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Hierarchical Bayes tutorial and Stan example.ipynb
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{
"cells": [
{
"metadata": {},
"cell_type": "markdown",
"source": "# Introduction"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "This notebook walks through a Hierarchical Bayes model and is a tutorial in Stan using logistic regression.\n\nI generate simulated data in which there are several different locations and features. Each location has a different bias term and feature terms, but they are related enough to prove loosely informative between locations.\n\nI compare the performance of a hierarchical model that understands this structure versus a model that simply treats all locations identically and which treats them separately. I also compare the performance of such a model with regularized logistic regressions with overall bias and feature terms, along with per location terms.\n\nNote: the summary/introduction assumes some familiarity with Bayesian statistics, but when I discuss each model below I spell out what the model I'm sampling from looks like. The links from further reading below, especially the first one, introduces Bayesian statistics enough to read this tutorial. \n\nPlease note that I am neither a Stan expert nor a Bayesian statistician, so I imagine that there are code speedups and other best practices that I missed. This notebook is the result of a weekend or so of learning. Please comment here or drop me a note at nkgar6@gmail.com to correct any errors/note any improvements. "
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## Main findings\n\n1. A hierarchical model outperforms standard logistic regressions that treat each location either independently or identically. \n\n1. Creating a logistic regression in which there are overall bias/feature terms and then terms for each location often outperforms (in this basic example) the hierarchical model in Stan. However, the coefficients are a bit hard to interpret, the regularization parameter must be tuned, and there are no theoretical guarantees in general. Furthermore, with more complex hierarchical structure, the performance of this approach would not scale.\n\n1. Stan (in Python) is a relatively easy piece of software to work with, and building a model helps one think through exactly how the data/locations relates to one another. In standard Bayesian fashion, it also helps formalize the assumptions one is making about the data. However, it is a bit slow when compared to standard regression in Python/Sk-learn.\n\nNote that real-world data is often far more hierarchical than this example. There is not just a single level. Building a model that takes advantage of this structure does not require much more model code on top of what is done here, but is omitted for simplicity reasons. If a certain application demands taking advantage of such structure, then this approach would be far better than standard regressions or regularized regressions.\n\n## Different models summary\n\n1. **Single Bayesian model for all locations**. Here, we estimate a single bias term and feature parameters for all locations. This model has decent overall performance, but the parameter estimates are way off. Furthermore, performance (and especially parameter estimates) for locations that deviate from the other locations is especially bad.\n1. **Separate Bayesian model for each location**. Here, we estimate separate bias terms and feature parameters for each locations, with no knowledge sharing. Overall performance is better than that of a single model, and parameter estimates for locations with many data points are good. However, performance for locations with very few data samples is terrible.\n1. **Bayesian Hierarchical Model**. Here, there are overall bias and feature parameters, and then location specific parameters that are allowed to differ from the shared parameters with enough data. Slightly better performance overall than the previous two in terms of AUC. Where it is especially good is the parameter estimates for each location are accurate. \n1. **Standard logistic regressions with regularization**. Here, we have central bias/feature parameters, and then _add_ location specific coefficients. Regularization should, heuristically, put as much information in the shared parameters as possible, with the location specific parameters being how the locations differ from the shared parameter (especially for those models with large number of data points). For locations with low number of data points, location specific parameters will be small. However, such a pattern of course depends on the regularization parameter. Such models, in the example here, have better performance (in terms of AUC) than the hierarchical model. However, it's hard to interpret what the shared and location parameters are doing as they don't completely match the intuition that the location parameters are just differences from the central one. This model also will not scale with a deeper hierarchical structure.\n\n\n## Links for further reading\n\n1. http://m-clark.github.io/workshops/bayesian/02_key_concepts.html, and https://m-clark.github.io/bayesian-basics/intro.html\n1. http://mc-stan.org/users/documentation/tutorials\n1. https://github.com/stan-dev/stan/wiki/Stan-Best-Practices\n1. https://github.com/stan-dev/stan/wiki/Prior-Choice-Recommendations\n1. http://mc-stan.org/users/documentation/case-studies/radon.html\n1. https://ourcodingclub.github.io/2018/04/17/stan-intro.html\n"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "# Setup"
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2018-09-18T03:42:10.505843Z",
"end_time": "2018-09-18T03:42:13.045886Z"
},
"trusted": true
},
"cell_type": "code",
"source": "%matplotlib inline\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns; sns.set_context('notebook')\nimport pystan\nimport scipy.stats as stats\nfrom sklearn import metrics\nimport sklearn\nimport arviz as az\nimport copy",
"execution_count": 1,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Stan compiles models in C++, and this process is expensive. Below code caches model compilations (using a hash of your model code). It is from https://pystan.readthedocs.io/en/latest/avoiding_recompilation.html."
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2018-09-18T03:42:13.049045Z",
"end_time": "2018-09-18T03:42:13.079023Z"
},
"trusted": true
},
"cell_type": "code",
"source": "import pickle\nfrom hashlib import md5\n\ndef StanModel_cache(model_code, model_name=None, **kwargs):\n \"\"\"Use just as you would `stan`\"\"\"\n code_hash = md5(model_code.encode('ascii')).hexdigest()\n if model_name is None:\n cache_fn = 'cached-model-{}.pkl'.format(code_hash)\n else:\n cache_fn = 'cached-{}-{}.pkl'.format(model_name, code_hash)\n try:\n sm = pickle.load(open(cache_fn, 'rb'))\n except:\n sm = pystan.StanModel(model_code=model_code)\n with open(cache_fn, 'wb') as f:\n pickle.dump(sm, f)\n else:\n pass\n# print(\"Using cached StanModel\")\n \n return sm",
"execution_count": 2,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## Some tools to evaluate the model performance"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Some quick functions to plot ROC curves, including for each location and overall. These functions rely on Sklearn functions that operate on the exponential coefficients in the logistic model, and so can be used for all types of models as long as we can extract the exponential coefficients for each test data point."
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2018-09-18T03:42:13.083648Z",
"end_time": "2018-09-18T03:42:13.266522Z"
},
"trusted": true
},
"cell_type": "code",
"source": "def plot_hist(pooled_sample):\n\n for param in list(pooled_sample.keys())[0:-1]: #not including dist of lp\n density = stats.gaussian_kde(pooled_sample[param])\n\n n, x = np.histogram(pooled_sample[param],normed = True, bins = 20)\n \n plt.plot(x, density(x), label = param)\n plt.legend()\n \ndef plot_roc_and_score(y, scores):\n fpr, tpr, thresholds = sklearn.metrics.roc_curve(y, scores)\n plt.plot(fpr, tpr)\n plt.xlabel('False positive rate')\n plt.ylabel('True positive rate')\n auc = sklearn.metrics.roc_auc_score(y, scores)\n print('AUC: {}'.format(auc))\n return auc\n\ndef plot_prec_rec_and_score(y, scores):\n precision, recall, thresholds = sklearn.metrics.precision_recall_curve(y, scores)\n plt.plot(precision, recall)\n plt.xlabel('Precision')\n plt.ylabel('Recall')\n auc = sklearn.metrics.average_precision_score(y, scores)\n print('Precision/Recall AUC: {}'.format(auc))\n return auc\n\n\ndef auc_per_location(y, scores, location_indices, n_locations):\n y_by_loc = {i:([],[]) for i in range(n_locations)}\n for i in range(len(y)):\n loci = location_indices[i]-1\n y_by_loc[loci][0].append(y[i])\n y_by_loc[loci][1].append(scores[i])\n \n aucs = []\n for loc in range(n_locations):\n print(loc, end = \" \")\n a = plot_roc_and_score(y_by_loc[loc][0], y_by_loc[loc][1])\n aucs.append(a)\n return aucs\n\ndef prec_rec_auc_per_location(y, scores, location_indices, n_locations):\n y_by_loc = {i:([],[]) for i in range(n_locations)}\n for i in range(len(y)):\n loci = location_indices[i]-1\n y_by_loc[loci][0].append(y[i])\n y_by_loc[loci][1].append(scores[i])\n \n aucs = []\n for loc in range(n_locations):\n print(loc, end=' ')\n a = plot_prec_rec_and_score(y_by_loc[loc][0], y_by_loc[loc][1])\n aucs.append(a)\n return aucs",
"execution_count": 3,
"outputs": []
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2018-09-18T03:42:13.270325Z",
"end_time": "2018-09-18T03:42:13.308024Z"
},
"trusted": true
},
"cell_type": "code",
"source": "def get_scores_from_coefficients(coef):\n cs = np.exp(coef)\n return [c/(c + 1.0) for c in cs]\n\ndef eval_accuracy_from_scores(y_true, scores, location_index_test, n_locations):\n \n plot_roc_and_score(y_true, scores)\n auc_per_location(y_true, scores, location_index_test, n_locations) \n plt.show()\n\n plot_prec_rec_and_score(y_true, scores)\n prec_rec_auc_per_location(y_true, scores, location_index_test, n_locations)",
"execution_count": 4,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## Code that fits model and evaluates performance of the MLE parameters."
},
{
"metadata": {},
"cell_type": "markdown",
"source": "The below code takes in a Stan model and the test/train data. It first compiles the model (using the cache if applicable), and then fits it using the train data. Then, using model_opt.optimizing, it extracts both the parameters that maximize the posterior probabilities given the train data and the exponential coefficients for the test data using these coefficients."
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2018-09-18T03:42:13.310956Z",
"end_time": "2018-09-18T03:42:13.344242Z"
},
"trusted": true
},
"cell_type": "code",
"source": "def sample_and_evaluate_model(model_str, gen_str, data, iter_num = 1000, chains =2):\n model = StanModel_cache(model_str)\n model_opt = StanModel_cache(model_str + gen_str)\n \n location_index_test = data['location_index_test']\n \n fit = model.sampling(\n data=data, iter=iter_num, chains=chains)\n \n pars_to_plot = [x for x in fit.model_pars if x not in ['exp_coef_train', 'exp_coef_test']]\n \n opt = model_opt.optimizing(\n data=data, init = fit.get_posterior_mean())\n \n scores_test = get_scores_from_coefficients(opt['exp_coef_test'])\n \n eval_accuracy_from_scores(y_test, scores_test,location_index_test, n_locations)\n \n return fit,opt",
"execution_count": 5,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## Set up Data"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "I simulate a situation with 5 locations. Four of these locations share similar but not identical parameters, while one of the locations has dramatically different parameters. The data model is as follows:\n\nLet $y_i$ be the $i$th data point, $x^i_j$ be feature $j$ for the $i$th data point, and ${k[i]}$ be the location of the $i$th data point. Then with per-location parameters $b^k, \\beta^k_1, \\beta^k_2$, \n\n$ y_i \\sim Bernoulli(\\frac{c_i}{1 + c_i})$, where $c_i = e^{b_{k[i]} + \\beta^{k[i]}_1 x^i_1 + \\beta^{k[i]}_2 x^i_2}$.\n\nI also have the different locations have different number of training data-points each. "
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2018-09-18T03:42:13.347600Z",
"end_time": "2018-09-18T03:42:13.369952Z"
},
"trusted": true
},
"cell_type": "code",
"source": "n_locations = 5\nn_features = 2\ndatapoints_per_location_min = 5\n\noverall_mean = [-.5, -.5, 1, -.5, -.5] #bias terms b for each location\noverall_slope = [[.8, -.4], [.8, -.4], [-1, 1], [.8, -.4], [.8, -.4]] #feature slope terms \\beta for each location.\n\nlocation_means = np.random.normal(loc = overall_mean, scale = .1, size = n_locations)\nlocation_feature_slopes = np.random.normal(loc = overall_slope, scale = .2, size = (n_locations,n_features))\nnumber_datapoints_per_location = [datapoints_per_location_min + x*200 for x in range(0, n_locations)]\n\nnumber_testpoints_per_location = 500",
"execution_count": 6,
"outputs": []
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2018-09-18T03:42:13.371741Z",
"end_time": "2018-09-18T03:42:13.399549Z"
},
"trusted": true
},
"cell_type": "code",
"source": "print(\"Location means\", location_means)\nprint(\"Location feature slopes\", location_feature_slopes)\nprint(\"Number datapoints per location\", number_datapoints_per_location)",
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"text": "Location means [-0.65495183 -0.63733907 1.04119405 -0.56687424 -0.5110217 ]\nLocation feature slopes [[ 0.57466563 -0.10446814]\n [ 0.85998957 -0.03824166]\n [-1.13315319 1.20645515]\n [ 0.39066619 -0.47314813]\n [ 0.41191865 -0.27377126]]\nNumber datapoints per location [5, 205, 405, 605, 805]\n",
"name": "stdout"
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2018-09-18T03:42:13.402101Z",
"end_time": "2018-09-18T03:42:13.428522Z"
},
"trusted": true
},
"cell_type": "code",
"source": "total_datapoints = sum(number_datapoints_per_location)",
"execution_count": 8,
"outputs": []
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2018-09-18T03:42:13.431384Z",
"end_time": "2018-09-18T03:42:13.557739Z"
},
"trusted": true
},
"cell_type": "code",
"source": "def draw_samples_from_model(location_means, location_feature_slopes, number_datapoints_per_location, do_noise=True):\n \n p_successes = []\n features = []\n location_index = []\n for en,points_location in enumerate(number_datapoints_per_location):\n for i in range(points_location):\n feature = np.random.uniform(low = 0, high = 3, size = n_features)\n if n_features == 1:\n features.append(feature)\n else:\n features.append(feature)\n \n exp_coef = location_means[en] + np.dot(location_feature_slopes[en],feature) \n if do_noise:\n exp_coef += np.random.normal(0, .1)\n c = np.exp(exp_coef)\n p = c/(1.0 + c)\n p_successes.append(p)\n location_index.append(en + 1)\n y = np.random.binomial(n = 1, p = p_successes)\n return y, features, location_index\n\ny_train, features_train, location_index_train = draw_samples_from_model(location_means, location_feature_slopes, number_datapoints_per_location)\ny_test, features_test, location_index_test = draw_samples_from_model(location_means, location_feature_slopes, [number_testpoints_per_location for x in range(n_locations)], do_noise = False)",
"execution_count": 9,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Below indicates the test and train probability that $y_i = 1$, for each location."
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2018-09-18T03:42:13.559373Z",
"end_time": "2018-09-18T03:42:13.566642Z"
},
"trusted": true
},
"cell_type": "code",
"source": "for i in range(n_locations):\n start_index = sum(number_datapoints_per_location[0:i])\n end_index = start_index + number_datapoints_per_location[i]\n print(np.mean(y_train[start_index: end_index]), np.mean(y_test[i*number_testpoints_per_location:(i+1)*number_testpoints_per_location]))",
"execution_count": 10,
"outputs": [
{
"output_type": "stream",
"text": "0.6 0.518\n0.668292682927 0.606\n0.740740740741 0.652\n0.328925619835 0.344\n0.467080745342 0.436\n",
"name": "stdout"
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2018-09-18T03:42:13.568797Z",
"end_time": "2018-09-18T03:42:13.599402Z"
},
"trusted": true
},
"cell_type": "code",
"source": "data = { \n'N_features' : n_features,\n'N_locations' : n_locations,\n'N_datapoints_train': len(y_train),\n'location_index_train' : location_index_train,\n'x_train': features_train,\n'y_train': y_train,\n'N_datapoints_test': len(y_test),\n'location_index_test' : location_index_test,\n'x_test': features_test,\n'y_test': y_test\n}\nprint(data['N_datapoints_train'])\nprint(data['N_datapoints_test'])",
"execution_count": 11,
"outputs": [
{
"output_type": "stream",
"text": "2025\n2500\n",
"name": "stdout"
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "# Single Regression"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Here, I build a Stan model that has a single bias term $b$ and two feature terms $\\beta_1, \\beta_2$, one for each feature. This model ignores the geoegraphic structure of the data completely, and implies the following structure for the data:\n\n$ y_i \\sim Bernoulli(\\frac{c_i}{1 + c_i})$, where $c_i = e^{b + \\beta_1 x^i_1 + \\beta_2 x^i_2}$ and $x^i_j$ is feature $j$ for the $i$th data point. \n\nThe parameters have the following priors:\n\n$b, \\beta_1, \\beta_2 \\sim Normal(0, 2)$. \n"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "A Stan model has three main components:\n1. data, which defines the forms of the data that will be given to it. It is best practice not to hard code things such as the number of features and datapoints, so the same model can be used with different data.\n2. parameters, which defines the parameters of the model to be learned.\n3. The model itself, which defines the structure I indicate above.\n\nNote that in the model we specify prior distributions for the parameters, a necessity in Bayesian statistics. Here, we specify $b, \\beta_1, \\beta_2 \\sim Normal(0, 2)$. \n\n\nIn all my models, I also have a string _single-gen-str_ that has an optional component for Stan models, _generated quantities_. This component, as the name suggests, generates terms that one may want to extract from the model. I use it to generate the exponential coefficients for both the train and test data so I can evaluate the model's performance using standard python functions."
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2018-09-18T03:42:13.602917Z",
"end_time": "2018-09-18T03:42:13.635646Z"
},
"trusted": true
},
"cell_type": "code",
"source": "single_model_str = \"\"\"\ndata {\n int<lower=0> N_features; \n\n int<lower=0> N_datapoints_train; \n matrix[N_datapoints_train,N_features] x_train;\n int y_train[N_datapoints_train];\n \n int<lower=0> N_datapoints_test; \n matrix[N_datapoints_test,N_features] x_test;\n int y_test[N_datapoints_test];\n}\n\nparameters {\n real beta ;\n vector[N_features] beta_feature;\n} \n\n\nmodel {\n vector[N_datapoints_train] exp_coef;\n beta ~ normal(0, 2);\n \n for (feature in 1:N_features){\n beta_feature[feature] ~ normal(0, 2);\n }\n \n for ( i in 1:N_datapoints_train) {\n exp_coef[i] = beta + (beta_feature .* x_train[i]')[1];\n } \n y_train ~ bernoulli_logit(exp_coef); \n\n }\n\"\"\"\n\nsingle_gen_str = \"\"\"\ngenerated quantities{\n vector[N_datapoints_train] exp_coef_train;\n vector[N_datapoints_test] exp_coef_test;\n \n for ( i in 1:N_datapoints_train) {\n exp_coef_train[i] = beta + (beta_feature .* x_train[i]')[1];\n } \n \n \n for ( i in 1:N_datapoints_test) {\n exp_coef_test[i] = beta + (beta_feature .* x_test[i]')[1];\n } \n }\n\"\"\"",
"execution_count": 12,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "I defined a function above, _sample-and-evaluate-model_ that takes in a Stan model and the data and runs standard evaluations -- the AUC by location, overall AUC, and similarly the precision-recall curves. "
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2018-09-18T03:42:13.637875Z",
"end_time": "2018-09-18T03:42:26.905357Z"
},
"trusted": true
},
"cell_type": "code",
"source": "fit, opt = sample_and_evaluate_model(single_model_str,single_gen_str, data)",
"execution_count": 13,
"outputs": [
{
"output_type": "stream",
"text": "AUC: 0.5530211638991981\n0 AUC: 0.6101667761418799\n1 AUC: 0.6828835167780737\n2 AUC: 0.2720541569705945\n3 AUC: 0.6399071185479296\n4 AUC: 0.5989979829526969\n",
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"text/plain": "<matplotlib.figure.Figure at 0x7f393caf48d0>",
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "stream",
"text": "Precision/Recall AUC: 0.5495680965794227\n0 Precision/Recall AUC: 0.613063823531351\n1 Precision/Recall AUC: 0.7613896115935875\n2 Precision/Recall AUC: 0.5182551752241953\n3 Precision/Recall AUC: 0.4737270996663786\n4 Precision/Recall AUC: 0.5105431479712934\n",
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"text/plain": "<matplotlib.figure.Figure at 0x7f38f00a7978>",
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "The Stan model also returns a _fit_ object that contains the posterior distributions for each parameter, as well as terms that help diagnose the quality of the sampling fit.\n\nBelow, we show the true parameters for each location and then, using the _fit_ object, the posterior distributions for the overall parameters. As expected, the posterior distributions roughly match the parameters of the majority of the locations, and the performance in the idiosyncratic location is poor. "
},
{
"metadata": {
"trusted": true,
"ExecuteTime": {
"start_time": "2018-09-18T03:42:26.910667Z",
"end_time": "2018-09-18T03:42:26.918923Z"
}
},
"cell_type": "code",
"source": "print(\"Location means\", location_means)\nprint(\"Location feature slopes\", location_feature_slopes)",
"execution_count": 14,
"outputs": [
{
"output_type": "stream",
"text": "Location means [-0.65495183 -0.63733907 1.04119405 -0.56687424 -0.5110217 ]\nLocation feature slopes [[ 0.57466563 -0.10446814]\n [ 0.85998957 -0.03824166]\n [-1.13315319 1.20645515]\n [ 0.39066619 -0.47314813]\n [ 0.41191865 -0.27377126]]\n",
"name": "stdout"
}
]
},
{
"metadata": {
"scrolled": false,
"trusted": true,
"ExecuteTime": {
"start_time": "2018-09-18T03:42:26.921760Z",
"end_time": "2018-09-18T03:42:26.956540Z"
}
},
"cell_type": "code",
"source": "fit",
"execution_count": 15,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 15,
"data": {
"text/plain": "Inference for Stan model: anon_model_38c27ad8b6b413a0761f094474360cf2.\n2 chains, each with iter=1000; warmup=500; thin=1; \npost-warmup draws per chain=500, total post-warmup draws=1000.\n\n mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat\nbeta -0.27 4.1e-3 0.09 -0.44 -0.33 -0.27 -0.22 -0.1 456 1.0\nbeta_feature[1] 0.18 2.6e-3 0.05 0.08 0.15 0.18 0.21 0.28 381 1.01\nbeta_feature[2] -0.07 0.08 2.02 -4.01 -1.24 -0.21 1.1 4.32 585 1.0\nlp__ -1399 0.06 1.22 -1402 -1399 -1399 -1398 -1398 357 1.0\n\nSamples were drawn using NUTS at Mon Sep 17 20:42:26 2018.\nFor each parameter, n_eff is a crude measure of effective sample size,\nand Rhat is the potential scale reduction factor on split chains (at \nconvergence, Rhat=1)."
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "We can also plot the posterior distributions of the parameters. "
},
{
"metadata": {
"trusted": true,
"ExecuteTime": {
"start_time": "2018-09-18T03:42:26.959653Z",
"end_time": "2018-09-18T03:42:27.768740Z"
}
},
"cell_type": "code",
"source": "_ = az.traceplot(fit)",
"execution_count": 16,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "<matplotlib.figure.Figure at 0x7f38effc4f98>",
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "From the above, we can see that training a single model for all the locations yields in relatively poor performance as some locations differ strongly from the others, and this model does not allow for that difference."
},
{
"metadata": {},
"cell_type": "markdown",
"source": "# Independent Regressions per location"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Here, we test the other extreme model -- in which there is a separate model trained for each location, with no knowledge used between locations. We'll see that this model performs very poorly for models with few samples.\n\nThis model assumes the following structure:\n\n$ y_i \\sim Bernoulli(\\frac{c_i}{1 + c_i})$, where $c_i = e^{b_{k[i]} + \\beta^{k[i]}_1 x^i_1 + \\beta^{k[i]}_2 x^i_2}$, $x^i_j$ is feature $j$ for the $i$th data point, and ${k[i]}$ is the location of the $i$th data point. \n\nThe (lack of) hierarchy is encoded as follows:\n\n$b^1, b^2, b^3, b^4, b^5 \\sim Normal(0, 2)$ i.i.d.\n\n$\\beta^1_1, \\beta^2_1, \\beta^3_1, \\beta^4_1, \\beta^5_1 \\sim Normal(0, 2)$ i.i.d.\n\n$\\beta^1_2, \\beta^2_2, \\beta^3_2, \\beta^4_2, \\beta^5_2 \\sim Normal(0, 2)$ i.i.d."
},
{
"metadata": {
"trusted": true,
"ExecuteTime": {
"start_time": "2018-09-18T03:42:27.773723Z",
"end_time": "2018-09-18T03:42:27.784475Z"
}
},
"cell_type": "code",
"source": "independent_model_str = \"\"\"\ndata {\n int<lower=0> N_locations;\n int<lower=0> N_features; \n \n int<lower=0> N_datapoints_train; \n matrix[N_datapoints_train,N_features] x_train;\n int y_train[N_datapoints_train];\n int location_index_train[N_datapoints_train];\n\n \n int<lower=0> N_datapoints_test; \n matrix[N_datapoints_test,N_features] x_test;\n int y_test[N_datapoints_test];\n int location_index_test[N_datapoints_test];\n\n}\n\nparameters { \n vector[N_locations] beta_locations;\n matrix[N_locations,N_features] beta_features_locations;\n \n} \n\nmodel {\n\n vector[N_datapoints_train] exp_coef_train;\n \n for (hex in 1:N_locations){\n beta_locations[hex] ~ normal(0, 2);\n for (feature in 1:N_features){\n beta_features_locations[hex,feature] ~ normal(0, 2);\n }\n }\n\n for ( i in 1:N_datapoints_train) {\n exp_coef_train[i] = beta_locations[location_index_train[i]]\n + (beta_features_locations[location_index_train[i]] .* x_train[i])[1]; \n } \n \n y_train ~ bernoulli_logit(exp_coef_train); \n }\n\"\"\"\n\nindep_gen_str = \"\"\"\n\ngenerated quantities{\n vector[N_datapoints_train] exp_coef_train;\n vector[N_datapoints_test] exp_coef_test;\n \n for ( i in 1:N_datapoints_train) {\n exp_coef_train[i] = beta_locations[location_index_train[i]]\n + (beta_features_locations[location_index_train[i]] .* x_train[i])[1]; \n } \n \n \n for ( i in 1:N_datapoints_test) {\n exp_coef_test[i] = beta_locations[location_index_test[i]]\n + (beta_features_locations[location_index_test[i]] .* x_test[i])[1]; \n } \n }\n\"\"\"",
"execution_count": 17,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Recall from the data generation above that the locations had, respectively, [5, 205, 405, 605, 805], number of samples each. As expected then, training an independent model per location yields very poor performance for the location with only 5 samples, even as overall performance slightly improves over the single combined model."
},
{
"metadata": {
"scrolled": false,
"trusted": true,
"ExecuteTime": {
"start_time": "2018-09-18T03:42:27.787577Z",
"end_time": "2018-09-18T03:42:59.790356Z"
}
},
"cell_type": "code",
"source": "fit, opt = sample_and_evaluate_model(independent_model_str, indep_gen_str, data)",
"execution_count": 18,
"outputs": [
{
"output_type": "stream",
"text": "AUC: 0.6596583501737832\n0 AUC: 0.38983322385812014\n1 AUC: 0.6828835167780737\n2 AUC: 0.7279458430294055\n3 AUC: 0.6399071185479296\n4 AUC: 0.5989979829526969\n",
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"text/plain": "<matplotlib.figure.Figure at 0x7f38efff6668>",
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\n"
},
"metadata": {}
},
{
"output_type": "stream",
"text": "Precision/Recall AUC: 0.6755337619304765\n0 Precision/Recall AUC: 0.45162685809994296\n1 Precision/Recall AUC: 0.7613896115935875\n2 Precision/Recall AUC: 0.8038319785664795\n3 Precision/Recall AUC: 0.4737270996663786\n4 Precision/Recall AUC: 0.5105431479712934\n",
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"text/plain": "<matplotlib.figure.Figure at 0x7f38efff6550>",
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "The parameters learned below also give some insight on the benefit of training independent models. For the locations that have enough data, the bias and feature terms approximately match the true values. For the location with very little, the parameters are far off: see, for example, beta_features_locations[1,1] and beta_features_locations[1,2]. These estimates are then reflected in the poor AUC for the first location."
},
{
"metadata": {
"trusted": true,
"ExecuteTime": {
"start_time": "2018-09-18T03:42:59.793050Z",
"end_time": "2018-09-18T03:42:59.800630Z"
}
},
"cell_type": "code",
"source": "print(\"Location means\", location_means)\nprint(\"Location feature slopes\", location_feature_slopes)",
"execution_count": 19,
"outputs": [
{
"output_type": "stream",
"text": "Location means [-0.65495183 -0.63733907 1.04119405 -0.56687424 -0.5110217 ]\nLocation feature slopes [[ 0.57466563 -0.10446814]\n [ 0.85998957 -0.03824166]\n [-1.13315319 1.20645515]\n [ 0.39066619 -0.47314813]\n [ 0.41191865 -0.27377126]]\n",
"name": "stdout"
}
]
},
{
"metadata": {
"trusted": true,
"ExecuteTime": {
"start_time": "2018-09-18T03:42:59.803045Z",
"end_time": "2018-09-18T03:42:59.871831Z"
}
},
"cell_type": "code",
"source": "fit",
"execution_count": 20,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 20,
"data": {
"text/plain": "Inference for Stan model: anon_model_937e3a019c59006a7c05a048dfe9dc0a.\n2 chains, each with iter=1000; warmup=500; thin=1; \npost-warmup draws per chain=500, total post-warmup draws=1000.\n\n mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat\nbeta_locations[1] 1.19 0.05 1.45 -1.59 0.22 1.15 2.16 4.11 799 1.0\nbeta_locations[2] -0.28 0.01 0.28 -0.8 -0.47 -0.28 -0.09 0.29 763 1.0\nbeta_locations[3] 2.91 0.01 0.31 2.34 2.7 2.9 3.1 3.54 602 1.0\nbeta_locations[4] -1.22 6.7e-3 0.18 -1.59 -1.34 -1.22 -1.11 -0.83 750 1.0\nbeta_locations[5] -0.72 4.9e-3 0.15 -1.02 -0.82 -0.73 -0.62 -0.42 939 1.0\nbeta_features_locations[1,1] -0.64 0.03 0.89 -2.48 -1.22 -0.59-1.5e-4 1.02 835 1.0\nbeta_features_locations[2,1] 0.69 6.7e-3 0.18 0.34 0.57 0.69 0.81 1.05 754 1.0\nbeta_features_locations[3,1] -1.08 6.2e-3 0.16 -1.4 -1.2 -1.08 -0.98 -0.79 662 1.0\nbeta_features_locations[4,1] 0.32 4.0e-3 0.1 0.11 0.25 0.32 0.38 0.51 633 1.0\nbeta_features_locations[5,1] 0.39 2.8e-3 0.09 0.21 0.33 0.39 0.44 0.55 918 1.0\nbeta_features_locations[1,2] 0.04 0.06 2.0 -3.77 -1.29 -0.01 1.4 4.08 962 1.0\nbeta_features_locations[2,2] 0.05 0.05 1.98 -3.68 -1.33 6.2e-3 1.37 3.8 1392 1.0\nbeta_features_locations[3,2] 0.05 0.05 2.02 -4.14 -1.3 0.03 1.32 4.19 1650 1.0\nbeta_features_locations[4,2] 0.05 0.05 1.94 -3.66 -1.36 0.06 1.39 3.73 1705 1.0\nbeta_features_locations[5,2] -0.04 0.05 1.88 -3.56 -1.4-5.2e-3 1.19 3.64 1406 1.0\nlp__ -1265 0.15 2.86 -1271 -1267 -1264 -1263 -1260 380 1.01\n\nSamples were drawn using NUTS at Mon Sep 17 20:42:59 2018.\nFor each parameter, n_eff is a crude measure of effective sample size,\nand Rhat is the potential scale reduction factor on split chains (at \nconvergence, Rhat=1)."
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true,
"ExecuteTime": {
"start_time": "2018-09-18T03:42:59.873917Z",
"end_time": "2018-09-18T03:42:59.876185Z"
},
"scrolled": false
},
"cell_type": "code",
"source": "# _ = az.traceplot(fit)",
"execution_count": 21,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "# Hierarchical Regressions"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Here, I build a Stan model that has a single bias term $b$ and two feature terms $\\beta_1, \\beta_2$, one for each feature. It then has per-location bias and feature terms that have a shared prior of the overall terms. The implied Bayesian model is as follows:\n\n$ y_i \\sim Bernoulli(\\frac{c_i}{1 + c_i})$, where $c_i = e^{b_{k[i]} + \\beta^{k[i]}_1 x^i_1 + \\beta^{k[i]}_2 x^i_2}$, $x^i_j$ is feature $j$ for the $i$th data point, and ${k[i]}$ is the location of the $i$th data point. \n\nThe hierarchy is encoded as follows:\n\n$b, \\beta_1, \\beta_2 \\sim Normal(0, 2)$\n\n$b^1, b^2, b^3, b^4, b^5 \\sim Normal(b, 1)$\n\n$\\beta^1_1, \\beta^2_1, \\beta^3_1, \\beta^4_1, \\beta^5_1 \\sim Normal(\\beta_1, 1)$\n\n$\\beta^1_2, \\beta^2_2, \\beta^3_2, \\beta^4_2, \\beta^5_2 \\sim Normal(\\beta_2, 1)$\n\n"
},
{
"metadata": {
"trusted": true,
"ExecuteTime": {
"start_time": "2018-09-18T03:46:37.586777Z",
"end_time": "2018-09-18T03:46:37.595278Z"
}
},
"cell_type": "code",
"source": "hier_model_str = \"\"\"\ndata {\n int<lower=0> N_locations;\n int<lower=0> N_features; \n \n int<lower=0> N_datapoints_train; \n matrix[N_datapoints_train,N_features] x_train;\n int y_train[N_datapoints_train];\n int location_index_train[N_datapoints_train];\n\n \n int<lower=0> N_datapoints_test; \n matrix[N_datapoints_test,N_features] x_test;\n int y_test[N_datapoints_test];\n int location_index_test[N_datapoints_test];\n\n}\n\nparameters {\n real beta ;\n vector[N_features] beta_features;\n\n vector[N_locations] beta_locations;\n matrix[N_locations,N_features] beta_features_locations;\n \n} \n\nmodel {\n\n vector[N_datapoints_train] exp_coef_train;\n\n \n beta ~ normal(0, 2);\n\n for (feature in 1:N_features){\n beta_features[feature] ~ normal(0, 2);\n }\n \n for (hex in 1:N_locations){\n beta_locations[hex] ~ normal(beta, .5);\n for (feature in 1:N_features){\n beta_features_locations[hex,feature] ~ normal(beta_features[feature], .5);\n }\n }\n\n for ( i in 1:N_datapoints_train) {\n exp_coef_train[i] = beta_locations[location_index_train[i]]\n + (beta_features_locations[location_index_train[i]] .* x_train[i])[1]; \n } \n \n y_train ~ bernoulli_logit(exp_coef_train); \n }\n \n\"\"\"\n\nhier_gen_quantities = \"\"\"\n\ngenerated quantities{\n vector[N_datapoints_train] exp_coef_train;\n vector[N_datapoints_test] exp_coef_test;\n \n for ( i in 1:N_datapoints_train) {\n exp_coef_train[i] = beta_locations[location_index_train[i]]\n + (beta_features_locations[location_index_train[i]] .* x_train[i])[1]; \n } \n \n \n for ( i in 1:N_datapoints_test) {\n exp_coef_test[i] = beta_locations[location_index_test[i]]\n + (beta_features_locations[location_index_test[i]] .* x_test[i])[1]; \n } \n }\"\"\"",
"execution_count": 47,
"outputs": []
},
{
"metadata": {
"scrolled": false,
"trusted": true,
"ExecuteTime": {
"start_time": "2018-09-18T03:46:38.088788Z",
"end_time": "2018-09-18T03:49:55.632227Z"
}
},
"cell_type": "code",
"source": "fit, opt = sample_and_evaluate_model(hier_model_str, hier_gen_quantities, data, iter_num = 1000)#(model_obj, data)",
"execution_count": 48,
"outputs": [
{
"output_type": "stream",
"text": "INFO:pystan:COMPILING THE C++ CODE FOR MODEL anon_model_6526bfde5dfabe37dede400f538211a6 NOW.\nINFO:pystan:COMPILING THE C++ CODE FOR MODEL anon_model_8e2f985f12bf0a6b3a82e892db884773 NOW.\n",
"name": "stderr"
},
{
"output_type": "stream",
"text": "AUC: 0.6664771315655343\n0 AUC: 0.38983322385812014\n1 AUC: 0.6828835167780737\n2 AUC: 0.7279458430294055\n3 AUC: 0.6399071185479296\n4 AUC: 0.5989979829526969\n",
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"text/plain": "<matplotlib.figure.Figure at 0x7f38d2e8f828>",
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\n"
},
"metadata": {}
},
{
"output_type": "stream",
"text": "Precision/Recall AUC: 0.6828306426710804\n0 Precision/Recall AUC: 0.45162685809994296\n1 Precision/Recall AUC: 0.7613896115935875\n2 Precision/Recall AUC: 0.8038319785664795\n3 Precision/Recall AUC: 0.4737270996663786\n4 Precision/Recall AUC: 0.5105431479712934\n",
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"text/plain": "<matplotlib.figure.Figure at 0x7f38daef2b38>",
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true,
"ExecuteTime": {
"start_time": "2018-09-18T03:50:03.594258Z",
"end_time": "2018-09-18T03:50:03.601675Z"
}
},
"cell_type": "code",
"source": "print(\"Location means\", location_means)\nprint(\"Location feature slopes\", location_feature_slopes)",
"execution_count": 49,
"outputs": [
{
"output_type": "stream",
"text": "Location means [-0.65495183 -0.63733907 1.04119405 -0.56687424 -0.5110217 ]\nLocation feature slopes [[ 0.57466563 -0.10446814]\n [ 0.85998957 -0.03824166]\n [-1.13315319 1.20645515]\n [ 0.39066619 -0.47314813]\n [ 0.41191865 -0.27377126]]\n",
"name": "stdout"
}
]
},
{
"metadata": {
"trusted": true,
"ExecuteTime": {
"start_time": "2018-09-18T03:50:07.225888Z",
"end_time": "2018-09-18T03:50:07.266151Z"
}
},
"cell_type": "code",
"source": "fit",
"execution_count": 51,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 51,
"data": {
"text/plain": "Inference for Stan model: anon_model_6526bfde5dfabe37dede400f538211a6.\n2 chains, each with iter=1000; warmup=500; thin=1; \npost-warmup draws per chain=500, total post-warmup draws=1000.\n\n mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat\nbeta 0.07 8.4e-3 0.26 -0.48 -0.1 0.06 0.24 0.58 986 1.0\nbeta_features[1] 0.1 7.6e-3 0.24 -0.39 -0.06 0.1 0.26 0.55 1015 1.0\nbeta_features[2] -0.12 0.12 1.91 -3.78 -1.44 -0.03 1.13 3.41 244 1.0\nbeta_locations[1] 0.14 0.02 0.51 -0.85 -0.22 0.14 0.51 1.1 999 1.0\nbeta_locations[2] -0.13 9.2e-3 0.28 -0.67 -0.33 -0.13 0.06 0.4 903 1.0\nbeta_locations[3] 2.06 8.5e-3 0.25 1.55 1.89 2.06 2.24 2.56 885 1.0\nbeta_locations[4] -1.07 6.0e-3 0.17 -1.4 -1.18 -1.07 -0.96 -0.75 776 1.0\nbeta_locations[5] -0.65 5.3e-3 0.14 -0.94 -0.75 -0.64 -0.54 -0.37 747 1.0\nbeta_features_locations[1,1] 4.1e-4 0.01 0.39 -0.7 -0.27 -0.01 0.25 0.77 932 1.0\nbeta_features_locations[2,1] 0.59 5.7e-3 0.18 0.26 0.47 0.59 0.71 0.95 953 1.0\nbeta_features_locations[3,1] -0.67 4.6e-3 0.14 -0.93 -0.76 -0.67 -0.58 -0.4 891 1.0\nbeta_features_locations[4,1] 0.25 3.2e-3 0.09 0.07 0.19 0.24 0.31 0.44 832 1.0\nbeta_features_locations[5,1] 0.34 2.9e-3 0.08 0.19 0.29 0.34 0.4 0.51 787 1.0\nbeta_features_locations[1,2] -0.13 0.12 1.96 -4.12 -1.42 -0.04 1.25 3.55 262 1.0\nbeta_features_locations[2,2] -0.13 0.13 1.99 -3.93 -1.46 -0.12 1.29 3.53 232 1.0\nbeta_features_locations[3,2] -0.13 0.12 1.98 -3.98 -1.38 -0.1 1.17 3.62 254 1.0\nbeta_features_locations[4,2] -0.14 0.12 1.96 -3.92 -1.4 -0.14 1.15 3.67 252 1.0\nbeta_features_locations[5,2] -0.12 0.12 1.94 -3.87 -1.45 -0.01 1.24 3.41 248 1.0\nlp__ -1283 0.16 2.99 -1291 -1285 -1283 -1281 -1279 358 1.0\n\nSamples were drawn using NUTS at Mon Sep 17 20:49:54 2018.\nFor each parameter, n_eff is a crude measure of effective sample size,\nand Rhat is the potential scale reduction factor on split chains (at \nconvergence, Rhat=1)."
},
"metadata": {}
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2018-09-18T03:50:04.629902Z",
"end_time": "2018-09-18T03:50:04.632491Z"
},
"trusted": true,
"scrolled": false
},
"cell_type": "code",
"source": "# _ = az.traceplot(fit)",
"execution_count": 50,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "In the hierarchical case, performance is slightly better than in the case with individual regressions, though not substantially. Note that the smaller the number of samples, the closer the location specific parameters are to the population parameters. For the first location, the parameters are especially close. Unfortunately, these population parameters are too affected by the idiosyncratic location, and so the hierarchical nature does not help as much as it could otherwise."
},
{
"metadata": {},
"cell_type": "markdown",
"source": "# Logistic regression using scikit learn"
},
{
"metadata": {
"trusted": true,
"ExecuteTime": {
"start_time": "2018-09-18T03:43:33.012317Z",
"end_time": "2018-09-18T03:43:33.102707Z"
}
},
"cell_type": "code",
"source": "from sklearn.linear_model import LogisticRegression",
"execution_count": 27,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## Single model "
},
{
"metadata": {},
"cell_type": "markdown",
"source": "This example from scikit learn is conceptually similar to the single model above through Stan. The performance here is near identical than that of the single model above, far worse than the hierarchical model as expected."
},
{
"metadata": {
"scrolled": false,
"trusted": true,
"ExecuteTime": {
"start_time": "2018-09-18T04:05:10.351555Z",
"end_time": "2018-09-18T04:05:10.739344Z"
}
},
"cell_type": "code",
"source": "logistic = LogisticRegression(C = 5000) # higher C is, the lower the regularization\nfitted = logistic.fit(features_train, y_train)\ny_pred = fitted.predict_proba(features_test)\nscores = [x[1] for x in y_pred]\n\neval_accuracy_from_scores(y_test, scores, location_index_test, n_locations)",
"execution_count": 53,
"outputs": [
{
"output_type": "stream",
"text": "AUC: 0.551492076664387\n0 AUC: 0.6126179528669154\n1 AUC: 0.672898761957414\n2 AUC: 0.2382236795712573\n3 AUC: 0.6513223199092456\n4 AUC: 0.6093597501463985\n",
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"text/plain": "<matplotlib.figure.Figure at 0x7f38d23f5668>",
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "stream",
"text": "Precision/Recall AUC: 0.5436297094682503\n0 Precision/Recall AUC: 0.6225539695079316\n1 Precision/Recall AUC: 0.7459995313574268\n2 Precision/Recall AUC: 0.49835908766093506\n3 Precision/Recall AUC: 0.4775693181986469\n4 Precision/Recall AUC: 0.5255223702342995\n",
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"text/plain": "<matplotlib.figure.Figure at 0x7f38db034978>",
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Compare the learned coefficients from those of the single model above, which had:\n\nIntercept: -0.27\n\nfeature1: 0.18\n\nfeature2: -0.07"
},
{
"metadata": {
"trusted": true,
"ExecuteTime": {
"start_time": "2018-09-18T04:05:45.431758Z",
"end_time": "2018-09-18T04:05:45.438617Z"
}
},
"cell_type": "code",
"source": "print('Intercept: {}'.format(fitted.intercept_))\nprint('Features: {}'.format(fitted.coef_))",
"execution_count": 54,
"outputs": [
{
"output_type": "stream",
"text": "Intercept: [-0.21377071]\nFeatures: [[ 0.17695821 -0.03546086]]\n",
"name": "stdout"
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## With group mean term, group feature terms, and per-location bias terms"
},
{
"metadata": {
"trusted": true,
"ExecuteTime": {
"start_time": "2018-09-18T03:43:33.518995Z",
"end_time": "2018-09-18T03:43:33.543947Z"
}
},
"cell_type": "code",
"source": "# Below function adds a column indicating that the given index belongs to a location,\n# for each location\n# this leads to both a group bias term, and a bias term per locations\ndef add_column_per_location(features, location_index, n_locations):\n features_new = np.matrix(features)\n cat_matrix = np.zeros((len(location_index), n_locations))\n for i in range(len(location_index)):\n cat_matrix[i, location_index[i]-1] = 1\n# features_new.append(cat_matrix, axis = 1)\n return np.concatenate((features_new, cat_matrix), axis = 1)",
"execution_count": 31,
"outputs": []
},
{
"metadata": {
"trusted": true,
"ExecuteTime": {
"start_time": "2018-09-18T03:43:33.546501Z",
"end_time": "2018-09-18T03:43:33.575569Z"
}
},
"cell_type": "code",
"source": "features_train_local = add_column_per_location(copy.copy(features_train), location_index_train, n_locations)\nfeatures_test_local = add_column_per_location(copy.copy(features_test), location_index_test, n_locations)",
"execution_count": 32,
"outputs": []
},
{
"metadata": {
"scrolled": false,
"trusted": true,
"ExecuteTime": {
"start_time": "2018-09-18T03:43:33.577927Z",
"end_time": "2018-09-18T03:43:34.010792Z"
}
},
"cell_type": "code",
"source": "logistic = LogisticRegression(penalty = 'l1', C = 5000) # higher C is, the lower the regularization\nfitted = logistic.fit(features_train_local, y_train)\ny_pred = fitted.predict_proba(features_test_local)\nscores = [x[1] for x in y_pred]\n\neval_accuracy_from_scores(y_test, scores, location_index_test, n_locations)",
"execution_count": 33,
"outputs": [
{
"output_type": "stream",
"text": "AUC: 0.6396521518637192\n0 AUC: 0.6123616206603759\n1 AUC: 0.6732840796770033\n2 AUC: 0.2386996685706227\n3 AUC: 0.6510209869540555\n4 AUC: 0.6090181534257272\n",
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"text/plain": "<matplotlib.figure.Figure at 0x7f38ed6ad128>",
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "stream",
"text": "Precision/Recall AUC: 0.584946971021798\n0 Precision/Recall AUC: 0.6219376411769291\n1 Precision/Recall AUC: 0.7465522967882838\n2 Precision/Recall AUC: 0.49858057863256655\n3 Precision/Recall AUC: 0.47656789679374933\n4 Precision/Recall AUC: 0.5246986734735412\n",
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"text/plain": "<matplotlib.figure.Figure at 0x7f38effe6780>",
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true,
"ExecuteTime": {
"start_time": "2018-09-18T03:43:34.013743Z",
"end_time": "2018-09-18T03:43:34.022018Z"
}
},
"cell_type": "code",
"source": "print(\"Group intercept term: \", fitted.intercept_)\nprint(\"Group feature terms: \", fitted.coef_[0][0:2])\nprint(\"Per location bias terms:\", fitted.coef_[0][2:])",
"execution_count": 34,
"outputs": [
{
"output_type": "stream",
"text": "Group intercept term: [-0.05428325]\nGroup feature terms: [ 0.18836694 -0.03662902]\nPer location bias terms: [ 0.23251732 0.52658842 0.87024963 -0.89855418 -0.3105306 ]\n",
"name": "stdout"
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### With various levels of l1 regularization"
},
{
"metadata": {
"scrolled": false,
"trusted": true,
"ExecuteTime": {
"start_time": "2018-09-18T03:43:34.025097Z",
"end_time": "2018-09-18T03:43:34.363459Z"
}
},
"cell_type": "code",
"source": "logistic = LogisticRegression(penalty = 'l1', C = 1) # higher C is, the lower the regularization\nfitted = logistic.fit(features_train_local, y_train)\ny_pred = fitted.predict_proba(features_test_local)\nscores = [x[1] for x in y_pred]\n\neval_accuracy_from_scores(y_test, scores, location_index_test, n_locations)",
"execution_count": 35,
"outputs": [
{
"output_type": "stream",
"text": "AUC: 0.6386122700926417\n0 AUC: 0.6122815168458322\n1 AUC: 0.6746578211120604\n2 AUC: 0.2411325012340456\n3 AUC: 0.6498511060692003\n4 AUC: 0.6084650920684496\n",
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"text/plain": "<matplotlib.figure.Figure at 0x7f38ebf2ab38>",
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "stream",
"text": "Precision/Recall AUC: 0.5839244744555006\n0 Precision/Recall AUC: 0.620366526444284\n1 Precision/Recall AUC: 0.7487562218281649\n2 Precision/Recall AUC: 0.49947137708987793\n3 Precision/Recall AUC: 0.47703240766797095\n4 Precision/Recall AUC: 0.5237892363003005\n",
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"text/plain": "<matplotlib.figure.Figure at 0x7f38effd59b0>",
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true,
"ExecuteTime": {
"start_time": "2018-09-18T03:43:34.365747Z",
"end_time": "2018-09-18T03:43:34.371766Z"
}
},
"cell_type": "code",
"source": "print(\"Group intercept term: \", fitted.intercept_)\nprint(\"Group feature terms: \", fitted.coef_[0][0:2])\nprint(\"Per location bias terms:\", fitted.coef_[0][2:])",
"execution_count": 36,
"outputs": [
{
"output_type": "stream",
"text": "Group intercept term: [ 0.]\nGroup feature terms: [ 0.18576991 -0.03263641]\nPer location bias terms: [ 0. 0.44819883 0.80094958 -0.94714714 -0.36192509]\n",
"name": "stdout"
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### With more regularization"
},
{
"metadata": {
"scrolled": false,
"trusted": true,
"ExecuteTime": {
"start_time": "2018-09-18T03:43:34.374863Z",
"end_time": "2018-09-18T03:43:34.747584Z"
}
},
"cell_type": "code",
"source": "logistic = LogisticRegression(penalty = 'l1', C = .1) # higher C is, the lower the regularization\nfitted = logistic.fit(features_train_local, y_train)\ny_pred = fitted.predict_proba(features_test_local)\nscores = [x[1] for x in y_pred]\n\neval_accuracy_from_scores(y_test, scores, location_index_test, n_locations)",
"execution_count": 37,
"outputs": [
{
"output_type": "stream",
"text": "AUC: 0.6421161081784397\n0 AUC: 0.611288229545491\n1 AUC: 0.6810239399574476\n2 AUC: 0.26399760242578096\n3 AUC: 0.6428140952921158\n4 AUC: 0.6010312967662177\n",
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"text/plain": "<matplotlib.figure.Figure at 0x7f38db10b940>",
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "stream",
"text": "Precision/Recall AUC: 0.5900760255864747\n0 Precision/Recall AUC: 0.6173399532328856\n1 Precision/Recall AUC: 0.7593078811009318\n2 Precision/Recall AUC: 0.5107337426209986\n3 Precision/Recall AUC: 0.47789191258733793\n4 Precision/Recall AUC: 0.5158493435402567\n",
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"text/plain": "<matplotlib.figure.Figure at 0x7f38effc7400>",
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"metadata": {
"scrolled": true,
"trusted": true,
"ExecuteTime": {
"start_time": "2018-09-18T03:43:34.750029Z",
"end_time": "2018-09-18T03:43:34.757435Z"
}
},
"cell_type": "code",
"source": "print(\"Group intercept term: \", fitted.intercept_)\nprint(\"Group feature terms: \", fitted.coef_[0][0:2])\nprint(\"Per location bias terms:\", fitted.coef_[0][2:])",
"execution_count": 38,
"outputs": [
{
"output_type": "stream",
"text": "Group intercept term: [ 0.]\nGroup feature terms: [ 0.15511223 -0.00663478]\nPer location bias terms: [ 0. 0.26345484 0.69552647 -0.8706795 -0.30972504]\n",
"name": "stdout"
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Especially with regularization, the model here performs almost as well as the fully hierarchical model. The coefficients are somewhat as desired -- there are (decently) accurate group feature terms, and the per-location bias terms for the location with only 5 samples is 0. For the locations with high number of data points, they are non-zero and in the correct direction."
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## With group mean term, group feature terms, and per-location bias and feature terms"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Here we build a model with parameters (both bias and feature) for each location, along with group terms. With regularization, the hope is that the low-sample locations will have near zero parameters and that the group parameters will be non-zero."
},
{
"metadata": {
"trusted": true,
"ExecuteTime": {
"start_time": "2018-09-18T03:43:34.760986Z",
"end_time": "2018-09-18T03:43:34.787691Z"
}
},
"cell_type": "code",
"source": "def add_column_per_location_and_separate_features(features, location_index, n_locations, n_features):\n features_new = np.matrix(features)\n cat_matrix = np.zeros((len(location_index), n_locations))\n feat_cat_matrix = np.zeros((len(location_index), n_features*n_locations))\n for i in range(len(location_index)):\n cat_matrix[i, location_index[i]-1] = 1\n for nn in range(n_features):\n feat_cat_matrix[i,(location_index[i]-1)*n_features + nn ] = features_new[i,nn]\n features_new = np.concatenate((features_new,feat_cat_matrix, cat_matrix), axis = 1)\n return features_new",
"execution_count": 39,
"outputs": []
},
{
"metadata": {
"trusted": true,
"ExecuteTime": {
"start_time": "2018-09-18T03:43:34.790317Z",
"end_time": "2018-09-18T03:43:34.851081Z"
}
},
"cell_type": "code",
"source": "features_train_local = add_column_per_location_and_separate_features(copy.copy(features_train), location_index_train, n_locations, n_features)\nfeatures_test_local = add_column_per_location_and_separate_features(copy.copy(features_test), location_index_test, n_locations, n_features)",
"execution_count": 40,
"outputs": []
},
{
"metadata": {
"scrolled": false,
"trusted": true,
"ExecuteTime": {
"start_time": "2018-09-18T03:43:34.855075Z",
"end_time": "2018-09-18T03:43:35.215760Z"
}
},
"cell_type": "code",
"source": "logistic = LogisticRegression(penalty = 'l1', C = 5000) # higher C is, the lower the regularization\nfitted = logistic.fit(features_train_local, y_train)\ny_pred = fitted.predict_proba(features_test_local)\nscores = [x[1] for x in y_pred]\n\neval_accuracy_from_scores(y_test, scores, location_index_test, n_locations)",
"execution_count": 41,
"outputs": [
{
"output_type": "stream",
"text": "AUC: 0.6502699594548561\n0 AUC: 0.47922908088883187\n1 AUC: 0.6828500108894139\n2 AUC: 0.8005606092659192\n3 AUC: 0.6446043675553035\n4 AUC: 0.615671156223567\n",
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"text/plain": "<matplotlib.figure.Figure at 0x7f38dafd40f0>",
"image/png": 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mf8DDr34cPVZy4Cokvw7TVs7QsiLyAtLhmtj6BHvQT/32bQTyQhSELuOI00b6o7FeT0nkSnLOoFaLoVt4KRLDgE8c36uBiTFrpgPzReQqoAQ41c2NRWQ6cAvA0KFD93Wfio+Il5EUDz+6kgA2bm/k/nuWUbajlrxAC9+PnhHknf8mEAgwpHhI4husgMeeeY3d215iT+0bAASC5QSCYwj1L8p+gXA25HOiLTZ6jJciIXGOmZjv5wGzjDEzRORY4HEROdQYE052Y2PMdCyBYfz48bH3VLKYbMpIctLY0sYdv/0TfapLGd7SDyihvrAJIY+A2P8r5dEnWJTsNuze9hLNDWsJR1pm9CmfSGnFZIDMGd7jBU6ByPGGfL2NlyJRDYxwfB9OZ3fSZcBUAGPMf0SkCKgAtni4L8VHdOql5HM3Uiwfbd3NKTMWcYp8wtE7xwJQV7CTmkHvM3JKAddOuCbp9bZLyWZPrfW/TtnAHIg3aMfWlOClSCwFxojI/sAm4Fzg/Jg1G4HJwCwRGQcUAVs93JPiA5IN+PGrGykRZzz+M04tHMKRm60OqutHrOeO6y/pFG9IhB1zsNNUc0IcbLRja0rwTCSMMW0iciUwDyu9daYxZrWI3AosM8bMAa4GHhKRH2O5oi42xqj7KEeJWwGdZW4lmxnLZvDM+3MpGLCF0RtuBqDtsM3cfUX3f7mHBlRw+e9n9vYWMxtndbRmKXmKp3USkZqHuTHHbnZ8XgMc7+UelMwnnjhkozA4mfPh89S2bsO09kVMH0L9i7jwivPSvS3/oNXRKUMrrpW0k63B6His2LiT8566iYIBmzGtfcnbdBNDSkvTvS1/EFvroNXRKUFFQskIsikYHY8fPDedV2qs9ysYsMv6Z/OR3Dl2JB8u/pRQ/+RZSwodYxBa65AyVCQUxQPWbd3NuQ++zrihZfxn3TYKRr1kFcK1lkFrPy44/CxO+OSrvP1vKyunu+mpzkK5rOutFA+NQaQNFQklLWTzDIfX1m3j/IesQrat9VayXgGQF+7L0ksWUBAMsOTpqqhA9KQK2ikQWddbySZeiqtaDylHRUJJC87eStmU1vqdR97glQ+3Rb9fPm01iza9yJbGegYXD6YgaDXeW7fcqmfYlzYZWZ3VFFs9rSmuaUNFQkkb2RSH2NHQwncfXcqKjbuixz741Rmc9Y/fsaVxS4e+SkuerqJ+R1OP2mTkjJvJtiC0OC7tqEgonpGsCV82uZh+++IH/PbFD6PfR/YvZvFPT2bGshnUNNRQWVLZoTOrbUV0Jw5hi0Pd1o4V1VmNZi9lBK5EQkQuAsYaY24UkVHAEGPM615uTPE/idp1g/8rp/+zbjvfe246FK8kbEy0fXdFaSFF+Xmc/tQd0WE/zs6sNt2xIhbNnsmyfz0DZHFFdWz31njzHpS00KVIiMj/YvVg+jxwI7Aba5BQbEdXRelENrmUwJoKN/Wxa9nJMgLllmspr60/eQFhUKiww9rKkkqmjJrC1eOv3qdn2r2ZsnrOdOwgIE1xzRjcWBKnAkcCKwCMMdtEpI+nu1KUDGRrfTNfuO1FSg5cFkln7cf5h36ZG475qavrlzxdxbrlW9i9q5nSvoVdrnfGH7J6zrSmt2Y0bkSiyRhjJNKuWEQCxG8DriiAo81GFsUdlm/Ywdfv3zs2dEDRQBad+2K37uEUiETxCGdX15yIPzizmNRyyEjciMQaEfkWICIyHLgeWOLtthS/svmuu9kx00rLtNts+J0dDS2c//TPKTlwFQDBwnqKgsXduoczo+nC24+LuyY29pC18QcnmsWU8bgRif/BikEMB1YCc4Afebkpxb/Y2Uz9L700o3swzVg2g/nr5ydd0xY27GhooaWtPdpKY2jJUITiuMHoZHSV0eQUiKyOPYD2YPIZbkSi3RhzifOAiJR4tB/Fp3RwMVVWZrRAAMxfP5/NjZsZXNzZHWYw7GlpZ0dDS/RYuKUvZ40+gztOvqHHz4zNaIrnWsoagUg0axp03rTPcCMSrwBHuTim5DDOGEQmu5hsC8IWCGf9wspPdnHD399hdU1dh2sWXHMS+1f07t+Lst61FJut5ESrp31FQpGIBKiD1kfJZ2+wuhzonkNWyQkyPd11xrIZzFo9C9ibngqwcXsjb32yk/9+4u0O63986lguOWEUZUX5ru5vZy/FIzajKWvTWm0LwhYIzVbyPcksiZ8Dt2BNjGuKHBOgAZjh8b6UDCZeJXWmZjI5Yw92cdvFh1wcrV24b0EVd897v8M1sy+byPGjB2Bn9LklWXprvIymrExrdQqEupGygmQicStwG/B74Ar7oDGm3etNKeknaUuNmLnTkHkV1LY42MJQWVLZqbht/baGDgLxo8ljuOz4/Skvdmc5OHGTvQRZ3HtJLYisJaFIRGZNtwE/SN12lHQRKwrxhMAmUyfIxbMaElU9L6naxrcffiP6/aPbzyQQ6H75j+1iqt9hGdtd9WPKqhbf8Vp52/EGJWtw05bjUOB+rLYc0fFZxpgCD/elpJjY4rdMFYJYEglDspYYd89by30L1kW//+0Hx7oSiHgxB1scQv2LOPDoQQmzl6LrIwLhqxbfiTKVnMKgweisxU120x+wXE93A1/Ccj3tSnqF4htiq6MzOfAcD2emUle9kua+8yn/9acVHY6t/sXplBS6a4YcL+YQTxygc/ZSdL3fLIjYuQ5OVBhyAjf/d/QxxswTkf81xnwCXCciC4A7PN6bkgL8kroK8Qvg4qWyxlLX1Mrh0zteN2nsQB69dEK391DatzBpzAGyqDDOKRBaEZ2zuBGJ1sg/d4rIYUA1MMqzHSkpY/Ndd9NaU0N+ZaUvLIh4BXDOYT5OjDGsqq5l2n0dO8gMDBXyzyuOp7Kvdz0qfZ/earuXbHeSCkRO40YknhKR/sCdwGtAAMv9pPiUqIspEpzOdAvCSVdWwzvVtdz78ofMX7O507k/f3cix41OnFGUrM4BOtc6JMN36a3JgtAqEDlNUpGIFNQ9Z4zZATwnIgOx3E87U7I7xROc7TP8EJwGOkx5S8TDr3zEr557r8Oxkw4ayPSzDmFUFxXTS56u4u1/W78cQ/2L4q5J1r3V9zjTV1UcFAdJRcIYExaRx4Dxke9N7C2sU3yM34LUdiwiUWO9tZ/VRQUiLyC8fv1kBobc/a3fKRBHnDay23Onswatb1Di4MbdtFZERkSC1oqSNipLKuNmLsVmLVXddobrauneEIjYVNesK5RTcho3ItEXeEdEFmONLgXAGHO+Z7tSlAixDfmctLWHuX3uWmYu+Th67MMUCUSi4UDgwzRXRUmCG5H4e+RHUVKOUyCcrqZ4PZc+/vWZKROIrOjgGjvXIV7HViXn6VIkjDGPpGIjihKLM1jtzGhavmFHB4G4+5zD+dpRw7vVkM/OYtoXgfBtiquNM1itDfmUBLgrNVV8Tae+TBnasTUWZ7C6Ztce5qys4ZUPt7Kkant0TXesBxtnM77uxiB8XwMRiwarlS7wVCREZCrW6NM84GFjTKcqbRH5JjAdqyX5So119C6xM6ch8zq2JmNgnyHc+9TnuJeXO51b+8up3RYI6HqUaCIWzZ5J3dYt/quBUJR9wDOREJE84D7gNKwq7aUiMscYs8axZgxwPXC8MWaniGRpEnr68MvM6Vhe/XAbOxpaaGxp63D8F2cfwphBpRx7oDXvoasCuHjs3tW8T1aEL4LSycaH2mgcQnGBK5EQkUnAOGPMA5Ff5CFjzLouLpsAVBljPorc4wlgGrDGseZy4D67OM8Y073/25VOxHUt+WDmNEB72PDXpZ/wiyV3EgytQvLrgDIAXv3ZyQzvZw1EXPJ0FY/P/ADo2IXVLT0pivONFRHbUiO2KZ8TjUMoLnDTKvwa4KvAIOABrHbhs4ATu7h0GOCsragGJsasGRt5xhIsl9R0Y0z8STcd9zQda2oeQ4cO7Wp51tGdgUB+cS29u6mWL9/7KoWD5lIwYDEAgfb+HDfsFO67bO+sh9jK6ERdWHuDeGmuGWtFxBMHrZpWegE3lsR3sCqu3wQwxmwUkb4urovnLDZxnj8GOAkYDrwiIocaY5K2IjfGTMeKYzB+/PjYe2Y9sbMfnPip1YbNnS+s5f6FlmEaDK0C4GsHfJtfnHhdp7U9zUrqLr5Ic9V+S0oKcCMSe4wxrTEBwrCL66qBEY7vw4GaOGteN8a0Ah+LyPtYorHUxf1zDr/PfojH5rom7l+4jsJBcwmGVpFfWM/g4sq4AmHTk3hCMuINB7Ith4zKYoqNM+jQHyUFuBGJahE5BjBiKcXPgPe6uAasX/RjRGR/YBNwLhCbufQP4DxglohUYLmfPnK7+VzA6VpyupL84EJyw8TbLaHbKxDxW397gS0OsRXT9ueMsxycdQ2gwqCkBDci8SNgNnAo0Ai8jvULPynGmDYRuRKYhxVvmGmMWS0itwLLjDFzIuemiMgaoB241hizPfFdcw+n5eBHV1Iibn/9Lv787rOUHGh5C4MRgUjWBtxZ39Ab2POmM1IQbOJVRWtdg5JC3FRc1wCniEgICBhjat3e3BgzF5gbc+xmx2cD/CTyoyQgW1xLTp5aOxeCtdBaRklhkH5dWBDOgHVvtuvO+HnTWhWtpBk32U3zgUeAfxhjmr3fkpILNLe2A2X84qi/cM7Rwzuci1f7YKe65mQrb7UelDTixt30/4CLgd+JyDPAH40xb3q6KyWrcM6mbgsbave0IPl1mNYyvnZk52Kudcu3dJoC19uprs66h4xl/k1WcDpZrYOieIwbd9M/gH9EptJ9G3hARAqMMYd6vrscJjaTyY/Y4lDTYAXcwy3OzOkyRhYd26H+wbYebIG48PbjPNmXM701o+seXrvX+qwuJiWNdKctRwvWVLoWoNyb7Sg2ToHwYybTjGUzmLV6FgAlgYHs3DqO5i1nAjCifx9+NvVzfOmwvYWQTuuht8eExqa4ZkR6a1dtM+z01uOu0uwlJa24iUmcgeVuOhl4DrjOGLPQ220p4O+A9bz1VpZSy/YvUh8RB4D3fzWVwmBe3Gv21XqIV+8AnYcCpTWbyW3bDE1vVTIEN5bEz7DacFxqjGnwdju5TYeaCB+7mZpa29m0cw/QN2o9XHjsfvzi7EM6dW213UyxMYjukKzewf6eMSmudraSioDiE9zEJE5KwT5ymmj8wVEs50c30/zVn3HVvF9Gm/OZ1jIWXHMS+1eUJLzGKRA9cTHFts/IGDFIhmYrKT4ioUiIyO3GmBtE5C907rmkM657kWj8wafFcuGw4cS7FrCt4Olocz7T2o+vHXRGUoFwFsf11M2UdUOAFCXDSGZJ2GmuL6ZiI7mIn3sx2ZlLbWHDZ7V7YAAUFFh9GS8cdxHXTrgm4bW2i8mufdjXIHXGt+9WFB+TUCQiqa9gzYRY5DwXmS+h7CN+zmCav34+mxs309ocih7rVzCYaWPO4OrxVye91nYxednmO+OwA9Y66EfxGW4C178BjnJxTHGJny0IsKyImoYahpYM5YM1/w3AmzdOZlCoe4N/euJiis1gqt++jdCAim7fJ+U4BULrHhQfkSwmcQAwGgiJiLOpTjlQ7PXGshk/WxBAtHp6w8YxAIQKg64FoqdN+hJlMIUGVGR2QZw251N8TjJLYhJwKTAE+LnjeB3wUy83lQv4yYJwttUA2Ny4mT5SEa1/ePii8a7vZVdVdycO4csMJtDmfEpWkCwm8UfgjyJymTHmkRTuSckw7PjD4GKrbqNPoD87towD4JDKMiYeMKDLezjrIdwMDYo3OtSXGUxqPSg+J5m7aaQxZiPWSNGxseeNMR94ujMlo7BnPTy9vJqr/7YSgOKCPJ77UfJR57GZTHawuivsWQ+hARX+sh4UJctI5m76A3AGEM8nYgBtTZljPP/Op1GBAFjx89O6vGZfMpkyftaDouQAydxNZ0T+OSLRGqX7bL7rblprasivrEz3VhISLwZRFOjPD/+0Inrs41+f2anFRiK6m8nkizbeipIjBLpaICIHikhh5PNkEblGRPp2dZ0SH7s3UyZnNdkxCJvS4AB2RmIQAGt/OdW1QPQEOxaRsVlLipJDuKmTeAqYKCL7ATOx3E+zgK94uK+sJr+yMiNbb9gWhB2k/vvZz3HwzR0Jyxo2AAAeJklEQVRnTnfHgugJTivCtzEILZxTsghX8ySMMS0i8mXg/xlj7hSRlV1epGQ8sW4lezhQZUklp4w4tYNAnDpuMA9+52jPBSLjhwG5QQvnlCzCjUgUishg4CzgZo/3k7Vk4qS52NTWypJKpoyawviyC7lo5t4JtbMu+QInHeR9fMD3zfpiLQhNfVWyADcicS+wDnjJGPOmiIzCKqhTXBCvDXi64xGxbqV55+y1GGa++jEXPbVXIO78+mEpEQgbX7mZYqfLOQcJqQWhZAlu5kncD9zvOPQJMCXBciWGTGsD7hwralsONjf+/R3+9MbG6Pe1v5xKUX78KXK9jS8zmmLjDjpISMlC3IwvFaz2HKdi1Ue8CPzR431lFZnUgsOOQVx8yMUdurU+ueyTqECcOKaCxy6dsM/xBzd9mmJ7MvkmFjH/JstyKB+pbiUlq3HjbroDmICV0QRwITAWuM6jPSkeU1lS2UEgvnLfEt7+ZFf0++OXTeyV57jp02RXVvuiqtrpXrJdS+pWUrIcNyJxBnC0MaYVIDKpbhkqEr4iNg4BsKWuiQm3d7RwPv71mb36XDd9mjK2sjpZzEFdS0qO4EYkAkC743s74F0epOIJToGw4xDfe3x59PzlJ+7PjV86uFee5WzmV9q3sNN5Z/O+jJ0HMf8meO1e63P5yL3/VGFQcgw3IvFv4DkR+SNWTOISYH7yS5RMSHl11kHEy2SyXUwvXz2JAwaW9tpznQJhu5ridXUtGzgoc+dB2BbEcVepKCg5jRuRuAb4L+B8LAtiLh2znZQ4pHOwkC0OzuI4pwUBsGnXnujnngiEbS3EwxaIC28/jkWzZ/LQlbd3EIaMjz84g9IqEEqO4yYFth2rVuJe77eTXaQjqyleimvszOk/vbGBG//+LgDD+vbp0XOSuZOcFoSvAtPQ0c2kQWlFcZUCOxj4LXAylrtpAfBjY8zmpBda104F7gHygIeNMXckWHcO8DfgC8aYZe63nxnYriUn6XIzJUpxtWlrD0cFAuCv3z/G9b2d1oPTWoDOs6fXLLB+7JhDRgamndhBajs4rW4mRQHcuZseB14HfoblbrokcixpQZ2I5AH3AacB1cBSEZljjFkTsy4E/Ah4o9u7zxDixR5S4WaK7b0EVuwhNsXVybcefD36ef0dX3L9rCVPV/H2v61foKH+RR2sBeg4JMhJxsYcnMQGqTU4rShR3IjEIGOMs2fTdBF528V1E4AqY8xHACLyBDANWBOz7pfAXVixD18RG5xOtWspNqUV6BR7cDLt96+ysroWgO9POiB6PFl8wcaeLHfEaSMTprT6wmKIxSkQaj0oSifciMQ6EdnfGPMxQKR3U+wv+ngMw2rhYVMNdKjSEpEjgRHGmGdFxLVIiMh04BaAoUOHur2s10lncNomNmMpEU2t7VGBOO7AAVx/xt75EMniCwC7t71ES8NaggUB1izIZ82CzmsyNpW1KzSLSVGS4kYkSoB3RGRR5PsXgQUi8mcAY8z5Ca6LV0thoidFAsBvgItd79a+iTHTgekA48ePN0kXe0wmtdxIxlPLqwEIBoQ/X945DhE7Pc4ZY9hTa1kZRf0SV077wq0EnQvk6jZpFpOiJMGNSPwt8mPzjMt7VwPO0afDgRrH9xBwKLAw0iNoCDBHRM72Y/A6kwmHDTf9wwpWn37IEFfXOGMMvslM6op4BXI680FRkuImBfaRHt57KTBGRPYHNgHnYtVa2PetBaL+CRFZCFyjAtF7LHm6iqrlm9m0cw/fw3IlVa7ew2M3vNZhXSJXky9jDMlQ15KidJsuZ1z3FGNMG3AlMA94D3jSGLNaRG4VkbO9eq6yl3XLt1C3ozn6vV9JAYE4XsDYTKWsRAvkFKVHuBpf2lOMMXOxKrSdx+JOtzPGnOTlXrKNGctmUNNQQ2VJZdzzdpvuegnzYHkz95x7BNOOyKF5y4ma86lrSVG6haciofQ+sS034qW7GmNY+Zp1/v2CdsqKgt0SCF8OAIL4rby1OZ+i7BOuREJEJgHjjDEPiMggIGSMWeft1jKbzXfdTWtNDfmV8f8m7xV2bUSilhsAF858k3GNLSCwqE8b66e7L5qDvbOmfZGt5MQ5KU5FQVF6BTdtOa4BvgoMAh4AirAGEJ3o6c4yHLsNRyrrI5wupkS1EVvqm3jlw22Mo5Ci/Dze+vlpPXqWr2ZNOykbppPiFKUXcWNJfAcYD7wJYIzZKCJ9Pd2VT8ivrPRsZnW8lhvJXEw2E257iUl7gpSbAKHSQvqVFHiyP0VRcgM3IrHHGNMaM+847NF+cp54bb5tkrmYABZ/sBWAg1rygORjQ7MKOxZhu5oURek13IhEtYgcAxixlOJnWCmtige4iTkk4pH7VvC9lkLKCLgaG+rEF9PiYont3GrHIRRF6TXciMSPgNlY1dGNWB1hz/VyU5lMKibOue3H5ORfj65mQnM+ACX9ul/34Kyw9kWLDe3cqigpwU3FdQ1wSqSldyBSKZ2zeNnUL7b2wU131ui+Il1aPxkU5I5bj+/Wc50pr76psNbqaUVJCW6ym6bEfAfAGJNTc65T0RbcDlTbgemuurM6aS0K8JZp5qxpY7v9XF+lvDrjD1o9rSie48bd9HPH5yLgMOBtIKdEIlVtwWMHBsV2Z43HQ4s/4rdzrTDRdZVlrp9lxyHs8aK+SHl1Bqg1/qAonuPG3dShHkJEDgOu8mxHGUwq24LbbTVC/YuSrmsPG26buzeP4HNDQl3e2xaHuq2WK8vu8uobtBZCUVJGt9tyGGPeEZHPe7EZZS92LCJZALquqZXDp1sGXUDgo193rqyOnT0NdBKHjLUgYvsvgaa5KkqK6W5MIgB8wc11ijucRXP7rZnAmB1H8diK19i9q7nLNNbH/7Mh+vnCY0d1Or9o9kyW/csa/+Hsw+QbcYjtvwTqZlKUFNPdmEQbsA74pjfbyT2cc6rH7DiKPs1lUOyuffcTS61ford99VCGf/gSD105o8N522IYf9bXMlcQ4uEMTGtqq6KklaQiERkxepsx5oUU7ScnGVw8mJvlPt5u2kiof1GXgWqbT3bsAeBrRw5n9uOvdiqCy3iLwYnTtWS7lDTuoChpJ6lIGGPCInIzoCLhMW5iEDat7WHG3Ph89PubTz7qvzoHm3iuJXUpKUrG4MbdtEJEjjbGLPd8NzmO21YaKzbsjH7+78lj+OCfTwM+qXOIRV1LipLRuBGJY4Dvi8h7wG77oDHGnU9EiYsdsLbjEd3hpbWW1XHZCfvz49PG8tA/fdzaG9S1pCgZjBuR+Jnnu8gxZiybwazVs4C9nV1Z4e7aptZ2Hlz8EQCHdKNwTlEUpSckFAkRecQYc5kxJjXVYzmEnfJ68SEXR6urH3vmtS6vM8bwuZ/vDQ8dP9oHnVoToe29FcUXJLMkjkzZLjIUu18T0OtdX2Pbb7jhnU17eysuvOYkBpclr8bOWOJ1cFUUJSPRorgkOPs1ed2zyQ3vbqoD4BtHD2dURUmH3ku+mP9gox1cFcU3JBOJw0QkXp9qAYwxJifGnu1rv6Z4Y0hjg9Vu+zTd8HcruDuk3FrnFAjfZTZpB1dF8QXJROID4MxUbSQbiQ1Q2wwuHsyZmy/msRusOIQ9CyJZjcSG7Q3Rz+dN2NumIjSgwj+1ERqHUBTfkUwkmo0xG5KcV7ogXoDa5rEbXovOigj1L+LAowclrJEwxvC9x6wylaHlRXz47F94zq9uJm3zrSi+IplItKRsF1mGswYiNkBtT5uzBaKrFhzNbe0cdNPejKaf9v+AZf96DvBRi+9YC0JrIhTFNyQUCWPMMancSDbhLJKbMmpKhzGktmvJth664j/rtkc/X933fT56+WXAJ0374rXcUAtCUXyFZjf1IrFV1PPOmQdYNRBuXUuxzH7d+gV7eeG7tLz1CuAjgYhNc9VAtaL4DhWJXiTWgnDixrUUj/c+tdJeyze/RzM+FAhNc1UUX6Mi0cs4LQhwn94aj+27m9m0y2oHXpAXoNAP/ZlUIBQlq1CR8JAlT1fx9r8td5Gb+EMs67buTXsV6bVt9R7xxova8QcVCEXJCgJe3lxEporI+yJSJSLXxTn/ExFZIyKrROQlEdnPy/24ZfNdd1N1ymRaN292fc2MZTOoaajpcMwOVh9x2kjXMYhYjt/xGldtfYL67dt6dL2n2BlLTspHqkAoShbhmSUhInnAfcBpQDWwVETmGGPWOJa9BYw3xjSKyA+Bu4BvebUntzjbcXTVisMOVtsCERuLcDsjIh7v/H02R9WutO6TqemumtKqKFmNl+6mCUCVMeYjABF5ApgGREXCGLPAsf514AIP99Mt3LbjcNZDTBk1pVtN++zeS4mwZ1QHP38yl9/QvWaAvUY8l5KNVk4rStbjpUgMAz5xfK8GJiZZfxnwfJLzUURkOnALwNChQ3u4vd4jNljthkWzZ7LsX88AVlFcLO1hQ10wRFXJATzwkx/1yj67TWwaayxaOa0oWY+XIhEv1GriLhS5ABgPTHJzY2PMdGA6wPjx4+Pe00ucTft6MlkOiFoQiVJa73xhLY8uXEeoMEioKH/fNtwTNEtJURS8FYlqYITj+3CgJnaRiJwK3AhMMsY0e7ifXsNZDxGvJgISp74623snGjna1NrO/QvXAfC/3/y8Ny8RD6drSbOUFEXBW5FYCowRkf2BTcC5wPnOBSJyJPAHYKoxJl5b8pRiDxlKNmDIzmKqLKlM6mKyM5tiU1/dtPd+4s2N0c+Txg7s7mu4I1n6avlIrZJWFAXwUCSMMW0iciUwD8gDZhpjVovIrcAyY8wc4G6gFPibWIUAG40xZ3u1p65IltXUVRaTE9uKkPAS1iyYzRpHeN4WiGTtvf+2vBqAX33lUIry8/bhjeIQr5+SjQqDoigxeFpMZ4yZC8yNOXaz4/OpXj6/JyTKanKTxWQ38qvf0URr4yLam5ezh46BaTcDgkoKrT+W0w8Zsm8vEw+7tkEFQVEUF2jFtQu642LauWk+4bYPCbdZ86i722tpR0MLb368A4C+xT0MWLtJW9XaBkVRXKAi4QI7k6krF9OO6nm0N1vDgexZD93ttbRu6+7o5/y8HhbEJ5v+pmmriqJ0AxWJJCQbHuRk0eyZrHj+pR5bD04Wf7AVgEuP379nm7ZRa0FRlF5ARSIJyVp/2ziL4gLBco46Y3KPBcIYw70vVwFwcGWZy03GcS1pJbSiKL2EikQXJKumdgpEn/KJlFZMZtIF3Z8ZYXPZo8uin4/er5+7i+K5ltSlpChKL6EisQ/YVdN9yidCsOfN9+qbWjn6ly/S0h4G4NrTD2L/ihL3N1DXkqIoHqEikQBnRlM8Fs2eSd3WLQSC5RA8gdK+hT2aGdHU2s5h0+dHv3/58KFccXKCrrHqWlIUJcWoSCSgq4wm24ooLPlcj0eTAqz9rD76+c+XT+S4AysSL1bXkqIoKUZFgsTtOJJlNAEUFPfDBI7fp2d/VtsEwPe/eEBngYi1HLTGQVGUFKMiQfeGDNkN+uq2bQMppaiwZ6NJwXI1/WC2VVfR0NLWeUGs5aBWg6IoKUZFIoLbIUN2g75AXgjJG92j0aRVW3bz7zWbufOFtdFjN5w5ruOi+TdZ/ZXKR6rloChK2lCR6AGhARUUln0XoEejSU/9v0Udvv/58okUF8T8UdhuJrUcFEVJIyoSQG1LHU1te7jiqdOjxxINE2pqaKWtJUxruJnSvoXdftZ3HbUQ95x7BIdUljF6UGjvAjsOYTfh0wZ8iqKkERUJoKltD+2mvcOxeFXWi2bPpKVxJxIo63HK64vvbQbgR5PHMO2IOKmrzjiEWhGKoqQZFYkIeZKXtMOrs7q6KDSu2ymv/1pZw1V/eQuAEf378JPTxu496cxi0gwmRVEyCBUJl9h1EXmFR1NaMdn1dfVNrR2K5QD+Z3KMQNizpMtHqgWhKEpGkdMiMWPZDIoe+Cun7GpnR9/kE+CaGlohUEZ+8STXbqZ1W3fz4j3f49XCNwAIBgIMLitEFgssjizSWdKKomQwOS0S89fP58Z3rIrnXccdHHeNPW2upcmKWbhJed3d3MZ3HnmDqTX38f3gcwC0hYYTDMSZD6ET4hRFyWByWiTAikXkVw7mS//3ZNzz65ZvYfeuZgIBIVgQ6FIgmlrbOfSWeVwf/FNUIFonXkn+Gbf1+t4VRVG8JudFwhVtrxJuq6WoX4ybKaZtRhjDtl17eLUQhss26+BxV5GvVoKiKD4lZ0Vi8113c/Mzmyiva4fijudsFxPA7l3NNDdYldFjj4lpBx5JVzVllWzd3UxLWzh6qrl0GIWHf03dSIqi+JqcFYn6F16gvK6d2rI8Bsb0a1q3fAs7N80n3PYhAOH2esoGDoo/ca5sGCe1/Y4NDY3RQ89edQLDh5V7un9FUZRUkLMiAVBblset1wxj3jnXdjoXbvsQE64nNKACKOxsRUSoa2plQ60lEOdNGMmvv3aYl1tWFEVJKTktEl0RGlDB5b+fmfD85vomWiPT5D4/vFwFQlGUrCNOTqaSjKbWdm6f+x6jrnsuKhAA/7hi3+ZKKIqiZCJqScSwaPZMtm94iXBbHTAQgLb2MP9aVcOP/7oSgOuDf+LVwjcYwg7qCgax/hdfSuOOFUVRvENFIoZVLy0g3FZHIFjG2GNO4JkV1fzkyZXR8876B1M+gv6HfCVdW1UURfEcFYkI9sS5lsZdEAix36nXc9G7G7h+7U+jbTWKC4L0b/3MuuC4qxBNb1UUJcvJWZGobamjzdEePDpxLlhGfsEoJqyf1qEozpSPQBBA22goipI75KxINLXtAWDK+gN48IpLqd++DSOl9AldQiiwjeHye6pNBeGyEQQO/YpaDYqi5CSeioSITAXuAfKAh40xd8ScLwQeA44GtgPfMsas93JPUUyYzZVfJbB8HfXhRgiECOaPIRTYxoFFr/EoZ3Hh9McRkZRsR1EUJRPxLAVWRPKA+4AzgIOB80QkttXqZcBOY8xo4DfAnV7tpxPGsKvvkRgCBALF9Ov3FQ4d1My3DriViVNCXDR9tgqEoig5j5eWxASgyhjzEYCIPAFMA9Y41kwDpkc+PwX8XkTEGGM83Bdt7WGWHfAF2pr+jgnvpj5Yyj1lJVTddjvBvDu6voGiKEqO4GUx3TDgE8f36sixuGuMMW1ALTCgqxuLyHQRMSJiampqur2x2j2tGEBMO62BUg465ng+/vWZBPO0tlBRFMWJl5ZEPF9NrIXgZk3nBcZMJ2KBjB8/vttWR9/iAr563/30KchjUKiou5criqLkDF6KRDUwwvF9OBD71357TbWIBIFyYIeHewIgLyDsN6DE68coiqL4Hi/9K0uBMSKyv4gUAOcCc2LWzAEuinw+B3jZ63iEoiiK4h7PLAljTJuIXAnMw0qBnWmMWS0itwLLjDFzgEeAx0WkCsuCONer/SiKoijdx9M6CWPMXGBuzLGbHZ+bgG94uQdFURSl52g6j6IoipIQFQlFURQlISoSiqIoSkJUJBRFUZSEiN8zTkVkK7Chh5dX0rl2I9vRd85+cu19Qd+5u+xnjBnoZqHvRWJfiLSJyqkufvrO2U+uvS/oO3uJupsURVGUhKhIKIqiKAnJdZH4Rbo3kAb0nbOfXHtf0Hf2jJyOSSiKoijJyXVLQlEURUmCioSiKIqSEBUJRVEUJSEqEoqiKEpCVCQURVGUhKhIKIqiKAnJCZEQkaki8r6IVInIdXHOF4rIXyPn3xCRUanfZe/h4n1/IiJrRGSViLwkIvulY5+9SVfv7Fh3jogYERmfyv15gZt3FpFvRv6sV4vIn1O9x97GxX/bI0VkgYi8Ffnv+8x07LO3EJGZIrJFRN5NcF5E5HeRfx+rROSoXt+EMSarf7BGp64DDgAKgJXAwTFr/gt4IPL5XOCv6d63x+97MlAc+fxDP7+v23eOrAsBi4HXgfHp3ncK/pzHAG8B/SLfB6V73yl45weBH0Y+HwysT/e+9/GdvwgcBbyb4PyZwPOAAMcAb/T2HnLBkpgAVBljPjLGtABPANNi1kwDHo18fgqYLCJ+bRbW5fsaYxYYYxojX18Hhqd4j72Nmz9jgF8CdwFNqdycR7h558uB+4wxOwGMMVtSvMfexs07G6As8rkcn3eGNcYsBnYkWTINeMxYvA70FZGhvbmHXBCJYcAnju/VkWNx1xhj2oBaYEBKdtf7uHlfJ5dh/U3Ez3T5ziJyJDDCGPNsKjfmIW7+nMcCY0VkiYi8LiJTU7Y7b3DzztOBC0SkGpgLXJWaraWN7v7/3m2CvXmzDCWeRRDbi8TNGr/g+l1E5AJgPDDJ0x15T9J3FpEA8Bvg4lRtKAW4+XMOYrmcTsKyFl8RkUONMbs83ptXuHnn84BZxpgZInIs8HjkncPeby8teP67KxcsiWpghOP7cDqboNE1IhLEMlOTmXiZjJv3RUROBW4EzjbGNKdob17R1TuHgEOBhSKyHst3O8fnwWu3/13/0xjTaoz5GHgfSzT8ipt3vgx4EsAY8x+gCKhIye7Sg6v/3/eFXBCJpcAYEdlfRAqwAtNzYtbMAS6KfD4HeNlEokI+pMv3jbhe/oAlEH73U0MX72yMqTXGVBhjRhljRmHFYc42xixLz3Z7BTf/Xf8DK0kBEanAcj99lNJd9i5u3nkjMBlARMZhicTWlO4ytcwBLoxkOR0D1BpjPu3NB2S9u8kY0yYiVwLzsLIjZhpjVovIrcAyY8wc4BEss7QKy4I4N3073jdcvu/dQCnwt0h8fqMx5uy0bXofcfnOWYXLd54HTBGRNUA7cK0xZnv6dr1vuHznq4GHROTHWG6Xi338Fz5E5C9Y7sKKSJzlFiAfwBjzAFbc5UygCmgELun1Pfj435+iKIriMbngblIURVF6iIqEoiiKkhAVCUVRFCUhKhKKoihKQlQkFEVRlISoSCgZh4isF5G1IvJ25Oc3Lq6pFpHPpWJ/3UFERojIi5HPARGZHinYtM/fJiLnpGlvB4jId9PxbMU/aAqsknFEqqK/bIyJ2x45wTXVwKnGmLWebWwfiYhDK9DHGON5k0ERCUZ6kSU6fyrwK2PMMV7vRfEvakkovkFEvhOZ9/GWiKwQkZMSrLs1YomsjKwLRY4fKyILRWRZ5OeMBNfPFpEHxZq18b6IPCAi+ZFzQ0Tkn5He/atE5NuR43ki8gfHcxdHjo8Wkc8it74v8s83IhZSKPKsH4hIqYhsF5H+jn3cIyI39mDv94jIPOC1yLEnIte8IyJPi0hfx34Oi+zlr5G140Tkhcj6lSJyobs/HSVrSXe/dP3Rn9gfYD2wFng78nN65PgAx5qDsSrF7e/VwOeAgVhdfIsix0NYnQX6Y81WGBw5PixyTVmc58+OrC3Bqm59CfhB5NzTwC2Oe3wGjAO+ALwHBCLn7BkOo4HPIp+DWFXARTHPsu/9KPBfkc/5kXuP6MHe3yAyLyRyrMLx+Q4s6wHgVOB1x7l8YAUwNvK9DPgQGJ3u/yb0J30/Wd+WQ/Et55jO7qYxkTYFlUAbMExEKowx2xxrdmL1J3o88rfpZ40x9SJyArA/ME/2jgoxWANs3o7z/CeMMQ0AIvIY8CXgAaxfrFcAGGM2icgLWG0TngAKsVpCLAB60pJ8FnAn8P+ALwMrjTGfiMjZ3dz738zeeSEAl4jIeVgiUAqsTvD8cVhC+6TjOfmR41U9eB8lC1CRUPzEX4ErjDHPikgesAergVsUY/X3+QJwAnAK8FbE9y7ACmPMKT14rtCx/XJsIM8YY3aKyMFYDfVOBe4Uq5Fid1iI1aNnHFZb81mO53dn77ujGxc5Gasz6gnGmG0R91EiF5JgWT1HdHPfShajMQnFT5QDH0c+f49IozMnIlKG5V5ZaIy5GcsFdAiwBDhYRL7oWDshybO+JSLFkVjEt4EFkeMvRp6NiFQCp2O1IB+E5UZ6HvgpVrO1Uc4bGiuI3Bh5j04YYwzwOHAtcCzw98ip7u7dSV8s99sOESkCLnWcq4vZyxqgXUTOdzznYBEpdfksJQtRS0LxE/8DPBvJZFqA9csvln5Y7pJirL8ZL8OaqdAsItOAuyKB23wst9SXEzzrFaw2zCOw/ob/SOT4FcCDIrIq8v0aY8zaiPXyh0gGUx5Wm+6lwIEx950BLBaRPcCJcZ47C2uO84MmkgEVsQC6s3cnz2KJ3HvApsi/D9tSeAtYLyLvAquNMd8SkS8DvxWR6yLv8RnwDRfPUbIUTYFVlBhEZDbwqrFaMStKTqPuJkVRFCUhakkoiqIoCVFLQlEURUmIioSiKIqSEBUJRVEUJSEqEoqiKEpCVCQURVGUhPx/DuM93BHAPFoAAAAASUVORK5CYII=\n"
},
"metadata": {}
},
{
"output_type": "stream",
"text": "Precision/Recall AUC: 0.6035798540112813\n0 Precision/Recall AUC: 0.5066593674309645\n1 Precision/Recall AUC: 0.7661444405136631\n2 Precision/Recall AUC: 0.8750562989212892\n3 Precision/Recall AUC: 0.46554193865516025\n4 Precision/Recall AUC: 0.5326106092565559\n",
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"text/plain": "<matplotlib.figure.Figure at 0x7f38ebb1f7b8>",
"image/png": 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ZRoYyEoqLDjcPI9nntLfh5tGOcefsMWq8te0fElr+5undkLgaogZDRI86ywgL8CLhpdHW/tUf/VXnuSqibZAtA2y/7/rx8a6POZJ5hOPZx53uftrkuuus7YIdNQxaVNgFZSQUFx2ZKba34I/uXcWOZcd1l1m2lGla8onyN8+fR1zyYL3luBkNfHm77XB+4kcb6j3necZGj2Vqt6nW/ke7P+Kq365i3C/jrDEUzsK7a1cCJ10DgN+woU7VpbGhjITiosMnwINWHW3bTBt/PsLGX/XNBeThZSt8VJhb5lwi8wTs+1kLoou53C6yLokJsbZ3nsgkJdt+Zwj3db+PzTdtZmq3qVwXa3t7/zvjb7vJqAtSSnKWai7AWQsWOFWXxoYyEoqLDt9AT8Y/0J2rHrZt7exYcpys1IIqnrIf/k1tX+JseA+kGTpdbbfMpp5uRo69aktN3vfllfy266Rd5gbwcffhvu738Z8B/7F6Ov14+Ee+3v81JRb9vcUqQgiBZ1ttOyxz7jzMOTlO0aMxooyE4qKlRVwTJk/vb+0nHUjTTVZ+dhZGNzfcvbxp1blMTYatpT7+Ed3tLvP1a7pa2w/O1SdKun+49vcnkbyx7Q0WH12si5ya0Pq7b63t1PdnqJxODkIZCcVFTWCorcaEd4BHFSPrx96VSzGXaG/ZH99zM3OefLD82cS3k8DOh7/X9WnJkZdt1dyi/v078Wey7Srjsd6P8cO4H6z9Z9Y/w8w9M52yohAGAz79+gGQMWcOuevWOVyHxoiuRkIIMUoIcUgIkSCE+Ee+AiFEKyHEKiHETiHEHiHEmIrmUSjswXmPJz0Iah6Bf0gonj4+FOXlkXLsCLMfncYHR4bxd3ZTbVCR/bdIjAbBmC7Nrf1R764jPc9+keZGg5EOTTswf/x867X3d75PVpFjqwCeJ+LVV6zt7MWLyVm50il6NCZ0MxJCCCPwITAa6AjcKIS4MEfys8APUsoewA3AR3rpo1C4exp1mztuwCDu+XAW//r4K8bc/yjBEZG4eXhSVGym0KLVnCDjmC6yP7q5Fz/da8tAuyvJvvETAHHBcXw7Rtvu8TR68trW13hz65sOj6dwDw/Hs0MHALIXLCT5vvud7p57saPnSqIvkCClTJRSFgNzgQkXjJHAeUf2QOCUjvooGiHSYvsCadM1pIqR9qPD4Eu5451PrCVNY/xLz0I+HQxH9dki6dXa5s115+xtbDmabncZbgY33AxuFJmL+OPoH3x14Ct2pjg+ZqH111/R8jNbidXD/fpz4s67OPvqaw7XpTEg9LLCQohJwCgp5d2l/VuAflLK+8uMCQeWAU0AX+ByKWW1ifOFEC8AzwOEh4dz6pSyLYqKOXEgjYXv70YYBF5+7hWOKcguJiwqgAkPdcfDy81ust+6fhwA/3rybvwW3KZdHPYUDNOn6lpCSg6Xv23LDVXWA8peZBVlUVBSwIj5I6zXtty8BW837yqesj9SSpLvnUbuBdHX7davwy3EMS8DDRkhxHYpZe/qR+q7kqjI3+9Ci3QjMFtKGQmMAeYIIarVSUr5gpRSSClFRJm6uArFhfgGeRLW2p/AUG88vd0q/ACkHMsm/ZT9SnZmn0uxtn2626KxydMnOR9ATJg/656wFe2x9yE2QKBnIM19mzNntK0uxu7U3XaXUx1CCFp+8jFxe3YTt9vm2ZWzapXDdbnYsd9r0z9JBlqW6Ufyz+2ku4BRAFLKjUIILyAESEGhsANNI/y49qk+VY75cOqfAJxLzrUmB6wvSfv3WtvLP/uQASZPAtyLbCnDdaJlsM2ba9S763RZTQB0D+uOu8Edk8XElGVT2HzTZnzcfap/0M6cr4vt07s3+du2kb14Mb4DBuARGelwXS5W9FxJbAXaCSHaCCE80A6mLwyVPAFcBiCE6AB4Aak66qRQVEpgiP22TOIGDrG2961axoGsMK3jFWQ3GZUx566+ussAmDPGtpr4+sDXDpFZGe6tWgGQv3ETyfdOw3TSfsGFjR3djISUsgS4H1gKHETzYtovhPifEOLK0mGPAlOEELuB74HbpXJVUDgJ3yDP6gfVEDd3d6Z88CVefv4YjEY6BJQujgfeX/WDdqCJj37xIGXp1LQT0YHRAJzIPlHNaH0Jf+lFAq+5GoCiv/8m4bLLKTqqqtjZAz23m5BSLgYWX3DtuTLtA8AleuqgUFRF+mntHMI/2Ism4fbdLjkZv5/C3By6DBtO4NnV2sWAFnaVURHztyfrLuM8fh5aCu+m3k0dJrMihBA0f+45fAcM5NRjjwFwfPItxG5Y71S9LgZUxLWiUbNt8TEABl3Xzq4lOy0WMxt/movBaKRfbJk3ezf7rVYqI665v+4yzmOxWAAI9w13mMzKMHh6EjhuLJEffgCAbz/HbLtd7CgjoWi05GUV8ffWswC06WZft8n4DWvJOH2STkMvI9C7dAfVQQe7w9uHWdsHTtnfw6ks+9L2AfDKllfYcNJ+acvrQ9HhwwD4Db+smpGKmqCMhKLRkpNmS9Nhz1UEwNbftDQWWSlnYP8v2sUet9hVRmU0C7DVthjz/jqW7Dutm6zFE227ySHerhGfkPPnKjAa8RukdrLtgTISikbL2WP6vWWbS0wAhLdrDydL40OLK6l/rQOz77C5/U79poJyqnai0KwZ2r7N+xIXHKebnJpizs6mcO9eMJs5Mm48JRn2T1HS2FBGQtFoOXU4E4CoLvY9dC0uLCDjtBYS1GvsVbYbhY5LijcsLoyNTw3XXc538d8B0MSrSTUjHYPB15fASdcgPDwwp6fbPfNuY0QZCUWjxGK2kByvBbYNvj7WrnMf2brJ2vb2L1Nj206V6WpK8zLbTnpxJFOr+HfeFdbZCKORsIcfRppMePfsgVtwcPUPKapEGQlFoyTtVB7FhVrRmgA7BtEBmEoT+wFa5leDGwRHQ4/JdpVTrR5m21v04r36nEsk52jutqkFqSRmJeoio7bk/fUXSEnBtu1k/PgjWYt+x1LgmKqEFyPKSCgaJcnx2l51z1Gt7T952R2ONW+ApQTy0qg4nZl+eLjZfr2nfbuDgmL7V3JLLdASJMw/PJ8Jv07g9a2vY7KY7C6nNnh16mRtn/nPc5x67DGyfv3ViRo1bJSRUDQ6igtL2LH0OB7ebvQY0cru85+PHTC6u0NeaaR1URaUOP5t9pERtq20Z37ZW8XIurHm+jU83vtxa3/OgTnMPzy/iif0xzM6mpg/VxL+yit4ddVKvHrG2ndLsTGhjISi0XFww2kKc00UF5SQooOHU9L+PQCEtm4D0aVZWftMAU/HBbmd54HL2lnbP++0fz6jYK9gbu10KztvsdWV8DA4Ji1IVbhHRBA44UqK4uMB8OrSxckaNVyUkVA0OoIjfK1tYbD/FlDGae3L+EzCYVZ//Zl2MdR57qHdWuqfVLDYXEygZyBBnkGMiXaNKsTm9HRksVbKNWfZcidr03BRRkLR6ChbxjSyvf1dN8c+8IS1vT29NGV1cBu7y6kpQ9rpH+S2KHERWUVZZBZlciDtAAVO2Fq7EGPTphj8tdWbLCqqZrSiMpSRUDQ69qzSPHKatQmwe6Q1QNPIlgyZfKe1X2w2QmaS3eXUlOISi7VttugTN+BusFX9u33J7fT9ti8vbnqR7+O/Z178PM4V6FdsqTKEELiFaSlKUt59R9XCriPKSCgaFRazhXNJOQBcdlsH3eS4e9piFOYc7QGLHoJVr4DFUsVT+tA/2hYsuPLgWV1kTGw3kZ+u/IkrWl9hvTbv0Dxe3vwyL25+kU92f6KL3OoIffABAMyp56znE4raoYyEolERv+kMGWfyaRHXhKL8EvIy9dmGiBs4mMBmzQHINJXGYax5TffKdBVxaZmEf/fM2a6LKyxAbJNY3hr2Fhtv3Mjbw97mzaFvEumnbbctOHJhvTHH4DdsmLXtrqrV1QllJBSNin1rtEPlk4cy+On17cx9cYsu2xDefv7E9BkAwID+590vJfg6Jwnel7fbat6/teyQrrL8PPwY0XoEI6NGclWMlpbEWWcUJSmaC7JnuxhKzuqzirrYUUZC0ajoPyGaniNbEdFO8/iJiAnS5VwiJ+0cu5ctxq9JMH1aln5BXvKQ3eXUlOHtm1nbn68/ikWns4kL6Rtuq+nw4qYXHSKzLIV7tdiQor8TSBw3nrNvvIE5MxNzTo7DdWmoKCOhaFS06tSUARNjrPER3S9vqYuctd/OoqS4iB6XX477Pi0JHp2v1kVWTVn6kK3utkEH19+K6BHWgw7B2tnPvEPzSMhIcIjc8/gOHkKzZ5+19tO/+JLD/QdwuE9fEidchSU/36H6NESUkVA0OkpMZkpM2gFyUUGJLjLiN6wBoHnLMuVKPx0CiWugIFMXma7K3HFzre3pm6aTVeS4bLhGP1+CJ99M+4MHaDrlbvwuuwy30FAAig4d4lDPXhy/9TaV26kKlJFQNDosZRLf/f7hHlbOPsCJ/Wl2m//U4YPW9m8ff8Ky4tG2m19fCYsetpus2pCeV2xtl3WL1RuDMNAzrCcAO1J2MGjuIH46/JPD5IPmDhv26KO0/PADYtauIeyxR8Goxcvkb9mijEQVKCOhaHR4eLmVC6KL33SG+I32y5JqMBgJCNXOAIoLCth7JBdGvgLepTLDOtpNVm0Y0NbmCvuHjtXqKuLjyz/m3m73WvvHso85VH5ZhBA0vftuYjfbUroLD+enEnFVlJFQNErG3d+Nm//XH/9gLxDQY6T9ssE2j4llygdfcM0z020XB0yDgtIqaV6BdpNVVx6cu8uh8nzcfZjWfZp1RTE3fi7jfxlPan6qQ/UoS8lpm6FM+/xzp+nh6igjoWiUGN0MJB9MJye9kJYdgjEYBGkncymxYwxBfqZmFJq3bVf+hgPLmF7IqseGWdt3zNricPn9w/sT2ySWQnMhJ3JOYJb6xGzUBI+YGHz6aGVe0z75VHk8VYIyEopGy87lJwBIOpDO3OlbmDt9C398Yp902iXFxWyc/z3CYGDk1AehOM92M9d5/vptQmzJDVcdSuVfc7Y5VP693e/lsd6PAdC5aWea+zZ3qPyyCCEIf/kla9+Sr84lKkIZCUWjpe/4aLoMi6TLMFsk7okD6RTkFFfxVM3YtvBnMs+epufo8YS0iirv0fT3snrPXx/2vmBLnbF0/1lScgodKn9t8loA9pzb41C5FeEWYgtuTPv0Uydq4rooI6FotMT1a86QG2IZckMso6fa6g24eRireKp6slNT2Pzrj/gEBjFg0k3axYAIiBqstYc/W/nDDsDfy52jr9jSeXu71+/nrS3hvuEAXB93vUPlVoTB25uAsWMB8IxzXjp3V0YZCYUC8A30tLbdPOr3a7FmzheUFBfh7R9AwtZSD5r4RXBsHUQPg07ODaoDykWZ+3m6OUyuRVr4JeEXjMLIHZ3vcJjcqihJTQUh8B9+qbNVcUmUkVAogLPHbAFe9U3TcSbxbwDSkk+w5KN3SE04AL9O026OeRN0SANSH9YcdpyH0ZqkNSRkJtA2qC0t/FpU/4DOmDMzyd++He+uXa1BdoryKCOhUABJBzVPJINb/b/Ab339A0bf/6i1v2XOe1BUWibVS/8qcbXl9llbHSZr4+mNABzOOOwwmVWRu2YNmM34XXaZs1VxWZSRUCjQssICtOwQXO+5PH186DjYtnVRZDrfEuAVUO/57cXz421BfY4qyNM9tLu1nV7o+LTpF5KzYiUAXh30qy3S0FFGQqEATEWav354W/sHusV6HtUak+eDm2fVgx3I2C7h1vZnaxMdYijaBrUFwCiM+Ln76S6vOgoPaWnTk6ZMIWvhQidr45roaiSEEKOEEIeEEAlCiH9XMuY6IcQBIcR+IcR3euqjUFREXpat8FD7AeFVjKw5ZxNt2U47WTZDYEto61pbGqH+NoP1yh/xtHlqse4yFx/VZDze53E8jM5PhdHi7bet7VOPP4HpbIoTtXFNdHNrEEIYgQ+BEUAysFUIsUBKeaDMmHbAU8AlUsoMIURYxbMpFPqxbfExa/vrp/76x32LRRLUzIfrn+2DWw3dRc8n+WvuV6ydU2clwexxMPgRiHENYyGEYPnDQxjxzlrrtY1H0srleLInGYUZfB//PaHeoUyKnaSG5XPWAAAgAElEQVSLjNri3bkTUT/+wLFrrwPgzPT/ETljhi41Rhoqeq4k+gIJUspEKWUxMBeYcMGYKcCHUsoMACmlMuMKh9O8TQDNowNpHh1IWFRAuY+7l2YUMs/ms+DdXRTmmqqZTePPWVpgVvSA4baLx9fDN1dDysFKnnI87Zr5c+zVsdb+jTM3sTdZn1TeX+3/ioKSArqHdcfT6Drbbt5dulhjJXJXrCRh2KWYUtRX0Xn0dJBuASSV6ScD/S4YEwsghNgAGIEXpJRLqptYCPEC8DxAeLh9tgcUjZe4/uHE9a/4/1HayVx+fWcnhbkmTh/JstahqCmnUwvg8SOw/xdYrKWj4Od74O6V4Ob87Zbz/DxtIFd/pK2ixn+wnvjpo/Cyc5DdihMrAFh+fDkJGQnENImx6/z1odkzT1N4KJ7ihCOUnD1LwpChBF07CfeICBAGAsaMxqNVK2er6RT0XElUtF678GTMDWgHDANuBD4XQlTrIyilfEFKKaSUIiIiot6KKhSV0bSFH1c+aPPI8fCu3Rfn0V3bkT5Noe8U28Uze2D925U/5AR6tmrCykeHWvsfrbJ/BbkZw2dY2yZLzVZkjsItOJi2ixbRavZs67XMH+eT+t77pL77Lkn3TkOW6FOgytXR00gkA2VrQ0YCpyoY85uU0iSlPAocQjMaCoXLsGuFlgiw34RoPLxqtvi+4l8PWNu/v/c6B9etgrtW2Abs/bF80j8XoG2ozdtoyzH7u6dmFtnyVwV5ul68CIBv/37E7dlNq9mzaPXlFwRdr6UOKT5yhPSvvnKyds5BTyOxFWgnhGgjhPAAbgAWXDDmV+BSACFECNr2U6KOOikUteLY3nMc3nyWkJZ+9Lyi5tsN4TGx1vahjetY/MFbnM5xA3cf7WJaAuSds7e6dmNCd/tHQ5d1eTUI1/W+N3h44Nu/P74DB9LsicetkdgFu3aR/vXXlKTZr4phQ0C3fykpZQlwP7AUOAj8IKXcL4T4nxDiytJhS4E0IcQBYBXwuJSycf0LKFya3z/UMpU2iwrAYKz5r0tIqyjunfktl91pq8Z25kQydLlW60QPgyb2K3Rkb/q1qX9Q4YWUjbIe/fNorl5wNWaL8+pJ1ASDry9+l2qBkTnLV3D25VfImDfPyVo5Fl3NuZRysZQyVkrZVkr5Uum156SUC0rbUkr5iJSyo5Syi5RybtUzKhTOISSy9oFfPgGBBDbT6iU0iYik62UjYUfplkWR8woP1YSnfrZPXY2ytA5oTa9mvYgKiMJkMVFsLnbpFcV5mj35BC0//QS/yzXXZa9Gli3W9f+FFAonYS6xeTKt+f4wMx9ey+ePrq1xPezCvFwWvvMqBqORcQ8+gdFQ5tdt0MP2VrfemMy2n/eliV2qGFk3Ood0Zvao2bRroh073tj+xgYRj2Dw9cVv6FBKUlLBzQ2ffhc6aV7cVHkKJ4R4var7Uson7KuOQuFCCGjTLYTTCVn4BnmSdjLXer0mrP12FqZCrdpZWFQ0bPvSdrPDODsrW38+X3fU2m4b6lvFyLpjkRaWH18OwKtbXmXjqY28NOglAj2dX/e7KkoyMijcswfh7Y3B03ViPBxBdSuJvGo+CsVFi9FoYMy9XbnrrcF0Ha5VrzMYBVmpBTXKc3R8zy5r+9ienRTumK91et6qi7715VyuLT1Jm6cWU2KuXUxITTAIA1O62NyB1ySv4erfnF9fozqKDmoBkLKggDMvvVTN6IuLKo2ElPK/VX0cpaRC4WzOncgBwGKW7Fp+AktJ9UYipKXNG+qnl/7Diq2l3kw7vobpofB+T0g7oou+deHZsR0wlFklrUvQx/vqgZ4PsH3ydoZEDgEgpSCFPJNrv3N6d+uGV7euABibNHGyNo6lSiMhhJhW1cdRSioUzmbQ9bH4NdG2GToOisDoXv1x3vA7pjJg0k0YjNqubpF3BFvORXLKEgnmYkg/AjN66ap3bRBCkPiKLUXHHbO28vuemp2/1BYPowdj29hk5RTn6CLHXhh8ffGKaw+A3+AhTtbGsVT3P71PFZ/e+qqmULgOGWfyyM3QtmNOHsqsZrRGYFgz2vUdgMWsReoeO1vCutQ2fH+oDeeMpauM2JG66FsfPrq5p7V933c7dJOTX5IPwPjo8TT3ba6bHHsgpSR33ToAshYuIOXtdyjJyHCyVo6hyoNrKaVrFKFVKJxMk+a2g9yOg2qeCiakVRQ3/O8NivJy2b3iDxK3bwFgSUJTJncphgkf2l3X+jKmSzjX9Y7kh23JAET9+/dySQDtxed7PwcgPiPe7nPbHSmx5GtGLfN7zVM/7bPPCH9xOkGTXCOjrV7U2AVWCBFXWvvh1vMfPRVTKFyJhG1nre0uw2oejSyEoEVcB6J79uGqx561Xg9wL4SJn4BviF31tBfPje9Urr94r/23nc57NP2d8Tc7U3aSXZxtdxn2QhgMxCxdQpvffiXitVet1/M2bnJYVT9nUSMjIYR4APgZ+AS4ufTPG3XUS6FwKc4es32BJWyvWxrpM0f+RggQSC6/8lJoe2n1DzkJP0+3cquHad/af9vpxva2r5Bb/7iVS76/hL8z/ra7HHthDArCKy6OwAkTCJmmHclm//77RZ/TqaYriXvQ6kOckFKOLG03jg05hQIIb2tLSGeuZbpw0ALrFr35PFLC1S334T36BTtqpx9TBrfRbe6rYq7ijaFvMLrNaOu1GTtnVPGE6xAwbiwYtYzAwt3dydroS02NRKGUMg8wCCGElHIf0FZHvRQKlyL9tOai2aZbCHH9a3/IunH+92RnasF4PyV14YtH7kNa7B+HYG+CfLSaF6M66XOwPCpqFK8PeZ3WAVoeq1VJqxrE9o1ndDT+I0YAWk4n06kLE1xfPNTUSOQLIdyB3cBrQoj/A3z0U0uhcB1OHspg6yItGnn4rR3qlEoism00rXxti++slDP89tbLdtNRL95YegiAiT3tnxW2LOdjJga1GNQgUnWAVoMCIH/TJgr27XOyNvpRUyMxDfAAHgWCgaHALXoppVC4EitmW8uyk5NWWKc52l0ynGvHd+Hutlus145s21Rv3RxFz1b6BZCZLCZWnVhlba84vqKaJ1yD5s/9B5/evUEI7c+LlBoZCSnlPillnpQyRUp5t5RykpRyV/VPKhQNHw9vm6f4Dy9vpSCnuPaTFGTAsQ0EehTTsk2kHbXTj7LbPqvi9av5nFOcQ0q+Nv/m05v54dAPusmyJ5a8PPJ378arY0frquJipKbeTT8JIYLL9JsKIRrGv6RCUU8uu60DwRG2OIlV39TSr99ihp+nQNYJGPZvAqM6Wm+58v67pYxqeu4ABXsF8+d1fxIdGA3AlK5TqnnCNcjbvAVMJnwHDXK2KrpS0+2maCmltZ5haWEg16lirlDoSFjrAEZPtaXOjmxfy62XNa9DwgqIGYF54COkHNPyNQ264VaX3n8vLpMq/ZvNJ3SVtTZ5LYlZibTyb0WeKQ+LdP1D/bz1WgS232BlJADchBDWCvClh9iNK1+uolGza7n2JdmuTzO6DKvFdtHhZbDmNQhqBVd/xrq5X5Ny9Ag+gUH0vepanbS1D94e1l95difVLBVJXVl4ZCEAJ3JO8H9//h+3LL7F5Q1F7voNAGQtXIQ517UTFNaHmhqJJcA8IcQgIcQg4PvSawrFRc/+dSfZv+4UTSP9uPSW9jV/+09P1LaZjB5w3RzwCWb7778CENv/EpdeRZzn8ZGOqcL2dL+neWHAC9b+nnN7MFlMDpFdV4SbdlaVOW8exceOOVcZHampkXga2Au8DbwD7AGe0ksphcJVOJOYxdp5h/H0dWPM1C64l3m7rhJTAbzfAwozYdzbENGdhK02b6bBN92uj8J2pmzxob4v6ed1FBUYxTWx13BrR1u2n2JzHRwEHEj0r7/g1qwZBn9/vOJina2ObtTUu8lUWkOir5Syj5Tyf1JK1/4XVCjqSV5WEUs+3Ys0S0be3ZmAEO+aP5y4xtburCWAO7pzm/WSm4eHvdTUlQFtbbmlUnKKqhhpH4rMNhkDvx/IV/u/YnHiYt3l1oXipCRKzp7Ft39/cKsyV2qDpqbeTWFCiG+EEGtL+12FEFP1VU2hcB4lJjN/fLKXvKxiBkyMoWWHWro4/vW+rX1sHZhLMBVpMRYe3t4I0TDKywd6uxPsazNoJ9LydZX3bP9neaTXI9b+m9ve5Ml1T3KuQJ8CSPUhb/NmAHKWL+f0vy/ejZWa/k+dCawHziewiUcLsFMoLjqklKz6Jp6zR7OJ7duM7iNa1n6SsA629reTSFzwAQfXr8bLP4Db3vyoQZxHnOd/E2wZYe+YvaWKkfbhjs538OP4H5kYM9F6LcAjQHe5tcWnV2+8OmruzFm//UbStPuwFF98Gyw1NRItpJSfAGaA0q0m13Y9UCjqyI6lxzm8+SzN2gTU7qC6LKNfh6jB1u4vc7X9/ODwFgSEhNpLVYcwrqutfsaRVMd48bQPbl/u4LrPt33ILNTXw6q2eMXF0urLL/BooyVBzP3zTyy5uU7Wyv7U1EiUlO0IIYKAhvMqpFDUkLNHs9n0ayIAV9zVCTf3Gh5UX0huirbNBHDXcuvlvKyGmTz5y9sdn3bi6X5PExUQBYBFWlzS28kYFETUjz8i3N3x7Njhooy8rqmR+EkI8SngL4S4HVgGzNJNK4XCSSTssKWfOF/Tuk7s/9nWDu9ubfr4B9Z9TifS1Nf2d7HvZJZDZPp7+NM/vD8AE2MmEurjmiuwvI1/IU0m/IcNc7YqulBT76Y3gLXAdmAM8B7wnY56KRQO5/j+NHavTMLLz53J0/tjMNbjcHnH19bmsZeHWdsjpz1UDw2dR7eWtnoa42asp8Ss/26zyWxi7iGtVOiy48tcNoVJ7urVAPgNHepcRXSi2t8CIURzIUQvYJ6U8nrgfqAXcEhv5RQKR5F2MpdFM3YjLZJ+V0YTGFrPTPhXfQxARrEXCw9qh65uHp6cjD9Q1VMuzazb+1jbB0/n6C7P3ehO++D2gOumEJcWC7lr14K7OyUZGUiz2dkq2Z0qjYQQ4i7gOPA7sFMIMRY4DLQALt7cuIpGR+KuVGt77dzDHNp8ptwncVcq5pJavD236AmPHCSr2AuTRTvXKCkuYvlnM/jkX7dweNN6e/8IupOaa4th6NxCf2+jlPwUTmRr6VByinN4b8d7mC2u9SVszszEnHoOTCaSp95Lfqlb7MWEqGoJJ4TYD1wnpdwvhLgEWAXcJKWc7ygFq6N3795y27Zt1Q9UKKrAbLLwyf+trnLM6KldiO5ew31xcwlMbwqAqe/9bMlpz6af51lvu3l68uDXP9VVXYdjsUiin9aC2j6+uSeju4TrLnPrma3cufTOf1x/a+hbjGg9wiVWFlJK8tav5/Tzz1Ny6jQxq1fh3lyfKn72RAixXUpZoxf96sIETVLK/QBSyg1CiERXMhAKhb0wuhuY+GhP0k/nlUuLvXtlEhln8mnawq922V+zkqxN985XckmrfvSbeD3v3XI1AB0HX0rheXdJAZ4+vi7xpVcZaw7bVlpNfB0TLd6neR9WXbeKgpIC3tj6BquStMJEj655lPnj5xMX7Ji8UlUhhMC7WzdKUlLx6ty5QRiI2lKdkfAQQnTA5u5qKduXUla5wSqEGIV2yG0EPpdSvlrJuEnAj0AfKaVaFiicQkS7ICLa2Q5oj+09R8YZLcI47WQuv7y1g+uf6VuzyQxlXGdLXTc3/2orwbJnxRL2rLDlyOw9/mqGTv7nW7Or0CHctr309cZj9I9u6hC5Id5aWpD3h7/P53s/570d7wHQ3Nd1voxz162DkhJK0tI4cc895e75XXIJwbfd5iTN7EN1RsIHuDBxyvm+BKIre7A0tfiHwAggGdgqhFhwoWERQvgDDwAX32aeokETHO5LTK8wErZrbrHnkmoRKLVjjq0d0QOA1p27k5Z0AlmaAjsnLY2ziX8DEBZV6a+SS2Aoc3r59JgOlQ/UkaNZR63tQXMHMSRyCCHeITzb71ncje5O0QnA4O0Nbm6UnD5NyenT5e6ZTiRd3EZCShlVj7n7AglSykQAIcRcYAJw4epjOvA68Fg9ZCkUdicgxJuRUzqTsP1P67Wf39xe6XiDQdB7TBSR7YNh7evaxZBY8NAyqUZ27Exkx84A5GakM+/5J63XEYKDG0qTAloseAcE0rprD5fZgioy2Q7t/b2c84V8f/f72ZO6h2PZxwCtUJGbwY0HejxAU2/HrGwqwn/4cOJ2bIcynk2F8fEcv/EmTCdPcmTUaCLeehPvTp2qmMV10TN1YQsgqUw/GehXdoAQogfQUkq5SAhRYyMhhHgBeB4gPFz/AzRF46Z5dCBnErUAstNHKggkK+P70aZbHpEtyuTvGXB/hXPuWbGEzLPaW2fygX0kH9j3jzE3vfQW4THO33cHaB7oZW0npOTSq3Utq/PZgXC/cBZOXEhBSQEvbnqRBUcWMKXLFKcaiPMYLsjq69GyJd7dulGUkEDxsWOYkpKVkaiAil6BrL9OQkuD+Q5we20nllK+ALwAmndTnbRTKGrINU/0qvReUUEJC97dScrxHDpeEk7XSyPhq3G2AVEVl7bsPnIs/k1DsFzgV39i326re+yCt16mw+BLad25O627dq9oGocxfobNZbd7mcA6Z7Dv3D4WHFlATFAMU7q4Zj1st5AQoubN5fgtt5K/bRs+/Wp4luWC6GkkkoGy6TMjgVNl+v5AZ2B16ZK6ObBACHGlOrxWNASKC0tYNGMXKcdzaNLch9adQzi65xwU9obC0tXE7HfgsucBbTsqIjYIDy83fAIC6TL8in/M2a7fQKuRyE1PY+tv8zl16KBTjYTFIok/owXP/Xt0e4wG522BFZQU8Pxfz2MQBv438H9OPYuoDkteHvm7duHVqRNuTRy/8rIXehqJrUA7IUQb4CRwA3DT+ZtSyizAWtFECLEaeEwZCEVD4dCmM5xJzAYg40w+f3y6t/TO0NIPkAn8vdf6TJ+xUfQdX/khtU9AIHfP+IKN879j/5qV+AQGMWLKffr8ADVk/6lsa7t1cD0j0evJ+zveJyknids63kaX0C5O1aU68rZuBZMJ34EDna1KvdDNSEgpS4QQ9wNL0VxgvywNyvsfsE1KuUAv2QqFI2jXpxnAPyOxN30EWclae+TLHFh/iowz+fg18SSuf/VnaEe2b7EaiOuee4WmkXWoZ2FHOoT7W9sr41McEkhXEZtOb+Kbg98AMK2765ezydvwFwC+l1ziZE3qh64196SUi7nAhVZK+VwlY4fpqYtCYW+8fN3pMiyy/MWUg7D+YygtDZ1ovouMM9qxWbfLWhIYWnUJ1B2Lf2PVVzPxDWrCtc+9TNMWzjUQQLntpcvahzlNj/d32Kr9/XH0D66JvcZputSEvA0bEN7eePdw7nlSfWkYNRQViobC+RVEKWeX/Whtt4itel/6+J5drPpqJgADJt3oEgYCoKjMSmlTYprT9Lil4y3W9subXyY1P7WK0c6lOCmJ4sREfPv1+4fnU0NDGQmFwp60GwH/SYPgaPbmj2JH3tV4eAqufrwXoa38q3z06C7bcVxkR9fZb08sU43uq43H2XI03Sl6DGs5jHBfbaur2FKMxHUdG3NXaSlEhLs72cuWNejssMpIKBR2Rm6bxfYTPVib/S+8fQxMfLw34W0rLzYkpWTzLz+w/fdf8Q4IZPKr77nMKgKgY0QAUwa3sfZbOenw2tvNu1y+pvd3vO+yq4miRK26Yc7y5Zx84EEKD8Y7WaO6o+uZhELRqJASljzFqmUGDhZMBuDqJ/sT1KzyL1UpJWu/ncW2hT/jHxLKpGdeJDiihaM0rjEz19lSYpQNrHM0MUExrE5aDcBvR37jtyO/MTFmIoXmQtoHt+fOzq6R/yrs4Yfx7duXMy++hCUnB882Uc5Wqc6olYRCYS8SV1O08SsOFlxuvZSTVsiphEwsln9ujVjMZpZ9OoNtC38mOCKSG/77uksaiLK0CfF1qvwHez7IkmuWMKL1COu1XxJ+4Y+jf/DO9nfo8lUX1iavdaKGGsbAQLy7dcOcno7PgP4YfJ3791Yf1EpCobAXPsFYMJa7tOD9XQBcdnsH2pdxf5UWC9889RCpx7U39GbRMRzbvZ2ul41ynL41JCWn0Np+5Wrnn5W08GvB28PeJq0gjXxTPiZp4uFVD5OYpW3xeLtV7UHmKHJWrQa03E4NGbWSUCjsxd75eIkchgfMoMfwZnj52aKBC3JM5YbmZ2dZDQTAwfWrWfvtLCwuVnkN4D+/2vJKOSpFeE1o6t2UlgEtiQ6MpnczrX7O5A6T6dO8TzVPOobzh9f5O3aQNms2sqTEyRrVDbWSUCjsQUkR7P8VIaCDz59wdTsMHklsX3IcgL9+SmDzgsRyj3g3/Rem/E2UFOwGQLiP5rMH11U4vZuHgTH3diUixrF5k46ey2Pp/rMAvHltN4fKrinrktfxw2GtVkehuRCLtGAQzn//NWdkAJC9YCHZCxYiTSa8OnXEd8AAhMH5+tUUZSQUivpiKoQ320FRafqKxxLAzYPWnZtyaPMZSkwWAkL+uQUipS9Ju3Zb+01bRmN08yk7gNQTOUgJRqPAy8exeYpOZxVw6ZurARjZqRmTekVW/YCTOJl70tqef3g+T/Z5Ei835x2un6fV119RfOw4xyZNAiD17bet1337NpyEf8pIKBT1JS3BZiDAWpUuPCaI216pPCXDtoU/k7TL1u810p12fbRtkxKTmVVz4kk5noN/Uy/G3teV4AjHHn4OeMVWR+OTyZVnwnU2V7a9ktn7Z3My9ySvDn7VJQwEgNHPD+/OnWiz4DcKtm8nbebnmE6dwqN1lLNVqxXKSCgU9UFKOPCrrd/tJijKAZ/gah/1Dwkt11/+2Qc0a9MWU5GZudO3IC0ScGfSk4PwCXBu1G6JReJudI0CSGWRUjJ903RO5p7kpvY3MTZ6rLNV+gdesbG4R7TgzMuv4NmxA+7NnJfapC4oI6FQ1IfkrbD2DVt/93dwdA08UmX5dwDiBgwmskNnPvmXlm6iIDuLmff9089/94pgmoRHARAQ4kVEO/3TTmfm2wonXd6hGe5G19xD//bgtyxKXERTr6Y82vtRZ6tTKXkb/wKTCf9hw5ytSq1RRkKhqA/Nu8LYt2DnN3Bqp3ZtSM0r8foEBjHw2ps5unMbTcIjAK0y18F1q6xjNv+6DHefwYBWk2LKu0Nw8zBWNJ3d8PGwfTWsOHhWV1l1Jac4h9e2vgZAWmEavb7pxTP9nuGa2GtwN7hWnYncNVppWr+hQ52sSe1RRkKhqA/uXtD5Gvi9zFtsrztq/LgQggGTbmTApBut10zFRZxNPE76Sc0bysvPQFHBHgBi+zTjwNrlRHXrSUCoftsWn6+3eWLNu6e/bnLqg5+7H1O7TeWT3Z9Yr720+SW6hnalY9OOTtSsPNJiIXfNGozBwXh1cX6cSW1xzTWkQtGQ+O4GW/uG70HUb+/+2M7tVgMBkJe2mZL8FZTkr+DAmm9ZPvMD1s+bUy8ZVfH9lhO8vuQQAI9dEUs/F4qNKIsQgvu638fWm7dajcLY6LF0CO7gZM3KU7BjB+bUc/hdOqxBub6eR60kFIr6Ysq3tefeCC9k1Wu66F59mPDYs5iKCjl5OJMD608ipYmS/BXWMWVXHvbmqZ9tlfTuH95ONzn2Ym78XA6kHaBLSBf+O/C/iHoaaXuTvVgrqRMwZoyTNakbDc+sKRSuxvh3be3AVvWezujmTtve/SgqjObvbYG4eXag02Db9olvk2CaNI+ot5yKKDFbqh/kQqw8vpK3t79NmE8Y7136Hp5GT2erVA5ZUkL2kqUYg4Px7dfP2erUCWUkFIq6IiXs+h5mj9f6Ha+C+zbVe9rCPBN/fLKXDfMT8PRzZ8iNISTv19xsje7uXPufl+otoyKklPR9eaW1v/bxS3WRYy9O5Z7iodUPIZGk5Kfw3F/PsTNlJyUW10l/kbdpM+b0dAJGjUS4NcyNm4aptULhbAqztcPqvT+AZwBc8wV0mVTvaZPi01k5+yB5mUVEtAskMu40Kz97mxJTMT1GjWfI5Dtxc9fHc+emmZtJz9NcXxf93yBaNXVO3Yia4m5wp1toN3analHr60+uZ/3J9Tw/4Hkmxdb/38IeZP/+OwABY10vfqOmKCOhUNSWkiJ4twsUZmr9YU/V20CYTRY2/XaEXSuSMBgEPUc1IzVxAWvnbMDL148xDz5Ouz4D7KB85WwsU5q0c4vKiyS5CqE+oXwz5htO557mkdWPsC9tH2E+YQxuMdjZqgFgzs0le8kS3Fu0wLtHD2erU2eUkVAoaoswaquH80bixF8wYFqdp0s7lcvyLw+QlpxLUDMfYvvksnbOkwBExHVk7AOPERCib5RuVr6p+kEuSkJmAvHp8fh7+PPp5Z/SzLeZs1UCtANrWVCA8PDgzPPPl7tnDGpCyNR/NYg6E8pIKBS15fTu8m6uI/5Xp2ksFsmOpcfZ/Jvm7tpxcASdB/vx9eNTrWMmPTsddw99D2MPns5m9Hu27LPbn728itGuxc6UnUxbqRnoa2OvpXVAaydrZMOSkwNA8dGjFB89+o/7/leMwLsBxE0oI6FQ1JSSIlj6DGydqfX73gOXvwAedXsb3PjLEXYtP2HtD76uLeu//8rab3/JUN0NRFJ6fjkDceTlMRgNruVCWhUf7vrQ2v5y35d8ue9LhrUcBsCZvDOMjBrJ3V3udopuwXfeif/IkVCmjoS0SI7feCMS8Grf3il61RZlJBSKmvL55XBGi3zmtoXQZki9ppNmW0lTS8lpPrhjCmbTOQxugQRFjKXPhHH1mr9a+VIy+HVb+o+/XxrdoAwEwNN9n2bT6U28suUV67XzNbAB4tPjCfMJY3jL4fh5+DlUNyEEHpHl06vn79yJOSuLwIkTETo5INgbZSQUiuooyoVVL9kMRM9b620gAG3rk8MAACAASURBVKJ7hLL7zyRMBRswF25By9oERs9LKC6MQOocsvDQPFue8tcndXXZJH5VER0UTXRQNNfHXU9+iS2ocezPY8ko0or+PLP+GaZ1m8adXe50ehxFztJlAPiPvMKpetSGhve/QqFwJLkp8EoL2PSR1h/1Klw5wy5TR7QLYsq7A5CmbZw3EACmvMV4+sDR3amYivUpZ5qeV8xvu05Z+8PiQqsY7foYDUb8Pfytn5lXzGRk1Ejr/Y92f8SlP1xKbnGu03SUZjPZS5Zg8PPDd+BAp+lRW9RKQqGoisLsCy7YdzvGw8ubO9/9jAXvLSUl4Qfr9ezUeLYvsVCYZ6JJeMVnHoGh3kR1CamTXD/P8r/6eUVm8K/TVC5JXHAcj/Z6FDeDG78narEKId4hmCzO8+LK+2sjJWfOEHTddRg8nFsfpDYoI6FQVEXTtuX77e0fFBUY1owbn7+Z926xGYmSgrUYPWLZv+5Upc8Z3QxMeWcIRvfabQgUmsy0/88Sa3/WHX1oE+L6rpi1JdwvnFFRo6xG4mjWUYb9MIwfx/9IbJNYh+uT+fNPAARdc7XDZdcHZSQUisowm2B6mTf13ndCUEtdRElK8AtuSm66FtDWPCaWfld3LjuA/etPkXQgHQDfQA+GTW5fawORkJLD5W+vtfYX3j+ILpGuHzhXVzo27cj1cdezOHExOaYcogKiaOHXwuF6lGRkkLtiJR4xbfHq2tXh8uuDMhIKRWUc32Brh8TBuHfsLkJKyeFNG1j77ZfkpqfhExjEoBtupdOwyzAYbIWFju5OtRoIAN8gz1pvNaXmFJUzEOueuJSWwa6deqO+hHqHEuwVTI4ph9YBrfn8is/xdXf8qinrp5+QJhNB10xyuSy11aGMhEJRGb+XqTBn9IBts6D7zeBmn/3ks0ePsGr2Z5yM3w9A7IDBXHHP/+Hp888v7tBW5Q8MUo7ncGDDKTpeUn02WJPZwld/HeO9FX9br8VPH4WXu77V7ZyNlJJ3drzDrH2zaOHXgs+v+JxQH8cf0MuSEtK//Q4At6bB5KxahTEwCO8e3RuEwdDVu0kIMUoIcUgIkSCE+HcF9x8RQhwQQuwRQqwUQrhOuKRCcT7tBsDZvbDoITi9q/LxtaAoP5+5zz9hNRAAhzeu49ThgxWO9w3ypEVck39cq461h1MZ9e5aXvz9IAaDYPqETiS8NPqiNxAAr299nVn7ZgEQ4RfBrhT7/NvVlqIjiZScPg3AqSeeJPneaRy/6SYKtm1zij61RbeVhBDCCHwIjACSga1CiAVSyrIV4ncCvaWU+UKIe4HXgev10kmhqDFmE8RcDru/t10bcD9E9rHL9B7e3gycdBNZKWfZvXyx9fqhv9bRpnuvcmNP/Z3BXz8f4ezRbAwGQaehLegzJgpv/8pXNEnp+dZAOYOAW/q35pERsTTxbTheNfXBbDGz4MgCa3/rma1sPbOVV7e8ikEYmNZ9msMyxXq2jSbi9dcoST0HSFLfnwFS4hET4xD59UXP7aa+QIKUMhFACDEXmABYjYSUclWZ8ZuAyTrqo1DUnPXvlDcQLXqBTzBkJdvl8FoIQZfLRvLnrE/LXTe4Gdm28GcA8rKLOb4vjYzTeQA0beFH685N8fbJYOeSrUgp6TlmAt5+5beicgpN5SKpF/3fYDpGBNRb54aE0WBk4cSFnM07y95ze5m+aToAaYWaY4AsE5eiN8LNjcArrwQgb9MmZFERAVeOx61Jk2qedA30NBItgKQy/WSgqtJMdwF/1GRiIcQLwPMA4eHhdVRPoaiC2FFalPV5Tm7XPnvnw7SNdhFxeON6Dq5bVe7a3pVLKx1/NkH7lOXQX2u5/e2PrYfcmxLTuOEzW+GjRf83qNEZiPMEewUT7BVMh6YdiAuOY9qKaWQXZ3NJi0to4dsCk9mEu9GxqTEyvtNePJrcqF/5WXujp5Go6ESmQvMthJgM9AaG1mRiKeULwAsAvXv3dtwrgaLxEN4VHvtbMwpLn7Jd92lqNxGdhl1OQFgzzCat0E9OWiFbFiZSmKclhAsM9aZd3/9v787joyrPBY7/nskkmSSELAQkCWEJyCYKCgouiAVt1duCt/VarN6qVWttq7ftbWlrr7dqbavttYsWqxVxu5VqrVV6awuCKIiyySY7IYQlJCYsyWSbycyc9/5xJiHLTDIhMxNCnu/nk8+c8573nHnfbM+857zLWQwa1r/VpLNv/OInzdsnyo7Q4Hbj7NefZ1YW8z9L9zQf23T/VX3m9lJnHln7CO5Ge2Dk6tLVrC61e65dUXAFfsvPPeffw/gB4zu6RLc1HjxIzbJlJI8bR8qkSTF9r2iKZZA4DLRslw8B2o0MEpErgR8BM4wx3hiWR6mu6TfIXieiZZAoWQV/vtWeuyl3on0b6hQlOJ0MP+/kYjQv/HA1Pt8wEoL/18+5opAp1wxvPh7w+/nbrx9pdQ1HgpO3fvcYv6vI40Cq3e9jzFnpLL7nUpKdZ/7D6Uj9YOoP2PTJJgAe++ix5vSmyQCPNRzjKxO+QrYrmwsHXxiTXkfHFjwLlkXaxRfTsHEjAAlZ2SQXjoj6e0WTGBObD+Ii4gT2ALOAUmA98CVjzPYWec4HXgOuNsbsDXmhTkyZMsVs6CW9BFQvteE5u2dTKD84CK7uDUazLMO+jRUsXbC9VfqXf3YJ6dmu5n2f18PCb93VPOCupbWZU1iXdSE3XlTAzz/fuwZrxZsv4OO45zgv7XiJF3a80O74P7/wz5gMuNszdRqB6up26SPefBPXmPiOABeRj4wxUyLKG6sgESzItcBvgARgoTHmpyLyELDBGLNYRJYB5wJlwVMOGmNmd+U9NEiouKjcDRtfhOpDsOPNk+n9zoI7lkHm0FO6rDGGVx5ex7HSuua0CTPymXHjmND5LYvlzz3NlqV/b5Xe4HCRlJjQbk6m5uNu+59Tcloat/3qKdIye8dD01iq9lazpGQJpbWlLNy2EIC8tDzumngXrgQXM4fOxOV0dXKVyLmXLsWzI9hvJxDg2LMLkeRkzn7vXRL6x/e50WkTJOJBg4SKqz1L4OUbWqd95meQXWgvPjR8eutV6yLwwg9XU3ui9Z3WQcPsHkuJLiefunkMGQPtAXaVNV6e//2z1GxbQ8Cy/3YTE4Sh2alhb5EcLz3ULm3mbXcx8aprcSToLanfbvwtCz5e0C79F5f/gmtGXBOT93S//Tal99xL5o1zyW2ztGk8dCVI6Ihrpbpiw8L2aUvuO7k99+UuTwI4dU4hy59vPYiu4oC99GVCooPGBnu68K2Hq5j9u9XAMMgbxl0zCrn1kuHkZqR0eH330UpeeeD7uCsrmtPeee5pPnxtEXMf+iXZefGfy+h0cus5tzIycyQ1jTX8+qNf0+BvIMWZQmltKX/c+UfGZo9l8lmn/uypLWMMx5+3b3Nl33RT1K4bK9qSUKoryrbA6sch52wINNq3oOoqTx6/cwXkX9Dlyx47UsufHlrXKm3y1cM4b+YQnvhgPyt2VbCrvKb52Dv/OYPCgZGvtGYsi1WLXmD94r+0Ozb3oV+SP2Zcl8t8pllSsoTvvvfddum5abksvX5p1N6n7oMPOPiV2+l3xRUUPPX7qF23K7QloVSs5E6ES++FxffYAaNJYqo9AeApBAiAj/5xoF3agUSLuT9b1rw/a+wgvnzJcKaPysHRxWVGxeFg6r/eQMmWjVQe2N/qWEXJPioP7MdbX8fEK6/B1S++y3yeLmYOncmTs57EE/CwpGQJS0rsMSsF6QXNt6POH3R+t1oVxgRHXAMD772n+4WOAw0SSnWFZcEzM8Hyt04fNP6UHl5XHqphx/tH2Lfx5K0gI7AiuZGPVu5pHm305E0XcO253Rs4uuXtf7QLEADvLHyqefv9RS9w4ZzrmXrdDSEnGjyTJToSmT5kOgBPbzk5En5d+TrWldutvHHZ43j1c6+GPD8Ste+8Q8PmzaRfdSWu8bEdlxEtertJqa7a+iq8fmf79EHnwNc/6PT0uiovOz8sY9eHZVRXNACQnOpk7CW5/OVEFX/ddzJgvPvdKxgepQWBGhvq2bdxPZb/ZIA7euhA8zQgbY29dAYzb7uLlPS+N2K7vK6coqoijDG8uONF1pStITM5k/mz5nPewFPrYmw1NFD8L5/FV1FB4ZtvkDxyZOcnxYjeblIqVtxHoOqg3ZvpeHHrYzPmQcVOe+0JR+gJlt3HGnjpR+2n9fDW+9my7BCjgItcTta5/Gz58afJSInetBFJKamMu7T9pAajp13Kujf+TNH6Na3Sd61+jwlXXMWw83rP6OBoGZw2mCxXFncuvZNNFfYgvHkXzsMhDrYd3UZBegEZyV0bH3P0qafxHTnCgDtu79EA0VUaJJSKlDHwzCyoCbOk6J9vsV+nfR2u/nnILK7URIaMzeLwrhMhj1c5LCoSLICoBoiO5I4aw4AhQ9sFCYDBo86OSxlOR4t2LmoOEAD3vX+yF9vIjJG8cd0bEV+rfuMmjj3zDM68XHLuvjuq5Yw1DRJKRUoEPvVDKNsKNWVQtAz8nhbHHfY4ifNuCHm6sQzHSmsZkN+Pqk/qm8dGJKQk8ElmAkvraigVCwRy+nW+VkQ0lRXtCZne6GkgOfXMW/86ElcOu5LDtYcRBJfTxYdHPmT3id0IwhfHRr6iQaC2liPz5oEx5D/6KI603vX91GcSSnXVsgfg/d8QZr5K+MoSGDqtedeyDEv+sI3izZWtstUPSGR5oIHdxocRGH1WP+ZMymf2xLy4LCvq83jYu+4Ddn+4iuKN65vTc0eNYfzlMzl76iUkpaYi4sCZGN/ZUk8nPsvHE5ue4Lltz5HiTOHn03/OrKGzIj6/9HvzcP/tbwCkTZ/e6pg4neR84xukTDgnqmXujD6TUCqWElPtnkxV7but2lp3T21s8LcLEAAb6uqpPsvJVycVMGdiPuNy0+O2nKUxhifv+BL+4Ay0LZUV7aasaDfLF9p9+EUcXP2NbzN++qfiUrbTSWltKfNWzmNr5VYK0gt4bMZjjBvQtTElviMnb0/WrVrV7ni/GTPiHiS6QoOEUl01+VZIyYK32gy8+vKbMOxSaLNGgSst9Kfw6R4nc+66OC6thrZEhPOv+RzrF/+FtKxsBg4djreujvJ9ezHGapU3O38I2XlD4l7GnhSwAizatYjHNz1Og7+Ba0dcy/3T7qdfUtfHkAx76UVMY+tg3LB5MwdvvY2kUSPJ/Py/RqvYMaFBQqnOWAEoWg57l0LR23CipPVxcdiT/HUwbfigYenNU200n4Zwx8MreeX7l7e6VP+clLi0KKZ/6VbOmXElxZvWU7xxHeXFJwNEztDhjJ52KaOnXsaAId1fia832XV8Fw988ADbj20nIzmD+6fdz2cLP3vKPxNxOBDXyYkCA7V1lD/wIAC5Dz6IJJ3ea35okFCqM299N/ScTfmT4Y7lEU3olzWif7sgATC7Pok//rh1r6LJ1wxj2pzYdJH0eT2U7txO8eYNFG9cT/Un5fYBEXJHjabwgosYPe3SPtdyaPLYhsd4fvvzzfuFGYUsO7CMZQeWhT+pCxLEwR1/qUVKSsi+7TZSJ0dvTqhY0SChVCiWBRU77EWGQgUIgKlfCxsgLMuw6VAV7+89yqq9lRQVV3GdI4ksS7CApBALNzqTE8gblUnhpIFRrIht/d9eZ+X/hq7HrNu/zuipl5CakRn19+1tmkZWN2nZBTYarl9lIe9bpEyaxKDvfDuq144V7d2kVFvrn4W/fyf0sc8vsFelSz+rw0t84fcf8NGBE6RYkO93MNw4mehy4ajyNXeKSnA6GDyyP0PGZJE/JptBw9NJSAg9CK87jGXxqxs7X6blqq/ew3mzPhP19+9N/JafOl9d5xm7oMHfwPdXfp8B/9zAnUssnEPyGbFoEc6B0f8wECnt3aRUd1S3X3+h2bjPQWLnC9EE9rj5XkObKbwbfIB9O2nImCwGF2bgTIr9eg7icHDn/IUc2bOLf87/FQG/P2S+lf+7sM8HCafD2eWR1B052nCUb6/4NoV/38rNKywSsrMYtmBBjwaIrtIgoVST4vfgxQ4+cRdMgw+e6PgaAS9W+XY+4/kyVpgs514xhLSM2A+WM8ZQc7SSsqI9lBXtprxoN4FAoFUecTgYMm4CQ8ZN4Lwrr455mfqSmsYabn7rZia8e4ibV9i/DTXeGtbcEro3k0McDB5xDoW//wMOV/RWxOsuDRJKNXn7/o6PH1pjf3XCASQyFy+hA8Gy53YwavKgdukJiQ4KJw0kyXVqf5ae2lrK9+2hvCko7NtLfXVV83ERB4OGFwaDwjnkjz2H1P7R+9SsWqvz1eENeEk0DqpTLcQAlp9kd/uWXJIfXD7w1e20p385jWiQUKrJza/Drv+DjAK7L2pXvHRdq920hBN4/ekhsx7edSLs3E3+aw8wYUqLT5HGshc1GnE5JJ68fVVdUc6Rvbup2L+PipJiKkuKaahxt7pWes5ARk+9lMGjRjN41GjOKhxFkqvjVexU9AxOG8yKG1ZA6FlaAPAGvHxr8Z1c96v1DK2E/J/+FEfK6fUz0iChVJO0HHugXBd5/YF2bYZrMh9lWfW9BEwiNYGBeE3ogAEWGQnlDHAeYGBiMUPXvUfd+hDPDIZdBtc/C8DBbZt464mHOyzTlx5+jNyzx3S5Lip+vAEv85Z+iyuftANE5s030f/Tn+7pYrWjQUKpbig5WscV//Mun3N8kyeSfsdOq4CdZhiWdROf+AZHcAUH1YE8qgN5FHsvZm1t6DWPTbmbwOqnMYFjWL6iTq9a767i0I6PW6VlnpVL+oCcSKqlYuyE5wTfWn4vtzy8gdymRmWjj7IHH4z4Gq6x48j6YgfNlCjRLrBKnYJ1+4/zg9e3UlzZvrvkTRcVcEtOMiWby8BbC94aaKwJvtbZt5Ba8JkUqk0B1b5BWCb0bS5P1VNg6rtV5kRXCvc8/2rc5odSoX145EP+a/V/0VjxCX94ItD5CeGIMHbL5lMasa1dYJWKoqr6Rooqalm+q4Lfv7sPgDQaKJBKrnJUMFQqGCKVDJUKxrmOk7ejAvwNjA6ebxkHdSaLGlch7qwJuB2FuK1c3I0ZVLuTqK9t3w9KBNIHuMg8K43s3FS89bfgrTmMr7GauhNHcR+toPb4sU4fcqZmZJKVm0dWbj7DJ16gAaIHfVL3CfM3z+evRX/FKU7unn4vI2+4DlPt7vzkNsr++8c0bNyIMSbEsMzo0iChVFuW/bDYuEv52vzFDJbj5MoxxstR3kiqpEAqGCAnp9jwWcnUWjnU+gdQXT+B0sRCaiUfd2Agbk9/amudWKH6wwqkZyUxZGwKmYNSScuExMR6kBoCjdXUHC/BfbSSA5sqcB+toK4q9MPuxGQXWbn5djDIyz+5nZuPK63rE9Kp6CqvK2fRrkW8vPNlPAEPozJH8fBlD3POgODMrwPb93TrjCOO649rkFB9T025vXCQ+7C9HGl1Kbibvo6AL3hbxwi/caZTF8imLpBFfSCL6sBkNjuH0eDIo9afTa03FW9j+D+jlP5J5AxNJq2/RVJqI05nAw5HPVagFp/HTe3xSqpLKzm0pRJvfeiRvo4EJ+k5ORSccx79cwaSnjOI/gMHkjFwMNl5+aRlZWsL4TQTsAKsLV/Lq7tfZcWhFVjGYlDKIO47/z5mj5xNgiP2gyijRYOE6luqS+HX45t3PVY6K913Uum7mHori0YT+aphjRhMSgL5o9JJyUgiLSuZ3Nx+pGe5OLB1GWv/+iKeE3Ai3LITEbICfqo/KW+ejO/Gn/ySvNFdW9NAxV69r541ZWtYVbqKdw6+w3HPcQDGZY/jxrE3cs2Ia3A5T59BcpHSIKHOXB43PDMTju3FGGg0qXisdLxmJA1WBg1WBsur7+3SJRenNnIswaJGDF7BXl/oaB0ctY8/8vlzmTs+m/WLd0S9Ok0qD5RokOhhxhjK68rZcXwHWyq2sLFiI9uPbcdv2d2Xs13Z3DD6BmaPms15Oef16paeBgnVq1iWwefx46nz46nz4a3z4an34Q3ue0q24927Fo9JtwOC9R94rH54TT8Mp9bET5I6XA43k9Ne5xupy/i+707eDFxCqM+ED7y+gdpaN0P7hxsX0X3LFsxn2YL5Heb56pPPa3fXKDDGUFZXxv7q/ZS4S9hfvZ99VfvYeXxnu4kAx2aP5eK8i7ks7zLOHXguTrH/vTYFjuiWK9ykL9GnQULFjbEMPm8Ab4OfxgZ/82tjm31vQyBMuh+fp7Mug6mAvcymAz/JjlpSHDVkOY6Q7KjBJbXBtGpSEmpIkSp72+EmxVFFong6XR7i0cRneDTxmfAZVsKK8kIgvyvfnqjy1NaQ4Ozen7cxhoDPnpSw/yk8XD0TPL7pcRZ8vCCivLuO72LX8V08t+25GJcK7jsSYBJgGYvozxvcmgYJFXMb3iph7eLimF3f1S+Ry+eOxpWWiCstkeQ0J660RBKTHIjltx9E+z3gawi+1oPPA/4G2Ps2rH0q6mWq9Eb+bCMWXpx3T1SvN+srdzPpM/8S1Wv2BhMGTGi1n56YzvCM4fRL7NleY07HGsCHz/LhJLbTeMQ0SIjI1cBvgQRggTHmkTbHk4EXgcnAMeCLxpiSWJZJxZaxDK/+fD1HD9XG7T09tT4+fuHVUzx7CBB6igt7oKnBYMAYDJa9b5rSLPu1Oc0K5jPUBA4B5adYptNP4kevwZEne7oYcTcL+JihJxO8QEU1UN1DJbL9I2Sf6tiIWZAQkQRgPnAVcBhYLyKLjTEtn+jdDpwwxowSkbnAo8AXY1UmFTljDPb/RoNp+WqFSLdO5vc1Bk45QBirAV/DCoxVExyVHPxqsW1MAHvVnuBrcH8/3Ri5ekZJAHECiYgkQvBLCL5KUvB4UpvjCdDBsKyy0iVQsSdelVCdqErIoCrFb7eI6R/T94plS+IioMgYUwwgIn8C5gAtg8Qc4IHg9mvA70RETBzmCtm2spS1bxa3+yeIFdzH2P+fmtJVzPm9G7Aad/V0MXq5QDBwelv93nb3V3hLPWxBJww8baRB2dnC+MbGGN9sim2QyAdaLvF1GJgaLo8xxi8i1cAAmjsUhiYiDwA/BsjNze1ywQIBi/de3t3l81RsOZMnYwLu4BxFDvtLHK22JUw6tD0W/GQsTZ+Qe28XRKXa8tcvxYjB4/ET6xVBYhkkQv1Vtv1AE0me9hmMeYBgC2TKlCld/pCUkODgC/Mms2dtOTgEERA5+UrLfUebfRHEESJfy3Ts89pdzxHmfUKk913TeroASp321rxRi6eqkn6ZA2L+XrEMEoeBghb7Q4AjYfIcFhEnkAEcj2GZmg0uzGBwoa7KpZTqfYafe1/c3iuWXWzXA2eLyAgRSQLmAovb5FkM3BLcvh54Jx7PI5RSSkUmZi2J4DOGbwJLsG8QLzTGbBeRh4ANxpjFwLPASyJShN2CmBur8iillOq6mI6TMMa8BbzVJu2/W2x7gH+LZRmUUkqduliP6FZKKdWLaZBQSikVlgYJpZRSYWmQUEopFZb09h6nIlIJnOraX3m0H7txptM6n/n6Wn1B69xVw4wxAyPJ2OuDRHcEp4nqU+Obtc5nvr5WX9A6x5LeblJKKRWWBgmllFJh9fUg8WBPF6AHaJ3PfH2tvqB1jpk+/UxCKaVUx/p6S0IppVQHNEgopZQKS4OEUkqpsDRIKKWUCkuDhFJKqbA0SCillAqrTwQJEblaRHaLSJGI/CDE8WQReSV4fK2IDI9/KaMngvp+R0R2iMhWEVkuIsN6opzR1FmdW+S7XkSMiEyJZ/liIZI6i8gNwZ/1dhF5Od5ljLYIfreHisgKEdkU/P2+tifKGS0islBEKkRkW5jjIiKPB78fW0XkgqgXwhhzRn9hL526DygEkoAtwPg2eb4OPBXcngu80tPljnF9PwWkBrfv7s31jbTOwXzpwEpgDTClp8sdh5/z2cAmICu4P6inyx2HOv8BuDu4PR4o6elyd7POlwMXANvCHL8W+AcgwDRgbbTL0BdaEhcBRcaYYmNMI/AnYE6bPHOAF4LbrwGzRKS3ThbWaX2NMSuMMfXB3TXAkDiXMdoi+RkD/AT4BeCJZ+FiJJI63wnMN8acADDGVMS5jNEWSZ0N0D+4nUEvnxnWGLMSON5BljnAi8a2BsgUkdxolqEvBIl84FCL/cPBtJB5jDF+oBoYEJfSRV8k9W3pduxPIr1Zp3UWkfOBAmPM/8WzYDEUyc95NDBaRFaLyBoRuTpupYuNSOr8AHCziBwG3gLuiU/RekxX/967zBnNi52mQrUI2s5FEkme3iLiuojIzcAUYEZMSxR7HdZZRBzAr4Fb41WgOIjk5+zEvuV0BXZrcZWITDDGVMW4bLESSZ1vBJ43xjwmIhcDLwXrbMW+eD0i5v+7+kJL4jBQ0GJ/CO2boM15RMSJ3UztqIl3OoukvojIlcCPgNnGGG+cyhYrndU5HZgAvCsiJdj3bhf38ofXkf5ev2mM8Rlj9gO7sYNGbxVJnW8HXgUwxnwIuICcuJSuZ0T0994dfSFIrAfOFpERIpKE/WB6cZs8i4FbgtvXA++Y4FOhXqjT+gZvvTyNHSB6+31q6KTOxphqY0yOMWa4MWY49nOY2caYDT1T3KiI5Pf6DexOCohIDvbtp+K4ljK6IqnzQWAWgIiMww4SlXEtZXwtBr4c7OU0Dag2xpRF8w3O+NtNxhi/iHwTWILdO2KhMWa7iDwEbDDGLAaexW6WFmG3IOb2XIm7J8L6/hLoB/w5+Hz+oDFmdo8VupsirPMZJcI6LwE+LSI7gADwPWPMsZ4rdfdEWOf/BJ4RkW9j33a5tRd/4ENEFmHfLswJPmf5MZAIYIx5Cvu5y7VAEVAP3Bb1MvTi759SSqkY6wu3m5RSSp0iDRJKKaXC0iChlFIqLA0SSimlwtIgoZRSKiwNEqrPEpESEdklIltEZJuIRK3rs4gs4zx9WgAAAk1JREFUEJHpneR5S0RGRus9lYoF7QKr+qzg6OvPGmO2BQcYfoA9v9PRFnkSjDGBniqjUj1NWxJKAcaYTUANcJuI/FNEXhKRj4BzRSRXRF4TkXUi8rGI3Nd0noiME5Glwbn8PxaRW4Lp74rIZ4PbXxWRnSKyOZhvbDC9REQmBLdHib22x1YR2dhyMr7g+hf3ich6ESkWkS/E8Vuj+rgzfsS1UpEQkU9hT+HgAy4DJhpj9gWPvQ38xBizMjgdxHIRWQ+sAN4EfmSM+XMwb6jZg38JTDDGHBKRZOzRwm39EfiDMeZZERkPrBSRccaYpikl3MaYC0XkUuy5if4Srbor1RENEqqve01EPIAb+AL2NMvvtwgQadjTIgxsscRIOjAOeyI1Z1OAAAgz7cU7wHMi8ibwd2NMq/mTRCQdmAQ8F7zGDhHZjD0R4d+C2f4UfF0D5ImIyxhzJqyLoU5zGiRUX3e9MaZ5aUgRuRWobXHcgT0H0IXGGF/LE5tuFUXg88CFwExghYh8zRjTcg2PcAtctXxg6AEwxgSCwUr/dlVc6DMJpTpgjKkBVgHN6ymLSIGIDAZ2AX4R+bcWx1rdbgpOPV9ojFlnjHkEWAqc3+Y93MBmgjMRB59ZTATWxqRSSnWBBgmlOncTMD74YPpj4BUgM7iK4Rzga8FjW7Bn5GwpAXi+xfFc7GnaQ73HzSKyFXgZ+PcWzyOU6jHaBVYppVRY2pJQSikVlgYJpZRSYWmQUEopFZYGCaWUUmFpkFBKKRWWBgmllFJhaZBQSikV1v8DUruRT5RPzzEAAAAASUVORK5CYII=\n"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true,
"ExecuteTime": {
"start_time": "2018-09-18T03:43:35.218538Z",
"end_time": "2018-09-18T03:43:35.228612Z"
}
},
"cell_type": "code",
"source": "print(\"Group intercept term: \", fitted.intercept_)\nprint(\"Group feature terms: \", fitted.coef_[0][0:2])\nprint(\"Per location bias terms:\", fitted.coef_[0][-5:])\nprint(\"Per location feature terms:\", [list(fitted.coef_[0][2+ 2*i: 2 + 2*i+2]) for i in range(5)])",
"execution_count": 42,
"outputs": [
{
"output_type": "stream",
"text": "Group intercept term: [ 0.05230095]\nGroup feature terms: [ 0.04473803 -0.04529938]\nPer location bias terms: [ 27.28293208 -0.59405289 1.50679012 -0.61547966 -0.51185023]\nPer location feature terms: [[-4.3300736527394985, -9.4940198130440532], [0.63360955962433263, 0.23389267621543106], [-1.3712416924950397, 1.3800872489021863], [0.32636835076570248, -0.46777018293824302], [0.33949161971425879, -0.12673760528962325]]\n",
"name": "stdout"
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### With various levels of l1 regularization"
},
{
"metadata": {
"scrolled": false,
"trusted": true,
"ExecuteTime": {
"start_time": "2018-09-18T03:43:35.233070Z",
"end_time": "2018-09-18T03:43:35.720477Z"
}
},
"cell_type": "code",
"source": "logistic = LogisticRegression(penalty = 'l1', C = 1) # higher C is, the lower the regularization\nfitted = logistic.fit(features_train_local, y_train)\ny_pred = fitted.predict_proba(features_test_local)\nscores = [x[1] for x in y_pred]\n\neval_accuracy_from_scores(y_test, scores, location_index_test, n_locations)",
"execution_count": 43,
"outputs": [
{
"output_type": "stream",
"text": "AUC: 0.7060041646496547\n0 AUC: 0.61104791810186\n1 AUC: 0.6848603642090098\n2 AUC: 0.7992913052676115\n3 AUC: 0.6424950368689732\n4 AUC: 0.6163543496649099\n",
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"text/plain": "<matplotlib.figure.Figure at 0x7f38daf14be0>",
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "stream",
"text": "Precision/Recall AUC: 0.7231975013909345\n0 Precision/Recall AUC: 0.6262476087183406\n1 Precision/Recall AUC: 0.7671743772735906\n2 Precision/Recall AUC: 0.8747567451564093\n3 Precision/Recall AUC: 0.46316682307234625\n4 Precision/Recall AUC: 0.5343713440054648\n",
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"text/plain": "<matplotlib.figure.Figure at 0x7f38daf32780>",
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true,
"ExecuteTime": {
"start_time": "2018-09-18T03:43:35.723495Z",
"end_time": "2018-09-18T03:43:35.732576Z"
}
},
"cell_type": "code",
"source": "print(\"Group intercept term: \", fitted.intercept_)\nprint(\"Group feature terms: \", fitted.coef_[0][0:2])\nprint(\"Per location bias terms:\", fitted.coef_[0][-5:])\nprint(\"Per location feature terms:\", [list(fitted.coef_[0][2+ 2*i: 2 + 2*i+2]) for i in range(5)])",
"execution_count": 44,
"outputs": [
{
"output_type": "stream",
"text": "Group intercept term: [-0.37808886]\nGroup feature terms: [ 3.50481495e-01 -2.05954904e-04]\nPer location bias terms: [ 0. 0. 1.75993069 -0.14187807 -0.04235999]\nPer location feature terms: [[0.0, -0.11126986776380454], [0.27269957989479104, 0.13485969826457009], [-1.5755170653821255, 1.314881672346599], [0.0, -0.51432215800246983], [0.015510032163074111, -0.17554159058390326]]\n",
"name": "stdout"
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## With more regularization"
},
{
"metadata": {
"scrolled": false,
"trusted": true,
"ExecuteTime": {
"start_time": "2018-09-18T03:43:35.736841Z",
"end_time": "2018-09-18T03:43:36.174703Z"
}
},
"cell_type": "code",
"source": "logistic = LogisticRegression(penalty = 'l1', C = .1) # higher C is, the lower the regularization\nfitted = logistic.fit(features_train_local, y_train)\ny_pred = fitted.predict_proba(features_test_local)\nscores = [x[1] for x in y_pred]\n\neval_accuracy_from_scores(y_test, scores, location_index_test, n_locations)",
"execution_count": 45,
"outputs": [
{
"output_type": "stream",
"text": "AUC: 0.6949931997879256\n0 AUC: 0.6101667761418799\n1 AUC: 0.6828835167780737\n2 AUC: 0.7635216134264156\n3 AUC: 0.6214017300056722\n4 AUC: 0.6112954648968704\n",
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"text/plain": "<matplotlib.figure.Figure at 0x7f38db078908>",
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "stream",
"text": "Precision/Recall AUC: 0.7112221136561213\n0 Precision/Recall AUC: 0.613063823531351\n1 Precision/Recall AUC: 0.7613896115935875\n2 Precision/Recall AUC: 0.8536783844604634\n3 Precision/Recall AUC: 0.4379779176377827\n4 Precision/Recall AUC: 0.5331693822026189\n",
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"text/plain": "<matplotlib.figure.Figure at 0x7f38ed6cd208>",
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"metadata": {
"scrolled": true,
"trusted": true,
"ExecuteTime": {
"start_time": "2018-09-18T03:43:36.177074Z",
"end_time": "2018-09-18T03:43:36.183506Z"
}
},
"cell_type": "code",
"source": "print(\"Group intercept term: \", fitted.intercept_)\nprint(\"Group feature terms: \", fitted.coef_[0][0:2])\nprint(\"Per location bias terms:\", fitted.coef_[0][-5:])\nprint(\"Per location feature terms:\", [list(fitted.coef_[0][2+ 2*i: 2 + 2*i+2]) for i in range(5)])",
"execution_count": 46,
"outputs": [
{
"output_type": "stream",
"text": "Group intercept term: [ 0.]\nGroup feature terms: [ 0.19993755 0. ]\nPer location bias terms: [ 0. 0. 0.13513641 -0.18465186 -0.07452027]\nPer location feature terms: [[0.0, 0.0], [0.25090838108495717, 0.0], [-0.7206677127592116, 1.2209339198129963], [0.0, -0.53318257759760679], [0.0052152439995722137, -0.2095251045677427]]\n",
"name": "stdout"
}
]
},
{
"metadata": {
"collapsed": true
},
"cell_type": "markdown",
"source": "After tuning regularization, the model here performs the best. The coefficients are somewhat as desired -- there are (decently) accurate group terms, and the per-location bias terms for the locations with small number of data points is zero. For the locations with high number of data points, they are non-zero and in the correct direction. The same pattern holds for the per location feature terms"
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "",
"execution_count": null,
"outputs": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"name": "python3",
"display_name": "Python 3",
"language": "python"
},
"varInspector": {
"window_display": false,
"cols": {
"lenName": 16,
"lenType": 16,
"lenVar": 40
},
"kernels_config": {
"python": {
"library": "var_list.py",
"delete_cmd_prefix": "del ",
"delete_cmd_postfix": "",
"varRefreshCmd": "print(var_dic_list())"
},
"r": {
"library": "var_list.r",
"delete_cmd_prefix": "rm(",
"delete_cmd_postfix": ") ",
"varRefreshCmd": "cat(var_dic_list()) "
}
},
"types_to_exclude": [
"module",
"function",
"builtin_function_or_method",
"instance",
"_Feature"
]
},
"latex_envs": {
"eqNumInitial": 1,
"eqLabelWithNumbers": true,
"current_citInitial": 1,
"cite_by": "apalike",
"bibliofile": "biblio.bib",
"LaTeX_envs_menu_present": true,
"labels_anchors": false,
"latex_user_defs": false,
"user_envs_cfg": false,
"report_style_numbering": false,
"autoclose": false,
"autocomplete": true,
"hotkeys": {
"equation": "Ctrl-E",
"itemize": "Ctrl-I"
}
},
"gist": {
"id": "dc09d3a6c5fe9af4ef6942f7fcecd130",
"data": {
"description": "Hierarchical Bayes tutorial and Stan example.ipynb",
"public": false
}
},
"_draft": {
"nbviewer_url": "https://gist.github.com/dc09d3a6c5fe9af4ef6942f7fcecd130"
},
"language_info": {
"name": "python",
"version": "3.6.3",
"mimetype": "text/x-python",
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"pygments_lexer": "ipython3",
"nbconvert_exporter": "python",
"file_extension": ".py"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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