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Patent_Classification.ipynb
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Patent_Classification.ipynb
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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Patent Classification.ipynb",
"version": "0.3.2",
"provenance": [],
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/github/rcmckee/dsi-Capstone/blob/master/Patent_Classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"metadata": {
"id": "YMXrGSVuFK0i",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import scipy.stats as scs\n",
"import statsmodels.api as sm\n",
"import matplotlib.pyplot as plt\n",
"\n",
"%matplotlib inline\n",
"%config InlineBackend.figure_format='retina'"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "QJ8RnnDnGfVl",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 51
},
"outputId": "8bb32283-2c6a-4ee4-c96f-beb047dc11cf"
},
"cell_type": "code",
"source": [
"# Code to read csv file into Colaboratory:\n",
"!pip install -U -q PyDrive\n",
"from pydrive.auth import GoogleAuth\n",
"from pydrive.drive import GoogleDrive\n",
"from google.colab import auth\n",
"from oauth2client.client import GoogleCredentials\n"
],
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"text": [
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"\u001b[?25h Building wheel for PyDrive (setup.py) ... \u001b[?25ldone\n",
"\u001b[?25h"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "QcA3OHBhGsVC",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"# Authenticate and create the PyDrive client.\n",
"auth.authenticate_user()\n",
"gauth = GoogleAuth()\n",
"gauth.credentials = GoogleCredentials.get_application_default()\n",
"drive = GoogleDrive(gauth)"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "NLQS3r_uGswm",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"downloaded = drive.CreateFile({'id':'1UcoQDxQe5MGruMUoD4013HrTz346OH8i'}) \n",
"downloaded.GetContentFile('small_700_through_710_descr_clm_code.csv') \n",
"df = pd.read_csv('small_700_through_710_descr_clm_code.csv')\n",
"# Dataset is now stored in a Pandas Dataframe\n"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "ESVoFcmdH6Uo",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
},
"outputId": "24bd29af-01f2-4f8d-e514-5d558fbb1502"
},
"cell_type": "code",
"source": [
"df.head()"
],
"execution_count": 14,
"outputs": [
{
"output_type": "execute_result",
"data": {
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" <th></th>\n",
" <th>Unnamed: 0</th>\n",
" <th>descr</th>\n",
" <th>clm</th>\n",
" <th>code</th>\n",
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" <td>700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
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" <td>700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>FIELD OF THE INVENTION \\n The present inve...</td>\n",
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" <td>700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>RELATED APPLICATION \\n This application cl...</td>\n",
" <td>What is claimed is: \\n \\n 1 . A me...</td>\n",
" <td>700</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 descr \\\n",
"0 0 This application claims priority under 35 U.S.... \n",
"1 1 BACKGROUND \\n 1. Field of Invention \\n ... \n",
"2 2 CROSS-REFERENCE TO RELATED APPLICATIONS \\n ... \n",
"3 3 FIELD OF THE INVENTION \\n The present inve... \n",
"4 4 RELATED APPLICATION \\n This application cl... \n",
"\n",
" clm code \n",
"0 What is claimed is: \\n \\n 1 . A pr... 700 \n",
"1 What is claimed is: \\n \\n 1 . A st... 700 \n",
"2 What is claimed is: \\n \\n 1 . A me... 700 \n",
"3 1 . A method for state-transition-controlled p... 700 \n",
"4 What is claimed is: \\n \\n 1 . A me... 700 "
]
},
"metadata": {
"tags": []
},
"execution_count": 14
}
]
},
{
"metadata": {
"id": "MDJWwaFtFdxz",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
},
"outputId": "f226c74a-7d23-492c-8080-f3db770a0a33"
},
"cell_type": "code",
"source": [
"df.drop('Unnamed: 0',axis=1, inplace=True)\n",
"df = df[(df['code']==705)|(df['code']==706)|(df['code']==700)]\n",
"df['descr_clm'] = df.descr + df.clm\n",
"df.drop(['descr','clm'],axis=1, inplace=True)\n",
"df['code'] = df['code'].astype('category')\n",
"df.head()"
],
"execution_count": 15,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
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"</div>"
],
"text/plain": [
" code descr_clm\n",
"0 700 This application claims priority under 35 U.S....\n",
"1 700 BACKGROUND \\n 1. Field of Invention \\n ...\n",
"2 700 CROSS-REFERENCE TO RELATED APPLICATIONS \\n ...\n",
"3 700 FIELD OF THE INVENTION \\n The present inve...\n",
"4 700 RELATED APPLICATION \\n This application cl..."
]
},
"metadata": {
"tags": []
},
"execution_count": 15
}
]
},
{
"metadata": {
"id": "5etELzCVJEGg",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"# Data exploration"
]
},
{
"metadata": {
"id": "6KDJrO7fIs8m",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 85
},
"outputId": "13bc5313-1a35-4334-9930-797816a22529"
},
"cell_type": "code",
"source": [
"df['code'].value_counts()"
],
"execution_count": 16,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"706 1000\n",
"705 1000\n",
"700 1000\n",
"Name: code, dtype: int64"
]
},
"metadata": {
"tags": []
},
"execution_count": 16
}
]
},
{
"metadata": {
"id": "JD8bXdGTIsrW",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"df['category_id'] = df['code'].factorize()[0]"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "EcJP6L6jIyEk",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 85
},
"outputId": "c4692e5a-f1f3-424a-cc0a-c02bf0d107d4"
},
"cell_type": "code",
"source": [
"df['category_id'].value_counts()"
],
"execution_count": 18,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"1 1000\n",
"2 1000\n",
"0 1000\n",
"Name: category_id, dtype: int64"
]
},
"metadata": {
"tags": []
},
"execution_count": 18
}
]
},
{
"metadata": {
"id": "MZp3DjrlI1Ak",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"category_id_df = df[['code', 'category_id']].drop_duplicates().sort_values('category_id')\n",
"category_to_id = dict(category_id_df.values)\n",
"id_to_category = dict(category_id_df[['category_id', 'code']].values)"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "GIw0FGdxI3sm",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "a7dcf91a-9b69-428b-b071-f4b450351120"
},
"cell_type": "code",
"source": [
"id_to_category"
],
"execution_count": 20,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{0: 700, 1: 705, 2: 706}"
]
},
"metadata": {
"tags": []
},
"execution_count": 20
}
]
},
{
"metadata": {
"id": "1aH1wZ_yI6tV",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
},
"outputId": "400ef8fe-be54-41cf-8f28-4823c2af6f34"
},
"cell_type": "code",
"source": [
"df.sample(5, random_state=0)"
],
"execution_count": 21,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" }\n",
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" vertical-align: top;\n",
" }\n",
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" .dataframe thead th {\n",
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"</style>\n",
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>code</th>\n",
" <th>descr_clm</th>\n",
" <th>category_id</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2077</th>\n",
" <td>700</td>\n",
" <td>CROSS-REFERENCE TO RELATED APPLICATION(S) \\n ...</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3401</th>\n",
" <td>706</td>\n",
" <td>BACKGROUND \\n Many systems are instrumente...</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4393</th>\n",
" <td>706</td>\n",
" <td>FIELD \\n The subject matter disclosed here...</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10648</th>\n",
" <td>705</td>\n",
" <td>This application is a continuation of U.S. pat...</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10687</th>\n",
" <td>705</td>\n",
" <td>BACKGROUND \\n 1. Technical Field \\n Em...</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" code descr_clm category_id\n",
"2077 700 CROSS-REFERENCE TO RELATED APPLICATION(S) \\n ... 0\n",
"3401 706 BACKGROUND \\n Many systems are instrumente... 2\n",
"4393 706 FIELD \\n The subject matter disclosed here... 2\n",
"10648 705 This application is a continuation of U.S. pat... 1\n",
"10687 705 BACKGROUND \\n 1. Technical Field \\n Em... 1"
]
},
"metadata": {
"tags": []
},
"execution_count": 21
}
]
},
{
"metadata": {
"id": "xJNaURyuJLcg",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 287
},
"outputId": "69ca4d0f-0fd8-4150-ce3d-b5d725a9c9c6"
},
"cell_type": "code",
"source": [
"df.groupby('code').descr_clm.count().plot.bar(ylim=0);"
],
"execution_count": 22,
"outputs": [
{
"output_type": "display_data",
"data": {
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8xZzzY9XF1ZnHqT1re3z39vje6nZjjBtdhVoAADg0DjrQv6nNsps7H+Pc0eB99EbYN1Zn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\nQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"image/png": {
"width": 378,
"height": 270
}
}
}
]
},
{
"metadata": {
"id": "o__UXblgJTYT",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"# Tf-idf Vectorizer"
]
},
{
"metadata": {
"id": "TjikdGnQJN04",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "63382c28-dbda-4afd-e866-cc5761bcefae"
},
"cell_type": "code",
"source": [
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"\n",
"tfidf = TfidfVectorizer(sublinear_tf=True, min_df=5, norm='l2', encoding='latin-1', ngram_range=(1, 2), stop_words='english')\n",
"\n",
"features = tfidf.fit_transform(df['descr_clm']).toarray()\n",
"labels = df['category_id']\n",
"features.shape"
],
"execution_count": 23,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(3000, 110325)"
]
},
"metadata": {
"tags": []
},
"execution_count": 23
}
]
},
{
"metadata": {
"id": "-uCj_tMaJiIz",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"**3000 applications, represented by 110325 features, representing the tf-idf score for different unigrams and bigrams.**"
]
},
{
"metadata": {
"id": "hnb2et8SKA8a",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"# Top 10 Uni-Grams and Bi-Grams for each Patent class"
]
},
{
"metadata": {
"id": "xoxJm48wJc5S",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"from sklearn.feature_selection import chi2"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "GwFF_gUtJ-z0",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1190
},
"outputId": "a7d6525f-de58-4005-d8a7-7ca2f1d5d448"
},
"cell_type": "code",
"source": [
"N = 10\n",
"for code, category_id in sorted(category_to_id.items()):\n",
" features_chi2 = chi2(features, labels == category_id)\n",
" indices = np.argsort(features_chi2[0])\n",
" feature_names = np.array(tfidf.get_feature_names())[indices]\n",
" unigrams = [v for v in feature_names if len(v.split(' ')) == 1]\n",
" bigrams = [v for v in feature_names if len(v.split(' ')) == 2]\n",
" print(\"# '{}':\".format(code))\n",
" print(\" . Most correlated unigrams:\\n . {}\".format('\\n . '.join(unigrams[-N:])))\n",
" print(\" . Most correlated bigrams:\\n . {}\".format('\\n . '.join(bigrams[-N:])))"
],
"execution_count": 26,
"outputs": [
{
"output_type": "stream",
"text": [
"# '700':\n",
" . Most correlated unigrams:\n",
" . temperature\n",
" . direction\n",
" . motor\n",
" . position\n",
" . sensor\n",
" . controlling\n",
" . power\n",
" . robot\n",
" . control\n",
" . controller\n",
" . Most correlated bigrams:\n",
" . electric power\n",
" . control signal\n",
" . robot according\n",
" . power supply\n",
" . control method\n",
" . method controlling\n",
" . controller configured\n",
" . control unit\n",
" . perspective view\n",
" . control device\n",
"# '705':\n",
" . Most correlated unigrams:\n",
" . card\n",
" . price\n",
" . sale\n",
" . financial\n",
" . transactions\n",
" . credit\n",
" . purchase\n",
" . merchant\n",
" . transaction\n",
" . payment\n",
" . Most correlated bigrams:\n",
" . debit card\n",
" . financial transaction\n",
" . account number\n",
" . payment transaction\n",
" . mobile device\n",
" . account associated\n",
" . goods services\n",
" . service provider\n",
" . credit card\n",
" . point sale\n",
"# '706':\n",
" . Most correlated unigrams:\n",
" . model\n",
" . classifier\n",
" . neurons\n",
" . neuron\n",
" . artificial\n",
" . prediction\n",
" . probability\n",
" . neural\n",
" . training\n",
" . learning\n",
" . Most correlated bigrams:\n",
" . learning algorithm\n",
" . model based\n",
" . training set\n",
" . artificial neural\n",
" . knowledge base\n",
" . artificial intelligence\n",
" . neural networks\n",
" . training data\n",
" . machine learning\n",
" . neural network\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "SiCi9DuFK9Rg",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"# Model training and evaluation"
]
},
{
"metadata": {
"id": "wzY8wzPVK3-w",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.naive_bayes import MultinomialNB\n",
"from sklearn.model_selection import cross_val_score"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "iMSeFmdLLCov",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 377
},
"outputId": "c8e476e3-9580-4241-a2b9-a2463564194f"
},
"cell_type": "code",
"source": [
"models = [\n",
" RandomForestClassifier(n_estimators=400, max_depth=50, random_state=0),\n",
" MultinomialNB(alpha=0.01),\n",
" LogisticRegression(random_state=0, C=0.9),\n",
"]\n",
"CV = 5\n",
"cv_df = pd.DataFrame(index=range(CV * len(models)))\n",
"entries = []\n",
"for model in models:\n",
" model_name = model.__class__.__name__\n",
" accuracies = cross_val_score(model, features, labels, scoring='accuracy', cv=CV)\n",
" for fold_idx, accuracy in enumerate(accuracies):\n",
" entries.append((model_name, fold_idx, accuracy))\n",
"cv_df = pd.DataFrame(entries, columns=['model_name', 'fold_idx', 'accuracy'])"
],
"execution_count": 70,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
" FutureWarning)\n",
"/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
" \"this warning.\", FutureWarning)\n",
"/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
" FutureWarning)\n",
"/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
" \"this warning.\", FutureWarning)\n",
"/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
" FutureWarning)\n",
"/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
" \"this warning.\", FutureWarning)\n",
"/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
" FutureWarning)\n",
"/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
" \"this warning.\", FutureWarning)\n",
"/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
" FutureWarning)\n",
"/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
" \"this warning.\", FutureWarning)\n"
],
"name": "stderr"
}
]
},
{
"metadata": {
"id": "Zpd2tHqDLKD_",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"import seaborn as sns"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "_2E8rXiDLPA-",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 333
},
"outputId": "1d82f2fe-f7ea-44e0-856b-903ff68a61d6"
},
"cell_type": "code",
"source": [
"sns.boxplot(x='model_name', y='accuracy', data=cv_df)\n",
"sns.stripplot(x='model_name', y='accuracy', data=cv_df, \n",
" size=8, jitter=True, edgecolor=\"gray\", linewidth=2);"
],
"execution_count": 71,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:454: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
" box_data = remove_na(group_data)\n"
],
"name": "stderr"
},
{
"output_type": "display_data",
"data": {
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48c6+wrYN3k9kGkQmA47Xo9C+lewdo/EFqTrk2OFsssiY5CZdOt/YR9ebLbjd\nyf7HY0m6t7fTvb0dX22Q6neMp2pGbRlaKiJSORQYRArgC0SIzDydzs1P4Gbv5dC53fvJx/ETmbEE\nf1VD/joictDceJLW53cS29k5eGUg2Raj7YVdJFq6ibxjvHobRETy0BwGkQJ5E8jfizOEYUVOIEz1\n7KUEag4pYctExE26tP55gLAwQBbotPvofH1f/goiImOcehhEhsBXVU/N3L8j3rqZ7r1vkszeBbq3\nXgPBcYcSbJiD4w8OcytFxp7O1/YS25UVFgIO/kNrCBxag9MQABeSu6Ik1rWT2NTZ7/zA+CpCU4aw\nMaOIyBihwCAyRI7PT7BhLsGGuSS69pLoaMJNRMF1cfwhfJGJ+COTNLxBZJi48SRdb7VklDnjg1Sd\nMQmnOm2FMwf8U8L4p4RJLowSfXI3pM1z6HxjnwKDiEgOCgwiB8EfHo8/XOAmbiJSEtEtbbjxtIUG\nQg5VSyfhRPLvleJrrCJ02gS6H+tb+jje3EV8fzeB+lApmysiUnE0h0FERCpa14b9Gc8Dh9UOGBZ6\n+KeE8U3ODAfRrGuJiIh6GEREiiYej/f9eV+UlhUDrJ4lReG6Lol9mSuX+RfUFHy+/9Aakrv6zu/a\n1Eq8pXuAM0a/+L5o758TifgANUVkrFBgEBEpkubmpr4ncZd4c1f5GjNWVfnw1RT+q803MWv4kf7e\nMjQ1NQ1eSURGPQ1JEhGR0cMdvMpB1RcRGYPUwyAiUiSTJjXS0pJazz/gEBhXVd4GjQGu65LY3TeE\nhu4kyf0xfPWFLWecbMoafqS/N29IUmoSeWNjY5lbIyIjgQKDiEiRBAJ9/6QGxlXRcNq0MrZm7Gh5\nahvxPWnj7te14zt+XEHnxt9sy3genltPzVETi9q+StOyYnvvsCy/Xx8TRERDkkREpMKF59ZnPI+v\nayfZNvhk3cTmTtzdsQGvJSIiCgwiIlLhQjNqcEJpv87iLt2PNZFsieU9J7G1k+6VezLKgodE8Ndq\nZ3YRkWzqaxQRkYrm+H1EDhtHxyt9AcBtSxB9cCf+2RH882vwjQ9CEhK7oiTWtZPcGe13nYgpbBiT\niMhYo8AgIiIVL7yggfi+KN1b2/sKXUhs7CSxsXPQ86uPmUhwUqSELRQRqVwakiQiIhXPcRxqF00m\nNLN2yOdWHz2RyPyGErRKRGR0UA+DiIiMCo7PoXZRI9FJYTrXtZBsyz+HASAwKUz1wvEEJ6tnQURk\nIBUbGIwxE4DrgQ8BU4Fm4CHgOmvtjgLOvwS4HDgGCAGbgQeBG621u7Pq+oDPA58BDgVagMeBr1pr\n1xfrNYmIyMFxHIfw3Hqq5tQHLPlnAAAgAElEQVQR29VJdGMr8b1R3FgSHPBV+Qk0RgjPrSfQEBr8\ngiIiUpmBwRgTAZ4CFgI/AFYBC4B/BZYaY4631u4d4PxvAV8BXgCuBdqAk4Ergfenzt+fdsrPgUtT\nj98BDgOuBs4wxhxlrW0u7isUEZGD4TgOoUOqCR1SXe6miIhUvIoMDMAXgKOAK6y1P+opNMasAZYD\n1+F9oO8n1TPxb8BG4DRrbc9SGT83xjQDXwYuA76fqv9+vLBwvbX2G2nXscDNwFLgnmK+OBERERGR\nkaJSJz1fCrQDP80qfwDYClxijHHynDsLLyi9kBYWeqxIPc5JK7sC2A/8r/SK1tr/sdbOsNYqLIiI\niIjIqFVxPQzGmHq8oUjPZH/gt9a6xpgXgPOBuUCu+QUbgCjeEKZsc1KPr6Tu5QfOAB6z1namykJA\nwlqbOPhXA42NdcW4TIZg0F/0a4qUUzDoL8l7pdj03pPRRu89kfIZSe+/SuxhmJ163Jrn+ObU47xc\nB621LcA3gXcaY243xsw3xkxODT36d2A1cGeq+lygClhnjLnYGPMaXtiIGmMeN8YcX4TXIyIiIiIy\nYlVcDwPQE7U68hxvz6rXj7X2JmPMTuB2vNWPejwIXGqt7Uo9n5B6XAp8GG9Y0nrgXcA1wApjzGJr\n7dohv4qUpqbWAz01r1isKJ0fIiNGLJYoyXul2PTek9FG7z2R8inF++9AeywqMTAcNGPM54DbgEeA\nu4Am4ES8EPCQMeYca+0+vOVWwVtK9Rhr7Zup5w8aY17H64m4Hi9MiIiIiIiMOpUYGHqWO63Jc7w2\nq14GY4zBCwuPW2vPTTv0x9QqS/fjLbV6Dd5yqwDPpoWFHncBPwHePaTWi4iIiIhUkEqcw7ABcIEZ\neY73zHFYl+f4UrygdF+OYw+nrn1G6vnG1GO/2VTWWhevZ6J+0BaLiIiIiFSoigsM1tp24GXgOGNM\nOP1YalWjk4Et1trNuc6nr2cinONYFeD0HEsNS3odOMIYk9EbY4wJ4u0wnW/ytYiIiIhIxau4wJDy\nU6Aa+GxW+SXAZLyhQgAYYxYaY+am1VmZevxIjr0almXVAW935ynA57LqfhYIAr8bcutFRERERCpE\nJc5hAPgx8DHgVmPMbGAVcATe7s5rgVvT6r4GWLy9G7DWrjTG/AYvHDxrjLkHb2jRCXibtO0Ebko7\n/zbgAuB7qeCxOlX3c8CWrLoiIiIiIqNKRfYwWGtjwFl4y6JeANwBfILUJGRrbb4lV3tcBFyJNwTp\nptT55wE/A45PH86UWmL1PXgh5LzUPT6cOudEa+2uIr0sEREREZERp1J7GLDW7sfrUbh6kHrZw45I\n7dL8g9RPIfdqA76U+hERERERGTMqsodBRERERESGhwKDiIiIiIjkpcAgIiIiIiJ5KTCIiIiIiEhe\nCgwiIiIiIpKXAoOIiIiIiOSlwCAiIiIiInkpMIiIiIiISF4KDCIiIiIiklfF7vQsIiIiIpVpdyLO\n69Eu9iYSdLsufseh2nGYHQoxP1iF33HK3URJo8AgIiIiIiXnui4bYt28HO1kRzyes86bsW7+5LRz\neFWYY6sihH0aDDMSKDCIiIiISEklXJcVHW283h0dtG6X6/JSVydvRKOcW1fPRL8+rpabYpuIiIiI\nlEzSdXmsvbWgsJCu3U1yf2sLexK5eyNk+CiyiYiIiEjJ/KWrg/Wx7n7ls3A40vExHoduYL2b5BWS\npMeKbtfl9237ubBuHFUanlQ2CgwiIiIiUhKdySSruzozyhqADzkBpjqZAWC+4+N01+UJN8Fqkr3l\nbckkr0S7OD5SPRxNlhwU1URERESkJF7v7kr76A8R4GIn2C8s9Ag5Dmc7fo7O+oj6arSLpOuWrqEy\nIAUGERERESk613X5W7Qro2yx46dhkCVTHcfhDMefMQymzU2yKcewJhkeGpIkIiIio0ZzIsEDrfvK\n3YxBJVyX1mSS+Cj+1twFYmnP/dCv5yCfiONwuOtjbVr/xCPtrQTaW4fcjoDjUO/z4auAvR2aE4ly\nNyEnBQYREREZNbpdl+151viX8joEh8gQPrTPcRzWpuWpJHAgfQzdrkvHCP0gXik0JElERERESm6o\n31IHGfk9AmOFehhERESkos2cObvcTRiyeDzO7t1NRKOjd1x+MpkgGu1bJHUPLq7r4hTYy9BM5nAt\nv99PKFQ15HZUVYWYNKkRf4VtADeS/r+urP9yIiIiIlkuvvjScjdBcujs7OBf/uVy4qkhYm3ABlzm\nFdBz4Loua93MYUQXXPAR/u7v3l+KpsogNCRJRERERIouEqlm0aITM8r+7CZwC5jo/QYue9Oe+/1+\nTjppSZFbKIVSYBABXDdBvKOJ2P4txFo2Em/dRrJ76CsxiIiISJ+lS8/MeL4Zl8cHCQ073CQPuZkT\n148//l00NIwrSRtlcBqSJGNaMtZObO9bxPa9hZuI9jvujzQSHH8ogfoZOI6/DC0UERGpXPPnL2D+\n/EN56603e8tWkWSH63ICfhbg4E/NadjturyY2uU5e02js846ZxhbLdkUGGRMct0k0V0vE9tjgfzf\nciQ6m0h0NuHsqiY8/SQC1Y3D10gREZEK5zgO//iPV3DTTdfT2rq/t3wbLtvcOFVAvevQjUtLnmtc\ncMFHmDfv0GFpr+SmIUky5rhukq5tK4nteZ2BwkLGOfEOOjc/Sbx1W2kbJyIiMspMnnwIV1/9Jerr\n6/sdiwJNA4SFc875e973vg+UtH0yOAUGGVNc1yW64y/EW7f2P+gLQs1MqJsHVZNynJykc9tKEh3N\npW+oiIjIKDJ79lyuvfbrLFz4joLq19bWcdlln2HZsosKXoZVSkdDkmRMSbTvINayIbPQH4GJx0H9\noV5o6BHdC3vXwv43+srcBJ3b/0zN/PfhOMrbIkPlui7x3V3Ed0dJxrxRyr6gn8DEMIGJVfpgIDKK\nTZ58CNdc81U2b97EU089xnPPPZuxTwN4cx6WLj2TRYtOJBgM5rmSDDcFBhlTuvesyywI1sGM90Ow\npn/lqvEw5TQIN8KuP/UWu7E2Eu1vE6idVuLWiowebixJdHMrXev3k2iN5azjrwsSnldP1aw6nKAC\nuchoNWvWbC699B+4+OJPsGfPbjo7OwgEgjQ0NFBbW1fu5kkOCgwyZiS7W0m078gsnHJ67rCQbtzh\n0LkDWtf3FnXvfVOBQaRA8b1RWp97m2RX9ronmRKtMdrX7KbzjX3UnTSFwLih7+gqIpUjEAgwefIh\n5W6GFEBf4ciYEWvZmFkQboTIlMJOHndkxtNE23aS8a7iNExkFIvt7qJlxfZBw0K6ZGeClqe3E9ut\n95iIyEigHoZRLtG1j45Nj5e7GQNy3SRurB03WfgHigOSzNwEhrr5hZ8bboRgPcT6loRrf/N3UIHz\nGByfHydYM+LnYCS69pW7CXKQEm0xWp97GxJZq5E54JsexjcpBECyuZvktq7MRcsSLq3PvU3DGdPx\n12gcs4hIOSkwjHbJGImOpnK3YmQK1hZe13G8+mmBATfh/VQYNxnDVe+IDIOOv+3B7U5mlPkPrSF4\ndD1OdeZGiG5HgtjL+0m82d5X1p2k45U91J2oIQsiIuU0sr9iFCmlRPcQ6/ffCVpEckt0xune3p5R\nFjiqjtDi8f3CAoBT7Se0eDyBIzMnPHZvbyfZGe9XX0REho96GEahmTNnl7sJQxKPx9m9u4lodIgf\n4Ieou7ubRCLtg0f7Jmg4rLCTY20Q3ZNRVFUVxuervMxdVRVi0qRG/P7KeftX2v/TAtEN+zOGGDkN\nAQJH99+0KVvgmHoSmztx96feqy50bWyl+vDxJWqpiIgMpnI+MUjBLr740nI3YUR6443Xufnmb/QV\ntG32gkAhQ5P2vUb6p5+ZM2dzww3f0prxkleipZuWFdvL3YxBuUmXZEccN54cvPJQxDPnLQQOqy3o\n/eI4DgFTS+wvfXNYOl/bS+e6yp/T4gR8+KoDOL6R/e9GoqW0X96ISOVRYJAxY8ECw/TpM9m2bUuq\nxIWdz8D0sweevNzVBPteySg644z3KizIgNxYkniz5or08M+MDKluemAA+gWQSuTGEySGsFqUiMhI\nUXnjKUQOkOM4LF16ZmZhxzbY+gfozvHtpZuE/etg60MZk5sjkQiLF59S4taKjDJVQ/h1M5S6IiJS\ncuphkDFlyZLTWbHiSTZt2tBX2LkdNv4WqqdB9QzwhSDWAq1vQbyj3zUuvPBjhMPhYWy1VIpKnGtR\nqjlEnZ2Z7x23M4FTW9ivHLez/7fwkUh1UdpVTpo/JCKVynHdyu/mrWRNTa36CxhmLS37+Pa3v86u\nXTuHfO773vcBPvzhj5agVSKjyw03XMvmzRt7nweOrSd45OCTngFir+wnvrpvCeNZs+Zwww3fKnYT\nRUTGnMbGugMaT61+XxlzGhrG8ZWv3MDcuYVv3OY4DsuWXcQFF3ykhC0TGT1OOilz2F7ijXbc7A3c\ncnATLok3MpdjPfnkJUVtm4iIDE3F9jAYYyYA1wMfAqYCzcBDwHXW2h0FnH8JcDlwDBACNgMPAjda\na3cPcJ4DPAmcDlxmrb3jYF6HehjKJ5FI8MILz/HEE4/y1lvrctapqqpi8eJTeM97zmbGjJnD3EKR\nytXW1sYXv3gFsVist8w/v5rg4vF5FwxwXZfYn/eSeKtvOFMoFOI//uMH1NQMYaNFERHJ6UB7GCpn\nIGUaY0wEeApYCPwAWAUsAP4VWGqMOd5au3eA878FfAV4AbgWaANOBq4E3p86f3+e0z+NFxakwvn9\nfk46aQknnbSETZs2smbNi7S0tBCLxaiujjBt2gwWLTqR6urKHzstMtxqa2s56aQlrFjxZG9Z4q0O\n3I6Et9PzpFBvcHBdF7e5m9jL+0nuyNwgcfHiUxQWRETKrCIDA/AF4CjgCmvtj3oKjTFrgOXAdcDV\nuU5M9Uz8G7AROM1a2/Pb6efGmGbgy8BlwPdznDsFuAV4CXhnsV6MlN/s2XOYPXtOuZshMqqcf/6F\nvPrqKzQ3N/WWJXdEie5owhkXxDcx6JXtjuHui/U7f9KkRs4//8Jha6+IiORWqXMYLgXagZ9mlT8A\nbAUuSQ0dymUWXlB6IS0s9FiRepyT59zbgSSg2XciIoOor2/gqquuoaFhXL9j7r4Yibc6vF6HHGGh\noWEcV131JerrG4ajqSIiMoCKCwzGmHq8oUgvZn/gt9a6eMOMGoG5eS6xAYjiDWHKNif1+Er2AWPM\nB4APA9fgzZcQEZFBTJ06na9+9RtD6sGbPXsuX/3qN5g6dVrpGiYiIgWrxCFJPYtCb81zfHPqcR6w\nPvugtbbFGPNN4EZjzO3A94BW4F3AvwOrgTvTzzHG1AE/xOuB+BlFnMPQ2FhXrEuJiIxIjY113Hbb\n93nxxRd56KGHWLVqFdkLbjiOwwknnMA555zDcccdh89Xcd9niYiMWpUYGHo+YfffUcvTnlWvH2vt\nTcaYnXhDjD6fduhB4FJrbVfWKd8GJgNnWmtdY8zQWy0iMob5fD4WLVrEokWLaGpqwlpLa2srAHV1\ndRhjaGxsLHMrRUQkl0oMDAfNGPM54DbgEeAuoAk4EW+40UPGmHOstftSdU8CPoe33OrrxW5LU1Nr\nsS8pIjLChTHmmH6l+vdQRKS0DnRkSyUGhp7lTmvyHK/NqpfBeN0DtwGPW2vPTTv0x9QqS/fjLbV6\njTEmBPwEeBNNdBYRERGRMagSA8MGwAVm5DneM8ch905csBTvdd+X49jDqWufkXr+JeBw4CKgMW0o\nUk+/+XhjzAxgj7U23xApEREREZGKVXGzyqy17cDLwHHGmHD6MWOMH28Dti3W2s25zqevZyKc41gV\n4KQde0/q+a+BLWk/96SO/2fquRYKFxEREZFRqeICQ8pPgWrgs1nll+BNTv5JT4ExZqExJn2J1ZWp\nx4/k2KthWVadrwB/n+Pn2tTx76WeP3bAr0REREREZASrxCFJAD8GPgbcaoyZDawCjsDb3XktcGta\n3dcAi7d3A9balcaY3+CFg2eNMffgTXo+AbgC2AnclKr7XK6bG2PaUn9cY619sLgvTURERERk5KjI\nHgZrbQw4C29Z1AuAO4BP4PUsvLuA+QQXAVfiDUG6KXX+eXh7LBw/wHAmEREREZExxcnePEeGV1NT\nq/4CRERERKTkGhvrsofjF6QiexhERERERGR4KDCIiIiIiEheCgwiIiIiIpKXAoOIiIiIiOSlwCAi\nIiIiInkVbR8GY8xfgP8G7rLW7i7WdUVEREREpHyKtqyqMSYJuEAceBj4JfA7a213UW4wSmlZVRER\nEREZDiNhWdXFwPfxdkr+AHAPsMMY87+NMScX8T4iIiIiIjJMSrJxmzHmFOAjeLswT8XredgA/AL4\npbV2Q9FvWqHUwyAiIiIiw+FAexhKutOzMcYBTgcuBj4MNKQO/Qn4Od58h66SNaACKDCIiIiIyHAY\nkYGhRyo4nIs3ZGluqtgF9gE/BL5tre0seUNGIAUGERERERkOIzIwGGOOBz4JLAMaAQdoAe4G/gb8\nE2BSf36PtXZXyRozQikwiIiIiMhwONDAULRlVXsYYyYDHwc+ARyBFxKSwGN4w5CWW2ujqeq3G2Ou\nBW7E6324qNjtERERERGRA1fMfRjOx+tNODt1XQdYB9wB/MJauy3Xedbabxlj3oU3ZElEREREREaQ\nYvYw/Db1uB9vSdU7rLUrCzz3OeD9RWyLiIiIiIgUQTEDw+N4vQn3HcAE5ruAZ4rYFhERERERKYKi\nT3o2xlQB4621b2eVHwW8OVZXQ8pHk55FREREZDiMhJ2eMcZcCLwN/EOOw9/A2/l5WTHvKSIiIiIi\npVO0wJDa3fnXQBWwN0eV51OPdxljlhbrviIiIiIiUjrF7GG4AWgGjrTW/ij7oLX2ZuBoYDfw5SLe\nV0RERERESqSYgeFE4JfW2vX5KlhrNwN3puqKiIiIiMgIV8zA4MPrPRjMXsBfxPuKiIiIiEiJFDMw\nrAMGnJtgjPHh7beQtxdCRERERERGjmIGhl8CS40xPzPGvCP9gDEmaIw5C3gIWAT8qoj3FRERERGR\nEinaPgzGGD/wIHA24AIxoAVv1aS6nvsBTwFnW2tjRblxhdM+DCIiIiIyHMq+D4O1NgG8D/hnYC3e\nLtKNQD2QAFYDXwDOUlgQEREREakMRd/puYcxJgRMApLAboWE3NTDICIiIiLD4UB7GEoWGPIxxlwD\nfNRae9yw3niEUmAQERERkeFwoIEhUOyGGGPqgMOBcI7D44GLAFPs+4qIiIiISPEVNTAYY27Gm6cQ\nHKCaAzxfzPuKiIiIiEhpFG3SszHms8A1eGFhE7AGLxysAyzeyklvA/8JXFis+4qIiIiISOkUcx+G\nT+Pt4vxOa+084PxU+TXW2ncAhwFvAQlr7ZYi3ldEREREREqkmIHhcOAX1tqXU88zJvNaa9cDFwCf\nMMZ8qoj3FRERERGREilmYAgCO9Oe9yyjGukpsNY2AXcD/1TE+4qIiIiISIkUMzDsInP1o+bU4/wc\n9Q4r4n1FRERERKREihkYngEuMsZcZYwZZ63tBrYClxljxqfVew/QXsT7ioiIiIhIiRQzMNwIxIFb\ngVNSZb/C62F4xRhzrzHmb8DpwLNFvK+IiIiIiJRI0QKDtfZVvKDwS2BDqvgG4ElgKnAe3sRoC3yx\nWPcVEREREZHScVzXHbzWQTLGvAuYC2wD/mytjZf8phWiqam19H8BIiIiIjLmNTbWOQdyXtECgzHm\nIuA1a+3qolxwjFBgEBEREZHhcKCBoZhzGP4LOLuI1xMRERERkTIrZmBYCZxUxOuJiIiIiEiZFXNI\n0lTgfwMJ4OfAS8Ce1PN+UsuujnkakiQiIiIiw+FAhyQFitiGv6YexwMfGqSue7D3NsZMAK5P3Wsq\n3kZxDwHXWWt3FHD+JcDlwDFACNgMPAjcaK3dnVX3KOAbeEvC1gI7gIeBr1lrdx3M6xARERERGcmK\nOSRpSuqnCnAG+Tmo+xpjIsBTwOeAe4FPAv8H+Ajwp6yN4nKd/y285V+DwLV4weEp4Ergz8aY+rS6\npwMvAicAtwD/CDwOfAZYaYypPZjXIiIiIiIykhWth8FaW8zwMZgvAEcBV1hrf9RTaIxZAywHrgOu\nznViqmfi34CNwGnW2mjq0M+NMc3Al4HLgO+nyv8v0AWcYq3dlCr7b2NMS6odlwK9bRARERERGU2G\n80N+MV0KtAM/zSp/ANgKXGKMyTdGaxZeUHohLSz0WJF6nANgjKnD25X6f6WFhR4PpR6PHnLrRURE\nREQqRNF6GIwxoaHUP9BJz6nhQguBZ7I/8FtrXWPMC8D5eBvFrc9xiQ1AFFiQ49ic1OMrqeu1Av+Q\npykNqcf9Q2m/iIiIiEglKeak584h1D2YSc+zU49b8xzfnHqcR47AYK1tMcZ8E7jRGHM78D2gFXgX\n8O/AauDOAtpxOd7ruKvwpvfX2Fh3MKeLiIiIiJRUMYckDTbR2QGSeB/otxzEfXo+YXfkOd6eVa8f\na+1NeJOXPw28CewEfoe3FOxSa23XQA0wxtwIvAf4gbX2pcKbLiIiIiJSWYZl0rMxZjJwPPA1vOE+\nnynWfQ+EMeZzwG3AI3g9BE3AicA1wEPGmHOstftynOcDbgf+CW++RM6J1UPR1NR6sJcQERERERnU\ngY5sKeaQpLxSexU8bIxZgfct/peAmw/wcj1zBmryHK/NqpfBGGPwwsLj1tpz0w79MbXK0v14S61e\nk3VeDV64+Hu8jek+Y62NH9ArEBERERGpEMO6SpK1th3vA/mnDuIyG/DmDszIc7xnjsO6PMeX4gWl\n+3Icezh17TPSC1Nh4RG8sHCdtfZTCgsiIiIiMhaUY1nVKDDzQE9OhY6XgeOMMeH0Y8YYP3AysMVa\nuznX+fT1TIRzHOvZdK73mDEmgLc53EnAp621Nx5o20VEREREKs2wBobUDs0fpG9i8oH6KVANfDar\n/BJgMvCTtHsuNMbMTauzMvX4kRx7NSzLqgPeyklnA1+01mbv+yAiIiIiMqoVcx+Gnw1SZRxwGjAe\n7xv7g/Fj4GPArcaY2cAq4Ai8SchrgVvT6r4GWLy9G7DWrjTG/AYvHDxrjLkHb9LzCcAVeCsm3ZR6\nTYfgzbfYBWw1xnw4R1varbUPH+TrEREREREZkYo56fmTBdZbC1x1MDey1saMMWcBNwAXAJ/H+1D/\nE+B6a22+JVd7XIS3q/Mn8cJBCNgO/Az4prV2W6re4UAk9XNPnmttom/DNxERERGRUcVxXbcoFzLG\nfGKAwy7QBay31q4qyg1Hiaam1uL8BYiIiIiIDKCxsS57OH5BihYY5MAoMIiIiIjIcDjQwFD0Sc/G\nmMXGmBNylF9kjFlS7PuJiIiIiEjpFC0wGGMcY8yPgD8B5+aociHwtDHmB8W6p4iIiIiIlFYxexg+\nDlwOvAr8Ocfxn+CtZvQ5Y0z2cqgiIiIiIjICFTMwXA38DTjOWvuH7IPW2t8Dp6TqKDCIiIiIiFSA\nYgYGAyy31sbyVbDWxoH7Se2JICIiIiIiI1sxA0NXgfWCQLSI9xURERERkRIpZmB4EVhmjKnKV8EY\nMwlvh+a1RbyviIiIiIiUSDF3er4NWA781RjzQ2ANsBeoAhqBM4BPAFOAfy3ifUVEREREpESKunGb\nMeZa4Ovk7rlw8HZ8/ra19qtFu2mF08ZtIiIiIjIcRsxOz8aYo4FPAYuAyUAS2Im3pOovrLVrinrD\nCqfAICIiIiLDYcQEBhkaBQYRERERGQ4HGhiKOekZAGPMYmPMCTnKLzLGLCn2/UREREREpHSKFhiM\nMY4x5kfAn4Bzc1S5EHjaGPODYt1TRERERERKq5g9DB8HLgdeBf6c4/hP8OYxfM4Yo52eRUREREQq\nQDEDw9XA34DjrLV/yD5orf09cEqqjgKDiIiIiEgFKGZgMMBya20sXwVrbRy4H1hYxPuKiIiIiEiJ\nFDMwdBVYLwhEi3hfEREREREpkWIGhheBZcaYqnwVjDGTgI8Ba4t4XxERERERKZFAEa91G7Ac+Ksx\n5ofAGmAvUAU0AmcAnwCmAP9axPuKiIiIiEiJFHXjNmPMtcDXyd1z4QAu8C1r7XVFu2mF08ZtIiIi\nIjIcRsxOz8aYo4FPAYuAyUAS2Im3pOovgGagzlr7elFvXKEUGERERERkOIyYwDAYY8x1wGestTOH\n9cYjlAKDiIiIiAyHAw0MxZzDAIAx5u+Ao4FwjsPj8TZ4yzsxWkRERERERo6iBQZjTC3wR2DxIFUd\n4NfFuq+IiIiIiJROMXsYrgNOArYCj+Hty3A5cB+wC3gPUAP8M97mbSIiIiIiMsIVcx+GDwGvA4dZ\naz8FfCdV/gtr7T8BR+AFhauAUBHvKyIiIiIiJVLMwDCb/9/efYfbUZWLH/+ek0B6qAFDEAIibxRB\nBJQqJiDqRQUFAQsiAldAQBG4ioUigg2UauECgqIXBZUq/AQhRYogSheWIIReEkggBUjbvz/WbLLP\nzp7Tcs7J2cn38zx5JnvNmpm158yemXdWGbg8pVT/xucWgJTSAuDLwKrAt3pwu5IkSZJ6SU8GDIuA\nV2s+V/8/vJqQUloIXAns3YPblSRJktRLejJgeALYoebzdPKL2raoy7cAGNOD25UkSZLUS3oyYLgG\n2CUiroiIjVNKi4AHgAMjYluAiBgNfBp4rge3K0mSJKmX9GTAcArwMPBRcn8GgLOAkcDNEfEi8CTw\nFvLISZIkSZL6uR4LGFJKM8jNjw4E/lWknQ+cCMwmv7RtAXAhcHxPbVeSJElS72mpVCq9vpGIGACs\nCUwrmiqpMG3arN7/A0iSJGmFN2rUiJbuLNcnAYPKGTBIkiSpL3Q3YOjJPgySJEmSljMGDJIkSZJK\nGTBIkiRJKmXAIEmSJKmUAYMkSZKkUgYMkiRJkkoZMEiSJEkqZcAgSZIkqdTAZV2A7oqI1YETgI8B\no4HpwLXAcSmlZzux/I6sb0UAACAASURBVL7AIcA7gZWBJ4BrgJNTSi/W5X07cBLwPmAk8Djwa+D7\nKaV5PfWdJEmSpP6mKWsYImIIMAk4FPgDsD9wLrAPcEtErNbB8t8FLgZWAr5BDhwmAUcAf4uIkTV5\nNwFuA3YATgMOACYDJwKX9tiXkiRJkvqhZq1hOBLYFDgspfTTamJE3ANcDhwHHNVowaJm4n+AqcCO\nKaXXi1kXRsR04Fjg88CZRfqPgeHADiml+4q030TEHODLEbFbSumqnvxykiRJUn/RlDUMwH7AHOCC\nuvQrgaeAfSOipWTZ9ciB0h01wULVlGI6FiAiRgO7ADfVBAtV5xTTz3a59JIkSVKTaLoahqK50Djg\nr/U3/CmlSkTcAewBbAA82mAVjwGvA29tMG9sMb2/mG4FtJCbJLWRUnokIl4Ctu7G13jDqFEjlmZx\nSZIkqVc1Yw3D+sX0qZL5TxTTDRvNTCm9DHwHeFdEnB0Rb4mItSLiI8A3gbuB3xTZx3ZiW2+OiKYL\nvCRJkqTOaMYb3eoj+bkl8+fU5VtCSumUiHgeOBs4vGbWNcB+KaXXurGtGe0Vusy0abO6s5gkSZLU\nJd1t2dKMNQxLLSIOBX4G3ETug/Ah8hCtE4BrI2LVZVg8SZIkqd9oxhqGV4rpsJL5w+vytRERAZwF\n3JhS+nDNrD8XoyxdQR5q9atd2JbVBJIkSVouNWMNw2NABVi3ZH61j8PDJfN3IgdKf2ww77pi3ROK\nz9VO0+1t67GU0oL2CixJkiQ1q6YLGFJKc4B7gS0iYnDtvIgYAGwHPJlSeqLR8iyuLRjcYN4g8qhI\n1Xl3AAuA7eszRsQ7gFWBm7v6HSRJkqRm0XQBQ+ECYChwcF36vsBawPnVhIgYFxEb1OS5tZju0+Bd\nDXvV5kkpTQeuAsZHxLvq8h5dTM9HkiRJWk61VCqVZV2GLouIlYC/AluSRzq6E9iE/Hbnh4FtUkpz\ni7wVIKWUxtUsfyk5OLgVuBSYBrwbOAx4CXhPtYYiIjYEbic3VToNeIbcSfozwAUppYOW5rtMmzar\n+f4AkiRJajqjRo0oe7Fxu5qyhiGlNB/4ADlY2BO4CPgc+Wn/+Gqw0I5PAUeQmyCdUiz/ceAXwJa1\nzZlSSo+SmzlNIneEvgDYAjiGJWs4JEmSpOVKU9YwLE+sYZAkSVJfWKFqGCRJkiT1DQMGSZIkSaUM\nGCRJkiSVMmCQJEmSVMqAQZIkSVIpAwZJkiRJpQwYJEmSJJUyYJAkSZJUyoBBkiRJUikDBkmSJEml\nDBgkSZIklTJgkCRJklTKgEGSJElSKQMGSZIkSaUMGCRJkiSVMmCQJEmSVMqAQZIkSVIpAwZJkiRJ\npQYu6wJIzWj27NnMmTOblpYWhg8fwdChQ5d1kSRJknqFAYPUSbNnz+bmmyczefKNPP/8c23mrbvu\nekyYsDPbbLMDQ4YMWUYllCRJ6nktlUplWZdhhTZt2iz/AP3c/PnzueyyS5g8+Ubmz5/fbt5Bgwbz\ngQ/8F7vvvietrbb4kyRJ/ceoUSNaurOcAcMyZsDQv82dO5ezzjqNf//7oS4tt/nmW3LooUew0kor\n91LJJEmSuqa7AYOPQKUS8+fP55xzflweLAwcDgOHNZx1993/4LzzfsqiRYt6sYSSJEm9zz4MUomr\nr76chx76V9vEAUNgtXfAyI1hYNFXYf4cePkhmPkALJr3RtY777yDiRNvYOedP9iHpZYkSepZ1jBI\nDbz++utMnHhD28TBo2DsnrD6OxcHCwArDYM1t4T194SVV22zyJ//fK21DJIkqakZMEgN3HHHbcyZ\nM2dxQuvKMOaDMGBw+UIrDYN1PgAti39W06dP4/777+3FkkqSJPUuAwapgb/+dVLbhFWi/WChauWR\nMOItbZKmTJnYcwWTJEnqYwYMUgNPPDG1bcIq4zq/8MiN21+XJElSEzFgkOosWLCAefPm1aS0wEoj\nO7+CQau1+Th37pySjJIkSf2fAYNUZ8kXrlWKf520aGGbjwMGOBiZJElqXgYMUp3W1laGDx/eNnHu\ns51fwavPtPk4fPiIHiiVJEnSsmHAIDXw9rdv2jbh5Qc7v/DMtnk32eQdPVAiSZKkZcOAQWpgwoT3\nt02YPbVztQyzHoPXXmh/XZIkSU3EgEFqYOONxzFmzLptE5+5HmY/DpUG/RkqFXjlYXhuUpvkt71t\nE0aPHtN7BZUkSeplBgxSAy0tLeyxx95tExfNh2dugCeuzM2OXpsGr74AM+6Hx/8Az02GyuIOz62t\nrey++559XHJJkqSe1VJp9LRUfWbatFn+Afqxq6++gssvv7Rby+6//3+z444TerhEkiRJ3TNq1IiW\n7ixnDYPUjo98ZHf23vvTXVpmwIABfP7zXzBYkCRJywVrGJYxaxiaQ0oPct11V3PfffdQ9ptpbW1l\niy22Ytddd2Ps2A37uISSJEnt624NgwHDMmbA0FymTXuBKVMm8uijjzBnzmygheHDh7PxxuN473sn\nsNpqq3W4DkmSVjRz5szmllv+yp133s5LL73I66+/zuDBgxk1ai222WZ7tt56OwYNGrSsi7ncM2Bo\nUgYMkiRpeTVz5gyuuOL3/O1vtzBv3rzSfEOGDOW97x3Pbrt9nKFDh/VhCVcsBgxNyoBBkiQtj554\nYipnnHEqM2fO6PQyo0evw1e+8jXWXHNUL5ZsxWWnZ0mSJPWae++9m3vvvbtTeZ999mlOPfWULgUL\nebln+MEPvsPLL8/sThHVSwYu6wJ0V0SsDpwAfAwYDUwHrgWOSymVvpI3IvYHLuxg9ZNTSuNrlvkw\n8GXg3cAw4FngeuDklNLj3f8WkiRJ/d/ChQv53e9+DbSwySabMmDAgNK88+bN48wzT2POnDlt0kcA\n72oZwMa0MgSYTYUHK4u4h0W8WpPvxRen85OfnMGxxx5Pa6vPtvuDpgwYImIIMAkYB5wD3Am8FTgG\n2CkitkwplYW0E4G9SuatC5wOPFCzrS8A5wIJOBmYBmwOHAp8LCK2MmiQJEnLs5tuuoFnn30GgIkT\n/8L73//B0ry3334rL7zwfJu0LWhl55YBDGhZ3CJmGC2s3dLKdpUK11YW8hCL3pj3yCP/5sEHH2CT\nTTbt4W+i7mjKgAE4EtgUOCyl9NNqYkTcA1wOHAcc1WjB4ua+4Q1+RFwBvAgcX3xuBU4BZgE7pJSm\nF1l/FREJ+HlRlq/0wHeSJEnqd2bPns1VV/3xjc9XXvkHttlme4YPH75E3kqlwk03Xd8m7W20skvL\nAFpaGjefX7mlhd0YwJxKhSdZ3LVz4sS/GDD0E81az7MfMAe4oC79SuApYN+I6FKnjoj4OLA78LWU\n0otF8khgTeDBmmChakoxHduV7UiSJDWTK6/8fTGUeDZnzmyuvPIPDfNOnfoojz8+tU3a+9oJFqpa\nW1rYsaVtM6e77rqTGTNe6l6h1aOaLmCIiJHkpkj/TCm9XjsvpVQB7gBGARt0YZ2DgDOLZX9Rs76Z\nwHPA+hGxct1iY4vp/V38CpIkSU3hmWeeZtKkG5dInzTpLzz77NNLpKf0UJvPG9DCqh0EC1Xr0sIa\nNZ8rlQqPPPLvLpVXvaMZmyStX0yfKpn/RDHdEHi0k+v8b+DNwGeLoKPWV4GLgF9HxAnkztWbAKcV\n2zq7k9toaNSoEUuzuCRJUq8555xLWLhw4RLpCxcu5I9//C0nnnhim/RFi9o8y2VMS+efTbe0tDCm\n0sqLNX0ZYL73Sv1A09UwkDvZA8wtmT+nLl+7itqFY4EpKaXJ9fNTSheTmyrtAvwLeIHccfpl4L0p\npRc6X3RJkqTlV/37vbp6o1mf3/eF9Q/NWMPQ0/YHxgBHNJoZER8Dfg3cB5xHrtnYBPgGcH1EfCCl\n9ESjZTtj2rRZ3V1UkiSpV+2xx6e45557lqhlGDBgAHvs8ckl7mNaW9u24H6usghayodgrfccbQOE\nRYsGeq/Ug7pbW9OMAcMrxbTsveHD6/J15CDyyEjX1M8o3vVwETCVXJuwoJh1fURMBO4iN03au5Pb\nkiRJahrrrDOG8eN35sYb2458NH78+xk9eswS+TfY4C1tPj9ChdmVCsM70Y/h+cqiJQKGDTd8S0lu\n9aVmbJL0GFAhvzOhkWofh4c7WlFEjAW2Av6UUprfIMvWwCrAVTXBAgAppbuBZ4AJnSu2JElS89l9\n908wbNjiIVSHDRvO7rvv2TDvxhuPY+21R7/xeRFwW2XJPhD1KpUKN9fle/vb38Faa63dvUKrRzVd\nwJBSmgPcC2wREYNr50XEAGA74MlONhOqvnXkppL51VqMwSXzB7czT5IkqekNHz6c3Xbb443Pu+++\nZ8N3MAC0trYyYcL726T9g0XcXllY2h+hUqlwU2UhD9fVLkyYsMtSllw9pekChsIFwFDg4Lr0fYG1\ngPOrCRExLiLKhljdspiWDY16Ozk4/liD4GQnYHXg1q4VXZIkqbnstNMujB69DqNHj1kiIKi3ww7v\nY+TIkW3SJlYW8qvKAu6vLGR+ETi8VqlwV2Uh51fm8/c2IyPB6NFj2HzzLXr2S6jbmrEPA+Q3LH8G\nOC0i1gfuJHdEPorcOfm0mrwPAon87oZ6GxfTqY02klJ6MiJ+BPwPcGdEXAQ8DbyN/HbnOcDXl/K7\nSJIk9WsDBgxgn332feP/7Rk6dCiHH34Up556CvPnL27x/SwVrqks5BoWMrACC0qWHzZsOEcccVSH\n21HfacoahqK/wQfI70DYk9wx+XPkmoXxKaWyIVfrrVZMS7vfp5S+Sq65mAEcB/wKOJD8VumtUkr/\n7MZXkCRJaiqbbbY5m222eafybrTRxnzpS8cwePCQhvPLgoVVVlmVY475Om960+iSHFoWWhzfdtma\nNm2WfwBJkrRcevrpp/jtby/mgQfuazdfS0sLW2zxbj75yX1ZY401+6h0K55Ro0Z07rXbdQwYljED\nBkmStLx77rlnmTTpL/z977czc+YMKpUKLS0trLHGmmy77Q687307sfrqayzrYi73DBialAGDJEla\nkSxatIh58+ax8sor09ralK3jm1Z3A4Zm7fQsSZKkJtTa2srgwY5K30wM6yRJkiSVMmCQJEmSVMqA\nQZIkSVIpAwZJkiRJpQwYJEmSJJUyYJAkSZJUyoBBkiRJUikDBkmSJEmlDBgkSZIklTJgkCRJklTK\ngEGSJElSKQMGSZIkSaUMGCRJkiSVMmCQJEmSVMqAQZIkSVIpAwZJkiRJpQwYJEmSJJUyYJAkSZJU\nyoBBkiRJUikDBkmSJEmlDBgkSZIklTJgkCRJklTKgEGSJElSKQMGSZIkSaUMGCRJkiSVMmCQJEmS\nVMqAQZIkSVIpAwZJkiRJpQwYJEmSJJUyYJAkSZJUyoBBkiRJUikDBkmSJEmlDBgkSZIklTJgkCRJ\nklTKgEGSJElSKQMGSZIkSaUMGCRJkiSVMmCQJEmSVMqAQZIkSVIpAwZJkiRJpQYu6wJ0V0SsDpwA\nfAwYDUwHrgWOSyk9285y+wMXdrD6ySml8TXLtAKHA18ANgJeBm4EvpVSerT730KSJEnq35oyYIiI\nIcAkYBxwDnAn8FbgGGCniNgypTSjZPGJwF4l89YFTgceqEu/ENivmP4A2Bg4CpgQEZumlKZ3/9tI\nkiRJ/VdTBgzAkcCmwGEppZ9WEyPiHuBy4DjyDf0SUkqPA483mhcRVwAvAsfXpH2EHCyckFI6qSY9\nAd8HdgIuXcrvI0mSJPVLLZVKZVmXocsi4kHgzcAaKaXXa9JbgCeAQcDaKaVOf7mI+DjwR+CglNIF\nNenXAdsBb0opvdpDX+EN06bNar4/gCRJkprOqFEjWrqzXNN1eo6IkeSmSP+sDRYAigDhDmAUsEEX\n1jkIOLNY9hc16QOACcBfq8FCRKxcpEuSJEnLvWZskrR+MX2qZP4TxXRDoLMdkv+bXGPx2bpaiQ3I\ntRUPR8SnyU2dxgELI2Iy8NWU0j+6Uvh6o0aNWJrFJUmSpF7VdDUMQPUOe27J/Dl1+dpV1C4cC0xJ\nKU2um716Md2J3Nn5Z8BHge8C2wNTImLTTpZbkiRJajrNWMPQ0/YHxgBHNJi3cjHdCHhnSumR4vM1\nEfEQ8Bvy0K6f6O7Gp02b1d1FJUmSpE7rbsuWZqxheKWYDiuZP7wuX0cOIo+MdE2DebOL6c01wULV\nJcCrwPhObkeSJElqOs0YMDwGVMjvTGik2sfh4Y5WFBFjga2AP6WU5jfIMrWYLtHJuejrMA0Y2dF2\nJEmSpGbVdE2SUkpzIuJeYIuIGJxSeq06rxi9aDvgyZTSE6UrWeyDxfSmkm3NLJoebRIRA1NKC2q2\ntRL5DdNlna8lSV302muv8be/3cLdd/+Dl1+eybx58xkyZAijR6/D9tvvSMTbaGnp1qiAkqRuarqA\noXABcBZwMHk41Kp9gbXI/QoAiIhxwOsppccarGfLYnp/O9uqvt35UODsmvSDgZWAq7taeElSWy+/\nPJNrrrmSW2+dwquvLvnKm0cffYRbbpnC6NFj2GWXD7HjjhNobW3GSnJJaj7NGjD8HPgMcFpErA/c\nCWxCfrvzfcBpNXkfBBJ5ONR6GxfTqe1s6yxgT+CMiNgAuBt4NzmAeBI4pdvfQpLE448/xhlnnMrL\nL8/sMO+zzz7Nr351Affc808OOeQIBg0a3AcllKQVW1M+nin6G3yA/MR/T+Ai4HPA+cD4lFLZkKv1\nViumpUMVFU2ediYHIR8vtvGJYptbp5Re6Po3kCQBPPHE4/zwhyd3Kliodc89d3Hmmacxf36j7meS\npJ7UUqlUOs6lXjNt2iz/AJJWSHPnzuG4477GjBkvtZ0xuJWBGw2jdZ3BtKzUSmX2AhY+NpeFT76a\nh7yoMX78zuy334F9V2hJamKjRo3oViewZm2SJElqcpMm3bhEsDAghrPSFqvQMqDmmrbaSgx48xAW\nzVrAvCkvUpmxuFZh8uSb2HXX3VhzzVF9VWxJWuE0ZZMkSVJzW7RoEZMm3dgmbcBGw1hpq7pgoUbr\niIEMev+atAxdPNJ1pVJh8uQbG+aXJPUMAwZJUp+7//57mD592uKEAbDSu0Z2OGRqy6ABDNy07etv\npkyZxIIFC0qWkCQtLQMGSVKfe+CBtqNZD1h/KC2DlnhHZkMDNhgCKy0OLGbNeoUnn+zMq3ckSd1h\nwCBJ6nOzZr3S5nPrWoM6vWzLwFZa11i53fVJknqOAYMkqc8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fXLPeR65tOqtB88QLyQHD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\nkkr9f/eE0O3JN+MAAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"image/png": {
"width": 390,
"height": 262
}
}
}
]
},
{
"metadata": {
"id": "MVxml5fBLShS",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 102
},
"outputId": "9542c5f6-7167-4dd5-ce44-cab57c04ad4f"
},
"cell_type": "code",
"source": [
"cv_df.groupby('model_name').accuracy.mean()"
],
"execution_count": 72,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"model_name\n",
"LogisticRegression 0.856000\n",
"MultinomialNB 0.847000\n",
"RandomForestClassifier 0.850667\n",
"Name: accuracy, dtype: float64"
]
},
"metadata": {
"tags": []
},
"execution_count": 72
}
]
},
{
"metadata": {
"id": "jCpJmGQhLXs-",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"# LogisticRegression Model Interpretation "
]
},
{
"metadata": {
"id": "o7iDRuz8LWwE",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"from sklearn.model_selection import train_test_split"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "fMATkCVQLdCq",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 105
},
"outputId": "4c9917af-9db5-4a8b-c1c0-2e9feb5fccf3"
},
"cell_type": "code",
"source": [
"model = LogisticRegression(random_state=0, C=0.9)\n",
"\n",
"X_train, X_test, y_train, y_test, indices_train, indices_test = train_test_split(features, labels, df.index, test_size=0.33, random_state=0, stratify=df['code'])\n",
"\n",
"model.fit(X_train, y_train)\n",
"y_pred_proba = model.predict_proba(X_test)\n",
"y_pred = model.predict(X_test)"
],
"execution_count": 81,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
" FutureWarning)\n",
"/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
" \"this warning.\", FutureWarning)\n"
],
"name": "stderr"
}
]
},
{
"metadata": {
"id": "KEY-nE8aLtYi",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 85
},
"outputId": "d8407e64-1e58-40f8-8bea-9c7c0f5a2c2f"
},
"cell_type": "code",
"source": [
"y_train.value_counts()"
],
"execution_count": 82,