MmZbFqqQfeVhOdsWy0mLaqqkdMkXPXEUYpVXP3ZqyDtmzaVFablXBW/dF2Kt1i3qlHc7tex77X+lv2mJ7E1exBo5wdgfRVgyUmPz7GSVarPWLWGyqTcKk0908sKST3GTclWrk5Ge6DTaf7q2M1kSpysRvq3SVo3w7gWFIyknWqlVcQVOIz3Sme7y6ast2nZumIyQtaL/WxU93lITUR0eN2itVWwIjV2xtjdkcufZ8ZLYtg4o1pheIl/wB21XG7EqmIdiKpFg1mv2tatC958apMmbU/3u0C0KX7vVMnnoblwO1kqbJkUhmDJe+Nvtx+0DSfaV07RsR4RkIjcMYbo0ngHCs0rIyTuI5GnVer1D87iysRNeX/ADOo1qb/AC6U7OHHZTWSPvUKHOvmITOzGinB0xgfCTZKoxmrPz01Fq9QhoreykoseWloPujYiZP7CBKw+2iXVix1jdm50JGOXAVhjat/+ESZ6caa1ibibf1kZFsukplNm2VZz3uauXS6tVK5SsPmocSrsVGg0y/75p1Ird3Xtd1Uo1fqtMgRitCnMx6aqiP0n6N4srGG/wAMn2e8CSGFMJ4cr/tCaT4MRa5Xq5D96bBm5OU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yExirBE3GjVCNh+QVYtaw2sVViRoMGSRVfOU1jlVYMWBrRIUNdSI1bIfSnRF7VFLxDKSFFxzUVpuIYUOFKMnZp6tkakrGoxsVIv8ABBmHpbtGPVEc9VcjrKplBhVG2PF2atHq1Aei7hLYqKJ9LdbJBlrvNTDePdTpx1S4ktOZcOHzUmadiSFGjUuNTq1AmFerI1PdKVGFEV99VWxJTs2q5yLlZ0NVuvcdkY9Uos/qTbpqnx9RP6uY7WC5rWu/li6yoiLvVrk57LGLXaSYk4RVO2bPn2rfUP6vWHV3025LBmWxKTOmUxbT6Cq7NSlRFOMNU+ZE1S8w64rpV7pEnUi0+nH4d+jDTHDxHimSreCp3/AZj7DU7h7SBK4kgxJCRqctGhOWQi02Wmmsix6nKx7Plo0KH/VNuuva50z9qrEuDXUWjTFGrst/hFwzWJWp4Zi02IyNMSjocRFmGTMWEqthyr2bYbnWiKllS9j5TZmc6g4oMQY+IE9NrXQiOhh8pSjTQJz6EETkmFILVuKcheq1R3yI0qUaUGaSIeS6VvYUxto0mqhUNHUhExjg9Y8SYl2yTWuxBToD3ucyWnZPKJNJLtVGJMy92xGtRzm6yqe/9AXti6OcR0ym0fHkaFg3FrIMGWmpqav+S1OMxrWrMwJ1UVks+Mv6nS0e2o5bNVW2U+wIrFvzmSlRKxR5TWm8TzU+G4lJdpK6XeSWhGfUfpHV6Ph/E9NjOkp6h1ySja2o+Xj02egq9yblhrCa163yzR2e873yGIsLVeXhTUpX6DPwWNR8OYh1KnxkYioqorYixVWHdLLtbzRD4fifjlZ+H8V5qNPi1y4FNq8VpVPfS+klnqSVzJDRrRHaSrQ18d9XFJFxHYDQz7M+PtKc/AmJqnTeHMKw4zPfq5U5eJLudCRUWJCp8vGRsSamHNyYiNSG293OW2XoLTX7UujfRDTZqFK1KUxRi+JBelOoNLmYczaOrVRkWpTMJXwpWXY6zn3csR7UVrG3UxpXPctWvKvT7jrcg5M+c7vGf+ZstJ1JmOwk/tGGEHutpLvUeqjMx9jMEYKoGj3DVNwphuUbKUymwla1NsaZju1feJyafZO0mpl6I+K7Yn6WNRGtPiZjjHGI9JOKapjHFc4s5VqpG13WukvKy7bpLyMmy9ocrKsXUhtTNy6z3Xc5VXwmp6adQ8ofZVsm3f8ALr6HjLE38dnzPpWEDhFeEwvu7UuVJa9qqW8RF6R7f0AL/wDJbwwqX/T+Y3/4vmPqeptPX+1dXP8At1L80noPwsfaUHpoXaZ6+Yh9FYy5onL6+i39DoSzNq78/khyqJXrksq5aHedm1mo25dVtVKNWKDXqTIXFqNLqUNwnI0uK8gzNK0rL4SFEbbraltOpW2pST/in5CTq0jM06fgQ5qTm4ToMxLxWo5kSG9M2L8WubZzVRHJmh/XJzk3TZuXqFPjxJWdlIrY0vMQlVsSHEbmipbbfY5Fyc26LczpYceEA4h02yoVp5hsu9rY01GmMtNJuSBLgU9VYWy0TSJdYoNYjyaezNXpvPPQTMnVqWvdQatwus1W9muEs9FmsNYkjUqBFe5UlY7YqrBRXX1IcxLua97EX+HtEaqIiIqqmZ2QovtAa0jDlsT0KDPx4TGtWPLrC1Y7kaqa74EZrmMcqLnqqqZu4nwzMntyMxmM9g1LCrBnDu2Mt1m1eHIpdQqdrSjnXeulyz3ZUOkyYzEamW6qQ2amnJkBtySSHXCb3FmS0+U4O9nbD9IqcGrYkqUbEkzAiMjQ5aMxWSjorFTVdH13PizDUVEux6o1VRLrbI8dxTp9q8/JxaZhynwaFLxGuhrNMVr5psN3/YGsRIUB1r/qRFVL5IfB9jipUPaQ5bnJRrcW9U7+S4664pbrj83Di70dK464aluuOvuG46tajW4tSlqNS1GZ+0dP86kXQtieVYqMa2HSIbGtREa2HDqkijIbWpZGtRGta1Eya1ERMkQ8H0KQ1XSpQI7taI961SJEeqq57nxKdOK+I9y3Vyq5yucqrdXKqryzS7QLa/XPldzVYh4Iv5WcLcRWbIploTLVvG6pilVeoRLjt2JV1vupVTJXijEeoOzYbLUd7eV4qp0yJS9B1M0ZaEn40w1LV2Hi2oUqHMx5uDMSUtDd2bHwI3Z2u2KxsVXwuziL2jbWfbYmfYvSFpchYRxBHo8bDcpUny8vLTEGajvYjlZMMVzVTWY5Wo16PZkt8t1zBtnN2jeYnPY1R6JiCVFszDmhTiqtLw3s4pKaKqqNpW3FqNZmytyTWJMBpa26eh5tuNDUtTrband1SO02jfRdhvRosaap6xahWZqGkGYq02je2SDdHOhSzEyl2RFRqxVRyviomqq6t0d1wx7pSr+Pmw5OabBkKTAi9tDp0qrla+IiWY+ZetljLCz7NttRqrrWVURU8Nkyz0Y6ZF7mqtZwtkQK3a9yE0d14dXI5J97VdfjJSiNUm1Rt5+l1qM0XQt1GIg1uRz6CQhxCWzb/s0naOsO6UJSA2spElqnJs7GRrEsjfepaE5Vc+XejrNjy+vd/ZvW7HqrmKiqqL/Bo+x/X9H0zGWlrDmadOREiTlMmVd7vFiIiNSOxzc4MZGJq67f4m/pci2RUydfZ/3W5aq4zkjwxbvJzV2RcBVeETjs3TXpvHUUj3WWk3SJRrcc6bdL7be4j0F/ix1B7fcnaRKi6nNTVZLrCmVZ2eyzpZY6y19XKyN1b2XVQ95rp/pzUbNpg+T/ADBbK+N2kvrIuapaN2Xbqmsu1XXRFXft6A1zaV4tXpnhs/PFXsPrTeuew4DNHtnD5FSqbNtRaVDoFy0WEw/UksnUHXmX7pqVVdeJlPTSeja3SbIzHvyQ0bUynaGanoekalMwJKqzKz03V+xY6PEnYk9ITcWJ7vrdm2FEbTpeX7NHXRms7+JUQ9M1PSLUp7SjS9JcaSgvjUiWSSlqVruSAsp7tPQFhujqixO0WJUI0wkS2Tka3+HM+J5uMz115x8c6tjnedr0S061VbYtO110W3pMuXTm4lpxZUaNJKTNQ2+t6Uc15bqTQSWkk2hJnoZjyDRjhqBoyws3C8pOxKm1KhO1GJORobIDnvnewasJsJiqjGQmQGI1b3e7WcuWR+FpAxVGx/XPz2ak4dOiJJS0i2XgRHRWoyWdFf2jnvRF14j4z7oiaqJZG7FOwND2kOItr5E6xkOgYYWc7ZFbtq8Lak3u7V6t74mkXlecy86jUU05LHuep9qVOkQ2GFOk10PRuKVvp0P13U9EErUtKLdJkSvTMOMlZkastKSVhrDX3KWgS7JZI+trIx3YoqvRLpdWomxV8zpelubpej5NH7aLAjQfyuepf5ksw9kVGz0aYjLHWAjVbrw0j6iMvqv1WqqoqqY+YKejItS5nxI+ZdenVy6u8e5o0wj3ZX8FyXpD1K7aqouaIlskzS9r9/pbLefRsMsQqthNiZh9irQIcKoVvDm8qBetJg1Lf9z5lQt6aidGjTOiPpSjuuoSTim/hkXLjz/GxFRpfFOHK1huajxZaWrdPj06NHg6qx4LI+rrRIaOyc5LW1VWyoq7Mj+ujVSYoNdo9flYTJiPR5+DPw4EVVSHGfB1rQ4ioiq1rtZbuaiqioi2PvucXPTiZnMvy0r9v6zbWt6qWfbbFsw49uuTnItQhsV2TXUOTDlkSm1k/KdYJLRadEZqPVfL8PRVgmT0VYfreHqZUZqoQK5PRZ6NFmYbIToD4shDpythNYqov9VCY9XOW+unCx5BpGxtP6R6vR6zPSkGnRqPJ+6Qocq50VkZPfFnUfEWI1FRWxFVqNbdFaqrke0539opifnrt/Cq279sC0bOj4Uza7Opkm2Z1TmPVJddptJpjzcxM9ttLKI6KShxrojUpxbqiVoSeP4mirRVTdFNUrVVkatN1SJWZeBKrDmYEOCktDgTESY17w3O7R73REauxGol0zdl+zpC0oVHSLTaXTJ6lS1PZS5mJNdpLxokVY8R8ukvqqj2pqNRrXOsi3VzkTchy8iW0SxCyDQ8TIti4a2lfRYmzbemVKTclWqtNep6begzoTEWKinsPIdbfKc6+tbuhtr+CkjSZmXHpc0XyulWdpE5N1mYpK0mUjyjYUGXhzDInbx+3dFVXq3Vcn8Oql0VG3U1oy0nTujaVqkrL0iBVG1KbgTSvjRokB0NYMFYPZpqNXWaqfqRVRFRVVN9zH+ch+fVKpV5KENyKrU51Teaa3ujbeqEt2W420pXwjQ246aEGfE0lqfEeyYDWS0nKybHK5krLQZdHrkrkgw2w9ZU3a2qiqm6+Ww8InppZ6bmZx7UY+ajxph8Nt7MdHiOiq1FVLq1quVqXsuSKudy/KaKQ2aTLUyLh5e36OsZWJqrZPHu5c+e4/jVL359J6539N5kEyXbUXMjkhpSbItxuk4lYTFJXJj4e3i/LaaoK31uOSfexWIxOSaWxIccU6qnqQuF0nFBNIM0j05pC0LYYx5GiVPtYtHrcT/XKhKMa9ky5ESzpqXcqI96IiIsRtnKmbkVUPbmA9L2IcGwodMcyFVaMxf6uUmXOZElWqqqqS0dt1axL3SG9FbfYqJkd/ZHhBUaM6dUomSayI9zkZvIqsquUlBlLNJn0qpcOle6Kkk7xNSVdKadT13jMeq2+zNPxUWDMY4jLKr+l0NkKZcitVET/W4j0g32ZWTysezpj2g6XCRI0vhOE6bRL68SJAZZ27+sZD7S2tddt89pibzkZ78fM8N827eGJLtPtqmWQ66/YNpWkqSxTrTkyX2JDtSj1B40zZdZW7FiqXUnCbWnxVhLaSJvU/fWjzRzh7R9S5qnU5r5yJUmoyrTc21uvPNax8NILoSXYyXRkSIjYSKt1iPVVuuXo/HOkOu47n5WbnVbJQKa9YlMlZZXaspF1kcsZIi2dEjq5rP1/psjUREMj2Ee3dxFtrDGi4eZg8AbUzAS7bhx4dNuqdKgR5lUKG0hmLJr9Mq0OVBVUUtoQT0+H+qSFEbqkpUrQeo6/wCzTJzFXmqnhbE0xh+XnIjnvkdSPaA2IquWHAjwHMiOhXcqNZEWyIurnme08Paf5iHTIFPxTQoNYjSsNrGTrVhIsw5iWbEjQY7VYyLZEu9l9Zc7op08zqbUHHjO1TqbZFQodIwpwjpEhqVEw6tOS8+xUpEUt2FIr89TbCZniSSPxOAw0mDGWe+glqJJp9maK9EVA0aTjqukaJWq+9HNSqTTEZ7ux+b2ysNXOVjn7HxnL2jky/SirfwTSHpbrWOZNaQyBDpNFRyOWRgPVzphWp+j3iIltZrNrYLU1Edmt7IMse0XxUytYEYnYBWvYVnXNbWKSrjXV6tW5VRj1SCq5qF7gy/FkxULZe8XjauMk4ZarIiVw4l/fpH0ZU/SDjKhYznazPSU5QmU5kKVgQYUSHG/LptJyEuu9zXQ1iPTVfZF/Sqqme38nAekyqYGwzV8MS1KlZ2Wqr596x40WLDiQVn5d0tERGsaqPRrV1mX5NWyIeFyS7RbG3IvVLhdsqkUS9LPuxbEmvWNcciXDgOT4pG2xVadOhpcehTij6R3TJtSJDTbZL0NOp70v6OaLpdg0+JU48WnVWmQnwJSpS0NsWIku9UcsCPDerUiM17uYquRWK5ype9j+HRdj+t6MZichU+DDqFLqUVI01ITLnQ2pHRFRI8GIxFVj9WzXIqWc1qX2GQ9/wAIjxHhSXp1tZQsMKdVpJK8aqT1yzW5D6lcVmt+FTESXUmrifSuGpXMx6Lgey5CeiQp3Gk/GgQ7dmxJRFa1rcks2JF1EsmX6bWzse7n+0W6GrosrhKSZGfdXvWZ1HOVf4lVzIest1zu5VVd9zGNnTz4YnZ6L5te/b8sy2rJkWhbx27R6RbcudOiHHVUV1JciVJnobeceVIWZbqUpShBEkjMyMx2N0T4Qk9FNDqtFp09M1FtWmlmpqPMMZCdrLL+7akNjFVERIe9drr7kPR+kzGc5pLqtJqc3JQKd+TwFgS8KWe+Nra0f3hYkR8RrVVVibG7ETLmZJbR8IGvql2TRLHxKyrWFfVOoVIpNIZXFuBw2JiaREahsS5lNrkF+Gl1aWUr0aJRIUat1XIde6l7LcOYqM3UqVjKYlHzUzMR9SLKOhvhJHiOiqxI0rER62VyprZax7sp/tE9nIy0lVsMQplsCXgwXOhzDXtirBhth67ocduqiqjUW2dlVbLY+H5mttzjtj3hpUsIMLMNLcy8WdXo71PuCbbU85tyT6VJSaJNLguxo0Wn0ePLbNTUp+KhUhxlRtFukZmPKsFezvRMN1eFXK/VYuJJyXe2JLQI0JWy0OMzOHGirEc+LHcxUuxr7NRUuqrsPHsV6eqpWKbFpFBpkKgy0ZroUeOx7XTCwnpZ0OC2GjWQde6o5zf1WuhiEpkbxdBal8I9NTMzNR73M1GfE1HxMzMzMz4jsU+JduqmSJnaybE2W5Js8j0Ml3LdyuXfdc7rfNb53XntXPM8tJc/UVdunr/kHE113Jmm3gqZeO+/p6VGqm+6X2cl+/ht4mwj4OURrxizLOF9qnD+0En/AFx11Z89OzQdU/axX/qbgxFtf36fVNmSdgnfx3r4HZv2ZkX3zFS7klpNP/uy7eeSm2kOkzNqdblO25+VPmHX/wBFjmSLXQixuxILT/dRUPk6vnH9TlyS9/4U37V2W8ttuScDGaIurn+rf1nnvPmtVjJlxloURGRkojI+RkZcj7deJH5ezgfGatzv4d/D4cuOZtK7HPOFYmZ3KnemzAx1vWRaN/QqHVYOBd2P1VdNn1OjPuKqNIgUOo9MytNyWbWiRLhRSc6aVT1rSyhRM7hlTYvVlt+6cSrw4/frd8D6/iltOscsC8K8QsrOebIfiJjDiFZFu162KRi/RbWh1/Cy+aNHpb0CiYi1SVUYclFLefjkxOqsiI42bT6HlEaHSMiioiqiovemaKnBLftzTcXPb16mnJKqTkhmRIQlDKZBvPNtMmfRNE4a3Uts6/5k2SiS3rqe4SdT1GlXO+xCH6Qux9pliVPZ+5Y8QqBYFp2ncFzYasMXBUqJb9LptSq8ujViq0V2ZUajGjImzHJZwEyHFSH3NXHFGY4nXRduS818fjuB1r21u09szKBgdc2DeH9xw6lmUxUoUygW/RadJbfk2PRKq0qJU7wrxMrNVPOPEddKjx3tx6ZNU1up6MlKIxu/P7A0GKDGcYZ33VOLdc1cccdWa3XXFq3nHXlnxcddWanHVmeq3FqUfEzG1z532+S+t+IT6Z9X4nnuk0ej6n/m7R/78i0Lz6exjgXa3m+Hz/8Abjd2/wCZ/TezHJyddf8AgL0ufI7j73wGldRts+tpJ8PPyH1spn/srpl//dbT7W2X9zl/kfLqoqi1KpW/92M//wCdxk2eGR46eyl5BlwMuJcuHHs085d4/va5U259c+vO5/AuW3Zfu222W5Kvx2mRvJ9tVMa8ploO4S3JatDx5wMcbWzHw6vVaFO0Bh5xbsiNQKhIZksqpzy3FrOj1BpcRtxRrYU2REgvSOkHQjSMZTyVqmTr8PV9YmvHnZZjlhTiomqjo8OGrXw4v6f9fgrrrvRVVVX29gPS/V8Ky35PUZZlcoSNRsKVjqnayrVW7ocF70Vr4Spe0GIitav8KomSdhq9two9m0qstZUslmFeB12V6K7GevqoIpk6pU43UkRuxYFGjNplElfw0RJEpmKpSUKWg93QeLSns6z9TmZV2M8bz9akZVyf6hgxJlWva1UsmvMxFSGtstdGuftseXT+nal0yFM/0TwjI02dmGrebiw5duq5djnMl2osSy56iqjdZM0zMJF73hfWJl33HiXiNclVu+97sqD9XuG4a0+p+bUJzxaFrqZojxo6CSxChsJRHhx0IZYQlKTM+22GJem4cp0rR6RKQZGnyUJIMtLQURrWon8TlWyK571/VEiOu57luq7k62YjqVQxFOzFTqkxEm5+ZdrxI8RbrbcxiXsyGxP0tY1ERqJvW6rtLZg85ScqWzj2cK52AOHePVsYl4d29R69ScRemVEokqh4etVWPMpRtRJJIny5Lcpg1mbKij9IRLURGR/PyjYIn8c6XNJiSGJp3D05TK9WJtsxI6yxJhkartgPY/ViMfqMSJrL/EmsxqKl8zupWcWy2ENGmA4k5Q5Wsy09SKVKvgziN7OFEZS2RWuzY9NeIkN3B1lXipiex62tmMOMOEs7ArCHCnDvK7hlXErjXVBwuQ63W7mp7jfRvU2TVExoaoMWU2amJrjJuy5cVRxunbbUZj3/AIQ0FUqg1uFifEdan8X1qXiMjSkSpq50CWjQ7LDipCiPirFdDciPhtcrWMeiOVq2svpLEumqo1KlRsP0CkyWGadHZEgzKSFkjRYURFR7GPY1iQddFVr3peIrVsiotrY1rPqlfsWv0K7bSq063botmqQa3b9cpTyos+kVWmupkQpsJ5PFtxh1KdEqJSFo3mnErbUpKve9RSVq9PnKXUoEOckJ6BElZyVjprw5iBFSz2REui53vdLORURzbORFPS0lMTdOnZWo0+O+VnZCMyYlJiEtokGLDVFY5q55JsVNjm3aqaqqZn0baV29KDR6ZmbycYO4+3PQocaFFviUTFLqc5DSN1x6oR5URfQKcMidVHhylxulU6tKEb2g6uP9nmeo1Qm4+B9INaw1ITz9aJT0dHVIVr6rWRYEVqRbXVEdEY16JZFVUOyEjpxlKnJQYOMcJ06rzcqxrWTbWwbxXI1bueyK1Vh3VVXUhuVt1VU5dU84G0JubNna9j4Y0TBzD/AvCaxa2i46fZtiMNmuqV1inzKVCn1iYiJFJZU2nz5rUaO2ThG7JckOuqXoReytEui6X0c1mp4nnq1PYlxPU5R9PdVag6Ir5eQjRoUxMy8BIkSK681HgQXRHOVLMhNYxqIrlPXuk3STGxvR5PDshTJaiUOTnIdQdISmraZnYEOLClo0VGNYxEgMjxdRE1lV0RXK7Ky+AxIztXbiHkiwiySTLDt6m2thDd8C7KZfESpz369WH4b95yXI8+nOtJhRyku3nJJS47i9xMRvdLV1Wn6FI0aS9J0u4h0sfnceYj16WnpdaO6WZDhy3v0OnQ1d7yiq+K2ClPR0JqtTOJd38KH5MzpCnJ7RpR9G8SlwWS9KiysVKqyM7tYqSszMzLWpAsjGOiOmdSIut/C26ZuW3z3JzmTuHJrjlTMebUtCiXpX6Val02tEo1wT51NpyWrrapzMuap+A06+p5hmn9Gy1u9Go31KWZbqdf3tKeE26TcMw8NRqpFpENlTlKkszCgtmFf7pDmIaQXwnq39L0mFdrot2qxMlup+Vo+xZH0f4hfiGDIQ6mrqfNyCysWI6Db3l0F6R2RGo5UcxYCIrbar0euaKiHzPF7Emr414y4p4zV2mQqJV8Ur8uS+6jR6a48/Apku4qg5OcgQ35BJfeYjb5NJedSS3NN9SSM9B/XhaiwsJ4YomGIM1EnIdDp0vT0m4jWw3zKQda8ZYbbpDVyuyYirqIiJ38WI67ExRXqriCNLMk4tUnHTb5Vj1iMgq5jIaMa9yI59kYiq5yIrlVVRD08y6Qt0+Jae3yejUftpERHIqLvtfaidbj8KJ/CqccjuGvO7fTGSVrI4VjWy5ZMa71Xgxe3j1QK5UzHLwmXguMcDo/c9TZypi4RO9J0iYyd/TfMx63kdG0rJaW/8K7KzNLNLAiQX0ZZeH7uvaUlKVf3m/aI1Gt94RqNziLqquqeeLpFnH6NHaNn0mXWWR12VdIz0mGp+afmf/W9uzc7XVYGtrf61+raU4F557/wCy5Y95b7esa1q5bmP7FcZuK4KtLqDNXpHu5a0O1VnTY8ZtUV4ocWGiYx06kmqStRL+ARDmx7o1ksfY7wrjqbrc7T5jCqU5IVPl4MGJBmkp9TfU23jPcj4SzD3rBjKiO/q0Rzc0sXBmkqo4MwpX8JwKVKVCWrqzyrNx4sSFElVn5BshFsxiK2KkNrUjQ9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D8bhlHa0OTMYgoMjMiHIxd3XHr75DSc2JWFOZXazbRfLQ1mlRVbnwg2b2HVCpMmHW6ZJiw6YixZjzNk2rVSfSSZFxTrkSuqVJbuslw6Ug30E2pBntck9fn9uSA/UIQTbKUNJJKEpSSEpSRJSkkkRJSRFoRJIiIkkRaEWhcOA4lu5VW37dfMGov4YrTatVcguC8akU2oVSQnMNSX1x6dBlTnyQxRHnN42orTqySXaadDPTTkNs2eIO82zX25WSXMlGy85W7JaxvpeLUmwbTsxli7cH7lotsu1+2LVhRqnHO5lpcgNNm5Bf8WkPk006SS4oNREDkVd2y67r9bk9UyHXXXkd1drvkRRtGsh+MmWeFMj027q5ChXRh1UZmhQol/Wu45Nt8pqtDNuLMWt6A+6WvRFIJ0yMkGMtWy2XLb58924GrXs8tvBibsoMNKTkN2q+XHG615mBbirOsbFi3rWmVlidacN91NPp01K22odcZhoMypdYpdQV4zCU2mQ2l1OhcoM2WVLwirK/nZzG4c5fstGBmZi+kXzU1QapiRUMPX7fsuyIbbDr71XuKbIKS23Ba3CbXpKacNbiOjQsy0GXJdNvns6++5ch1M8IqyRZgW8Usqe1Xyh2jNvnFzJtXosq+rGokR2ZXLhsaHUkVRuqwYTBLkz1U5PjUCZHjocfbgyTeabWpGgjV3bbZXTh39ct4PYLE8La2a0qzoEnFu28wOFuKbUNtFz4cPYWzqtKpdeJpBS4EWopkQ1SGTl76I6nozTyWzQTqd8jMFaqrty9dls12/Hb4F63/sZS9mltNvsksfE69LNy2Yw4P4N2dKpVPsnEXFeEiiKxOlzScXOXQqE4wzLZh09lKFrkrN5pxSyQl5SuUVLa2xd9t+3auxU8C3RbIt7J59fLxv7btb889f2cOz6zDZw7RsSn4j3fhdTLNp9o2tXXKoxa7tz4iYiWlhpQ6rd0ijE3UCtm36jdzFcqsGNOo0mvt09FsQa/QqhWodViZRLqmfrdePgv77QuzZvve1k3bOvAw1eDo7fXMBtY8TMfMDMy+EuGNq3thnYtPxYtK+cFaLeFAs+ba710U+1qval2Um8b0vuVGuSNUa/SJlsz6fWWWKxRI1banUxmbRFVOr1zUREz8N/w7vqZO8XhG+THEXOvsx8VbJwipEi4cSrArNt4rW1bsTeXMr7VmzinVukU+OnU5VSl0gpSYEfTV2QSUp1UZEdZbxVfkoNbrZqeFAxcrGTGycnuL2WjF7EPMtgzS38NcNqdbUJTBXkluVIRa9JuOnyoZ1mj1WkvyU0yY0iOpT0VhlLSW3EqMbXZw67027Nu8ddbjHRfuG+0RtPba5Bs3W0LsuZbV3ZlMcsLMSLUp0Fciq06z7MiXVBiUuwFojMKbodQtinzIqn6I44uURyXZr2866/uksqenWag2kPCEdqniPhtDibNHJBR7wu/OdmDi0uj1yXZtJqMyVhnYN4SX6YUlmbFZW1FuC4kofiRJKlttUiCcqpOvNraa1W/Vffbpfl+4OoGIvgsNsQtklRsPLUOHK2hlnypOOknEhhxZOXLfcmCh6o4SqqaldO5QGoDfubS5ritUXNv1rXddIgul9ufAHaTYDbZqZiPZjGQTP8ATZmD+bzAmD71LemYqpctN/FG0raQqnMRJUuuFFYVetsRYfiktS5Cmq/TWGahDfefQ70pU81yvn8d3zBtARb/AMN6ilK4N9WdMQ58JtcS6KDISvsNCmqioleYzGEaqZpkvBevvzNXva+7f8+/xPLor1qaapuKhmR8SMqvTTLzGUoWzlVFW2S+nH6eoyS9lVbptRLfE8kr3EuOlVGnMTIdSgzIsmBMKHKjykGzMacjvNqNhxwi6RC1p0My148OBjCIqKnddU5bbKRVv8+eVuu9TQFw8uDNX4L9nox/hVzA6+8btm1mVu966KJX7Epsieu2WGptUn25LYfjR5MejXRZsKsTrbrNLqiGYlwUdqHLYkHISwlvmT5kMurHha+z3uOFAiYZYV5psSb6qKY7cexKBhVKOouy5C0tFERLaXNQs+kUlBOIjGlRmWmmugipfLxRefXx8QbN2Gd4P4h4c2Ffsq261Z0m9rNtm7X7SuRpDNw2u9cVFhVhy366yj4DVXo6ph0+oto+CiXHeSWmmhcTksvXXPlcGhDnYzT2Lkf8K/dzQYzUi+X8J7Gwgt6kV2ZZNp1O6Ksgrsy5Vq26W9Ep0NtPjjaa9VIrMtSHtGGTdc4qZUguVFyuu9E+q+lwm1ea5eSG3tkY2neVraKt3qjLtPxBcm4eQ6JPuenX/h5XrGlxYlfkTItNdiKqzfi08nHoL6HExnjcaIkqWndVvDKrmq3vw4bc9m1Uy379m4qZdZ9fS2xTIcRfzOZdh6/uTI/kFbstv3om665enoF28efHj6n55uwc2uOW3ZyVrPhhZmhoeMlBk4vZwruxGtOt2rhnX7oojMJp6t29UY9TOM0y9BlNyY0Zwk7rhuR1pPRJtqI7dNl0vwuRN3h10p+gBhbiRZ2NOGdj4q2FUFVqxcS7RoN6WrUnoUmA5UrcuamsVOmvPwZzTMuG4/CkpS/EktIejuG404neSY43Kt04pwXLrcpeabF+31NFLBHEHFjwYjaBZrMP8XMEcR8Rdm/msvNOIuHGKWHtAlXB7w0QKhV5NDdfajMOQ2apb9EuCVZ12UOY7Dk1BuiUOsUx9bPSoTyXvs6Tdlzul9i2XbsIZTb38LH2e50hUXADDXMzmAxGlt7tEw+tzCyfRpVSnO7zcaK7OWdT8X6WQbbZqKG4ojWnd+EfDOqu1VzvdOGXFPoa1rJbdZUVbZ58PTbttuPtG3hvC88Y/B5MccQrkw+rOH17Yi4V5Zb0uXDOYl2fW7JrFx4uYR1qrWnOJhpTj023ZEt2nzVpa4ORHVnoST00iW7tyW2fW5k7TeD7NutbHfIk08y7HdYwYix3mX2XGHW3mLjuBt1K2nkIcSolpPXVJa66lqRkYxE3ePyBrl7fNibN8I/2PiI8WStqGxlhW4+1GfdbQk80N7vOGtaEKQlKEJ+GZqImyUSl6JPUbTYicU+QN5m/7VZvqxLzsqQ6bMe77VuW133iLeNpm4aNPo7jmnX0aJpr0/qRlV/X4Wz8/sD8+XYybSmytgtWc3OSLaKYbYw4cTKhjzVb/tC4qFZE24qbUn0Ual2tUGoqkHEbn0yfFoFMq1Mq8CRIiPty1tqJDqTItZZ+vz8QbZ2zy2xmBW0zxEv218vGFmOkaxLDt2JWn8Xb/sd+07Lq06ZLRGbt6mOSlLXIqxoNyV0bL76SjMrW50e8glZVqIi87Wv3ol/C/qDLuguOvYMr+lLZLdb35ZWBdGAABgA28CJrFsZYqvEp1RqSaLiBc1Uebp0CXN3UwUWxO3HTisvdCbpRVIbU4SUmrXQz3T07o+xvMS7axpBkI87KSLqnhuUloEabmIUuzXjPqEBF1ozmI7UWK1zkS+XgdMfbKbPNoej2akqfNVFZHFEeajQJWDFjP7OEyRjfq7Jj9XXWErEVyWup3cyj7Q3A3NFXo2Gljw73pV4Uuzma3MhXNbUulQjYpxRYNQbjznSJt55h9xvRBpSa2lktPWRei9J+hDF2jqTfXqzO0Kcp05VYkpBi0upQZuK6JHWJGhvWDDVXJDcxFTX2I66d/unRTpywxpInP6O0unVynVWn0yHNR4VTp8SXhoyEkOFFY2O6yK+G9zbJ/aRyKnLoljjAeTt1MsMo2ZKml4KT3CeTGfNhPQQqs0ojf6PotTM/hfqnwTMkqIj5+1cGzkD/ABT8eyrokNIrcTwUSGr2I9Vc6WiNVGK7WtbYqJnmqLa1vBMVSkx/jO4HmUY9YCUCOroiNVWIrGzDVbrWtfYq8Na1ynMfKdkbdDJpHUzJ6KDhDVG0PFGfNjpX2rgkGk3+j6HUiX8IyWW4ehK0My13gx0GH7LmkZqxYaRY9ZgL2avZrrqRJXV/RfWztlluXcc2OO3j+0do3RIblgylMmf16q6qK9Jx63W1stdPNE4nh83FSkJ24GRxo25PQN2YuP0iYz6mdH49yvKI3ktm19sZEo9/RCjIlaakP18CxpVvsm6TpdY0JseNVmu7NXs7RVbEkdWzFcjtiKiZLdOKH4ukCDNxfaY0ZRGQojoEtIvRYuq5WN14c6513Wtf9SXz32Xap1yx5zLWJlN24d+YyYmN1hyzouFdLtqpuUGnHVanGKrW3TjjPMwUrbU8ROxNxREtBlva66akPYuDNGda0o+yrTsPYafKJWW1eLUIEGdmWysGYbKzUVsWGkZyOa1+q/WS6W/TxzPC8daQqXo49pd1fr8OaWkOw9LyEWLKwVjxIDpiUhakXs0VFe1HQtVyIqLZ102KfZ8yG1BvLO3ZdYy0ZEsEcV6/X8T2/exXcR7mtuVQqFbds1BaGqm+28tKmWXHY6lJdkvSzSzEN40NLcURDwTA+gij6H61K470xYuw7JwqA78wkMN06fhT0/PVKCl5eFFYz9Sw2PRHarYf6omrdyNRTzPFWmaf0oUeawjouw7XJ2YrsP3KPW5qUiSclJSUZdWYja7skVWXbd0T+FXIjFWxlXym5Kbey9ZNYmWKfKZqkm4LTuCFf1ZZZLcqNw3hAfYrUlglfCWzEckdHE39T3WEqLTeIi9B6RtKVQxxpMmtILEfLPgT8nEo8u96qsvJ0yMx8lCW2TVckNFiI1LJrqm5T3XgHRvI4N0fS2CEVkVIkjNQqnGa1E7ebqEJzZuMl87XiKjFXc1LbTBnlNzB3DsesWMT8tmam0rrg4N3Ncblw2LidQqLNq9DSRGuPFqDTzSeidh1KD0KZsfpm5UOahRrbNCiHazSjh6R9qbDWGsdaO6tS0xzSaaym4gwpUZ2FJTz2w2tc5YSRVs50OLrOhOs5sWE6yKjkW/WnRlWav7OWIMQ4PxrTJ+Ng6p1B9Ro1fkpd8xAlu0VzWtiWTJkRmrrtRUcyIiuVNVUOXnLzY1ra0Vywsq2UGzLyqlhJu2FXb6xFrVHk0ujkzFV0Lcp4lpUzDplLaW9LJUiQqRMkG220zofDGiDAlO9mSVreknSlW6TAxLFpcaSoGG5Oahzk2x8RFet1hrrPjzL2th/oajIMPWVztZDGlXG077Qk5SNHWjilVWLQ2VSDN1+vzUvElpJsOC6zUcjrta2AutERXP1ojlRGtVEM992Ya07B3JJdOFNvb8iBYuAVftiGaGlqclrg2rKZfkIabI16ypKn3yQSTV+qbuhmQ6f0zEcxiTSnTsS1N7Wx6xjKTqEdz3IjIfb1GG5rXOctrQ26rb3yRL7DtZPUOBhvRxO0GRYroNLwvNyUBGoquesKnRGqqNRFW8R+s/Vam1bcjoxsJCei5EqfGdZfYU1ibe+iJEd6Os0muDookuobUpJnqSVkRkeh/C4GQ9v+11GgxdMk2+FGgxm/kNJRz4ERkVqKjYmTnMc5Eda36b3S6ZHqr2VoExB0Uy8OZl4sByVurOY2LDfCc5jokNdZEeiKqXv47zMkSzMzPsP29uzkOsiLfd1v8jsnls66ysek4rWkeIeF1/2MhZNu3XaFwUFhZmREmRUabIjRzM+RJ6ZbZKM+SdT5j9jD1VdQq7SKyxNd1MqUnO6uzWZLx2RXNTmrEciKu1cj8DElKbXKFWaPs/MqbOSTV4Pjy74TVXuc5FXlzNcbIJntsnZ92LfOWDNRaeItkV20r9rs+h1OHaFTqcKtImG1HktMLSlptSXVQmpUGQw68xIYe4GSyPTu7pt0XzenOt0fSRoyrOHqtJ1SiSMvVJGNVZeVnpCahI56rGgxHK9qM7V0J+s1qteze1bnRvQfpMTQTQqxo60lUSvSEWmVuoTVMqMGnRY8pOwZlUuyE5tmqipCa+E5quRzXqi2VDN/lPzfWPm/pd33Jh1at/UW2bWqUKmx67edBcoUW43pbTzziqK29+qPIhE0lMoz3iQp1siXqY6kaQ9HFR0az9PpdYq1EqE/PSsSZiy1HnWzr5BGOa1Ic26HdsOI9XfoRbKqNVdWx260daSqdpLp09VaTSqxT5GTmmS8GPVZR0q2dVWq5XyyOsr2Nt+pUul1T9W47hNJ7er4+fq+Yevtq/5S/8AJT1S/qewjmpLQiIYct1VQa7XhP2ZirYAbLDFG0bUOed7Zj6zRcC7fYpzMp+S/T7ndU/dyWkQ23Hyc968SpJbNBam4pKC1NREORiZX4/IGNLZh+C05HcQMkmAeJ+bC3MYJWOOKFlwr9vKnUzEKu2bTqKzcDz823aMihwko6JyJQFU9+QqSXjJSZbyHEpNCUlXKibfBOkUvh8fqYt9vbsacMNkrXcpmcLI1RMTIFp0bE9iPiCdVrlYvmXQLqoMiHdFo3A1UXWlyqXCkMUmp019TppjnLchESyWsiMi3bnvyXxIbBO1m22tTw6yZYC2fkzplbvzOHnzwmtmuYT0e0aLUK3Kw9oF6UmntVy7JbMWO8aazTKlUZdFocJZ6oq8dyTLU3HhOb9smV91s+K+fHjtXiDophn4KRatz7MK7KLi5MI9o/iassZmcUKhMflqsm9VRJNQpuF0+Sbi/H6TUlTnmL4nLU5IkV6Scxh3/EyNvMr57dm3raD6h4OvtP8AGCwrulbIPaE0y6LJzAYVSKjb2A1cvmNPYmXXRreU+UvDWRVJraW6xKpMZlyfZFYYedZq1BacgoeUuNEQCpf19Ut9OIPnnhHOzrzMYXZrMGNsvkdt6sXPfWEUmy5GL1sWvAkVSvQ5uHlRXMtW/mqPCQubWqRLpbjlq3dDYbffKnM05aWls9ORRq63dbxv3py7gd0sC/C1dmtdWEdOr+PMrEfBXGSlU5Ee+8KVWRUa89FuWM0TdRjWxPadj+6EF2Yh1UNqY1GmRGltxpe8+2tRxWIufNNibus7+gM1Gzt2gGHm0hwQrOP+FNhYlWRYEXEGvWPbz+JdBO3593RqHCpUxV0UWIr4TlEknVExW3TNZImRpTHSuG0oywrbef7eYNebwzOFVZ2RfKymmUqo1QomcOhT5SabAlTnmWIuE+Jh7y0xWnlNpUpe4lSiSk3DSnU1GRHtn8Pj5d3h8QZIMmG34yD5nbxwXy8WE9jhTcSb6btmxrfi3fg7clEob9wtW+wg2JVwOdJDix1yIT8dua7oyt3ozVuIc3k1c1TNMvj6pyTgvkXu6TmZ3mNN8+J6GRHy6uHl48foES6Zom2+XBUTJPO99ma7SGk/t9swNCynbdPZIZmL3t+8azhzgphvct0XmuzKBPrtXKjVK9bwoM1MCJFaUiXJjtz0yVwydQ6uNv6mjpGzPSZJnlv5Zqu8Wz77J15mzJkI2mmVnaLUa8J+XCu3hLk4eN26u8KJe9kVuy6xSm7mRUk0h5LNUaKPNYkPUaosm5EkOKZdYInm0pdbUvD037crbc0su3w2KXj0nXAwb+EgZJ8yTGLmTja4ZMrPnYhY2ZH63T27/sWkw3ahV7gw1oN1O3tQKhCpcMlTqxApNRqd3UG6KZCSuc5QbsOXEbcVCcbVWLlt5Jx39JyQh7XYXhaWzhl2RTJeL9n5hsJsV2oDJXRhe9hXPq8mj13dQl+mxKqh2D44z0uvQPORGXCaNBOINZK1qoqrttu52t9fPwQqLa/Pjsyz2b+txk72Xu09e2mh42Xja2WvFjBbBjDqqWRRsO7/AMVoKqRNxdn1g69JuOTRaO5FjLiQLbagUZLrm9IS47V9wpC1NLSmI2y8ERVsnG9rZ37sgq39PTI18vBUG5EXPLtqUzIkqKufi3ZciMcmK+wTqI+KmYdEgm1utoQs2lyGicShRmg3EEoi1IV2x1/D0+ZDJh4VxGlS9kDiZHiRpElxzF/BBJtxmHZDhI988zeUaGkLUSSPdI1GWhGZJ11URHlm/wAPmDuhsFVTT2Q2Q5iexKiyoeAlsU92PMjvRpDJ09yZCS24y+ht1G6hlJJJSS3kklSdUmRjTlRLLa63yBrK7U+RiJsxPCNMNtqPfmGWId0ZVb2ti3Sua6bIt+TWI8KOnB1WE900eVIjtrjx6rR6nAptyJhzlRSqFPcZTGeN3e3Le9vPzBmTw68J9yQZgsX8H8DMr2F+Y3HHELFe+rbtFdPhYfuW3BtGn1ufHi1C6a3UJzs5sqVbsJyTVKiWjH8zQ3SS8SzSR51eGXglrbbWTyXPiDZJJG8klGk0moiPjrwM9NSPTs6+XI+sREzztt2WS9r8Vz+drcQac2K3hRd84U7YpzZ91fKVC+odS8wlsZZq3dkuq3NGxsO5bnuih2oziXRqEzCl0OoWq1KrKKvSrOZpEiq3bbyoNSpV0wnqhHiuXV22Xflbdt4L15oDcb4dXL0cxhyWsufBb9b+lBWlOvE+Xxi31dm1UzXhy6/a9dfLh5l3lwIY25qQ64ZvsutuZtcseOGW+61oZouMOHdwWa5Lcb6VFOnzo3S0Sqm1zc9ya3Hp9R3C0NRR1JI9VDTXW7gaM+yI2kNT2B+IGNuzf2ltq3xYeG9OxBrNy4T4oQrcnVOgsSJqzKeuIpCW2ana90IbarUOoU11xcKbIkR5TSTMiTyL+pNipnnsW2Xr8Sott1z0naDZo7t8Jaz2ZcsoWS217ykZS8H7lOv37iXXKTOo9KmImTIZXdeFT+CtmmxI1AilRrZhSn1zZk13xhDZIW5ukSyLn4/XNe8h3G8MUslrD3Jns9rCtSnSXqLYWIdx0GCmJBddQzBtqxbZgwjf8XaUhk3URNfh7pKUa93XRQN2cr5cNtk87eee8H3m2fCssqdCyu4d2jgRgXmKxnzE0HDG0rSjYdwMO6kxRW7wpduwqOXjlchNz1y6W3UIxOKKHFbfkxyNLW4o+GURLtz2cLbvnvXb371vVs+uR7F4Pns6M0svMnmC2uGfW2Zlm45ZhnK01h9YtbieKV6hUK55SJlXrs2CtS10mPLgtQqTSKe/uykw4vTvIbW4aBpVTLPffdbpL2Tml9iKpDbnUalvlu8kmXHQ9OHl4DN1uvpmmSbf7KKvwy37FNZauxb3y4deZpg+GdEqRgHkRp7ZGuRMzH1xtttPFS1Lt+ntpIiLiZmtREWnWY03Zlszt5qZNuPBChxp+XDCG3aqwT0SXglh/RalFc1InGJFiUqDNjr6y3mnHWlcOs+8hxqllTbff55Lt+C7i3v5qvmaMmDl3YoeDL7RnMyjG7B3EHEPIhmirEy47WxUw9oMisFbTSq1IrNKff6FpbDc2hlNkUiqUWc/GddjIZkRXlKM9OXaibuSbPO3la3IhlpvXwsnZ+SIMam5dMNcyWYvEqqk0zRsP7Zw2mUWVLnSTJqNGfnuqqXQmb6kIXuxj0LU98iLUY1FVVz662/M1fJEzW3H5fe5nTxCw1VnryMXHhri5aEvDl7MhgY9Tbos6etMyfZk+86CUhqmzl6JJyZQ5T0XxstEn0rLqC3TLhMmu27s/n38U52M5fbkaVmzz2gWYHwceu4mZDc/+XzFC4cvDF+1e7MMcZbBokurUuKzVXW48qq02QbKqbVqJcEaNElrpxTItSpc9t1o0G2alK5MlsvO/fw+vkDN7hv4UNkxx+xZw0wdy0YK5ncaLsxDuujWw4UHDt6hwbaZq05qI5WatKeOe2mBTm1qlyVLVHR0TS9XSEVt0VMs+W/j1+42M8Q7dcvCwL3tNo0pdue0rkt5tS1aJSusUibTm1qV1JJchKjPTgWvZqMNsjtv79dbgfn57HraY4c7Bat5sckO0Hw3xgsF08aanddhXbQLJn16DW40Xx2lr6Fo1RG5kCrU/wBzKpTatT334ykqcYeSl0iJPKDat2cO2iwF2n+KmJNjZeML8a4NnYb2rHuWTirftqKtu06w9JqkOmM0Cnoc6RZ1dzxpyUlnxl0zjRX1mlJJIxhWojVy/frw7sxy37T6/tmCcVsqc/iWmXX3V5XsVkNsstqddcUq2phElDaCUtajM+CUpMzPkRiQ9/gDVL8HP202T7JbkXtzKpmF+rFQsS4+Lt6V2EqhYU3HctulQbrattulSHarBSRsGcuLMKQ14ufRIJDm8slkRa2KuaZ2tfj6bbc/kXLh62+vy+u+VRaxAuCkUquUxxb1NrdMgVenvLacYW9BqcRmbEcUy8lLrKnIz7SzadQlxtSjQ4lKkmQymS7kuuzgu1OCLa/wsNqd30z87X4JY/P98JCwqxx2Yu0fwb2s+U1DlvP4vUqfaN81NimLnUOLibAt9+3HWLhjNkTK41+2PI8VS0+aUvTbfUpKjlPI13kt+uC/P7WIZd/BVsiN1YB5R76zgY1Uue3jvnVut+95Ey4IzjVxMYbQ6hOmUZ+YclJSmHb0uKfV7qloVoUuKmhyVEeidMPXKydW+/WYOxHhImzIxA2jGSykScDKeirY/Zdby+qjYFv77bUi8qUumSaVd9nwnXjJlNSn015qp0ptZpTKqtIhxDWgn98oxd3FQdCMn/hSeVrCjAfD3CDPzhxjzgfmRwns+jWBfVLThhUqlTrlqll05m3vfBTyeVT5MB2rNU1l6VAeQ6hmoLk+Lvriqa0rkXdvsltq9/LYnfvNNVLqq8/Pw49xlK2ce2zsHac5hrxwxy+ZecaaTg5YmH9QvGrY/wCItHXblt1KqlWKRSaJalGpq2DN2pVI58ycZnOfWiJT5DhoQSdTaq7b5aqr6Wtlls3pwIq3+vHv6+prg7YhuVP8Kx2ajiYM9yJSqjkkQ6+1CkrZJCcer0mOOm8hpSOia6ZBPObxJa475p04b3J4evx6zJ1wP0BzNCTPhx3lcuJl8I+v5NRxu1s73teyftv7wUuHqy4fa2v+9MZSyKmeSKmYNJjZupfieFsbUqTNhSo7NVwdxIhU2Q/DkojyHo8zLFLNLD6miZUpyHDfdLRzVxttZp1HMuxe5Qbtg4AYCPCMdmde20ryEv2vg5CZqWPGBl7xcYMMqI48mM5ebLVFqduXrYcaS4aWWJ1wW9UinUjpTS3IrdCpkJa20yjcTti528uuvVQYidnX4TtgTlmyuWHlm2jth44YVZj8ulmU7C2oLZw7qNS9/wBT7Dilbtv1V+NKXT5dMuB2k0+DCrCH0vxpdRiSZ8Z42JCUjbkvbK+afH6FRL3z3X+x1gyOYQ447d7bgRNqZfOE124X5Lsu9es6s4WFekJ+I7X14VU8jwmtahOSmWU1WY/dri8Sbwn09CqVBfU7S2XXXZKSbtrJvsme7dn8iHqu3PzRWnk28JZyfZnMQ4dXqtlYN4SYPV65olBjpmVlVBqCMVqJU106Io0pky2G645Mbj6pU90ZoQpKlJMiZ2Xl8Qd484vhLX13ODl05bNlNlyzF4rY8Y0Uabh7S78m2BUKTS7Ch3JDXSqxVoCGUyEKryIcx2PSp02fDgUqU8mc+o1tMEJkrr8PomfHim/wKuW+/dcy7+D77MG59mVklRaWLCaerMDjPdsrFfGA4D/jzVDqk6nwqbQLOaqHAphWxRYTDUx5GjL1alVR9n9TdSMvXd0n343IYNM4uGGM+w121l67U2zMELtxjyY5qqbdMHF08Paa7U63h7XL+k0eu3omZEjMPlCdZu+hN3PRJr7aYM+lz5tMU8xLbSpdRbol9v7XTZzTLfkl1zBkPrHhbmy7RQG37RpOYu9LylR0nDsenYUS4dRk1BRF/iac1UuQ2270mqOkKO4k9N7c6hFbntVfVUXLb4LfuLe33QzxZIsxN05sMsOGeYO78JLkwOqWJtJqFxQ8NruWpy5aFQHqhPbtt6s7zTCmp1UojcKqOx1MtKjql9F0aTToFkRWpvunG2a9JzQhqxeCSwqjT8QNqVHqMCfCdXmFpMts50GXCN5l1+6G0rb8aZaJZbyD1JJmpPNRaKSZ1+zZ9ePlkDdTHEC26gnG1IUWqVJNKur4KiNKvQkzMu8aYtnd+XXiD88fZ5Z2rd8H0zv5/sCs9eF+LVu4d4v4qyrvw7xHte0J1eplTREqNUXCkxNCjRatTKzR5kKSxLgTFORXm3WZbSVGkk8w6663Gz1kC27uWfaS5h6jgXlpwxx1qNGo9m1O76tixdtmqtuy6cVOehR26PJW8p5aalUXJa/Em/Gt50oz59Hup1LjVqZrs65bticgZJs6xqLJ1msNKFrV9bhjZuoQk1rUZ4cXERJSlJGalGfAkpIzM+BEfIGLtTxBoheDRbXXK5s/MtuMOCWZWNjBSbju7GeRe9rPWlhXcl30pdClUOFCdflyqc2nxV0pzDqei6NeqCJze46Cqiquy+7ds3ou9N+ad3AuVrb0X422fO/gb+Tcqwsz2Az7kTxuoYcY34bT4afHqfIps6Va18UGRAc8Yps9tuTDknBnrM48ltLjTqSJZcNRhEVHW4/DpOlIaMmRHMje/gzmYTM5lSzn4M4nVbKniriGeI2EuO2HFsy7korcVJKp0JxZMoSwtqTSCiszaeqXGmQJ7DqjS404kxy7di2Xb8t/r5Ay213wq/I5d9XoNjZWcJsx+YvFK6qxS6Db1p0zDuZbcVc+rzY8Fh2dU31z+giMKfN2QvokElptZm6gtVFjVyW7s815ePl6ci7/AIbu5TMjntzeYoZQMp31ydnZdbsx2rtDnWQ/euEtnvuquym21XW0quqoUtDDEpydMtlatxTCWHSe3VGpJp4ioibL3S/qmfinLx4kMJ1weE47HTGWwapZuZqwsWaG8/BkR7kwnxVwKXcJsvuNbkimpTMNtpyT9s0l5uPEcM9FJNJgjVTYqePLh4ZbeIMJ2wTwkexu242J+bTJbg1iDgVkQo0K/wCTGKs06o0y33qJXoLUajWsgpKW4chVWqaXahHpkVyYmjt7pGtCi1Fda2eeabNvXdmD9CycRG6ZkeunP5T+jqGFTe3dnbbbxvn4X37D+g4IwAAL6ftS8nx8QOdv8Kd3xzJAoAAAAAAAAAAAAAVILU/MfzfKAIUWhmRAC6jlz19uQAq9vKAKVGWnPTU9NePt1aACjQz1LTjqWvHXq7z4+nuAEKIi0Lr6/kAEER8DLr5eYAVGep6Hw084Ak95PHXXXnw7OQAgk8ePnLs7PLr3ACoz0Mj3fhH8np6gAM0kZl2/baa+vTt+XiAIUotdNNdO0/k6wBJp1ItTIiLXX5i+IAV/KAKFa9vDr4cvlP21AFJamRac08u/Xy9gAjnp18/g9nn69efcAJ5nwLUi83pM/b1gCCI1GZ8+XDlqAKzM+RH8Lyc/kLgAJLiXHiR+Uvb1ACDUfHd4/J8/t3gCoz08vUXaAKdSMzP7nlp16gCklJLXQvb5PWAKtVFponh2a6/SQAg+Wp8+pPZ5+vt7OoAVGRcFFrw5c+JdgAg066KTwPn6QBSe9p8Ll5uYAciSWmuuhl8xe3XyAFZGSurgZaGevq0+UgBRrpruloZcz114ecASRq4ERadevDl5OQAL9PDl8uvq0AEERkZlprw7dNSPrABWuhdRctOfn1AEnpwV9sXLTkZeU+35wBBlqRfB0Lnrrrw+MAVJ4kZHy5a/y+rgAB8tNNSLTr06vmADQuBknhpqXVx7DAFBcVcuvU+7jx7ABKi3TPjxPXhp1GAGvwdDIz04EfIuXrMviAFAAAAAK0dnI+3nw7O7ygCDIyV368PTwAFwt3U9D48dfTxAA9T4ci04nz8wAF9zroZcC9HPQAWzItSIj17+Xt6wBSAJItT09tABVxJRFxIjPQvIZ9XZ8YAr3iI93zfR5fiAEFw+18hl2H269enYQApMzLUjPXUvJ7eQATprpqWnDQvL1cuQAoLXXTy69ehdYAuK10Ii5aFx6+727wBTumZcy4cPJp3+3lAFJcDI+wB8Coy0+EZczPh2a8v5ABQAAAvkevHTTs8gAkAWVERHoWvnA4XIiLZL+JQfI/IfxARNvgvwU8vb3+SZX+kp+MaT+z/lL8jjfs8fkp7UOY4iS06z07xFvuS/IFZ8SUfV8HTzDCZK1N/6r+IKC4HxLXuG1zTJbcwQKAAJ6i4eft4ib1z8OGQK0cPPy79OfoGX5+G3lfZ5gpVxUfXx/kFbk1N2X7gpGgVJ+2Lz/EYAvAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAsHxcX/Y/EC5ICABUWuh8dC+Xs9Qi2umV1+XH1BTyF2gesPQAAAAAEn5efMRO7ZsA5a6d5eYNtr8l8QQKCpOpnwIj4dftzEdZEzVUz3A4NRL+YpOvUgv79BCLmicF+iqVu1O9DxzKuBGR9RH6uPzDGxLd6X5LZUX52ORUuq88070yt6nPQotefAxM803pmnzt6Kce5F4Lb5py4nJSrTgfLqD+LO6Iu++xfvyFuGfLf18N5BqM/J2AqomTc77Vt6J14jZ338uf08eRx1r1Iy/Y8j7/bs6/UFv7O1eG1EvuTd3/YbEz25WT5r8vhY9epdv0GhqmuUSiUijuVJ/xmoLpdNhU9U+SZqUciaqIw0qU/vLWrpX+kc3lrPe+EY5okeLEczt40eOkNupDSLGiRkhtSyajEiOVIbck/QzValkyySyHCgwUe6DAhQViOR0RYUJkJYjv5nqxqK9c1/U667c8zy44M1yS+e40jv1XVePhluPFyKFRJdRiViXRqTKq8DhBqkmnQ36jCLjwiTnWVSY5cT4NOoLiZ6aj+lHxWw3wWxozYERUWJAbGiNgvVNivhI5GPVLZK9FscLoUF8RsZ8GC+NDS0OM6Ex0WGm/UerVcy+xdVUuh5MyI+Y4rPTJLWTrO+/6nK5yr1vGu6XDgXDlw/lGUV17Lz29bMt2XwMnqlxWPYl49H777Ote6ei/WffFQKXWTZItTImlVCJIUgtTM9EqJPE+GpmP6paZnJJXPkZyck3vzesnNx5VXL/fdBiQ1dl/NfgcEzIyM8jUnJOUm9VMveZaFMaqf3Uiw4iNzzultp5OgUC2rUglTLWt+iW3Tkq30wKFS4VJhEvj8PxaDHYZNXE/hGjXifHiMxoseZidrNR481GtZY0zFiR4qpe9lfFc9ypyVe6xyy0rLybeylZeDLQk2QpeCyFDuts0ZChsZ5JcuO0KgS6zCuOTRKRJr9OacjwK29TYjtXgsOpWl1mJUXGjmR2nUuOJW208hCkrWRke8rWpHmWwYks2ZmWSsVyOiyrI0VktFc1UVrokuj+yiORUSznMVcmqmxDTpWVdGZNOloD5qG1Wwpl0CG+ZhNW6K2HGc3tWNVHORWteiLeyoqXReS9Q7fm1eDX5dFpMmu0xtxqnViRTob1UgNvJUl5uHPcZVLituJcWlaWXkoUS1EojJR64SLGbCfBbMTDYEVUWJLsjxWwIqpZUWJBR6Q4rksior2qqWS1rIZfAlnxWTD5aA+ZhIqQph8GG6PCRbo5IcVze0hoqKqKjHWW6ot8y8/b1vzqrDrc2iUeXWacndp9Xk02FIqcJJGoyRDnusrlRk6rWejLqC+Gv7pWrtozIMSBDjzDYERU7WXbGitgPVEyWJBRyMeqIiWV7V2JuSxh8vLxI0OYiS0u+YhZQph8GG6PCRb/63FVqvZkqp+lU2rxPPD+Y5QAAA+UXngRgniNWIlwX/AIR4a3tXIJp8UrF12RbdfqbBISSG0Nz6pTZMkm20l8Bo3TbR+xSQ1rLa3kvAHt9ZsmzLitw7PuC07arlpKYjRVWvWKHTKlbyo0Lc8TjnRZkV6nGxENts4rXi3RxzbQbKUGhOkuvpbw+gOJVMOcP643brVZsa0Kq3aCDbtNupW1RZyLXbNhiLuW8iVCdTRU+LRY0fdpxRk9BHYa03GWyTUcqZ7e/klge5JSSSJKS0Ii0Ii4ERF1EXV5C4CKt1uD0y7cOMPr9OGd8WPaF4nTnG3qf76bao1f8AEXmXikNPQzqsKUqK628ROocYU2tDhEtJkotRUcqZbfimVsv2B7ceiSJCCJKUkRESdCLTTQiIi6iLhoNIlkVzs14Lt9erAx+Wfsrtnjh/mqrmdyzcpuFFv5oLgqFWrk/FKFTKh07FzV9Dia/eFFtN6pO2Jbd83Ab812vXxb1sUu7q3JqtblVStS5Ncq7s0qotmpdc89uzenEHf5C93mf8gy618vHvS6GtVfPZmifE9PvDDvDrEOMmHflj2jekRKFITFuu2qNcTCEqPeUlDdWhSyQRq46N7pa8RWrZbZWzzXLryvuMnqdlYB4EYbTvdXD/AAcwxsupmZqOp2tYts0KoFqSi4T6fTWJSSUS1EaUupIyUZHqR8aq3umaLnzVb7U5Z9ID614yZHp1EfZw08vPzjG3gn1+CX28E2FstuHJVsvkel3nh3hviRFag4g2HZ97w2dehi3dbVGuKOwZnqamGqvDlpZUZmZ6tbhn19Y2mSXVUW2dkzz5ruz9V42UZ7r9bTxlmYPYQYcvLk4e4X4fWRIcSaXZNo2ZbtuyHUnoW67IpNPivuJ0Ii3VOGky5kLr5Lx3J8722dX4Q9zrtAoF10mTQrnolIuKizNwplHrtNhVemSibWTjZSYFQYkRHyQ4lLiCdZWSVpJSdFFqMXzvvvttbdZb2XK3LxL9Ovp6nPpFIpNCp0Wk0Om0+j0uC0liHTaXDjU+nw2UF8FmLCiNMxo7Sf2LbLSEF1FoDlXYu7ZzTj18SHkhkEGRGRkfEjIyPyHwMVFstwej2phrh5YMitTLIsa0bQl3JNVUK/Jtm3aRQn61PWZmqZVXaZEjOT5JmajN6Spxepmeup6jWsq3va23f3Jv42B7WpXwjM+o+fk+UTO6InDzuiX8LWTlYHg67bdr3bFbhXVbtCuWHHdKQxFr1Hp9YjMyCSaUvNx6lGkstvbpmnpUIJe7wJRFz1e262ezblxy3pZLZc81UHiaRh1h5b0pqbb1iWdQpjGpsSqPa9Cpkhk1FotTT8GAw62pRGZKNC06kfEFclsl47l3r1fj4qXZt9FRfgp7q24aeCj1Lr6/KXzesZyVdnwTx3Jf9uY3d3Xj1zPS7ww1w0xCbQ1ftg2ZezLJETTV22vQ7iQ0RGZl0RVeDLNvQ9T/AFMy4i/ryS3jw55bLbuCbCE2lhvhvh80tiwbCs2ymXE7rjVp2xRLdbdTrro4VIgxOk48f1Q1admoXVOV127+a2XZyyTvyKnh49fbie4qWlaDbdSlaFEaVkoiMlJNJkaVEepKJRGZGRlxI+RiJa/DhwTPb4eu9SHxCqZa8t9aqrldrOA2DtWrTzhPv1WoYa2dLqD7+u90z8p6jqede3vhG44tSzVxMzMcl0zW+aX3p35bdpdVeC+R9Yp9NplGgR6XRKbBpFMhtpZiU+mxI8GDFaT9q3GiRGmY7DaS5IaaSkuzQZV1+Nr/AC2bt/NMuK5jJOfy8dmzZ38s/UsS8McO8Z7AuzCrFuyraxGw3vuiyrdvGyLwpEOu23cdGmkRPwKpSqg09Fktb6G3mVKQTsaUyxLjLZksMuoiZ3RMs0tfx9c8t/xLmrUyvZVT4KdesnGz9yabP+2bttTJ7gDZeCVKv2rw63esmguV2tXHdc2mNymqO1X7wu+r3FdtUpVBRUKp73KFLrbtEt1dYrTtDp8B2tVRcs5dyovnl35J9r+KGTucTqTI0rLUjLQyMtdS05dhl5eIzl180v8AC9i22fVOk8du4+RN4AYCtXV7+GsGcLG7yVI8cO6UYf2om4Dmb+/457rFSym+OdJ8PxrpSfNXwuk3uI1ddXjzuq29Pmtl5kPfK5Zdl3RMpFQuW1LauGfb8op1BmVyh0uqy6JOIyUUykyZ8V96nSyNJH4xDW06RkR7/AhEvnbxt1ly2A454d2Cd2Lvw7JtI73cjsxF3gdu0g7oVFj73QxlV44h1Q2Gt9XRteNbiCUokkRGYqPVEt6rmD3EZuu2634g+IYg5asv+Kqqm/iFg1htdk+sQZVNn1msWdQpNediTI64r6EV3xJNXZWbLi0IfZmoea11bcSoiMuRHZLnnnnu68AYTbt8GR2cVfnzKhQarmdsNUuS9JRAtLMTfsGk0/pnFOeL02CqaaIsRk1bjDCVGTbaUp1PQVVRL5+Xy2g9bh+DB5Fo/B7GLOlKQXAkKzM380RF1adHPI+BcOoZ1+/by6z8bAyP5D9ltl52edWvus4JXPjhX5eIVMpVJrjeLGLd0YjQWI1ImOzozlIgV6S/Hpctx95ZSZUckuvtaNKPdEV2VuN8s8vHavW6yAyHVy3qDc1PepNx0WlV+lSNCkUytU6HVafIIuSX4U5mRGdL+vbMZRVTYD53b2AOBdo1BFXtTBrCy2aq2slt1Og4f2pSag2tKiWlTcyDSWJDZksiURocSZGRGWhi6y8fhu+fMH1wZB6FW8K8MrlqzteuLDuxa9XH2WY71YrNo2/VKq7HjpNLDDlQnU5+W4yykzS00t40NkZkhKSMa1ltbLLZfrcDylFs+1LWKQm2Lat+3Slk2UkqHRqbSCfJk1G0l8qfGj9KlvfWbZObxI3lbump66vdqXRcl2p3Kl07lB7C2RJ59+vt6hnLOy22Wvle23le+fomQPnKsGcHnXXpDmFeG6335Dsp59djWup56S+s3HpLriqWa3H3nFKcdeWZuOLM1LM1GZnpVddMkVURL5X88svDzB7/AAIcKmRYtPp0SNAp8NluLDhQ2GosSLGZSSGWI8ZhLbLDLSSJKG2kIQhJbqEkkiIRUVVXfZM1655In3BwLita2rvprtGuu36Jc1HfMlP0qv0qBWaa+ZJUgumg1GPJjOfAWpOqmjPdUZa6GYyjlTYoPQrdwCwMtCot1e08G8LbYqzK0uNVS37AtSkVFpaFEtKmpsGksSWjJZEojbcSZKIlEZGWousv0y2dwPotboNEuWlyaJcVHpdeo00m0zKTWqfEqtMlpadQ+0mVAnNPxZBNvNNvNk60skOtocTotCTIjlRb8dvMFdGotHt2mRKLb9JptDo9Pa6GBSqPAi0ymwmd41dFEgwmmIsZveUpW4y0hO8oz01MzEVb7QevVnDfDy47ipN33BYlnV266Chpuh3NWLZotTr9HQxIXLYTS6xNhP1CAliU45JZKLIa6KQtbze64o1G1lta6g90EB80vbBjCHEqTHm4iYXYfX1Mho6OJLu6zreuKVFb59ExJq1PlPNN68TbQska8d0b1rIm9b53z2fXLjYHstq2XZ9i01NFsm1bdtCjoVvopVsUWm0GmoXukk1phUuNFjksySkjX0e8ZERa6ERDKuVdvW36g9mEAAAAeLqtGpNYaQ1VqZT6m00o1NtVCFGmtoUsiSpSESWnUoUZERGaSIzItD1Ic0CNFhOXsokSEr0s5YUR8NVRLqiKrFaqpfOy78ziiwIEdESPBhRkaqqiRYbIiIq5KqI9FRFVNtjw1PtC0qLKVPpFs29Spy21MqmUyi0yBLU0syUttUiLFZeU2o0kakGvdUZFqWpEZbixpiYTUjzU1HhpmjIszHiwmqmSWhxIjmIqXXNERc8rCWlJGUesaWkpSXjO/S6LAloMKK9q2ujokOG16otkuiuzsl+JyHLct+TWI1wSKJSJFdgMLjQq09TYjlViR3eLjEaoLZVLYZX+zaadShWp6pPUxGuishugNjxkgPVHPgNixWwHvbbVe+Ej+ze9NiOc1XW2Lkcj4Uu+K2YdLwHTDMocw6FDWOxHJ+pGRVb2jWrvRrkRVW63K3rZtyXWoVxSqBRZNfpzS2IFbfpkJ2rwmXNd9qJUnGFTI7a9T3m2XkJVqepcTGu1jsgvgMmJhsvEXWiS7Y0VJeI5LWdEgtekN6pb+J7VVLJnlYw6BLvjMmXy0u+ZhpaHMPgw3R4aLtRkVW9oxF3o1yIts0uVy7WtqdV4VwzbfokyvUxpTVNrUqlQZFWp7at7eRCqLrCpcVCt5WqWHUEe8rXmYyyZmGwnSzJiYZLxXIsWXZHitl4qoqWWJBR6Q3uSyZuaq5GXy8tEjsmYktLvmYV0hTDoMN0eGipZUZFVuu1LKuTXIi77pka9VJsRFf292JDd3WYqvWdUsD0rQ5XraXU7bdltUSiLiqKTOhu0tUhtRukzo4byVEpJEXEdu5vEHunspUeWpdYdJVSXxP8AwSU/7tPNhOmZntUVsCKyOjHIrboqaqptzOsa0X372kZyLPUpk9TnYbVHPnJJJmU10l5fs1R0aE+Drousif2k80Ng2iWzbFrsHGtu36HQY5kkjYolKgUtkySWiSNEFhlJ6dW9rp1DqNMzUxOuSJOTMzNxUunazceJMxLLt/XFe96+ConwOzcvJykixYUnKS8pCX/23LQIcvDy/uQmsblzRbep5Rbp6mZHp2n8he3zjitkm22xG71XivD5eh/R9e/rq56xcVrWld8UoV22vQLnhpPVMS4KPT6xGSf3SGahHkIQrr1QSeI55eNMycXt5OamZKNa3bSkeLLxf/gkF7HqnJVscMzLS07DSFNysvNMv/BMQYcdn+ZFa9t+dsjjW9Z9mWew7GtC1LbtWO8ojfYt2iUyjNvGXI3k06Mx0pkf3euh8eA1NzU5PxGxJ6dm56I1LNiTczGmXtS39l0d8RWplsRd/LOScjI09rmycnKyTXfxNlYEGA1y32ubBa27uapdLbTzzpNPNOMPJS8y82tp1p1BONOtLSaVtONrSaFoUlRpUhSTSpJmSiPXj/Nqo1UddyK1UVHItla5FyVqpZUci5oqbFTcf2KrXorXWciorVRUuioqLdFRclRUyVNi7zxdLo9IoUNFOolKptGgNrW4iFSoManxErcPVa0xojTLJLWfFSibI1HxMzGokR8Z7oseNFjRV/iiRor4sR6ImSOiRHOe62aIl8tiWMwoUKCxIcCFDgw0VVSHBYyHDRVzVUYxGtRV2qqJnvPJo+2Lz/EFsrJl1n4k1s78rfv4nNbTyI+s9fby6DKr/Eqbv09Js3mDwVZsayLmdbfuW0bYuB9nQ2na3QqXVHG937Xo3ZsV5xOmvAiWRdw54E3Oyet7nOTkoj83LKTMaArr/wA3YxIaO71RfU/mmpGQnre+yMpOav8AB7zLQI+p/k9tDiavhxPOU2j0mixEQKPTYFJgtmZtwqZDjQYjZq01NEeK00ykz0LUyRx6x/PEe6LEWLEiRI8Z9teJGc6JEfw13vcrnLnvcvfY5YUGDLw2wZeDCgQm/wAMKCxsOG2+1Gsho1jbrwah5RCS8xesF/Tn/adv4W4HIXhxg9YuayrNvVmFHvG07auuPTZaahTmLkodMrjMCehCkImw2qnFlNxpaELUhMhlKHUpUpJL0MyGtZUS3SZ3Fz2NppphpthltDTLLaGmmmkJbbaabSSG220IIkoQhJElCEkSUpIiIiIiIRVVdoPDXHa9tXhTHqJdlvUO56NINJyKTcNJgVqmPmk9Um9AqUeTFcNJ8UmtozI+JGRiouaJeyX6v99gPXYeFOF9OnUOqU/DmxYFTtiIVPtuow7SoMadb8Al9J4jRZbMBD9Kh7+q/FoLjDRLM1EglGZnpXpuz7038ePwB7/3dQxdVW+8HodRwtw1q9102+6rh/ZdSvWjOoepN3TrXokq56Y60ncacgV56EuqRFtpM0tmzKSaEqMkbpGLrLay58F4ddXB7w60082tp1tDjTiVIW2tJLQtCi3VIUhRGlSVEZkpKiNJlwMjIZ2ZoDqbcGQzJjdV2e/m4cr2BlWuw30Sl1yXhtbK5rslC+lKQ+aYCGn3zc/VFuutLU4szUs1GZ66114J0uzu3WB2eoFu0C1aVFoVsUSk27RIKTbg0eh02HSaXDbM9424sCAzHix0GozM0tNJIzMzPUzMxFVV2g41x2lat4w2afdttUC6IEeQmWxCuKjU6txGZSW1tJktRqnGlMtyEtuuNk8hCXCQ4tBK3VKIyLYHgKbhVhlQpkWo0TDyxqPPguE5CmUu0rfp8uI6RGknIsmJTmXo7hEZkS2loURGeiiG0cq3REzzW6fP691we6pTunr7e3ARFRVRM9t1zzvnki8PXMHrVxYf2Fd8qLNuyy7RueZBZcjwpdw27Rq1JiR3V9I6xFfqUKU7HZcc0W400tCFrIlKSZlqCK5EtZfIF237Ls60PGCtO1bbtnxwm0yyt6h0qjFJSwazZKQVNiRumJo3Fm30m9uby93TeVrf1r5ptTkqLt9UHXXwPajSSi0URK7SMtSPhofDv4l3keg4+4HxupZc8v1ZqTlYrGB+EVVq7zpPPVSpYcWfNqDzxGRk69LkUdx91wjItFrWpXAuI1rLe/hsQH1SlUil0KBHpVFp0Gk0yIjo4lPpsSPAgxm941dHHiRG2Y7KN5Rq3Wm0lqZnpqYK5V3g8FblgWLZ8yq1C07LtO2J9dfVJrc63rco9FmViQp52Sp+qyabDjPVB45D775uy1ur6Z95zXfdWairewPJXFbFtXfS3aJdlvUS56M+4y89SbhpMCtUx52O4Tsd12BUo8mI44w4ROMrW0amlkS0GlRajIOZSqTSqFT4lJolNgUilQWkx4VNpcONT4ENhH2jMWHEbZjR2ka/BbZbQhPUkhVVV2qDx9zWla16Ut6h3fblCumiyf8AJNIuKkwK1TJBaGnR6BUo8mK5wUZaqaMy14GCKqbO75g9EszAPBDDmpe7Ng4QYZ2VV91TfuratjW1Qal0ayUS2in0ymx5RNqJSiUhLpJURmSiMuA0r13Zevy687j6s850TTrhIW6ptta0stbnTOmlJmTbfSLbb31mRJR0i0I3jLfWlOqiyiKu7f4d3Sg0nskGT3MTtXds5iFtX83WXWrZb8Ectt5ycP8AL3hVfFlQrUva66rhRJqVuWlWL2W1FamXhWrdmql3DWbnlT6zTotwJplq2nWZ1p2pSGmOa6Jkm/Zwv9+swbr5kRcC5F7H6xxuXJEXbmq/L9twK0H1egRUyRU4JfwyKqKn1LgyQjUi6w5lsqnxPF/LrgJj5AZp2NOEWHuJ8SMpKoqL1tSkV5yLunvaRZE6M5Kjo14qQy+hBnxUkxtqrxTxvv3cOfz2oQ5WFGBeCmBFHOhYNYWWHhnSVFo7Dsu16TbyJHwjWk5TlPjMvyjSoz3TkPO7uvwSIL7UVb3z/Tn3p4ZrbdtuD227rLsS/obFPvqzbWvOBFdW9GhXXb1JuGJHecSSFvMR6tEltMurQRIU40lK1J4GrTgDNqpxROG368OAPVrawWwSs6UmbaOEeGVrzULJxEy3rEtajy0uJ4ktMmBS2X0qLmRpWRl1GQ05Mr3sqZ3smfwB9VJ1KtUpIyIuRFzP26v5BhEva68b33efH6qXr9zBfnw2+eVbZ5Y91bATHTC/MMur0636VXod12rhvMrFn1xFXYN+PFpNXb/UJTrWhMyjS4fQPHuLQRloORrUtw3a23xTLLhsG/gnj9+uBrUZvcf8b/CTc5uT7CjLdlpxew9yuYE3/EvO+cT8SaHMo8BbL1RhSK1W35LkWPAiITSIXudTacy7KnPSX0uLIkC5NTLvRPp1vIfoM0Cjw7bodHoEAlpg0Sl0+kQkrPeWUWmw2YUclqL7ZRMsI3j7dRwrmqrf78+uIOHc9o2ne1Ldol4W3QLqoz/6/SbjpFPrdNe4afqsGpx5MZfDhqbeunDUVFXZa6cOtneD51aOXHL/AGFUCq9kYJ4UWnVkr6RFVtzD+1aRUW1666tzYVLakNHrx/U3Egrnb8uSp65/Ed3Xoh9pItOBDO0HqV22FY9/Qfcu+LPte8aZqZ+5900ClXBB1PrKLVYktlJ96UpPr11GkcqJbd9dtuvmD16zsFMHcPJRzrBwqw6smcojSuballW5b8xaT5oVKpdNiyDSf3PSaCay7L9dbgfThAfL76wSwcxPkR5eI+FeHl+S4hEmLLu+zbfuKVGQkzUTceTVafKfZb3j16NDhI15pGkcqb1+/wA+4HsFo2BYmHsBdNsOzLVsumuGlS6falv0m34Ti067q3I1JiRGnFp1PRa0qURGeh8RUVzsr9+7IHnapTqXXKdMpFbpsGr0qox3IlQplTiR59PnRXUml2NMhS23Y0ph1OqXGH21trTqSkmXAEsi3Rfj65bOfHdvB8+i4J4MU9aXafhLhlBdQSNx2FYNqRXE7n2pE4zSULIkmRGnQ/g8NBdbLcicluvK1ronWwH0dDaW91LaEobQlKEIQRJQhCUklKEJToSUpIiJKSIiIi0LgM333z+d/LZ8k2IW+Spx62nq974fWHibRStzEWzbYvig+Nx5xUa7KFTLgpZTYiydiyyhVSPJjpkx3C3mX0tk42ZnuqIjMVEtmllumy635oll+/iQ9rhQ4VOiRafTosWDAgx2YkKDCYajRIcWO2lpiNFjMJQzHjsNJS0yy0hDbTaUoQlKUkRZW+1b+IOQZpPgZi6q2vYHyK7cAcCr8qKqxe+DuFt31dSt5dVuWwrXrdSWepH8ObPpb8pwtSI/huq7efEEui3W/DPL1UHutq2XZliU8qTZVq25aNKI0mVMtih0ygU8lISaUq8TpcWJHNRJMy3jQauJ8eJiq5VyyRPP1S462/LaeOqmHGHdbueBetYsOzateNKbjtUu66na9EnXJTW4jy5MVECtyoTtSiJiyHHJEYo8lsmHnFutbi1KUZHWTbdfHzvl3W5JuB7n5RlVvmC6jkflFVLatuCL11uB6tCsKyKdcs+84FnWrBu+qNraqV0w7epEW46g04lhtbc6uMQ0VOY2tuLGbUiRKcSpEZhJkZNNkkrst/Nbqt/ttB7YMgGRGWh8SMAfFL2y3ZfsSaui4MQMFMK7zrqFk4VZuewrYrVUUovtTcnz6a9KeJJmZkTzrhEZmZcxrWXv3Z+P1B9ToFvUG1aVDoVs0Wk29Ra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- } - }, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Applying Honegumi\n", - "\n", - "We will now use the [Honegumi website](https://honegumi.readthedocs.io/en/latest/) to generate a script that will help us optimize the polymer parameters. From the description, we observe that our problem is a **multi objective** optimization problem with a **constraint on the fractional sum of monomer components** and a **custom threshold** on the optimized strength. Additionally, we would like to include some **historical data** in our model training. To create an optimization script for this problem, we select the following options:\n", - "\n", - "![Selection.jpg](attachment:Selection.jpg)\n", - "\n", - "The Honegumi generated optimization script will provide a framework for our optimization campaign that we can modify to suit our specific problem needs. In the code sections below, we will make several modifications to this generated script to make it compatible with our problem." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Modifying the Code for Our Problem\n", - "\n", - "We can modify this code to suit our problem with a few simple modifications. Wherever a modification has been made to the code, a comment starting with `# CHANGE:` has been added along with a brief description of the change." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[INFO 04-17 12:10:39] ax.service.ax_client: Starting optimization with verbose logging. To disable logging, set the `verbose_logging` argument to `False`. Note that float values in the logs are rounded to 6 decimal points.\n", - "[WARNING 04-17 12:10:39] ax.service.ax_client: Random seed set to 12345. Note that this setting only affects the Sobol quasi-random generator and BoTorch-powered Bayesian optimization models. For the latter models, setting random seed to the same number for two optimizations will make the generated trials similar, but not exactly the same, and over time the trials will diverge more.\n", - "[INFO 04-17 12:10:39] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. If that is not the expected value type, you can explicitly specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.\n", - "[INFO 04-17 12:10:39] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicitly specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.\n", - "[INFO 04-17 12:10:39] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x3. If that is not the expected value type, you can explicitly specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.\n", - "[INFO 04-17 12:10:39] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x4. If that is not the expected value type, you can explicitly specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.\n", - "[INFO 04-17 12:10:39] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x5. If that is not the expected value type, you can explicitly specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.\n", - "[INFO 04-17 12:10:39] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x6. If that is not the expected value type, you can explicitly specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.\n", - "[INFO 04-17 12:10:39] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x7. If that is not the expected value type, you can explicitly specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.\n", - "[INFO 04-17 12:10:39] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x3', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x4', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x5', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x6', parameter_type=FLOAT, range=[0.01, 0.1]), RangeParameter(name='x7', parameter_type=FLOAT, range=[120.0, 200.0])], parameter_constraints=[ParameterConstraint(1.0*x1 + 1.0*x2 + 1.0*x3 + 1.0*x4 <= 1.0)]).\n", - "[INFO 04-17 12:10:39] ax.modelbridge.dispatch_utils: Using Models.BOTORCH_MODULAR since there is at least one ordered parameter and there are no unordered categorical parameters.\n", - "[INFO 04-17 12:10:39] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=7 num_trials=None use_batch_trials=False\n", - "[INFO 04-17 12:10:39] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=14\n", - "[INFO 04-17 12:10:39] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=14\n", - "[INFO 04-17 12:10:39] ax.modelbridge.dispatch_utils: `verbose`, `disable_progbar`, and `jit_compile` are not yet supported when using `choose_generation_strategy` with ModularBoTorchModel, dropping these arguments.\n", - "[INFO 04-17 12:10:39] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+BoTorch', steps=[Sobol for 14 trials, BoTorch for subsequent trials]). Iterations after 14 will take longer to generate due to model-fitting.\n", - "[INFO 04-17 12:10:39] ax.core.experiment: Attached custom parameterizations [{'x1': 0.3, 'x2': 0.2, 'x3': 0.1, 'x4': 0.0, 'x5': 0.4, 'x6': 0.05, 'x7': 150.0}] as trial 0.\n", - "[INFO 04-17 12:10:39] ax.service.ax_client: Completed trial 0 with data: {'strength': (46.660238, None), 'biodegradability': (9.078739, None)}.\n", - "[INFO 04-17 12:10:39] ax.core.experiment: Attached custom parameterizations [{'x1': 0.0, 'x2': 0.0, 'x3': 0.3, 'x4': 0.7, 'x5': 0.0, 'x6': 0.1, 'x7': 160.0}] as trial 1.\n", - "[INFO 04-17 12:10:39] ax.service.ax_client: Completed trial 1 with data: {'strength': (25.79598, None), 'biodegradability': (19.168606, None)}.\n", - "[INFO 04-17 12:10:39] ax.core.experiment: Attached custom parameterizations [{'x1': 0.2, 'x2': 0.2, 'x3': 0.2, 'x4': 0.2, 'x5': 0.2, 'x6': 0.09, 'x7': 184.0}] as trial 2.\n", - "[INFO 04-17 12:10:39] ax.service.ax_client: Completed trial 2 with data: {'strength': (41.652192, None), 'biodegradability': (11.474355, None)}.\n", - "[INFO 04-17 12:10:39] ax.service.ax_client: Generated new trial 3 with parameters {'x1': 0.222494, 'x2': 0.114947, 'x3': 0.5139, 'x4': 0.020708, 'x5': 0.829686, 'x6': 0.021624, 'x7': 181.654276} using model Sobol.\n", - "[INFO 04-17 12:10:39] ax.service.ax_client: Completed trial 3 with data: {'strength': (58.799907, None), 'biodegradability': (8.839131, None)}.\n", - "[INFO 04-17 12:10:39] ax.service.ax_client: Generated new trial 4 with parameters {'x1': 0.154027, 'x2': 0.242956, 'x3': 0.386497, 'x4': 0.173289, 'x5': 0.617998, 'x6': 0.026884, 'x7': 162.506085} using model Sobol.\n", - "[INFO 04-17 12:10:39] ax.service.ax_client: Completed trial 4 with data: {'strength': (65.371678, None), 'biodegradability': (9.692252, None)}.\n", - "[INFO 04-17 12:10:39] ax.service.ax_client: Generated new trial 5 with parameters {'x1': 0.179133, 'x2': 0.005902, 'x3': 0.101222, 'x4': 0.461255, 'x5': 0.171468, 'x6': 0.092874, 'x7': 195.245879} using model Sobol.\n", - "[INFO 04-17 12:10:39] ax.service.ax_client: Completed trial 5 with data: {'strength': (33.640468, None), 'biodegradability': (13.557441, None)}.\n", - "[INFO 04-17 12:10:39] ax.service.ax_client: Generated new trial 6 with parameters {'x1': 0.088445, 'x2': 0.425975, 'x3': 0.134375, 'x4': 0.143051, 'x5': 0.807816, 'x6': 0.059075, 'x7': 186.147521} using model Sobol.\n", - "[INFO 04-17 12:10:39] ax.service.ax_client: Completed trial 6 with data: {'strength': (62.787792, None), 'biodegradability': (8.18435, None)}.\n", - "[INFO 04-17 12:10:39] ax.service.ax_client: Generated new trial 7 with parameters {'x1': 0.026292, 'x2': 0.082697, 'x3': 0.27327, 'x4': 0.60958, 'x5': 0.650338, 'x6': 0.02124, 'x7': 155.349769} using model Sobol.\n", - "[INFO 04-17 12:10:39] ax.service.ax_client: Completed trial 7 with data: {'strength': (53.265418, None), 'biodegradability': (14.337877, None)}.\n", - "[INFO 04-17 12:10:39] ax.service.ax_client: Generated new trial 8 with parameters {'x1': 0.412713, 'x2': 0.228674, 'x3': 0.047212, 'x4': 0.091807, 'x5': 0.307525, 'x6': 0.033846, 'x7': 150.470045} using model Sobol.\n", - "[INFO 04-17 12:10:39] ax.service.ax_client: Completed trial 8 with data: {'strength': (101.00477, None), 'biodegradability': (7.90621, None)}.\n", - "[INFO 04-17 12:10:39] ax.service.ax_client: Generated new trial 9 with parameters {'x1': 0.117707, 'x2': 0.10513, 'x3': 0.039212, 'x4': 0.253231, 'x5': 0.552579, 'x6': 0.072357, 'x7': 146.750515} using model Sobol.\n", - "[INFO 04-17 12:10:39] ax.service.ax_client: Completed trial 9 with data: {'strength': (30.385234, None), 'biodegradability': (14.123691, None)}.\n", - "[INFO 04-17 12:10:39] ax.service.ax_client: Generated new trial 10 with parameters {'x1': 0.353, 'x2': 0.132346, 'x3': 0.124854, 'x4': 0.13, 'x5': 0.906883, 'x6': 0.018858, 'x7': 181.506837} using model Sobol.\n", - "[INFO 04-17 12:10:39] ax.service.ax_client: Completed trial 10 with data: {'strength': (89.00033, None), 'biodegradability': (7.147958, None)}.\n", - "[INFO 04-17 12:10:39] ax.service.ax_client: Generated new trial 11 with parameters {'x1': 0.145964, 'x2': 0.46252, 'x3': 0.193453, 'x4': 0.071685, 'x5': 0.407677, 'x6': 0.083634, 'x7': 157.186764} using model Sobol.\n", - "[INFO 04-17 12:10:39] ax.service.ax_client: Completed trial 11 with data: {'strength': (60.091989, None), 'biodegradability': (9.398967, None)}.\n", - "[INFO 04-17 12:10:39] ax.service.ax_client: Generated new trial 12 with parameters {'x1': 0.043166, 'x2': 0.023531, 'x3': 0.564187, 'x4': 0.201326, 'x5': 0.449078, 'x6': 0.05358, 'x7': 150.02964} using model Sobol.\n", - "[INFO 04-17 12:10:39] ax.service.ax_client: Completed trial 12 with data: {'strength': (24.454797, None), 'biodegradability': (15.363259, None)}.\n", - "[INFO 04-17 12:10:39] ax.service.ax_client: Generated new trial 13 with parameters {'x1': 0.080444, 'x2': 0.145896, 'x3': 0.441666, 'x4': 0.103938, 'x5': 0.221277, 'x6': 0.047577, 'x7': 134.169413} using model Sobol.\n", - "[INFO 04-17 12:10:39] ax.service.ax_client: Completed trial 13 with data: {'strength': (54.110101, None), 'biodegradability': (13.494714, None)}.\n", - "[INFO 04-17 12:10:39] ax.service.ax_client: Generated new trial 14 with parameters {'x1': 0.008678, 'x2': 0.049031, 'x3': 0.155088, 'x4': 0.11705, 'x5': 0.87693, 'x6': 0.043737, 'x7': 176.119587} using model Sobol.\n", - "[INFO 04-17 12:10:39] ax.service.ax_client: Completed trial 14 with data: {'strength': (65.61865, None), 'biodegradability': (14.216473, None)}.\n", - "[INFO 04-17 12:10:39] ax.service.ax_client: Generated new trial 15 with parameters {'x1': 0.033407, 'x2': 0.200164, 'x3': 0.361986, 'x4': 0.280265, 'x5': 0.334687, 'x6': 0.056508, 'x7': 183.545813} using model Sobol.\n", - "[INFO 04-17 12:10:39] ax.service.ax_client: Completed trial 15 with data: {'strength': (40.479783, None), 'biodegradability': (12.629707, None)}.\n", - "[INFO 04-17 12:10:39] ax.service.ax_client: Generated new trial 16 with parameters {'x1': 0.30056, 'x2': 0.032063, 'x3': 0.286449, 'x4': 0.149094, 'x5': 0.191202, 'x6': 0.025519, 'x7': 148.145571} using model Sobol.\n", - "[INFO 04-17 12:10:39] ax.service.ax_client: Completed trial 16 with data: {'strength': (64.053814, None), 'biodegradability': (9.970079, None)}.\n", - "[INFO 04-17 12:10:42] ax.service.ax_client: Generated new trial 17 with parameters {'x1': 0.623365, 'x2': 0.264021, 'x3': 0.0, 'x4': 0.112022, 'x5': 0.346625, 'x6': 0.02824, 'x7': 151.606778} using model BoTorch.\n", - "[INFO 04-17 12:10:42] ax.service.ax_client: Completed trial 17 with data: {'strength': (101.039555, None), 'biodegradability': (7.070961, None)}.\n", - "[INFO 04-17 12:10:44] ax.service.ax_client: Generated new trial 18 with parameters {'x1': 0.036143, 'x2': 0.083187, 'x3': 0.0, 'x4': 0.123627, 'x5': 0.741901, 'x6': 0.024407, 'x7': 169.50347} using model BoTorch.\n", - "[INFO 04-17 12:10:44] ax.service.ax_client: Completed trial 18 with data: {'strength': (79.395803, None), 'biodegradability': (14.41337, None)}.\n", - "[INFO 04-17 12:10:47] ax.service.ax_client: Generated new trial 19 with parameters {'x1': 0.185593, 'x2': 0.230212, 'x3': 0.0, 'x4': 0.120535, 'x5': 0.341583, 'x6': 0.027248, 'x7': 150.241196} using model BoTorch.\n", - "[INFO 04-17 12:10:47] ax.service.ax_client: Completed trial 19 with data: {'strength': (84.672987, None), 'biodegradability': (9.178258, None)}.\n", - "[INFO 04-17 12:10:50] ax.service.ax_client: Generated new trial 20 with parameters {'x1': 0.030186, 'x2': 0.05624, 'x3': 0.0, 'x4': 0.145763, 'x5': 1.0, 'x6': 0.021301, 'x7': 180.761653} using model BoTorch.\n", - "[INFO 04-17 12:10:50] ax.service.ax_client: Completed trial 20 with data: {'strength': (70.805624, None), 'biodegradability': (14.205617, None)}.\n", - "[INFO 04-17 12:10:53] ax.service.ax_client: Generated new trial 21 with parameters {'x1': 0.483281, 'x2': 0.223379, 'x3': 0.003256, 'x4': 0.093533, 'x5': 0.438249, 'x6': 0.029267, 'x7': 159.783298} using model BoTorch.\n", - "[INFO 04-17 12:10:53] ax.service.ax_client: Completed trial 21 with data: {'strength': (114.991376, None), 'biodegradability': (7.265252, None)}.\n", - "[INFO 04-17 12:10:56] ax.service.ax_client: Generated new trial 22 with parameters {'x1': 0.171795, 'x2': 0.080973, 'x3': 0.0, 'x4': 0.113554, 'x5': 0.655177, 'x6': 0.027367, 'x7': 168.605868} using model BoTorch.\n", - "[INFO 04-17 12:10:56] ax.service.ax_client: Completed trial 22 with data: {'strength': (82.802476, None), 'biodegradability': (10.980162, None)}.\n", - "[INFO 04-17 12:10:59] ax.service.ax_client: Generated new trial 23 with parameters {'x1': 0.0, 'x2': 0.094322, 'x3': 0.0, 'x4': 0.0, 'x5': 0.837995, 'x6': 0.019695, 'x7': 151.13371} using model BoTorch.\n", - "[INFO 04-17 12:10:59] ax.service.ax_client: Completed trial 23 with data: {'strength': (52.382159, None), 'biodegradability': (15.915414, None)}.\n", - "[INFO 04-17 12:11:01] ax.service.ax_client: Generated new trial 24 with parameters {'x1': 0.0, 'x2': 0.15087, 'x3': 0.0, 'x4': 0.198818, 'x5': 0.63805, 'x6': 0.026114, 'x7': 181.20332} using model BoTorch.\n", - "[INFO 04-17 12:11:01] ax.service.ax_client: Completed trial 24 with data: {'strength': (65.184941, None), 'biodegradability': (12.632999, None)}.\n", - "[INFO 04-17 12:11:04] ax.service.ax_client: Generated new trial 25 with parameters {'x1': 0.036207, 'x2': 0.0, 'x3': 0.0, 'x4': 0.250386, 'x5': 0.908954, 'x6': 0.029536, 'x7': 163.175483} using model BoTorch.\n", - "[INFO 04-17 12:11:04] ax.service.ax_client: Completed trial 25 with data: {'strength': (52.719962, None), 'biodegradability': (15.157616, None)}.\n", - "[INFO 04-17 12:11:07] ax.service.ax_client: Generated new trial 26 with parameters {'x1': 0.076866, 'x2': 0.173221, 'x3': 0.0, 'x4': 0.11133, 'x5': 0.797933, 'x6': 0.013231, 'x7': 160.373806} using model BoTorch.\n", - "[INFO 04-17 12:11:07] ax.service.ax_client: Completed trial 26 with data: {'strength': (79.935322, None), 'biodegradability': (11.222913, None)}.\n", - "[INFO 04-17 12:11:11] ax.service.ax_client: Generated new trial 27 with parameters {'x1': 0.479459, 'x2': 0.346647, 'x3': 0.0, 'x4': 0.097935, 'x5': 0.484028, 'x6': 0.026186, 'x7': 167.117139} using model BoTorch.\n", - "[INFO 04-17 12:11:11] ax.service.ax_client: Completed trial 27 with data: {'strength': (121.019677, None), 'biodegradability': (5.346441, None)}.\n", - "[INFO 04-17 12:11:14] ax.service.ax_client: Generated new trial 28 with parameters {'x1': 0.580115, 'x2': 0.041994, 'x3': 0.0, 'x4': 0.096781, 'x5': 0.463182, 'x6': 0.025367, 'x7': 169.884797} using model BoTorch.\n", - "[INFO 04-17 12:11:14] ax.service.ax_client: Completed trial 28 with data: {'strength': (103.120131, None), 'biodegradability': (7.851948, None)}.\n", - "[INFO 04-17 12:11:17] ax.service.ax_client: Generated new trial 29 with parameters {'x1': 0.0, 'x2': 0.008422, 'x3': 0.0, 'x4': 0.114974, 'x5': 0.570566, 'x6': 0.01, 'x7': 172.607485} using model BoTorch.\n", - "[INFO 04-17 12:11:17] ax.service.ax_client: Completed trial 29 with data: {'strength': (68.035485, None), 'biodegradability': (15.315641, None)}.\n", - "[INFO 04-17 12:11:20] ax.service.ax_client: Generated new trial 30 with parameters {'x1': 0.0, 'x2': 0.049395, 'x3': 0.0, 'x4': 0.127239, 'x5': 0.817694, 'x6': 0.025151, 'x7': 152.918907} using model BoTorch.\n", - "[INFO 04-17 12:11:20] ax.service.ax_client: Completed trial 30 with data: {'strength': (72.020162, None), 'biodegradability': (16.439821, None)}.\n", - "[INFO 04-17 12:11:24] ax.service.ax_client: Generated new trial 31 with parameters {'x1': 0.257347, 'x2': 0.645363, 'x3': 0.0, 'x4': 0.09729, 'x5': 0.480215, 'x6': 0.022957, 'x7': 166.427894} using model BoTorch.\n", - "[INFO 04-17 12:11:24] ax.service.ax_client: Completed trial 31 with data: {'strength': (91.473496, None), 'biodegradability': (4.18437, None)}.\n", - "[INFO 04-17 12:11:27] ax.service.ax_client: Generated new trial 32 with parameters {'x1': 0.413376, 'x2': 0.072098, 'x3': 0.0, 'x4': 0.099215, 'x5': 0.457643, 'x6': 0.031045, 'x7': 154.361334} using model BoTorch.\n", - "[INFO 04-17 12:11:27] ax.service.ax_client: Completed trial 32 with data: {'strength': (97.744146, None), 'biodegradability': (8.924053, None)}.\n", - "[INFO 04-17 12:11:31] ax.service.ax_client: Generated new trial 33 with parameters {'x1': 0.50416, 'x2': 0.273746, 'x3': 0.0, 'x4': 0.094571, 'x5': 0.503533, 'x6': 0.022394, 'x7': 167.617223} using model BoTorch.\n", - "[INFO 04-17 12:11:31] ax.service.ax_client: Completed trial 33 with data: {'strength': (119.509999, None), 'biodegradability': (5.995635, None)}.\n", - "[INFO 04-17 12:11:36] ax.service.ax_client: Generated new trial 34 with parameters {'x1': 0.0, 'x2': 0.058864, 'x3': 0.0, 'x4': 0.121596, 'x5': 0.762225, 'x6': 0.02506, 'x7': 165.611183} using model BoTorch.\n", - "[INFO 04-17 12:11:36] ax.service.ax_client: Completed trial 34 with data: {'strength': (77.437982, None), 'biodegradability': (15.832145, None)}.\n", - "[INFO 04-17 12:11:40] ax.service.ax_client: Generated new trial 35 with parameters {'x1': 0.005073, 'x2': 0.182147, 'x3': 0.0, 'x4': 0.112601, 'x5': 0.727606, 'x6': 0.02698, 'x7': 167.409672} using model BoTorch.\n", - "[INFO 04-17 12:11:40] ax.service.ax_client: Completed trial 35 with data: {'strength': (86.774828, None), 'biodegradability': (13.339966, None)}.\n", - "[INFO 04-17 12:11:44] ax.service.ax_client: Generated new trial 36 with parameters {'x1': 0.472891, 'x2': 0.132407, 'x3': 0.0, 'x4': 0.095867, 'x5': 0.440817, 'x6': 0.032109, 'x7': 160.997823} using model BoTorch.\n", - "[INFO 04-17 12:11:44] ax.service.ax_client: Completed trial 36 with data: {'strength': (109.173001, None), 'biodegradability': (8.197528, None)}.\n", - "[INFO 04-17 12:11:48] ax.service.ax_client: Generated new trial 37 with parameters {'x1': 0.005355, 'x2': 0.259832, 'x3': 0.0, 'x4': 0.107173, 'x5': 0.701674, 'x6': 0.028271, 'x7': 168.553791} using model BoTorch.\n", - "[INFO 04-17 12:11:48] ax.service.ax_client: Completed trial 37 with data: {'strength': (91.075536, None), 'biodegradability': (11.375168, None)}.\n", - "/Users/andrewf/miniconda3/envs/ax_env/lib/python3.9/site-packages/ax/modelbridge/modelbridge_utils.py:878: UserWarning: FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", - " frontier_observations, f, obj_w, obj_t = get_pareto_frontier_and_configs(\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "from ax.service.ax_client import AxClient, ObjectiveProperties\n", - "\n", - "\n", - "import pandas as pd\n", - "\n", - "obj1_name = \"strength\" # CHANGE: add name of first objective\n", - "obj2_name = \"biodegradability\" # CHANGE: add name of first objective\n", - "\n", - "\n", - "# CHANGE: remove the moo_branin dummy objective function, we will use the above function\n", - "\n", - "# CHANGE: update the total quantity for the composition constraint\n", - "total = 1.0\n", - "\n", - "# CHANGE: add the historical data that was pulled from the literature\n", - "X_train = pd.DataFrame(\n", - " [\n", - " {\"x1\": 0.3, \"x2\": 0.2, \"x3\": 0.1, \"x4\": 0.0, \"x5\": 0.4, \"x6\": 0.05, \"x7\": 150.0},\n", - " {\"x1\": 0.0, \"x2\": 0.0, \"x3\": 0.3, \"x4\": 0.7, \"x5\": 0.0, \"x6\": 0.1, \"x7\": 160.0},\n", - " {\"x1\": 0.2, \"x2\": 0.2, \"x3\": 0.2, \"x4\": 0.2, \"x5\": 0.2, \"x6\": 0.09, \"x7\": 184.0},\n", - " ]\n", - ")\n", - "\n", - "# CHANGE: calculate the y_train values using the polymer_properties function\n", - "y_train = [polymer_properties(**row[1]) for row in X_train.iterrows()]\n", - "\n", - "# Define the number of training examples\n", - "n_train = len(X_train)\n", - "\n", - "ax_client = AxClient(random_seed=12345) # CHANGE: add random seed for reproducibility\n", - "\n", - "ax_client.create_experiment(\n", - " parameters=[\n", - " {\"name\": \"x1\", \"type\": \"range\", \"bounds\": [0.0, 1.0]}, # CHANGE: update parameter\n", - " {\"name\": \"x2\", \"type\": \"range\", \"bounds\": [0.0, 1.0]}, # CHANGE: update parameter\n", - " {\"name\": \"x3\", \"type\": \"range\", \"bounds\": [0.0, 1.0]}, # CHANGE: add new parameter\n", - " {\"name\": \"x4\", \"type\": \"range\", \"bounds\": [0.0, 1.0]}, # CHANGE: add new parameter\n", - " {\"name\": \"x5\", \"type\": \"range\", \"bounds\": [0.0, 1.0]}, # CHANGE: add new parameter\n", - " {\"name\": \"x6\", \"type\": \"range\", \"bounds\": [0.01, 0.1]}, # CHANGE: add new parameter\n", - " {\"name\": \"x7\", \"type\": \"range\", \"bounds\": [120.0, 200.0]}, # CHANGE: add new parameter\n", - " ],\n", - " objectives={\n", - " obj1_name: ObjectiveProperties(minimize=False, threshold=70.0), # CHANGE: set minimize to False and change threshold\n", - " obj2_name: ObjectiveProperties(minimize=False, threshold=0.0), # CHANGE: set minimize to False and change threshold\n", - " },\n", - " parameter_constraints=[\n", - " f\"x1 + x2 + x3 + x4 <= {total}\", # CHANGE: update composition constraint\n", - " ],\n", - ")\n", - "\n", - "# Add existing data to the AxClient\n", - "for i in range(n_train):\n", - " parameterization = X_train.iloc[i].to_dict()\n", - "\n", - " ax_client.attach_trial(parameterization)\n", - " ax_client.complete_trial(trial_index=i, raw_data=y_train[i])\n", - "\n", - "\n", - "for _ in range(35): # CHANGE: increase number of trials\n", - "\n", - " parameterization, trial_index = ax_client.get_next_trial()\n", - "\n", - " # CHANGE: pull all added parameters from the parameterization\n", - " x1 = parameterization[\"x1\"]\n", - " x2 = parameterization[\"x2\"]\n", - " x3 = parameterization[\"x3\"]\n", - " x4 = parameterization[\"x4\"]\n", - " x5 = total - (x1 + x2 + x3 + x4) # CHANGE: update composition constraint\n", - " x6 = parameterization[\"x6\"]\n", - " x7 = parameterization[\"x7\"]\n", - "\n", - " results = polymer_properties(x1, x2, x3, x4, x5, x6, x7) # CHANGE: switch to polymer function\n", - " ax_client.complete_trial(trial_index=trial_index, raw_data=results)\n", - "\n", - "pareto_results = ax_client.get_pareto_optimal_parameters()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Show the Pareto Optimal Parameters\n", - "\n", - "After the optimization loop has completed, we can view the set of parameter combinations that are found to be Pareto optimal. This will help us understand the tradeoff between the two objectives of interest." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[INFO 04-17 12:11:49] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.\n", - "/Users/andrewf/miniconda3/envs/ax_env/lib/python3.9/site-packages/ax/modelbridge/modelbridge_utils.py:878: UserWarning: FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", - " frontier_observations, f, obj_w, obj_t = get_pareto_frontier_and_configs(\n" - ] - }, - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " x1 x2 x3 x4 x5 x6 x7 strength biodegradability\n", - "21 0.48 0.22 0.0 0.09 0.44 0.03 159.78 114.99 7.26\n", - "36 0.47 0.13 0.0 0.10 0.44 0.03 161.00 109.16 8.20\n", - "33 0.50 0.27 0.0 0.09 0.50 0.02 167.62 119.51 6.00\n", - "27 0.48 0.35 0.0 0.10 0.48 0.03 167.12 121.00 5.35\n", - "32 0.41 0.07 0.0 0.10 0.46 0.03 154.36 97.75 8.92\n", - "37 0.01 0.26 0.0 0.11 0.70 0.03 168.55 91.05 11.38\n", - "35 0.01 0.18 0.0 0.11 0.73 0.03 167.41 86.79 13.33\n", - "18 0.04 0.08 0.0 0.12 0.74 0.02 169.50 79.40 14.41\n", - "34 0.00 0.06 0.0 0.12 0.76 0.03 165.61 77.41 15.84\n", - "30 0.00 0.05 0.0 0.13 0.82 0.03 152.92 72.03 16.44" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "p_op = ax_client.get_pareto_optimal_parameters()\n", - "\n", - "# parse p_op values to get parameters and values\n", - "p_op_index = list(p_op.keys())\n", - "p_op_params = [p_op[i][0] for i in p_op_index]\n", - "p_op_values = [p_op[i][1][0] for i in p_op_index]\n", - "\n", - "# organize the results into a dataframe\n", - "pareto_results = pd.DataFrame(p_op_params, columns=[\"x1\", \"x2\", \"x3\", \"x4\", \"x5\", \"x6\", \"x7\"])\n", - "pareto_results[\"strength\"] = [v[\"strength\"] for v in p_op_values]\n", - "pareto_results[\"biodegradability\"] = [v[\"biodegradability\"] for v in p_op_values]\n", - "pareto_results.index = p_op_index\n", - "display(pareto_results.round(2))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot the Optimal Values Found During Optimization\n", - "\n", - "We can visualize the set of pareto optimial soltuions relative to the entire dataset by plotting them.\n", - "\n", - "We observe that our historical data was indeed of poor quality, but that our model was able to find many candidates with signficantly higher strength and biodegradability scores. Additionally, we can now see a clear tradeoff between the two properties." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[WARNING 04-17 12:11:49] ax.service.utils.report_utils: Column reason missing for all trials. Not appending column.\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "plt.style.use(\"ggplot\")\n", - "\n", - "fig, ax = plt.subplots(figsize=(4, 4), dpi=100)\n", - "\n", - "all_trials = ax_client.get_trials_data_frame()\n", - "ax.scatter(\n", - " all_trials[\"strength\"], \n", - " all_trials[\"biodegradability\"], \n", - " color='#818180', \n", - " facecolor='none', \n", - " s=25,\n", - " label='All Trials'\n", - ")\n", - "ax.scatter(pareto_results[\"strength\"], pareto_results[\"biodegradability\"], color='#0041FF', label='Pareto Optimal')\n", - "historical = np.array([[d['strength'][0], d['biodegradability'][0]] for d in y_train])\n", - "ax.scatter(historical[:,0], historical[:,1], color='#FF9A00', label='Historical data')\n", - "ax.axvline(70, ls=':', color='k')\n", - "ax.set_xlabel(\"Strength\")\n", - "ax.set_ylabel(\"Biodegradability\")\n", - "ax.legend(facecolor='w', fontsize=8, loc='center left', bbox_to_anchor=(1, 0.1))\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "ax_env", - "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": "3.9.19" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -}