{ "cells": [ { "cell_type": "markdown", "id": "34e305f2", "metadata": {}, "source": [ "# Classification with Naive Bayes\n", "\n", "```{note}\n", "This is typically not required but can fix issues. Mine was caused because I installed a package that reverted the version of my jupyter. This was the same problem that caused the notes to not render correctly.\n", "\n", "The solution to this is to use environments more carefully.\n", "```" ] }, { "cell_type": "code", "execution_count": 1, "id": "a6fd623d", "metadata": {}, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "id": "2802dee0", "metadata": {}, "source": [ "We'll start with the same dataset as Monday." ] }, { "cell_type": "code", "execution_count": 2, "id": "37657ef8", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import seaborn as sns\n", "import numpy as np\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.naive_bayes import GaussianNB\n", "from sklearn import metrics\n", "iris_df = sns.load_dataset('iris')" ] }, { "cell_type": "code", "execution_count": 3, "id": "247be98a", "metadata": {}, "outputs": [], "source": [ "# dataset vars:\n", "# ,\n", "feature_vars = ['petal_width', 'sepal_length', 'sepal_width','petal_length']\n", "target_var = 'species'\n", "X_train, X_test, y_train, y_test = train_test_split(iris_df[feature_vars],iris_df[target_var],random_state=0)" ] }, { "cell_type": "markdown", "id": "55eb52ca", "metadata": {}, "source": [ "Using the `random_state` makes it so that we get the same \"random\" set each type we run the code. [see docs](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html#:~:text=the%20test%20size.-,random_state,-int%2C%20RandomState%20instance)" ] }, { "cell_type": "code", "execution_count": 4, "id": "cebe72e7", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " sepal_length sepal_width petal_length petal_width species\n", "0 5.1 3.5 1.4 0.2 setosa" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "iris_df.head(1)" ] }, { "cell_type": "code", "execution_count": 6, "id": "20faa58d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((112, 4), (150, 5))" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train.shape, iris_df.shape" ] }, { "cell_type": "code", "execution_count": 7, "id": "e8303999", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.7466666666666667" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "112/150" ] }, { "cell_type": "markdown", "id": "67aba76d", "metadata": {}, "source": [ "We can see that we get ~75% of the samples in the training set by default. We could also see this from the docstring.\n", "\n", "\n", "Again we will instantiate the object." ] }, { "cell_type": "code", "execution_count": 8, "id": "dd0cd774", "metadata": {}, "outputs": [], "source": [ "gnb = GaussianNB()" ] }, { "cell_type": "code", "execution_count": 9, "id": "d6e0f0be", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'priors': None, 'var_smoothing': 1e-09}" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gnb.__dict__" ] }, { "cell_type": "code", "execution_count": 10, "id": "ba55f64c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
GaussianNB()
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" ], "text/plain": [ "GaussianNB()" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gnb.fit(X_train,y_train)" ] }, { "cell_type": "markdown", "id": "b8fa3c4d", "metadata": {}, "source": [ "we fit the Gaussian Naive Bayes, it computes a mean\n", "$\\theta$ and variance $\\sigma$ and adds them to model parameters in attributes\n", "`gnb.theta_, gnb.sigma_`." ] }, { "cell_type": "code", "execution_count": 11, "id": "9c8ca323", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'priors': None,\n", " 'var_smoothing': 1e-09,\n", " 'classes_': array(['setosa', 'versicolor', 'virginica'], dtype=' 3\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m th, sig \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(gnb\u001b[38;5;241m.\u001b[39mvar_,\u001b[43mgnb\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msigma_\u001b[49m)]),\n\u001b[1;32m 4\u001b[0m columns \u001b[38;5;241m=\u001b[39m feature_vars)\n\u001b[1;32m 5\u001b[0m gnb_df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mspecies\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m [ci \u001b[38;5;28;01mfor\u001b[39;00m cl \u001b[38;5;129;01min\u001b[39;00m [[c]\u001b[38;5;241m*\u001b[39mN \u001b[38;5;28;01mfor\u001b[39;00m c \u001b[38;5;129;01min\u001b[39;00m gnb\u001b[38;5;241m.\u001b[39mclasses_] \u001b[38;5;28;01mfor\u001b[39;00m ci \u001b[38;5;129;01min\u001b[39;00m cl]\n", "\u001b[0;31mAttributeError\u001b[0m: 'GaussianNB' object has no attribute 'sigma_'" ] } ], "source": [ "N = 20\n", "gnb_df = pd.DataFrame(np.concatenate([np.random.multivariate_normal(th, sig*np.eye(4),N)\n", " for th, sig in zip(gnb.var_,gnb.sigma_)]),\n", " columns = feature_vars)\n", "gnb_df['species'] = [ci for cl in [[c]*N for c in gnb.classes_] for ci in cl]" ] }, { "cell_type": "code", "execution_count": 13, "id": "d9aee695", "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'gnb_df' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[13], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m sns\u001b[38;5;241m.\u001b[39mpairplot(data \u001b[38;5;241m=\u001b[39m\u001b[43mgnb_df\u001b[49m, hue\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mspecies\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", "\u001b[0;31mNameError\u001b[0m: name 'gnb_df' is not defined" ] } ], "source": [ "sns.pairplot(data =gnb_df, hue='species')" ] }, { "cell_type": "markdown", "id": "bd9b06b1", "metadata": {}, "source": [ "````{margin}\n", "```{admonition} Further Reading\n", "There are other classifiers that use roughly the same model but don't make the\n", "naive assumption. The more flexible is called Quadratic Discriminant Analysis.\n", "- [mathematical formulation](https://scikit-learn.org/stable/modules/lda_qda.html#lda-qda-math) for both\n", "- [QDA](https://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.html#sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis) code\n", "- [LDA](https://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html#sklearn.discriminant_analysis.LinearDiscriminantAnalysis) code\n", "```\n", "````\n", "\n", "This one looks pretty close to the actual data. The biggest difference is that\n", "these data are all in uniformly circular-ish blobs and the ones above are not. \n", "That means that the naive assumption doesn't hold perfectly on this data." ] }, { "cell_type": "code", "execution_count": 14, "id": "890a1084", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "ufunc 'isfinite' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[14], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m 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diag_kws\u001b[38;5;241m.\u001b[39msetdefault(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwarn_singular\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m-> 2148\u001b[0m \u001b[43mgrid\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmap_diag\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkdeplot\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mdiag_kws\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2150\u001b[0m \u001b[38;5;66;03m# Maybe plot on the off-diagonals\u001b[39;00m\n\u001b[1;32m 2151\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m diag_kind \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/seaborn/axisgrid.py:1507\u001b[0m, in \u001b[0;36mPairGrid.map_diag\u001b[0;34m(self, func, **kwargs)\u001b[0m\n\u001b[1;32m 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\u001b[0;36mmasked_invalid\u001b[0;34m(a, copy)\u001b[0m\n\u001b[1;32m 2332\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmasked_invalid\u001b[39m(a, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[1;32m 2333\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 2334\u001b[0m \u001b[38;5;124;03m Mask an array where invalid values occur (NaNs or infs).\u001b[39;00m\n\u001b[1;32m 2335\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 2357\u001b[0m \n\u001b[1;32m 2358\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 2360\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m masked_where(\u001b[38;5;241m~\u001b[39m(\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43misfinite\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgetdata\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m), a, copy\u001b[38;5;241m=\u001b[39mcopy)\n", "\u001b[0;31mTypeError\u001b[0m: ufunc 'isfinite' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "filenames": { "image/png": "/home/runner/work/BrownFall22/BrownFall22/_build/jupyter_execute/notes/2022-10-19_20_1.png" } }, "output_type": "display_data" } ], "source": [ "sns.pairplot(data=iris_df,hue='species')" ] }, { "cell_type": "markdown", "id": "89a88897", "metadata": {}, "source": [ "When we use the predict method, it uses those parameters to calculate the\n", "likelihood of the sample according to a Gaussian distribution (normal) for each class and then calculates the probability of the sample belonging to each class and returns the one with the highest probability." ] }, { "cell_type": "code", "execution_count": 15, "id": "d8c068d9", "metadata": {}, "outputs": [], "source": [ "y_pred = gnb.predict(X_test)" ] }, { "cell_type": "markdown", "id": "dde988ee", "metadata": {}, "source": [ "We can check the performance by comparing manually" ] }, { "cell_type": "code", "execution_count": 16, "id": "12f4c0a8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.0" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sum(y_pred ==y_test)/len(y_test)" ] }, { "cell_type": "markdown", "id": "543705cc", "metadata": {}, "source": [ "Or by scoring it." ] }, { "cell_type": "code", "execution_count": 17, "id": "d00d1357", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.0" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gnb.score(X_test,y_test)" ] }, { "cell_type": "code", "execution_count": 18, "id": "ca894b4f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[13, 0, 0],\n", " [ 0, 16, 0],\n", " [ 0, 0, 9]])" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "metrics.confusion_matrix(y_test,y_pred)" ] }, { "cell_type": "markdown", "id": "f67423ed", "metadata": {}, "source": [ "## Interpretting probabilities" ] }, { "cell_type": "code", "execution_count": 19, "id": "2fa729a3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[2.05841140e-233, 1.23816844e-006, 9.99998762e-001],\n", " [1.76139943e-084, 9.99998414e-001, 1.58647449e-006],\n", " [1.00000000e+000, 1.48308613e-018, 1.73234612e-027],\n", " [6.96767669e-312, 5.33743814e-007, 9.99999466e-001],\n", " [1.00000000e+000, 9.33944060e-017, 1.22124682e-026],\n", " [4.94065646e-324, 6.57075840e-011, 1.00000000e+000],\n", " [1.00000000e+000, 1.05531886e-016, 1.55777574e-026],\n", " [2.45560284e-149, 7.80950359e-001, 2.19049641e-001],\n", " [4.01160627e-153, 9.10103555e-001, 8.98964447e-002],\n", " [1.46667004e-094, 9.99887821e-001, 1.12179234e-004],\n", " [5.29999917e-215, 4.59787449e-001, 5.40212551e-001],\n", " [4.93479766e-134, 9.46482991e-001, 5.35170089e-002],\n", " [5.23735688e-135, 9.98906155e-001, 1.09384481e-003],\n", " [4.97057521e-142, 9.50340361e-001, 4.96596389e-002],\n", " [9.11315109e-143, 9.87982897e-001, 1.20171030e-002],\n", " [1.00000000e+000, 7.81797826e-019, 1.29694954e-028],\n", " [3.86310964e-133, 9.87665084e-001, 1.23349155e-002],\n", " [2.27343573e-113, 9.99940331e-001, 5.96690955e-005],\n", " [1.00000000e+000, 1.80007196e-015, 9.14666201e-026],\n", " [1.00000000e+000, 1.30351394e-015, 8.42776899e-025],\n", " [4.66537803e-188, 1.18626155e-002, 9.88137385e-001],\n", " [1.02677291e-131, 9.92205279e-001, 7.79472050e-003],\n", " [1.00000000e+000, 6.61341173e-013, 1.42044069e-022],\n", " [1.00000000e+000, 9.98321355e-017, 3.50690661e-027],\n", " [2.27898063e-170, 1.61227371e-001, 8.38772629e-001],\n", " [1.00000000e+000, 2.29415652e-018, 2.54202512e-028],\n", " [1.00000000e+000, 5.99780345e-011, 5.24260178e-020],\n", " [1.62676386e-112, 9.99340062e-001, 6.59938068e-004],\n", " [2.23238199e-047, 9.99999965e-001, 3.47984452e-008],\n", " [1.00000000e+000, 1.95773682e-013, 4.10256723e-023],\n", " [3.52965800e-228, 1.15450262e-003, 9.98845497e-001],\n", " [3.20480410e-131, 9.93956330e-001, 6.04366979e-003],\n", " [1.00000000e+000, 1.14714843e-016, 2.17310302e-026],\n", " [3.34423817e-177, 8.43422262e-002, 9.15657774e-001],\n", " [5.60348582e-264, 1.03689515e-006, 9.99998963e-001],\n", " [7.48035097e-091, 9.99950155e-001, 4.98452400e-005],\n", " [1.00000000e+000, 1.80571225e-013, 1.83435499e-022],\n", " [8.97496247e-182, 5.65567226e-001, 4.34432774e-001]])" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gnb.predict_proba(X_test)" ] }, { "cell_type": "markdown", "id": "5af31fe3", "metadata": {}, "source": [ "These are hard to interpret as is, one option is to plot them" ] }, { "cell_type": "code", "execution_count": 20, "id": "9167ecf5", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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indexpredicted_speciestrue_speciesi,predi,truecorrectspeciesprobability
00virginicavirginica0,virginica0,virginicaTruesetosa2.058411e-233
11versicolorversicolor1,versicolor1,versicolorTruesetosa1.761399e-84
22setosasetosa2,setosa2,setosaTruesetosa1.000000e+00
33virginicavirginica3,virginica3,virginicaTruesetosa6.967677e-312
44setosasetosa4,setosa4,setosaTruesetosa1.000000e+00
\n", "
" ], "text/plain": [ " index predicted_species true_species i,pred i,true correct \\\n", "0 0 virginica virginica 0,virginica 0,virginica True \n", "1 1 versicolor versicolor 1,versicolor 1,versicolor True \n", "2 2 setosa setosa 2,setosa 2,setosa True \n", "3 3 virginica virginica 3,virginica 3,virginica True \n", "4 4 setosa setosa 4,setosa 4,setosa True \n", "\n", " species probability \n", "0 setosa 2.058411e-233 \n", "1 setosa 1.761399e-84 \n", "2 setosa 1.000000e+00 \n", "3 setosa 6.967677e-312 \n", "4 setosa 1.000000e+00 " ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# make the prbabilities into a dataframe labeled with classes & make the index a separate column\n", "prob_df = pd.DataFrame(data = gnb.predict_proba(X_test), columns = gnb.classes_ ).reset_index()\n", "# add the predictions\n", "prob_df['predicted_species'] = y_pred\n", "prob_df['true_species'] = y_test.values\n", "# for plotting, make a column that combines the index & prediction\n", "pred_text = lambda r: str( r['index']) + ',' + r['predicted_species']\n", "prob_df['i,pred'] = prob_df.apply(pred_text,axis=1)\n", "# same for ground truth\n", "true_text = lambda r: str( r['index']) + ',' + r['true_species']\n", "prob_df['correct'] = prob_df['predicted_species'] == prob_df['true_species']\n", "# a dd a column for which are correct\n", "prob_df['i,true'] = prob_df.apply(true_text,axis=1)\n", "prob_df_melted = prob_df.melt(id_vars =[ 'index', 'predicted_species','true_species','i,pred','i,true','correct'],value_vars = gnb.classes_,\n", " var_name = target_var, value_name = 'probability')\n", "prob_df_melted.head()" ] }, { "cell_type": "markdown", "id": "c628c4d9", "metadata": {}, "source": [ "Now we have a data frame where each rown is one the probability of one sample belonging to one class. So there's a total of `number_of_samples*number_of_classes` rows" ] }, { "cell_type": "code", "execution_count": 21, "id": "71d7f2ef", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(114, 8)" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "prob_df_melted.shape" ] }, { "cell_type": "code", "execution_count": 22, "id": "571933e7", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "114" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(y_pred)*len(gnb.classes_)" ] }, { "cell_type": "markdown", "id": "8084da1e", "metadata": {}, "source": [ "One way to look at these is to, for each sample in the test set, make a bar chart of the probability it belongs to each class. We added to the data frame information so that we can plot this with the true class in the title using `col = 'i,true'`\n", "\n", "````{margin}\n", "```{tip}\n", "I used `set_theme` to change both the fond size and the color palette.\n", "Seaborn has a [detailed guide](https://seaborn.pydata.org/tutorial/color_palettes.html#palette-tutorial)\n", "for choosing colors. The `colorblind` palette uses colors that are distinguishable under most common\n", "forms of color blindness.\n", "```\n", "````" ] }, { "cell_type": "code", "execution_count": 23, "id": "87d3892e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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S9PHHH+vHH390ehtWrFghk8mUbl1UVJSioqJ09OhRLViwQG+99ZaeeOIJJ7W04Ln3xCI1NTXTcnv37jWW27Rpk+NFXMuWLVWyZEndvn1biYmJOnz4sB588ME8t69Zs2aqV6+e/vzzT8XFxem3337Tww8/nON2W7duNU5Q6tSpk2Wnd15cv35dr7/+ug4cOJBj2T179ujNN9/UzZs3070fFhamsLAwrVq1Si+++KJeeuklm9uVlQMHDuj111/X9evX071/6dIlXbp0SWvWrNGkSZM0fPhwu9QXGxurDz/8UD///LPMZnO6dcnJycax//zzz5o7d6527tyZYR/Tp0/XwoULM2wvSdHR0YqOjtbp06f1ww8/qH///vrkk0+yHOkOKKiIm4VbbuOmszg6bp48eVLvvPNOpk91X7t2TdeuXdPWrVv1n//8R/Pnz88xCSlNbr8XCQkJevPNNzPtgDaZTLpz546uX7+uAwcO6Ntvv9XHH3+sYcOG5aoNWQkODtZ7772nsLCwDOvSYtPRo0e1ZMkSPfvsswoICMh0P0eOHFFAQID++uuvdO8nJiYqPj5eYWFhWrlypTp16qSZM2dmmPbbWitWrNC0adN069atTNt+4sQJLV68WE888YTGjx8vDw+PXO23IPyWAc5UEL4DxHPrOSqeW+733r+JNVJSUjR58uRMP2cmk0nh4eEKDw/XkSNH9MMPP2jcuHF6/fXXs9zfzz//rE8++UTh4eEZ1l24cEEXLlzQqlWr9NBDD+lf//pXljeWbWHPeBgYGKh3331XMTExGdbdunVLt27d0tmzZ7V582YtXLhQQUFBGcpdvXpVgwcPVnR0dKZ1XL9+XdevX9f27dv1xRdfaPbs2Q5J7gZwF/G1cHNUfD179qyxnDally3sfV25bNkyzZkzJ8M1V1JSkm7duqU///xT33//vYYOHapJkyY5JDnq999/1wcffJAhxiclJSk2NlZnzpzRsmXLNGDAAH388cfZ3k/98ccf9c9//lN37tzJsC7tOvLUqVPasGGD1q9fr++//z5DuSNHjmjs2LFKSEjIsC45OVkJCQm6fPmyAgMD9cUXX2j+/Plq2rSpFUcOuA5iZOFmbYwMDAzUL7/8Ikl65plnVK9ePbu3TaIv1hb0xboGEsZQ5FSuXFkjR46UlH66wLT37uXr65vlvqZOnWqcoDRs2FCNGzeWp6enwsLCMp2v2d4SEhL09NNP6+DBg8Z7NWvWVLNmzVS6dGnFxMTo4MGDunHjhu7cuaMpU6YoLi5O48aNc3jbCoPTp08byyVKlFC5cuUyLffnn38ay7m5ePPy8lLDhg115MgRY3trEsYkaeDAgZozZ44kaf369bk6SVm/fn267W2VlJSk559/XqGhofL09FSbNm1Uo0YNJSUlGU+Qpzl8+LDGjRun27dvS7p7ItiiRQvVr19fycnJCgkJUVhYmD799NMs/71tdebMGc2aNUsJCQmqUKGC2rdvr7Jly+r69evas2eP7ty5I5PJpA8//FANGza0OUP8+vXrGjNmjM6fP2+8V7p0abVt21Z+fn5KSUnRlStXFBoaqri4OCUmJma6n2vXrslsNsvNzU116tRRnTp1VLZsWXl6ehqd2mkdCxs3blRcXJz+85//OOSJCsAScZO4mSa3cdOZHBU39+/fr3Hjxhk3Aby8vNS8eXPVrl1bnp6eunz5sg4cOKDExESdP39eI0aM0I8//pirGxm5/V689dZb6W7q16pVS02aNFGZMmWUkpKiyMhInT592m6jumzcuFHjx49XcnKy8V7t2rXVtGlTlSpVSnFxcTpz5ozOnj2r1NTULOPb/v379eyzz6Y7N2jZsqXq1aun5ORkHTlyxIhvO3fu1N///nd9//33NieNffPNN5oxY4bxulixYvL391fVqlUVGxurvXv3Kjo6WiaTSYsXL9bVq1c1b968XMVVZ/+WAdYgnhPP0zginiclJaVLLq5atarN+5wxY0a6DqHKlSurZcuWKl++vFJTUxUdHa2zZ8+muw7LyqJFizRt2jTjhrCvr69at26tKlWqKDU1VWfOnNGxY8dkNpv1+++/a9SoUfr+++/T3RRu2bKlRo4cqevXryswMFCSVKlSJfXu3TtDfZk9LW/PeBgSEqJXX31VKSkpku7+HVu1aqVq1aqpWLFiiouL08WLF3X69GmjvswkJCQYyWJlypRR/fr1dd9998nb21vJycm6dOmSjhw5osTEREVHR+u5557TkiVL0o0QALg64ivxNY0j4uvu3bv1xx9/GK9zmvUiN+x5XfnJJ5/o22+/NV6XK1dOrVu3lp+fnxITE3XixAmdPn1aZrNZK1eu1I0bN/Tf//433ee5Y8eO8vb21rlz57R7925JUt26dTMd8bNWrVoZ3tu0aZMCAgKM5A0PDw+1a9dONWvWVEJCgoKDg3Xjxg1J0oYNG3T58mUtXrw405FjAgMDNXHiROO15fmCh4eH4uLiFBYWptOnT6e7Tr5XTEyMkSxWoUIF1a9fX1WqVFHJkiV1584dXbhwQSEhIUpJSdHly5f1xBNPaPXq1ZkeH1CYESOJkWmsiZFxcXH66KOPJN29H/n88887rH0SfbHWoC/WdZAwhiKndu3axom/5UmK5cVAbly7dk3fffedqlatqpkzZ6p9+/bp1iclJdne2Bx89NFHxglK7dq1NXnyZHXo0CFdGZPJpB9//FFTp05VUlKS5s2bpw4dOqhNmzY21b127VojIcpeRo8erdq1a9t1n9mxHNo6u2mULIPPfffdl6t9V61a1fj3OXfunNVtfOSRRzR37lyZzWYFBQXlOFRrdHS0tm/fLunuCcIjjzxidd1pNm/erJSUFPn7+2vq1KkZhiFN+6wnJiZq/PjxxglK9erVNWfOnAxZ9Zs2bdK7776radOm2dy2zEyfPl0mk0nvvPOORo0alW5asKtXr+q5557T6dOnlZqaqtmzZ6e7sZFXKSkpeu2114zPSIkSJfT2229r+PDh8vLySlc2KSlJO3bs0MqVKzPdV7NmzdSlSxd17949y07y4OBgvfvuu7pw4YK2bdumdevWadCgQVa3H8gN4iZxM01u46YzOSJuhoeH69VXXzWSxQYPHqw333xTlSpVSlfu5s2bmjRpkn799VfdunVLr732mtasWZPtqFW5/V6cPHnS6Jz29vbW3Llz1a1bt0z3efHiRW3YsEEVKlTIst6cHD9+XBMmTDBugjdt2lSTJk3KdAqP8PBwrVu3LtOnA2NiYvTmm28a5wa1a9fWrFmz1Lx583Tl1q1bpw8++EB37txRWFiY3n33XX355ZdWt//gwYOaNWuW8bpr166aOnWqKlasaLyXlJSkuXPn6ptvvpEkbdmyRYsWLdKTTz6Z7b4Lwm8ZYA3iOfE8jSPi+ebNm41pstzc3PTAAw/YtL+oqCjjc+rh4aFPPvlEgwcPzrStN27c0ObNm1WiRIlM97V7925Nnz5dZrNZXl5eeuWVVzRq1KgMTwifOHFCAQEBOnv2rE6cOKHp06dr0qRJxvpu3bqpW7du2rt3rxGTLb9X2bF3PPzyyy+NZLE+ffpoypQpKlOmTIZyiYmJ2rNnj7Zu3Zppu0qUKKFRo0Zp4MCBat68eaadbXFxcfrss8+0YMECpaSkaMKECfr5559JkAb+P+Ir8TWNPeKr2WxWXFyc/vzzT23atEnfffedcZ31yCOP2DzKoz2vK1esWGHcU/X19dU777yjwYMHZ7gfumfPHr399tvGiJXffPONnn32WWP9oEGDNGjQIK1atcpIGGvVqlWuvkN//fWX3nvvPSNZrGXLlpo5c2a6xKvU1FQtXrxYM2bMUGpqqg4dOqR//etfev/99zPsz3Lq6SeeeEIBAQGZjigSHx+voKAghYaGZtquMmXKaNy4cerfv3+W03BFRERo+vTpWrt2reLj4/Xhhx9q0aJFOR4zUJgQI4mRaayJkTNmzDASfidNmpSrKSJtQV9s3tEX6zpIGAOyYDKZVLJkSS1cuFB16tTJsN7Rc88HBwdrzZo1ku5msmf15KmHh4f+8Y9/qESJEpowYYJMJpM+++wzff311zbVv3v37nRB3h769OmTbycpv//+e7qhKP/xj39kWdZyegbLjsbs+Pn5GcuZTRGRW9WqVVP79u21f/9+JScn65dfftGIESOyLP/zzz8bnbtt27ZVjRo1rK47TUpKiho2bKivvvoq05vwaZ/1VatWGU+Vp303atasmaF8v3795OXl5bBhUJOSkjR58mQ9/vjjGdZVrVpVs2bN0sCBA2U2m7Vv3z7duHEjQ6d/bq1evdq4UPDy8tI333yT4YIlTbFixdSjRw/16NEj0/XPPPNMjvW1b99eCxYsUL9+/ZSYmKilS5e69EkKChfipuvETWdyRNycM2eOIiIiJEmjRo3K9MaydPcc4d///reeeuop7dmzR6dPn9bmzZvVr1+/LOvP7fciODjYeG/06NFZ3tSXpBo1atj81N2UKVOMp7CaN2+ub7/9Vj4+PpmW9fPz09NPP53pusWLFxtDkpcpU0aLFi3KdNSZgQMHqmTJksa5we+//679+/fr/vvvt6r9s2fPNjoN2rRpo88++yzDb0yxYsX09ttvKykpSUuWLJF0t4Ng2LBh2T7V6uzfMsDZnP0dIJ7bxhHxPCEhQbNnzzZe9+3b16akZenu09JpCVH9+vXTkCFDsixbqVIljRo1KtN1qampmjRpktHZPmfOnExHBJOkJk2aaNGiRRo8eLBu3rypFStWaNy4capSpYpNxyLZPx6mnRcUK1ZMU6dOzTJGFy9e3Eh0y0y1atWyPK9J4+vrq/HjxyshIUE//PCDwsLCtH379mzPRQDkHfHVdePr559/rn//+99Zrvf09NSTTz6pN954w6Y2Sva7royLi9P06dMl3b0fumDBgkwfLpLuJgYsXLhQQ4YMUWJior7++ms98cQTdpna6bPPPjNG8qpVq5YWLFiQYUppd3d3Pfnkk3Jzc9PUqVMl3U1cGTNmTLrr//j4eJ04cULS3fvH77//fpYJDT4+Pnr44YezHH2mVatWWf57pKlQoYJmzJihqKgoBQUFaffu3frzzz8dNt0aUJgRI10vRu7fv18//fSTpLsP7mY26qS90Rebd/TFug4eFwOyMXLkyExPUPLDwoULjeXx48fnOG3P0KFDVbduXUnSjh07FBUV5dD2FWQ3btzQBx98YLzu1KlTthfIaReeknKdxW5ZznJ7a1gOZbpu3bpsy1qut8cQqGkCAgKyfGI7zYoVK4zlMWPGZHqCkqZ3794ZnsCwl4YNG2Z6gmK5vkWLFpLuPrl37Ngxq+tasGCBsTx27NgsT1DsqXr16sa/XUhIiDHiDVAYEDcLp7zGTWezZ9yMjIw0yvj5+emtt97Kdn8eHh56/fXXc12/lLvvheVvva1TNebkyJEjxgW4m5ubpk+fnmVHdHbMZrNxc0eSXnjhhWynKOvdu7e6du1qvP7+++/zXKd0dyrw/fv3G68nTpyY7c3DN954wxiaPS4uThs2bMixDmf+lgEFAfG8cHJUPJ8yZYquXLki6e7Nass4aC17xb3ffvvNuJHeq1evLJPF0vj5+WnMmDGSpOTkZP38889W153GEfEwbTS3kiVLWhWjrfHoo48ay2kjwACwL+Jr4eTI6+X69etr+fLlCggIsMvIjvaKrytXrlRsbKykux3/OSVH1atXT4MHD5aUfkQWW8TGxmrTpk3G67feeitDspil0aNHq0GDBpLuJpRbxmYp/b9N2bJl821Edcuk+F27duVLnUBhRIwsnKyJkYmJiXr//fdlNptVtmxZjR8/3tHNNNAXmzf0xboORhgDstG/f3+n1JuSkmJcQPj6+uqhhx7K1XYdOnTQuXPnZDabdfDgQfXs2dPqNkybNs1hw1g6UlJSkl555RWFh4dLunsBmPZ0UVYs5zm+d2jLrFh2TN65c8eKlv5P3759NWXKFCUlJengwYO6fPmyqlWrlqHcpUuXdOjQIaOduZljOzfKlCmjzp07Z1smLi4u3RzauTlBGjhwoPbu3Wtz++7Vt2/fHMs0adJER48elSRdvnzZqnouX76cbrrRJ554wqr9ZObKlSs6evSowsLCFBsbq8TERJnNZmP9pUuXJN09yTp58mS+nBwB9kDcdI246Wz2jJu7du0ynhbr3bt3rhLHW7VqJW9vbyUkJBiJV9nJzffCcnSTtWvXavjw4XZ5IjszljfvO3bsqPr161u1nz///NP43Hh4eOTqKaxhw4YpKChIkrRv3z6r6t2zZ4+x3KRJEzVt2jTb8t7e3howYIAxytjevXuzfYJQct5vGVBQEM+J52mWLl2qVatWGa8nTJiQbhooa1kmVP3666/6v//7P6tGLUuLKZI0YMCAXG1jOZ3mgQMHcpyqOCeOiIdVqlTRxYsXFRMTo02bNmU7mmluJScn68iRIzp16pTCw8MVHx9vjNYp/S9JTZIxAgsA+yK+umZ8bdGihUaOHCnp7ig6sbGxOnnypM6dO6ezZ89q2LBhGjp0qMaPH5/tSMi5Ya/rSmvj648//ijpbnz929/+lud6LR06dMiYmqtcuXI5fm7d3d316KOPGp+ze+9JlytXTsWLF1diYqLOnDmjAwcOqF27dja1UZJu376tw4cP6/Tp04qMjFR8fLwx8qkkYwRSifgKZIcY6Tox8rPPPjMe+slNgp490RebN/TFug4SxoAseHl5ZTkHvaOdOnXKGLXK09NTn3zySa62CwkJMZavXbvmkLYVZGazWe+88066QD579mxVrlw52+2KFy9uzAWd1mmcE8t503PKBs9J6dKl1b17d23ZskVms1nr16/XuHHjMpRbv369Eci6d++uMmXK2FRvmsaNG8vDwyPbMqdOnTIudn19fY0nKLKT09Nn1mrUqFGOZdJGEpFkdVa45bzxtWvXtstUJYcOHdKsWbMUHByc7qQkO678hAoKF+Jm4WNt3HQ2e8bNw4cPG8unTp3S5MmT89SWmJgYJSQkyNvbO9P1uf1edOvWzUhCCw0N1cMPP6zHHntM3bp1U9OmTXOM03lhecy2PIFmefOiTp066WJvVtq2bWssh4eH6/r163n+vFneZG/Tpk2utmnbtq2RMGbZ7sw487cMKAiI54WPo+L5b7/9pn/+85/G66FDh2b7dHFetGrVSlWrVtXVq1d15coV9e/fX0OHDlWPHj3UsmXLXE87k3bMkrRly5Z0I1Bm5datW8by1atX8974ezgiHj788MP673//K+nuSJlpSWMdOnTIc2LdnTt39OWXX+qHH37I9bUl16CA/RFfCx97xdcuXbqoS5cuGd4PDQ3V5MmTdfjwYf300086duyYlixZYlPSmL2uKy3j608//WRM1ZYdy8+IveNry5Yt5emZc1emZXw9fvy4zGazMZJYsWLF1KtXL23cuFEpKSkaM2aM+vXrpz59+uj+++9X6dKl89S+6OhozZs3T2vWrEmXdJ0d4iuQOWJk4WNtjDx58qS++eYbSZK/v7+GDh3q8LZaoi82b+iLdR0kjAFZKF26dK4uRBzhxo0bxnJ0dLSWLVuW533ExMTYs0mFwuTJk7Vx40ZJd58qmjZtmjp16pTjdt7e3kbCmOVoY9mxLJdVJ3FeDBw4UFu2bJGkbE9SLMvbS24y+C0DZZUqVXI1bLc9gnpmcnPjxPK7m5KSYlU9N2/eNJarV69u1T4srVixwhhqNy9ye9MBcDbiZuFjbdwsCOwVNy0/OwcOHNCBAwfy3JbY2NgszwVy+70oV66cPv74Y40fP17Jycm6evWqPv30U3366afy9vZWq1atdP/996tHjx5q0qRJnttoKSIiwliuUaOG1fuJjIw0lu+7775cbVOxYkXjqW7p7vlFXjtcrKnX8mnBnC7+nflbBhQExPPCxxHxfN++fXr99deNEageeughTZkyxea2pvHy8tKMGTP0f//3f0pISFBUVJS++eYbffPNNypevLiaN2+u+++/X127dlXbtm2zvP60/MxYTluVW2nTbdnCEfHw+eef1759+3T48GGZzWb9+uuv+vXXXyXdvYHerl07dezYUQ899FC218cxMTEaM2ZMnkc04RoUsD/ia+Hj6OvlZs2aafHixRo9erSOHDmi48ePa8aMGXl+iMmSPa4r4+Pj08WB5cuX57kdzoqvltd9ycnJio+PTxcnJ0yYoNDQUIWFhSk5OVlr167V2rVr5e7urvr166t9+/bq1KmTunbtmm3y+uXLl/XEE08YU3bnFvEVyBwxsvCxJkaaTCa9++67SklJUbFixfTRRx/lR1MzoC829+iLdR22T4wOFFG2jhplC8snXq1lObWAK5g1a5a+++474/XEiRNzPWR22bJljWXLwJSdtGFWJdklu7xbt25GO86ePZth9IvQ0FD9+eefRn3du3e3uc40ufmsWwbK3H437JFIl5ncnCDZg+Ux+/j42LSvs2fP6sMPPzROUBo0aKD33ntPy5cv165du3T06FGdOnXK+G/IkCHGtpbDmAMFGXGzcLElbhYE9oqb9vjsZHcxnJfvRf/+/bV8+XL17t073RTZCQkJ2r17t+bNm6fBgwdr6NChCg4Otrq9lvHNllid9gSmpDxNc2JZ1poLcct6c9v+vNTpzN8yoCAgnhcujojnISEhGjdunO7cuSPp7pPf//73v+3eiePv769169Zp8ODB6T53iYmJOnDggL788kv94x//UN++fRUYGJjpPqx9gjmNPT4vjoiH3t7eWrJkid5+++0MU6SEhYVp5cqVCggIUOfOnTVjxgzjb3WvyZMnG8liXl5eGjZsmD7//HNt3rxZBw8e1PHjx41r0K1btxrb5fXGOoCcEV8Ll/y6Xi5RooTefvtt4/WqVatsTjyw9brS1tgqOS++3lvu3vjq5+enlStX6vnnn1fFihWN91NTU3X69Gl99913evHFF9W5c2f997//zfI43nzzTSNZzMfHR2PHjtXXX3+twMBAHTp0SCdOnDDi67fffmtsR3wFMkeMLFysjZFLly5VaGioJOn//u//cjVyliPQF5t79MW6Dh6dBpwgpx8dyx/3Ro0aad26dY5uUgZr165NNwykPYwePVq1a9e26z4l6YsvvjCma5CkgIAA/f3vf8/19nXq1DFOAHL7ZJDl0Nr2OLEpVqyY+vTpox9//FHS3Qz2pk2bGustM9r79u2b6yk67MUySGd1M/peaaO2FVaWx2xrZvnixYuNhILOnTvriy++yPZv6MqZ7EBmiJv2ZWvcLAjsFTctbyhPmDBBY8eOdUyDc6lJkyaaP3++YmNjtX//fh04cEAHDx7UsWPHjGmzQ0NDNXr0aM2aNUsPP/xwnuuwjG+WN+HzyvJ7l5eYb1nWmpsAlvXmtv221gnAPojn9uWIeH7q1Ck988wzxvVIy5Yt9cUXX6h48eI27TcrNWrU0PTp0/Xhhx8aI30ePHhQR44cMa47w8LC9OKLL+qdd97Rk08+mW77kiVLGp08q1evTncukF8cFQ+LFSump59+Wk899ZROnTql/fv369ChQwoODtb169eNfXzzzTcKDg7Wt99+m65D4fr16+me+v/666/1wAMPZNkerkGBwov4al/5fb3ctm1blSxZUrdv31ZycrKOHj2a6RSWeWHLdeW9SVf79u2z21RceWFNfL23XGbx1dfXV6+99ppefvllHTt2TMHBwTp48KAOHDhgjOoSExOjWbNm6fDhw/rss8/SdZgfPHjQmILN29tbP/30k+rXr59lm4ivgHMRI+3LlhhpOerx1q1bFRQUlGXZv/76y1j+448/NHz4cOP1/PnzValSpbw0Ox36Ygse+mKdj4QxwA7yOuRiTk/qVKhQwVjO7YhX9rZ7926tXr3arvvs06eP3U9SFi1apLlz5xqvn3/+eT377LN52ke9evWMJ5bvzSbPTEpKik6fPp1ue3sYOHCgcZKyYcMGvfXWW3J3d1dqaqo2bNiQrlx+sxyFLe3mdE4K+9ztlk+aXbp0yaZ97d6921h+7bXXcjzJvHz5sk31AQUdcTN3CmrcLCjsETctf+stRw91ttKlS6tnz57q2bOnpLvfgS1btujTTz/VlStXZDKZ9NFHH+mhhx7K85OYlt8XW+Kb5TDqlon02YmIiEg3rXe5cuXypV7LuGpNnQAyRzzPncISz8+dO6ennnpK0dHRkqSGDRvqq6++ytU0FLby9vZWly5djA7yO3fu6I8//tBnn31mXHvPmjVL/fr1Szd1Y4UKFYyEMWfFcUfHQzc3NzVu3FiNGzfWqFGjJN29b7FkyRKtWrVKknTkyBEtW7ZMTz/9tLHd7t27jaequ3btmm2ymJT7h+cAOB7xNXcKS3zNibu7u3x9fY3O3rQ4bA/WXFeWLl1axYoVU1JSkqS7nxlnJIzZet3n5eWV7cNCHh4eatWqlVq1aqWnn35aqampOnjwoL755hv99ttvku4mNGzevFl9+/Y1trO8xztkyJBsk8Uk4itgb8TI3CnoMTI3/bBpoqKi0k3TmBafbEFfbMFCX6zzMSUlYAeWN1Bzc1F36tSpbNc3adLE+BGLiIjQhQsXbGpfUfXjjz9q6tSpxuvRo0frtddey/N+OnToYCwfPnw4xxOOkJAQ4yK+ePHiat26dZ7rzEy7du2M+Zlv3LihvXv3SpL27Nlj3PyuXr262rVrZ5f68qJx48Zyd78bMm7dumWMyJado0ePOrpZDtWqVStjOSwszKaTrhs3bhjLjRo1yrbsrVu30iUkAkURcdM57BU3Cwp7xM2WLVsaywcPHnRga23j6+uroUOHavHixcZnPSoqyniyOS8sz1v27NljdZssn747d+5crr7Llv/Gfn5+6Tr9c6tJkybGcm6P37JeZ4w+AxRVxHPncEQ8v3jxop588kmjk6R27dpauHBhupvV+alEiRLq27evlixZYtw8Tk5O1vbt29OVs7xms1ccz+u0G86Ih02bNtXUqVM1bNgw4720zu00ltegDRs2zHGf+/fvz1MbADgO8dU5nHW9nJycnO7v7MjkrNxeVzriOtmW+Hr06NFcTflmeRxNmzbNU53u7u5q3769Pv/8c3Xq1Ml4n/gKFCzESOfgnnL+oS+WvlhnYIQxFGnFixc3ntpMTk6Wl5eXQ+qpVq2asXzy5Mkcy//yyy/Zri9RooQeeOABY0jO7777ThMmTLCtkXk0bdo0TZs2LV/rzIu1a9fqww8/NF4/9thjevfdd63al7+/v0qVKqVbt24pLi5Ov/76q/r3759lects/wcffNBu80O7ublpwIAB+vLLLyVJ69atU8eOHdMNgztgwIB8mzfakq+vr5o0aWLMMb5+/focTwidMXyvPVWrVk316tUzTsiWLVumN99806p9pZ3gSXeHh80uq3358uXG8PBAfiNuWs+V4mZBYY+42aVLF3l6eiolJUWHDh3SyZMn1bhxY4e33Vo1a9ZUgwYNjHgcERGR53107dpVn376qaS7T139+eefVo2WWq9ePfn5+Sk8PFwmk0nr1q3T6NGjs91mxYoVxrJlwn5eWI6Qcvz48Rz/Zrdv39amTZsy3R4oqojn1nPFeH79+nWNHTvWuClbrVo1LVq0KN1Tvs5StmxZtW3bVlu2bJGUMe51795da9eulSStXLlSL7zwgs3TZ1pun5vrMmfFQ0nq0aOHli9fLinjiAj3XoNm5/bt28a/I4CsEV+t54rxNbd27tyZLt7YayaL7OR0Xdm9e3cFBwdLkr7//ns99thjNt+Pzmt8bdOmjTHSWWRkpP744w9jpLTMpKamauXKlcZra6/73Nzc9NBDD2nnzp2SMv7bWMbXnKYKu379urZu3WpVO4DChhhpPVeJkXk5znfeecfohx0yZIjd/33oiy1Y6It1PkYYQ5FmzdCN1mjRooUROI4cOZJtxu+yZct05syZHPdpOZTn0qVLtWvXrly3pyBNqeQIW7Zs0YQJE4zpFQYMGKApU6ZYHby9vLzSPZk7b968LC/4Tp8+bUz7IEkjR460qs6sDBo0yFjesmWLYmJijJvj967Pb48++qixvHjxYl28eDHLslu3bk039Gdh9eSTTxrLCxcuNG6W5FWNGjWM5XufTLMUFham+fPnW1UHYA/EzaLJ3nGzILE1blauXNkYXtxsNuvtt9/Ocbj6NKmpqYqMjLSi1Rnldj8mkyndk1KWQ+fnVsuWLdW2bVtJ/zvm+Pj4PO/Hzc1Nw4cPN15/9tln2f5ubN26VX/88YfxesSIEXmuU7rbgXL//fcbr6dMmZLtxf3cuXONm/y+vr4aMGCAVfUChQnxvGhyRDyPiIjQmDFjjGkfKlWqpEWLFqlq1ap2aXNWLKcUyYnlNFSW01NJd6daqVWrlqS7f/9JkyYZ/z45iY+PV0JCQob3Lb8/ljE3K/aOh0lJSbmOy5b/NveeE1hegwYFBWU7Msu0adOcNgUPUJgQX4sme8fXvMS46OhoTZ8+3XjdrFmzdMkQeWWv68oRI0aodOnSkqTQ0NA83auMjIzMNObkNb6WLl1a/fr1M17PmDEj22v1pUuXGqOEuLu7p4vN0t1p6XI7jVl25x6W8TW7ZDCTyaSJEyfSEQ2XQYwsmrin7Bz0xdIXm99IGEOh1KNHDzVq1EiNGjXSO++8k2W5Bg0aGMs5ZZLbws/Pz3hqxWw264033sgwZGJKSooWLFigTz75JMc5c6W7o14NGTLE2Pa5557Tf/7znyxvHCYmJiowMFDPP/+8nn/+eRuPqOAKCgrSG2+8YVx49uzZU9OnT0+XNWyN5557zrgQDgsL0wsvvJDhAv/kyZMaN26ccaHXoUMHdenSJct9Xrp0yficNmrUKF2iWVbq1q2r5s2bS7p7IfvBBx8Yf/PmzZurbt26Vh2fPTz66KPGzfiEhAQ9+eSTOnbsWIZyv/zyiwICAnL1OS/ohgwZojZt2ki6+2TMM888o2XLlmV6sZ+UlKTffvtNL774YoZ1Dz30kLE8bdq0DFOpSHdHeRk1apTi4+PtNmodkIa4mRFxs+BdBjgrbr722mvy8/OTdHeo+scee0w7duzIsvy1a9e0aNEi9e3bN93IVbb417/+pZEjR2rNmjWKjY3NtExUVJTef/9942aUr6+vEaPy6v333ze+V8eOHdMTTzyhI0eOZFo2PDxc33zzjb7++usM68aMGWNMoxUdHa0xY8boxIkTGcpt3Lgx3ZNhDz30ULqkr7x644035OHhIUkKDg7Wyy+/nOHJ76SkJM2aNUuLFi0y3nvppZfk4+Njdb2AsxHPMyKeWx/PY2Ji9NRTT+n8+fOSpHLlymnhwoWqWbOmze21jOdpo1paWrp0qQYNGqTvvvsuy06W+Ph4zZkzRyEhIZIkDw8Pde7cOV0ZDw8PTZo0yYgJq1at0nPPPZdtx9GJEyf0r3/9S927dzcS5SxVr15dJUuWlCRdvnw5V9N72DMe3rhxQ927d9f06dONY8/Mzp070/3bdu3aNd36Bx54wDiOCxcuaPz48RnOMdLOnX744QeuQeHSiK8ZEV+tj68ffPCBXnnlFQUFBSklJSXTMqmpqdq2bZsef/xxnTt3znj/rbfeynK/ublettd1ZalSpdKNsDN//nyNHz9eV65cyXSfZrNZBw4c0KRJk/TQQw9l+iC25ffnyJEjWe7L0osvvmjEp7CwMD3zzDMZOs1TU1O1ePHidKPPjBw50phqLE1oaKh69OihTz/9VGfPns20PpPJpE2bNmnp0qXGe/fG127duhmJEvv27dP06dMzHG94eLhefvll/fHHH8RXFHrEyIyIkdxTdjT6YumLzW9MSYkirU+fPkaH38yZMxUUFKQGDRqk+/EcN26cypQpY3Ndr7/+uvbu3avU1FSdPHlSffr00QMPPKDKlSsrOjpawcHBioiIkLe3t958801NmTIlx31OnjxZ4eHh2rFjh5KTkzV79mx98cUXatmype677z4VK1ZMsbGx+uuvv3TmzBnjKZlmzZrZfDwFUWRkpF5++WUjQHh4eKh8+fL65z//mavtBw0alG4uZEvlypXT7NmzNW7cOKWkpGjnzp166KGH1LFjR5UvX15//fWX9u/fb2TSV65cWf/617/sc2D3GDhwoBH8N2/enK79zlSiRAlNnTpVTz31lO7cuaOLFy/qscceU6tWrVSvXj0lJyfr6NGjCgsLkyRNnDhRkydPlqRC+8SBp6en5s6dqzFjxigsLEy3b9/W5MmTNXfuXLVt21Z+fn4ymUy6fPmyQkNDFRcXp1KlSmXYz5gxY7R8+XJFRkYqOjpazzzzjJo1a6Z69erJzc1Nx48fN5526dy5sypUqMCUIHAK4mbR4si4ef36dT333HMZ3v/rr7+M5R9++EGBgYHp1leqVElfffVVbg8hV2yNm5UrV9bnn3+u5557TlFRUTp//ryefvppVa5cWS1btlT58uWVnJysqKgonTlzJtPOZVuZzWYFBwcrODhYHh4eqlu3rurWrasyZcrozp07un79ug4ePJjuInn8+PEqUaKEVfU1a9ZMn3zyiSZMmKCUlBQdP35cw4cPV506ddS0aVP5+voqLi5OZ8+e1ZkzZ5Samprp9FplypTRrFmz9Oyzz+r27ds6f/68hgwZku7c4MiRI7pw4YKxTe3atXP9GcxK27Zt9eabb2rGjBmSpN9//13du3dXhw4dVLVqVcXExGjv3r2Kjo42tundu7fGjh1rU71AYUE8L1ocFc8nTpyYbhqXBg0a6LvvvsvVPmvVqqUxY8bkqmxWTp48qY8++kiTJ082psYqV66cUlJSFB4eroMHD6YbAezZZ5/NdOSzBx98UJMmTdKkSZNkMpkUFBSk7du3q379+mrUqJF8fHx0584dhYeH6+TJkzmOvuLh4aGePXtqw4YNkqTRo0erS5cuqlq1qpGYVqZMGY0bN87Yxt7xMDY2VgsWLNCCBQtUtmxZNWnSRJUrV1bx4sUVERGhU6dOpeswr127doY4XaZMGT3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" ], "text/plain": [ " sepal_length sepal_width petal_length petal_width species\n", "114 5.8 2.8 5.1 2.4 virginica\n", "62 6.0 2.2 4.0 1.0 versicolor\n", "33 5.5 4.2 1.4 0.2 setosa\n", "107 7.3 2.9 6.3 1.8 virginica\n", "7 5.0 3.4 1.5 0.2 setosa" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "iris_df.iloc[X_test.index[:5]]" ] }, { "cell_type": "markdown", "id": "c75f34d7", "metadata": {}, "source": [ "and compare it to the actual test set" ] }, { "cell_type": "code", "execution_count": 25, "id": "e3e5e687", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " petal_width sepal_length sepal_width petal_length\n", "114 2.4 5.8 2.8 5.1\n", "62 1.0 6.0 2.2 4.0\n", "33 0.2 5.5 4.2 1.4\n", "107 1.8 7.3 2.9 6.3\n", "7 0.2 5.0 3.4 1.5" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_test.head()" ] }, { "cell_type": "markdown", "id": "e69b22df", "metadata": {}, "source": [ "the columns are out of order because of how we created the train test splits, but the data are the same. \n", "\n", "We split the data so that we can be sure that the algorithm is generalizing what it learned from the test set.\n", "\n", "### y_pred = gnb.predict(X_test). What is this predicting?\n", "\n", "This uses the model to find the highest probability class for each sample in the test set.\n", "\n", "### What is the best number of samples to use to have the most accurate predictions?\n", "\n", "That will vary and is one of the things you will experiment with in assignment 7.\n", "\n", "### need more clarification on the confusion_matrix. not sure what its doing if we already proved y_pred and y_test are the same\n", "\n", "In this case, it does basically repeat that, except it does it per species (class). Notice that all of the numbers are on the diagonal of the matrix.\n", "\n", "### Can we use this machine learning to predict MIC data of peptides based off their sequences and similarly output data for new synthetic AMPS?\n", "\n", "Possibly, but maybe a different model. I would need to know more about the dat here and I am not a biologist. We can discuss this in office hours.\n", "\n", "### If more samples of data increases accuracy of the model, is there ever a point where adding more samples of data does not increase accuracy?\n", "\n", "More samples increases the reliability of the estimate, and generally increases the accuracy, but not always. We will see different ways that more data does not increase over the next few weeks. You will also experiment with this in your next assignment (I give you the steps, you interpret the results)" ] } ], "metadata": { "jupytext": { "text_representation": { "extension": ".md", "format_name": "myst", "format_version": 0.13, "jupytext_version": "1.14.1" } }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.16" }, "source_map": [ 12, 22, 24, 27, 37, 43, 46, 50, 54, 58, 60, 66, 70, 74, 76, 81, 83, 133, 141, 143, 161, 163, 168, 170, 173, 175, 178, 182, 184, 189, 191, 195, 212, 216, 220, 222, 235, 240, 244, 248, 250, 262, 264, 267, 269 ] }, "nbformat": 4, "nbformat_minor": 5 }