Amazon Fine Food - Decision Tree.ipynb

{"cells":[{"metadata":{"colab_type":"text","id":"IXmtUCbYiWM3"},"cell_type":"markdown","source":"# Amazon Fine Food Reviews Analysis\n\n\nData Source: https://www.kaggle.com/snap/amazon-fine-food-reviews <br>\n\nEDA: https://nycdatascience.com/blog/student-works/amazon-fine-foods-visualization/\n\n\nThe Amazon Fine Food Reviews dataset consists of reviews of fine foods from Amazon.<br>\n\nNumber of reviews: 568,454<br>\nNumber of users: 256,059<br>\nNumber of products: 74,258<br>\nTimespan: Oct 1999 - Oct 2012<br>\nNumber of Attributes/Columns in data: 10 \n\nAttribute Information:\n\n1. Id\n2. ProductId - unique identifier for the product\n3. UserId - unqiue identifier for the user\n4. ProfileName\n5. HelpfulnessNumerator - number of users who found the review helpful\n6. HelpfulnessDenominator - number of users who indicated whether they found the review helpful or not\n7. Score - rating between 1 and 5\n8. Time - timestamp for the review\n9. Summary - brief summary of the review\n10. Text - text of the review\n\n\n#### Objective:\nGiven a review, determine whether the review is positive (rating of 4 or 5) or negative (rating of 1 or 2).\n\n<br>\n[Q] How to determine if a review is positive or negative?<br>\n<br> \n[Ans] We could use Score/Rating. A rating of 4 or 5 can be cosnidered as a positive review. A rating of 1 or 2 can be considered as negative one. A review of rating 3 is considered nuetral and such reviews are ignored from our analysis. This is an approximate and proxy way of determining the polarity (positivity/negativity) of a review.\n\n\n"},{"metadata":{"colab_type":"text","id":"syeYHDR_iWM7"},"cell_type":"markdown","source":"# [1]. Reading Data"},{"metadata":{"colab_type":"text","id":"D_eZrQC0iWM9"},"cell_type":"markdown","source":"## [1.1] Loading the data\n\nThe dataset is available in two forms\n1. .csv file\n2. SQLite Database\n\nIn order to load the data, We have used the SQLITE dataset as it is easier to query the data and visualise the data efficiently.\n<br> \n\nHere as we only want to get the global sentiment of the recommendations (positive or negative), we will purposefully ignore all Scores equal to 3. If the score is above 3, then the recommendation wil be set to \"positive\". Otherwise, it will be set to \"negative\"."},{"metadata":{"colab":{},"colab_type":"code","id":"53fLFqf8iWNA","trusted":true},"cell_type":"code","source":"%matplotlib inline\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\n\nimport sqlite3\nimport pandas as pd\nimport numpy as np\nimport nltk\nimport string\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom sklearn.feature_extraction.text import TfidfTransformer\nfrom sklearn.feature_extraction.text import TfidfVectorizer\n\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn import metrics\nfrom sklearn.metrics import roc_curve, auc\nfrom nltk.stem.porter import PorterStemmer\n\nimport re\n# Tutorial about Python regular expressions: https://pymotw.com/2/re/\nimport string\nfrom nltk.corpus import stopwords\nfrom nltk.stem import PorterStemmer\nfrom nltk.stem.wordnet import WordNetLemmatizer\n\nfrom gensim.models import Word2Vec\nfrom gensim.models import KeyedVectors\nimport pickle\n\nfrom time import time\nfrom sklearn.metrics import roc_auc_score\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.metrics import classification_report\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.tree import export_graphviz\nimport pydotplus\nimport collections\n\nfrom tqdm import tqdm\nimport os","execution_count":1,"outputs":[{"output_type":"stream","text":"/opt/conda/lib/python3.6/site-packages/smart_open/ssh.py:34: UserWarning: paramiko missing, opening SSH/SCP/SFTP paths will be disabled.  `pip install paramiko` to suppress\n  warnings.warn('paramiko missing, opening SSH/SCP/SFTP paths will be disabled.  `pip install paramiko` to suppress')\n","name":"stderr"}]},{"metadata":{"colab":{},"colab_type":"code","id":"j3kshrC-iWNI","outputId":"07826916-2c3f-4a0a-e928-ddbc6d567a11","trusted":true},"cell_type":"code","source":"# using SQLite Table to read data.\ncon = sqlite3.connect('../input/database.sqlite') \n\n# filtering only positive and negative reviews i.e. \n# not taking into consideration those reviews with Score=3\n# SELECT * FROM Reviews WHERE Score != 3 LIMIT 500000, will give top 500000 data points\n# you can change the number to any other number based on your computing power\n\n# filtered_data = pd.read_sql_query(\"\"\" SELECT * FROM Reviews WHERE Score != 3 LIMIT 500000\"\"\", con) \n# for tsne assignment you can take 5k data points\n\nfiltered_data = pd.read_sql_query(\"\"\" SELECT * FROM Reviews WHERE Score != 3 LIMIT 50000\"\"\", con) \n\n# Give reviews with Score>3 a positive rating(1), and reviews with a score<3 a negative rating(0).\ndef partition(x):\n    if x < 3:\n        return 0\n    return 1\n\n#changing reviews with score less than 3 to be positive and vice-versa\nactualScore = filtered_data['Score']\npositiveNegative = actualScore.map(partition) \nfiltered_data['Score'] = positiveNegative\nprint(\"Number of data points in our data\", filtered_data.shape)\nfiltered_data.head(3)","execution_count":2,"outputs":[{"output_type":"stream","text":"Number of data points in our data (50000, 10)\n","name":"stdout"},{"output_type":"execute_result","execution_count":2,"data":{"text/plain":"   Id                        ...                                                                       Text\n0   1                        ...                          I have bought several of the Vitality canned d...\n1   2                        ...                          Product arrived labeled as Jumbo Salted Peanut...\n2   3                        ...                          This is a confection that has been around a fe...\n\n[3 rows x 10 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Id</th>\n      <th>ProductId</th>\n      <th>UserId</th>\n      <th>ProfileName</th>\n      <th>HelpfulnessNumerator</th>\n      <th>HelpfulnessDenominator</th>\n      <th>Score</th>\n      <th>Time</th>\n      <th>Summary</th>\n      <th>Text</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>B001E4KFG0</td>\n      <td>A3SGXH7AUHU8GW</td>\n      <td>delmartian</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1303862400</td>\n      <td>Good Quality Dog Food</td>\n      <td>I have bought several of the Vitality canned d...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>B00813GRG4</td>\n      <td>A1D87F6ZCVE5NK</td>\n      <td>dll pa</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1346976000</td>\n      <td>Not as Advertised</td>\n      <td>Product arrived labeled as Jumbo Salted Peanut...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>B000LQOCH0</td>\n      <td>ABXLMWJIXXAIN</td>\n      <td>Natalia Corres \"Natalia Corres\"</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1219017600</td>\n      <td>\"Delight\" says it all</td>\n      <td>This is a confection that has been around a fe...</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"colab":{},"colab_type":"code","id":"-7Iy8yWfiWNR","trusted":true},"cell_type":"code","source":"display = pd.read_sql_query(\"\"\"\nSELECT UserId, ProductId, ProfileName, Time, Score, Text, COUNT(*)\nFROM Reviews\nGROUP BY UserId\nHAVING COUNT(*)>1\n\"\"\", con)","execution_count":3,"outputs":[]},{"metadata":{"colab":{},"colab_type":"code","id":"wuK6OYpOiWNV","outputId":"5cb5082e-47b9-4d4b-cf90-fc6ffe0dceeb","scrolled":true,"trusted":true},"cell_type":"code","source":"print(display.shape)\ndisplay.head()","execution_count":4,"outputs":[{"output_type":"stream","text":"(80668, 7)\n","name":"stdout"},{"output_type":"execute_result","execution_count":4,"data":{"text/plain":"               UserId   ...    COUNT(*)\n0  #oc-R115TNMSPFT9I7   ...           2\n1  #oc-R11D9D7SHXIJB9   ...           3\n2  #oc-R11DNU2NBKQ23Z   ...           2\n3  #oc-R11O5J5ZVQE25C   ...           3\n4  #oc-R12KPBODL2B5ZD   ...           2\n\n[5 rows x 7 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>UserId</th>\n      <th>ProductId</th>\n      <th>ProfileName</th>\n      <th>Time</th>\n      <th>Score</th>\n      <th>Text</th>\n      <th>COUNT(*)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>#oc-R115TNMSPFT9I7</td>\n      <td>B005ZBZLT4</td>\n      <td>Breyton</td>\n      <td>1331510400</td>\n      <td>2</td>\n      <td>Overall its just OK when considering the price...</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>#oc-R11D9D7SHXIJB9</td>\n      <td>B005HG9ESG</td>\n      <td>Louis E. Emory \"hoppy\"</td>\n      <td>1342396800</td>\n      <td>5</td>\n      <td>My wife has recurring extreme muscle spasms, u...</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>#oc-R11DNU2NBKQ23Z</td>\n      <td>B005ZBZLT4</td>\n      <td>Kim Cieszykowski</td>\n      <td>1348531200</td>\n      <td>1</td>\n      <td>This coffee is horrible and unfortunately not ...</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>#oc-R11O5J5ZVQE25C</td>\n      <td>B005HG9ESG</td>\n      <td>Penguin Chick</td>\n      <td>1346889600</td>\n      <td>5</td>\n      <td>This will be the bottle that you grab from the...</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>#oc-R12KPBODL2B5ZD</td>\n      <td>B007OSBEV0</td>\n      <td>Christopher P. Presta</td>\n      <td>1348617600</td>\n      <td>1</td>\n      <td>I didnt like this coffee. Instead of telling y...</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"colab":{},"colab_type":"code","id":"OMKVKkH7iWNZ","outputId":"acc9d31a-27a1-48a1-d2f2-ce1f2e511793","trusted":true},"cell_type":"code","source":"display[display['UserId']=='AZY10LLTJ71NX']","execution_count":5,"outputs":[{"output_type":"execute_result","execution_count":5,"data":{"text/plain":"              UserId   ...    COUNT(*)\n80638  AZY10LLTJ71NX   ...           5\n\n[1 rows x 7 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>UserId</th>\n      <th>ProductId</th>\n      <th>ProfileName</th>\n      <th>Time</th>\n      <th>Score</th>\n      <th>Text</th>\n      <th>COUNT(*)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>80638</th>\n      <td>AZY10LLTJ71NX</td>\n      <td>B001ATMQK2</td>\n      <td>undertheshrine \"undertheshrine\"</td>\n      <td>1296691200</td>\n      <td>5</td>\n      <td>I bought this 6 pack because for the price tha...</td>\n      <td>5</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"colab":{},"colab_type":"code","id":"s37uqS_jiWNe","outputId":"489f08d4-48a2-4e82-bfc1-4f982f35b2e4","trusted":true},"cell_type":"code","source":"display['COUNT(*)'].sum()","execution_count":6,"outputs":[{"output_type":"execute_result","execution_count":6,"data":{"text/plain":"393063"},"metadata":{}}]},{"metadata":{"colab_type":"text","id":"efnNNqNfiWNh"},"cell_type":"markdown","source":"#  [2] Exploratory Data Analysis"},{"metadata":{"colab_type":"text","id":"Eq0hnbxhiWNi"},"cell_type":"markdown","source":"## [2.1] Data Cleaning: Deduplication\n\nIt is observed (as shown in the table below) that the reviews data had many duplicate entries. Hence it was necessary to remove duplicates in order to get unbiased results for the analysis of the data.  Following is an example:"},{"metadata":{"colab":{},"colab_type":"code","id":"3CKLJ2FFiWNj","outputId":"c995e0c8-7ef1-48a0-d7a9-b0cc50efaa83","scrolled":false,"trusted":true},"cell_type":"code","source":"display= pd.read_sql_query(\"\"\"\nSELECT *\nFROM Reviews\nWHERE Score != 3 AND UserId=\"AR5J8UI46CURR\"\nORDER BY ProductID\n\"\"\", con)\ndisplay.head()","execution_count":7,"outputs":[{"output_type":"execute_result","execution_count":7,"data":{"text/plain":"       Id                        ...                                                                       Text\n0   78445                        ...                          DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...\n1  138317                        ...                          DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...\n2  138277                        ...                          DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...\n3   73791                        ...                          DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...\n4  155049                        ...                          DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...\n\n[5 rows x 10 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Id</th>\n      <th>ProductId</th>\n      <th>UserId</th>\n      <th>ProfileName</th>\n      <th>HelpfulnessNumerator</th>\n      <th>HelpfulnessDenominator</th>\n      <th>Score</th>\n      <th>Time</th>\n      <th>Summary</th>\n      <th>Text</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>78445</td>\n      <td>B000HDL1RQ</td>\n      <td>AR5J8UI46CURR</td>\n      <td>Geetha Krishnan</td>\n      <td>2</td>\n      <td>2</td>\n      <td>5</td>\n      <td>1199577600</td>\n      <td>LOACKER QUADRATINI VANILLA WAFERS</td>\n      <td>DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>138317</td>\n      <td>B000HDOPYC</td>\n      <td>AR5J8UI46CURR</td>\n      <td>Geetha Krishnan</td>\n      <td>2</td>\n      <td>2</td>\n      <td>5</td>\n      <td>1199577600</td>\n      <td>LOACKER QUADRATINI VANILLA WAFERS</td>\n      <td>DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>138277</td>\n      <td>B000HDOPYM</td>\n      <td>AR5J8UI46CURR</td>\n      <td>Geetha Krishnan</td>\n      <td>2</td>\n      <td>2</td>\n      <td>5</td>\n      <td>1199577600</td>\n      <td>LOACKER QUADRATINI VANILLA WAFERS</td>\n      <td>DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>73791</td>\n      <td>B000HDOPZG</td>\n      <td>AR5J8UI46CURR</td>\n      <td>Geetha Krishnan</td>\n      <td>2</td>\n      <td>2</td>\n      <td>5</td>\n      <td>1199577600</td>\n      <td>LOACKER QUADRATINI VANILLA WAFERS</td>\n      <td>DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>155049</td>\n      <td>B000PAQ75C</td>\n      <td>AR5J8UI46CURR</td>\n      <td>Geetha Krishnan</td>\n      <td>2</td>\n      <td>2</td>\n      <td>5</td>\n      <td>1199577600</td>\n      <td>LOACKER QUADRATINI VANILLA WAFERS</td>\n      <td>DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"colab_type":"text","id":"GmHe67iviWNn"},"cell_type":"markdown","source":"As it can be seen above that same user has multiple reviews with same values for HelpfulnessNumerator, HelpfulnessDenominator, Score, Time, Summary and Text and on doing analysis it was found that <br>\n<br> \nProductId=B000HDOPZG was Loacker Quadratini Vanilla Wafer Cookies, 8.82-Ounce Packages (Pack of 8)<br>\n<br> \nProductId=B000HDL1RQ was Loacker Quadratini Lemon Wafer Cookies, 8.82-Ounce Packages (Pack of 8) and so on<br>\n\nIt was inferred after analysis that reviews with same parameters other than ProductId belonged to the same product just having different flavour or quantity. Hence in order to reduce redundancy it was decided to eliminate the rows having same parameters.<br>\n\nThe method used for the same was that we first sort the data according to ProductId and then just keep the first similar product review and delelte the others. for eg. in the above just the review for ProductId=B000HDL1RQ remains. This method ensures that there is only one representative for each product and deduplication without sorting would lead to possibility of different representatives still existing for the same product."},{"metadata":{"colab":{},"colab_type":"code","id":"wgmdXIJviWNn","trusted":true},"cell_type":"code","source":"#Sorting data according to ProductId in ascending order\nsorted_data=filtered_data.sort_values('ProductId', axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last')","execution_count":8,"outputs":[]},{"metadata":{"colab":{},"colab_type":"code","id":"ComjQ0rHiWNp","outputId":"1c155404-4bb7-4078-c46b-1a9a33ac0602","trusted":true},"cell_type":"code","source":"#Deduplication of entries\nfinal=sorted_data.drop_duplicates(subset={\"UserId\",\"ProfileName\",\"Time\",\"Text\"}, keep='first', inplace=False)\nfinal.shape","execution_count":9,"outputs":[{"output_type":"execute_result","execution_count":9,"data":{"text/plain":"(46072, 10)"},"metadata":{}}]},{"metadata":{"colab":{},"colab_type":"code","id":"yFrWiU23iWNt","outputId":"b9d26952-d8af-4fbb-eb3d-c6a28abcc029","trusted":true},"cell_type":"code","source":"#Checking to see how much % of data still remains\n(final['Id'].size*1.0)/(filtered_data['Id'].size*1.0)*100","execution_count":10,"outputs":[{"output_type":"execute_result","execution_count":10,"data":{"text/plain":"92.144"},"metadata":{}}]},{"metadata":{"colab_type":"text","id":"g4lvCHn6iWNw"},"cell_type":"markdown","source":"<b>Observation:-</b> It was also seen that in two rows given below the value of HelpfulnessNumerator is greater than HelpfulnessDenominator which is not practically possible hence these two rows too are removed from calcualtions"},{"metadata":{"colab":{},"colab_type":"code","id":"6ehzwEdgiWNw","outputId":"60e18da4-784f-49d8-84b7-59823cfa3e85","trusted":true},"cell_type":"code","source":"display= pd.read_sql_query(\"\"\"\nSELECT *\nFROM Reviews\nWHERE Score != 3 AND Id=44737 OR Id=64422\nORDER BY ProductID\n\"\"\", con)\n\ndisplay.head()","execution_count":11,"outputs":[{"output_type":"execute_result","execution_count":11,"data":{"text/plain":"      Id                        ...                                                                       Text\n0  64422                        ...                          My son loves spaghetti so I didn't hesitate or...\n1  44737                        ...                          It was almost a 'love at first bite' - the per...\n\n[2 rows x 10 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Id</th>\n      <th>ProductId</th>\n      <th>UserId</th>\n      <th>ProfileName</th>\n      <th>HelpfulnessNumerator</th>\n      <th>HelpfulnessDenominator</th>\n      <th>Score</th>\n      <th>Time</th>\n      <th>Summary</th>\n      <th>Text</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>64422</td>\n      <td>B000MIDROQ</td>\n      <td>A161DK06JJMCYF</td>\n      <td>J. E. Stephens \"Jeanne\"</td>\n      <td>3</td>\n      <td>1</td>\n      <td>5</td>\n      <td>1224892800</td>\n      <td>Bought This for My Son at College</td>\n      <td>My son loves spaghetti so I didn't hesitate or...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>44737</td>\n      <td>B001EQ55RW</td>\n      <td>A2V0I904FH7ABY</td>\n      <td>Ram</td>\n      <td>3</td>\n      <td>2</td>\n      <td>4</td>\n      <td>1212883200</td>\n      <td>Pure cocoa taste with crunchy almonds inside</td>\n      <td>It was almost a 'love at first bite' - the per...</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"colab":{},"colab_type":"code","id":"EBa1d6gViWNz","trusted":true},"cell_type":"code","source":"final=final[final.HelpfulnessNumerator<=final.HelpfulnessDenominator]","execution_count":12,"outputs":[]},{"metadata":{"colab":{},"colab_type":"code","id":"KNQ01asjiWN1","outputId":"0d2433fb-dd33-41cd-a18d-110d776f60b4","trusted":true},"cell_type":"code","source":"#Before starting the next phase of preprocessing lets see the number of entries left\nprint(final.shape)\n\n#How many positive and negative reviews are present in our dataset?\nfinal['Score'].value_counts()","execution_count":13,"outputs":[{"output_type":"stream","text":"(46071, 10)\n","name":"stdout"},{"output_type":"execute_result","execution_count":13,"data":{"text/plain":"1    38479\n0     7592\nName: Score, dtype: int64"},"metadata":{}}]},{"metadata":{"colab_type":"text","id":"92IZ5ea0iWN4"},"cell_type":"markdown","source":"#  [3] Preprocessing"},{"metadata":{"colab_type":"text","id":"NTwQITJKiWN5"},"cell_type":"markdown","source":"## [3.1].  Preprocessing Review Text\n\nNow that we have finished deduplication our data requires some preprocessing before we go on further with analysis and making the prediction model.\n\nHence in the Preprocessing phase we do the following in the order below:-\n\n1. Begin by removing the html tags\n2. Remove any punctuations or limited set of special characters like , or . or # etc.\n3. Check if the word is made up of english letters and is not alpha-numeric\n4. Check to see if the length of the word is greater than 2 (as it was researched that there is no adjective in 2-letters)\n5. Convert the word to lowercase\n6. Remove Stopwords\n7. Finally Snowball Stemming the word (it was obsereved to be better than Porter Stemming)<br>\n\nAfter which we collect the words used to describe positive and negative reviews"},{"metadata":{"colab":{},"colab_type":"code","id":"kTCxT5aWiWN5","outputId":"64588cda-64b3-47f0-8d01-941e1cc85cc5","trusted":true},"cell_type":"code","source":"# printing some random reviews\nsent_0 = final['Text'].values[0]\nprint(sent_0)\nprint(\"=\"*50)\n\nsent_1000 = final['Text'].values[1000]\nprint(sent_1000)\nprint(\"=\"*50)\n\nsent_1500 = final['Text'].values[1500]\nprint(sent_1500)\nprint(\"=\"*50)\n\nsent_4900 = final['Text'].values[4900]\nprint(sent_4900)\nprint(\"=\"*50)","execution_count":14,"outputs":[{"output_type":"stream","text":"My dogs loves this chicken but its a product from China, so we wont be buying it anymore.  Its very hard to find any chicken products made in the USA but they are out there, but this one isnt.  Its too bad too because its a good product but I wont take any chances till they know what is going on with the china imports.\n==================================================\nthis is yummy, easy and unusual. it makes a quick, delicous pie, crisp or cobbler. home made is better, but a heck of a lot more work. this is great to have on hand for last minute dessert needs where you really want to impress wih your creativity in cooking! recommended.\n==================================================\nGreat flavor, low in calories, high in nutrients, high in protein! Usually protein powders are high priced and high in calories, this one is a great bargain and tastes great, I highly recommend for the lady gym rats, probably not \"macho\" enough for guys since it is soy based...\n==================================================\nFor those of you wanting a high-quality, yet affordable green tea, you should definitely give this one a try. Let me first start by saying that everyone is looking for something different for their ideal tea, and I will attempt to briefly highlight what makes this tea attractive to a wide range of tea drinkers (whether you are a beginner or long-time tea enthusiast).  I have gone through over 12 boxes of this tea myself, and highly recommend it for the following reasons:<br /><br />-Quality:  First, this tea offers a smooth quality without any harsh or bitter after tones, which often turns people off from many green teas.  I've found my ideal brewing time to be between 3-5 minutes, giving you a light but flavorful cup of tea.  However, if you get distracted or forget about your tea and leave it brewing for 20+ minutes like I sometimes do, the quality of this tea is such that you still get a smooth but deeper flavor without the bad after taste.  The leaves themselves are whole leaves (not powdered stems, branches, etc commonly found in other brands), and the high-quality nylon bags also include chunks of tropical fruit and other discernible ingredients.  This isn't your standard cheap paper bag with a mix of unknown ingredients that have been ground down to a fine powder, leaving you to wonder what it is you are actually drinking.<br /><br />-Taste:  This tea offers notes of real pineapple and other hints of tropical fruits, yet isn't sweet or artificially flavored.  You have the foundation of a high-quality young hyson green tea for those true \"tea flavor\" lovers, yet the subtle hints of fruit make this a truly unique tea that I believe most will enjoy.  If you want it sweet, you can add sugar, splenda, etc but this really is not necessary as this tea offers an inherent warmth of flavor through it's ingredients.<br /><br />-Price:  This tea offers an excellent product at an exceptional price (especially when purchased at the prices Amazon offers).  Compared to other brands which I believe to be of similar quality (Mighty Leaf, Rishi, Two Leaves, etc.), Revolution offers a superior product at an outstanding price.  I have been purchasing this through Amazon for less per box than I would be paying at my local grocery store for Lipton, etc.<br /><br />Overall, this is a wonderful tea that is comparable, and even better than, other teas that are priced much higher.  It offers a well-balanced cup of green tea that I believe many will enjoy.  In terms of taste, quality, and price, I would argue you won't find a better combination that that offered by Revolution's Tropical Green Tea.\n==================================================\n","name":"stdout"}]},{"metadata":{"colab":{},"colab_type":"code","id":"iX3VndCPiWN8","outputId":"7f8cad02-65a6-4530-e7e1-bb6775877737","trusted":true},"cell_type":"code","source":"# remove urls from text python: https://stackoverflow.com/a/40823105/4084039\nsent_0 = re.sub(r\"http\\S+\", \"\", sent_0)\nsent_1000 = re.sub(r\"http\\S+\", \"\", sent_1000)\nsent_150 = re.sub(r\"http\\S+\", \"\", sent_1500)\nsent_4900 = re.sub(r\"http\\S+\", \"\", sent_4900)\n\nprint(sent_0)","execution_count":15,"outputs":[{"output_type":"stream","text":"My dogs loves this chicken but its a product from China, so we wont be buying it anymore.  Its very hard to find any chicken products made in the USA but they are out there, but this one isnt.  Its too bad too because its a good product but I wont take any chances till they know what is going on with the china imports.\n","name":"stdout"}]},{"metadata":{"colab":{},"colab_type":"code","id":"DyO7iqEriWN_","outputId":"2a010a1c-574d-409f-d725-c726a25d3e70","trusted":true},"cell_type":"code","source":"# https://stackoverflow.com/questions/16206380/python-beautifulsoup-how-to-remove-all-tags-from-an-element\nfrom bs4 import BeautifulSoup\n\nsoup = BeautifulSoup(sent_0, 'lxml')\ntext = soup.get_text()\nprint(text)\nprint(\"=\"*50)\n\nsoup = BeautifulSoup(sent_1000, 'lxml')\ntext = soup.get_text()\nprint(text)\nprint(\"=\"*50)\n\nsoup = BeautifulSoup(sent_1500, 'lxml')\ntext = soup.get_text()\nprint(text)\nprint(\"=\"*50)\n\nsoup = BeautifulSoup(sent_4900, 'lxml')\ntext = soup.get_text()\nprint(text)","execution_count":16,"outputs":[{"output_type":"stream","text":"My dogs loves this chicken but its a product from China, so we wont be buying it anymore.  Its very hard to find any chicken products made in the USA but they are out there, but this one isnt.  Its too bad too because its a good product but I wont take any chances till they know what is going on with the china imports.\n==================================================\nthis is yummy, easy and unusual. it makes a quick, delicous pie, crisp or cobbler. home made is better, but a heck of a lot more work. this is great to have on hand for last minute dessert needs where you really want to impress wih your creativity in cooking! recommended.\n==================================================\nGreat flavor, low in calories, high in nutrients, high in protein! Usually protein powders are high priced and high in calories, this one is a great bargain and tastes great, I highly recommend for the lady gym rats, probably not \"macho\" enough for guys since it is soy based...\n==================================================\nFor those of you wanting a high-quality, yet affordable green tea, you should definitely give this one a try. Let me first start by saying that everyone is looking for something different for their ideal tea, and I will attempt to briefly highlight what makes this tea attractive to a wide range of tea drinkers (whether you are a beginner or long-time tea enthusiast).  I have gone through over 12 boxes of this tea myself, and highly recommend it for the following reasons:-Quality:  First, this tea offers a smooth quality without any harsh or bitter after tones, which often turns people off from many green teas.  I've found my ideal brewing time to be between 3-5 minutes, giving you a light but flavorful cup of tea.  However, if you get distracted or forget about your tea and leave it brewing for 20+ minutes like I sometimes do, the quality of this tea is such that you still get a smooth but deeper flavor without the bad after taste.  The leaves themselves are whole leaves (not powdered stems, branches, etc commonly found in other brands), and the high-quality nylon bags also include chunks of tropical fruit and other discernible ingredients.  This isn't your standard cheap paper bag with a mix of unknown ingredients that have been ground down to a fine powder, leaving you to wonder what it is you are actually drinking.-Taste:  This tea offers notes of real pineapple and other hints of tropical fruits, yet isn't sweet or artificially flavored.  You have the foundation of a high-quality young hyson green tea for those true \"tea flavor\" lovers, yet the subtle hints of fruit make this a truly unique tea that I believe most will enjoy.  If you want it sweet, you can add sugar, splenda, etc but this really is not necessary as this tea offers an inherent warmth of flavor through it's ingredients.-Price:  This tea offers an excellent product at an exceptional price (especially when purchased at the prices Amazon offers).  Compared to other brands which I believe to be of similar quality (Mighty Leaf, Rishi, Two Leaves, etc.), Revolution offers a superior product at an outstanding price.  I have been purchasing this through Amazon for less per box than I would be paying at my local grocery store for Lipton, etc.Overall, this is a wonderful tea that is comparable, and even better than, other teas that are priced much higher.  It offers a well-balanced cup of green tea that I believe many will enjoy.  In terms of taste, quality, and price, I would argue you won't find a better combination that that offered by Revolution's Tropical Green Tea.\n","name":"stdout"}]},{"metadata":{"colab":{},"colab_type":"code","id":"qzIaKh8niWOC","trusted":true},"cell_type":"code","source":"# https://stackoverflow.com/a/47091490/4084039\nimport re\n\ndef decontracted(phrase):\n    # specific\n    phrase = re.sub(r\"won't\", \"will not\", phrase)\n    phrase = re.sub(r\"can\\'t\", \"can not\", phrase)\n\n    # general\n    phrase = re.sub(r\"n\\'t\", \" not\", phrase)\n    phrase = re.sub(r\"\\'re\", \" are\", phrase)\n    phrase = re.sub(r\"\\'s\", \" is\", phrase)\n    phrase = re.sub(r\"\\'d\", \" would\", phrase)\n    phrase = re.sub(r\"\\'ll\", \" will\", phrase)\n    phrase = re.sub(r\"\\'t\", \" not\", phrase)\n    phrase = re.sub(r\"\\'ve\", \" have\", phrase)\n    phrase = re.sub(r\"\\'m\", \" am\", phrase)\n    return phrase","execution_count":17,"outputs":[]},{"metadata":{"colab":{},"colab_type":"code","id":"Esa29-JwiWOD","outputId":"be726596-752d-489b-d311-f4180ac83ac4","trusted":true},"cell_type":"code","source":"sent_1500 = decontracted(sent_1500)\nprint(sent_1500)\nprint(\"=\"*50)","execution_count":18,"outputs":[{"output_type":"stream","text":"Great flavor, low in calories, high in nutrients, high in protein! Usually protein powders are high priced and high in calories, this one is a great bargain and tastes great, I highly recommend for the lady gym rats, probably not \"macho\" enough for guys since it is soy based...\n==================================================\n","name":"stdout"}]},{"metadata":{"colab":{},"colab_type":"code","id":"qf0c7-nXiWOG","outputId":"6104976d-bda8-4de1-9d91-808f3661d53b","trusted":true},"cell_type":"code","source":"#remove words with numbers python: https://stackoverflow.com/a/18082370/4084039\nsent_0 = re.sub(\"\\S*\\d\\S*\", \"\", sent_0).strip()\nprint(sent_0)","execution_count":19,"outputs":[{"output_type":"stream","text":"My dogs loves this chicken but its a product from China, so we wont be buying it anymore.  Its very hard to find any chicken products made in the USA but they are out there, but this one isnt.  Its too bad too because its a good product but I wont take any chances till they know what is going on with the china imports.\n","name":"stdout"}]},{"metadata":{"colab":{},"colab_type":"code","id":"1Z5VK6wxiWOI","outputId":"d3405347-aa6a-4df5-c3d9-2c11441c2385","trusted":true},"cell_type":"code","source":"#remove spacial character: https://stackoverflow.com/a/5843547/4084039\nsent_1500 = re.sub('[^A-Za-z0-9]+', ' ', sent_1500)\nprint(sent_1500)","execution_count":20,"outputs":[{"output_type":"stream","text":"Great flavor low in calories high in nutrients high in protein Usually protein powders are high priced and high in calories this one is a great bargain and tastes great I highly recommend for the lady gym rats probably not macho enough for guys since it is soy based \n","name":"stdout"}]},{"metadata":{"colab":{},"colab_type":"code","id":"MfzZtIP1iWOL","trusted":true},"cell_type":"code","source":"# https://gist.github.com/sebleier/554280\n# we are removing the words from the stop words list: 'no', 'nor', 'not'\n# <br /><br /> ==> after the above steps, we are getting \"br br\"\n# we are including them into stop words list\n# instead of <br /> if we have <br/> these tags would have revmoved in the 1st step\n\nstopwords= set(['br', 'the', 'i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', \"you're\", \"you've\",\\\n            \"you'll\", \"you'd\", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', \\\n            'she', \"she's\", 'her', 'hers', 'herself', 'it', \"it's\", 'its', 'itself', 'they', 'them', 'their',\\\n            'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', \"that'll\", 'these', 'those', \\\n            'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', \\\n            'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', \\\n            'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after',\\\n            'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further',\\\n            'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more',\\\n            'most', 'other', 'some', 'such', 'only', 'own', 'same', 'so', 'than', 'too', 'very', \\\n            's', 't', 'can', 'will', 'just', 'don', \"don't\", 'should', \"should've\", 'now', 'd', 'll', 'm', 'o', 're', \\\n            've', 'y', 'ain', 'aren', \"aren't\", 'couldn', \"couldn't\", 'didn', \"didn't\", 'doesn', \"doesn't\", 'hadn',\\\n            \"hadn't\", 'hasn', \"hasn't\", 'haven', \"haven't\", 'isn', \"isn't\", 'ma', 'mightn', \"mightn't\", 'mustn',\\\n            \"mustn't\", 'needn', \"needn't\", 'shan', \"shan't\", 'shouldn', \"shouldn't\", 'wasn', \"wasn't\", 'weren', \"weren't\", \\\n            'won', \"won't\", 'wouldn', \"wouldn't\"])","execution_count":21,"outputs":[]},{"metadata":{"colab":{},"colab_type":"code","id":"g1C43tjNiWOM","outputId":"a997afc9-894c-4a56-d538-dadea8a21e6d","trusted":true},"cell_type":"code","source":"# Combining all the above stundents \nfrom tqdm import tqdm\npreprocessed_reviews = []\n# tqdm is for printing the status bar\nfor sentance in tqdm(final['Text'].values):\n    sentance = re.sub(r\"http\\S+\", \"\", sentance)\n    sentance = BeautifulSoup(sentance, 'lxml').get_text()\n    sentance = decontracted(sentance)\n    sentance = re.sub(\"\\S*\\d\\S*\", \"\", sentance).strip()\n    sentance = re.sub('[^A-Za-z]+', ' ', sentance)\n    # https://gist.github.com/sebleier/554280\n    sentance = ' '.join(e.lower() for e in sentance.split() if e.lower() not in stopwords)\n    preprocessed_reviews.append(sentance.strip())","execution_count":22,"outputs":[{"output_type":"stream","text":"100%|██████████| 46071/46071 [00:19<00:00, 2312.77it/s]\n","name":"stderr"}]},{"metadata":{"colab":{},"colab_type":"code","id":"MWDrCFxViWOP","outputId":"5618c9c3-a86a-42a4-e9c4-d24d93fd5a7a","trusted":true},"cell_type":"code","source":"preprocessed_reviews[1500]","execution_count":23,"outputs":[{"output_type":"execute_result","execution_count":23,"data":{"text/plain":"'great flavor low calories high nutrients high protein usually protein powders high priced high calories one great bargain tastes great highly recommend lady gym rats probably not macho enough guys since soy based'"},"metadata":{}}]},{"metadata":{"colab_type":"text","id":"fEu8sShciWOS"},"cell_type":"markdown","source":"<h2><font color='red'>[3.2] Preprocessing Review Summary</font></h2>"},{"metadata":{"colab":{},"colab_type":"code","id":"XP1URIERiWOU","trusted":true},"cell_type":"code","source":"## Similartly you can do preprocessing for review summary also.\n# Combining all the above stundents \npreprocessed_summary = []\n# tqdm is for printing the status bar\nfor sentance in tqdm(final['Summary'].values):\n    sentance = re.sub(r\"http\\S+\", \"\", sentance)\n    sentance = BeautifulSoup(sentance, 'lxml').get_text()\n    sentance = decontracted(sentance)\n    sentance = re.sub(\"\\S*\\d\\S*\", \"\", sentance).strip()\n    sentance = re.sub('[^A-Za-z]+', ' ', sentance)\n    # https://gist.github.com/sebleier/554280\n    sentance = ' '.join(e.lower() for e in sentance.split() if e.lower() not in stopwords)\n    preprocessed_summary.append(sentance.strip())","execution_count":24,"outputs":[{"output_type":"stream","text":"100%|██████████| 46071/46071 [00:12<00:00, 3557.19it/s]\n","name":"stderr"}]},{"metadata":{},"cell_type":"markdown","source":"### [4.3]. Split Dataset into Train and Test"},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn import model_selection as ms\ny=final['Score'][:50000]\ndw_train,dw_test,y_train,y_test=ms.train_test_split(preprocessed_reviews, y, stratify=y, test_size=0.3, random_state=0)","execution_count":25,"outputs":[]},{"metadata":{"colab_type":"text","id":"p2lyiwJ_iWOW"},"cell_type":"markdown","source":"# [4] Featurization"},{"metadata":{"colab_type":"text","id":"T_LFck8iiWOX"},"cell_type":"markdown","source":"## [4.1] BAG OF WORDS"},{"metadata":{"colab":{},"colab_type":"code","id":"OVHZ9f8biWOX","outputId":"b121f6f4-e30a-4184-b083-a77cdab3ca19","trusted":true},"cell_type":"code","source":"#BoW\ncount_vect = CountVectorizer(dtype='float') #in scikit-learn\nd_train =count_vect.fit_transform(dw_train)\nprint(\"some feature names \", count_vect.get_feature_names()[:10])\nprint('='*50)","execution_count":26,"outputs":[{"output_type":"stream","text":"some feature names  ['aa', 'aaa', 'aaaa', 'aaaaaaaaaaaa', 'aaaaaaaaaaaaaaa', 'aaaaaaahhhhhh', 'aaaaaawwwwwwwwww', 'aaaaah', 'aaaand', 'aaah']\n==================================================\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(\"the type of count vectorizer \",type(d_train))\nprint(\"the shape of train-text BOW vectorizer \",d_train.get_shape())\n\nd_test = count_vect.transform(dw_test)\nprint(\"the shape of test-text BOW vectorizer \",d_test.get_shape())","execution_count":27,"outputs":[{"output_type":"stream","text":"the type of count vectorizer  <class 'scipy.sparse.csr.csr_matrix'>\nthe shape of train-text BOW vectorizer  (32249, 33270)\nthe shape of test-text BOW vectorizer  (13822, 33270)\n","name":"stdout"}]},{"metadata":{"colab_type":"text","id":"mjfrB76yiWOa"},"cell_type":"markdown","source":"## [4.2] Bi-Grams and n-Grams."},{"metadata":{"colab":{},"colab_type":"code","id":"mzOdOY6MiWOb","outputId":"5ca07a6a-fb12-4deb-b0f4-21cdb42ccb7a","trusted":true},"cell_type":"code","source":"#bi-gram, tri-gram and n-gram\n\n#removing stop words like \"not\" should be avoided before building n-grams\n# count_vect = CountVectorizer(ngram_range=(1,2))\n# please do read the CountVectorizer documentation http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html\n\n# you can choose these numebrs min_df=10, max_features=5000, of your choice\ncount_vect_gram = CountVectorizer(ngram_range=(1,2), min_df=10, max_features=5000)\nd_train_gram = count_vect_gram.fit_transform(dw_train)\n","execution_count":28,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Vectorize cv and test data\nprint(\"the type of count vectorizer \",type(d_train_gram))\nprint(\"the shape of train-text BOW vectorizer \",d_train_gram.get_shape())\n\nd_test_gram = count_vect_gram.transform(dw_test)\nprint(\"the shape of test-text BOW vectorizer \",d_test_gram.get_shape())","execution_count":29,"outputs":[{"output_type":"stream","text":"the type of count vectorizer  <class 'scipy.sparse.csr.csr_matrix'>\nthe shape of train-text BOW vectorizer  (32249, 5000)\nthe shape of test-text BOW vectorizer  (13822, 5000)\n","name":"stdout"}]},{"metadata":{"colab_type":"text","id":"0tcbbZxSiWOd"},"cell_type":"markdown","source":"## [4.3] TF-IDF"},{"metadata":{"colab":{},"colab_type":"code","id":"mQ3EDEsoiWOd","outputId":"b305ed29-861d-40e9-c1df-b12c4e6492a8","trusted":true},"cell_type":"code","source":"tf_idf_vect = TfidfVectorizer(ngram_range=(1,2), min_df=10)\nd_train_tf =tf_idf_vect.fit_transform(dw_train)\nprint(\"some sample features(unique words in the corpus)\",tf_idf_vect.get_feature_names()[0:10])\nprint('='*50)","execution_count":30,"outputs":[{"output_type":"stream","text":"some sample features(unique words in the corpus) ['ability', 'able', 'able buy', 'able chew', 'able drink', 'able eat', 'able enjoy', 'able figure', 'able find', 'able get']\n==================================================\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Vectorize cv and test data\nprint(\"the type of count vectorizer \",type(d_train_tf))\nprint(\"the shape of train-text BOW vectorizer \",d_train_tf.get_shape())\n\nd_test_tf = tf_idf_vect.transform(dw_test)\nprint(\"the shape of test-text BOW vectorizer \",d_test_tf.get_shape())","execution_count":31,"outputs":[{"output_type":"stream","text":"the type of count vectorizer  <class 'scipy.sparse.csr.csr_matrix'>\nthe shape of train-text BOW vectorizer  (32249, 19259)\nthe shape of test-text BOW vectorizer  (13822, 19259)\n","name":"stdout"}]},{"metadata":{"colab_type":"text","id":"u566lt93iWOf"},"cell_type":"markdown","source":"## [4.4] Word2Vec"},{"metadata":{"colab":{},"colab_type":"code","id":"pxlbMwFjiWOg","trusted":true},"cell_type":"code","source":"# Train your own Word2Vec model using your own text corpus\ntrain_list=[]\ntest_list=[]\nfor sent in dw_train:\n    train_list.append(sent.split())\nfor sent in dw_test:\n    test_list.append(sent.split())","execution_count":32,"outputs":[]},{"metadata":{"colab":{},"colab_type":"code","id":"NOd5wp0yiWOh","outputId":"ae28439d-438c-4d88-ed12-db5f3317218f","scrolled":true,"trusted":true},"cell_type":"code","source":"# Using Google News Word2Vectors\nis_your_ram_gt_16g=False\nwant_to_use_google_w2v = False\nwant_to_train_w2v = True\n\nif want_to_train_w2v:\n    # min_count = 5 considers only words that occured atleast 5 times\n    w2v_model=Word2Vec(train_list,min_count=5,size=50, workers=4)\n    print(w2v_model.wv.most_similar('great'))\n    print('='*50)\n    print(w2v_model.wv.most_similar('worst'))\n    \nelif want_to_use_google_w2v and is_your_ram_gt_16g:\n    if os.path.isfile('GoogleNews-vectors-negative300.bin'):\n        w2v_model=KeyedVectors.load_word2vec_format('GoogleNews-vectors-negative300.bin', binary=True)\n        print(w2v_model.wv.most_similar('great'))\n        print(w2v_model.wv.most_similar('worst'))\n    else:\n        print(\"you don't have gogole's word2vec file, keep want_to_train_w2v = True, to train your own w2v \")","execution_count":33,"outputs":[{"output_type":"stream","text":"[('awesome', 0.8581539988517761), ('fantastic', 0.8050481081008911), ('good', 0.7907675504684448), ('wonderful', 0.783013641834259), ('excellent', 0.7822737097740173), ('amazing', 0.780458927154541), ('terrific', 0.7747030854225159), ('perfect', 0.7131232619285583), ('nice', 0.6884153485298157), ('decent', 0.6836135387420654)]\n==================================================\n[('best', 0.7539863586425781), ('closest', 0.7286052703857422), ('nastiest', 0.7254273295402527), ('greatest', 0.7198406457901001), ('tastiest', 0.6958309412002563), ('experienced', 0.6741902232170105), ('awful', 0.6519714593887329), ('ive', 0.6479312181472778), ('ever', 0.6441928148269653), ('tasted', 0.6303834915161133)]\n","name":"stdout"}]},{"metadata":{"colab":{},"colab_type":"code","id":"teE01Qk2iWOj","outputId":"f063436c-8b62-49d3-e093-723395e66ccd","trusted":true},"cell_type":"code","source":"w2v_words = list(w2v_model.wv.vocab)\nprint(\"number of words that occured minimum 5 times \",len(w2v_words))\nprint(\"sample words \", w2v_words[0:50])","execution_count":34,"outputs":[{"output_type":"stream","text":"number of words that occured minimum 5 times  10791\nsample words  ['delivered', 'quickly', 'bought', 'grain', 'free', 'dog', 'however', 'not', 'care', 'think', 'kibble', 'rather', 'small', 'aroma', 'salmon', 'based', 'foods', 'donating', 'remainder', 'animal', 'rescue', 'group', 'baby', 'likes', 'smells', 'delicious', 'seem', 'stain', 'thin', 'cereal', 'needs', 'added', 'overall', 'good', 'highly', 'recommended', 'looking', 'cracker', 'daughter', 'put', 'gfcf', 'diet', 'glutino', 'close', 'normal', 'come', 'across', 'rest', 'us', 'even']\n","name":"stdout"}]},{"metadata":{"colab_type":"text","id":"mUNnof40iWOl"},"cell_type":"markdown","source":"## [4.4.1] Converting text into vectors using Avg W2V, TFIDF-W2V"},{"metadata":{"colab_type":"text","id":"heHLB_FCiWOm"},"cell_type":"markdown","source":"#### [4.4.1.1] Avg W2v"},{"metadata":{"colab":{},"colab_type":"code","id":"iCYoNRANiWOm","outputId":"96ebb104-44d7-4b7d-aa45-62614bbfcf44","trusted":true},"cell_type":"code","source":"# average Word2Vec\n# compute average word2vec for each review in train dataset.\n#train dataset\nd_train_w2 = [];\nfor sent in tqdm(train_list): # for each review/sentence\n    sent_vec = np.zeros(50) # as word vectors are of zero length 50, you might need to change this to 300 if you use google's w2v\n    cnt_words =0; # num of words with a valid vector in the sentence/review\n    for word in sent: # for each word in a review/sentence\n        if word in w2v_words:\n            vec = w2v_model.wv[word]\n            sent_vec += vec\n            cnt_words += 1\n    if cnt_words != 0:\n        sent_vec /= cnt_words\n    d_train_w2.append(sent_vec)\nprint(len(d_train_w2))\nprint(len(d_train_w2[0]))","execution_count":35,"outputs":[{"output_type":"stream","text":"100%|██████████| 32249/32249 [01:04<00:00, 501.55it/s]","name":"stderr"},{"output_type":"stream","text":"32249\n50\n","name":"stdout"},{"output_type":"stream","text":"\n","name":"stderr"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# average Word2Vec\n# compute average word2vec for each review in test dataset.\n#test dataset\nd_test_w2 = []; # the avg-w2v for each sentence/review is stored in this list\nfor sent in tqdm(test_list): # for each review/sentence\n    sent_vec = np.zeros(50) # as word vectors are of zero length 50, you might need to change this to 300 if you use google's w2v\n    cnt_words =0; # num of words with a valid vector in the sentence/review\n    for word in sent: # for each word in a review/sentence\n        if word in w2v_words:\n            vec = w2v_model.wv[word]\n            sent_vec += vec\n            cnt_words += 1\n    if cnt_words != 0:\n        sent_vec /= cnt_words\n    d_test_w2.append(sent_vec)\nprint(len(d_test_w2))\nprint(len(d_test_w2[0]))\n","execution_count":36,"outputs":[{"output_type":"stream","text":"100%|██████████| 13822/13822 [00:28<00:00, 493.49it/s]","name":"stderr"},{"output_type":"stream","text":"13822\n50\n","name":"stdout"},{"output_type":"stream","text":"\n","name":"stderr"}]},{"metadata":{"colab_type":"text","id":"ylTn3LgiiWOo"},"cell_type":"markdown","source":"#### [4.4.1.2] TFIDF weighted W2v"},{"metadata":{"colab":{},"colab_type":"code","id":"XBxf_qKSiWOo","trusted":true},"cell_type":"code","source":"# S = [\"abc def pqr\", \"def def def abc\", \"pqr pqr def\"]\nmodel = TfidfVectorizer()\ntf_idf_matrix = model.fit_transform(dw_train)\n# we are converting a dictionary with word as a key, and the idf as a value\ndictionary = dict(zip(model.get_feature_names(), list(model.idf_)))","execution_count":37,"outputs":[]},{"metadata":{"colab":{},"colab_type":"code","id":"5FHQMSyUiWOp","outputId":"6b5b71be-e3f7-4933-cd74-f78fb703132d","scrolled":true,"trusted":true},"cell_type":"code","source":"# TF-IDF weighted Word2Vec\ntfidf_feat = model.get_feature_names() # tfidf words/col-names\n# final_tf_idf is the sparse matrix with row= sentence, col=word and cell_val = tfidf\n\nd_train_tf_wt = []; # the tfidf-w2v for each sentence/review is stored in this list\nrow=0;\nfor sent in tqdm(train_list): # for each review/sentence \n    sent_vec = np.zeros(50) # as word vectors are of zero length\n    weight_sum =0; # num of words with a valid vector in the sentence/review\n    for word in sent: # for each word in a review/sentence\n        if word in w2v_words and word in tfidf_feat:\n            vec = w2v_model.wv[word]\n#             tf_idf = tf_idf_matrix[row, tfidf_feat.index(word)]\n            # to reduce the computation we are \n            # dictionary[word] = idf value of word in whole courpus\n            # sent.count(word) = tf valeus of word in this review\n            tf_idf = dictionary[word]*(sent.count(word)/len(sent))\n            sent_vec += (vec * tf_idf)\n            weight_sum += tf_idf\n    if weight_sum != 0:\n        sent_vec /= weight_sum\n    d_train_tf_wt.append(sent_vec)\n    row += 1","execution_count":38,"outputs":[{"output_type":"stream","text":"100%|██████████| 32249/32249 [11:14<00:00, 47.84it/s]\n","name":"stderr"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# TF-IDF weighted Word2Vec\ntfidf_feat = model.get_feature_names() # tfidf words/col-names\n# final_tf_idf is the sparse matrix with row= sentence, col=word and cell_val = tfidf\n\n#test dataset\nd_test_tf_wt = [];\nrow=0;\nfor sent in tqdm(test_list): # for each review/sentence \n    sent_vec = np.zeros(50) # as word vectors are of zero length\n    weight_sum =0; # num of words with a valid vector in the sentence/review\n    for word in sent: # for each word in a review/sentence\n        if word in w2v_words and word in tfidf_feat:\n            vec = w2v_model.wv[word]\n#             tf_idf = tf_idf_matrix[row, tfidf_feat.index(word)]\n            # to reduce the computation we are \n            # dictionary[word] = idf value of word in whole courpus\n            # sent.count(word) = tf valeus of word in this review\n            tf_idf = dictionary[word]*(sent.count(word)/len(sent))\n            sent_vec += (vec * tf_idf)\n            weight_sum += tf_idf\n    if weight_sum != 0:\n        sent_vec /= weight_sum\n    d_test_tf_wt.append(sent_vec)\n    row += 1","execution_count":39,"outputs":[{"output_type":"stream","text":"100%|██████████| 13822/13822 [04:46<00:00, 48.31it/s]\n","name":"stderr"}]},{"metadata":{"colab_type":"text","id":"IwObBT2UiWOr"},"cell_type":"markdown","source":"# [5] Assignment 8: Decision Trees"},{"metadata":{"colab_type":"text","id":"k_mwmumRiWOr"},"cell_type":"markdown","source":"<ol>\n    <li><strong>Apply Decision Trees on these feature sets</strong>\n        <ul>\n            <li><font color='red'>SET 1:</font>Review text, preprocessed one converted into vectors using (BOW)</li>\n            <li><font color='red'>SET 2:</font>Review text, preprocessed one converted into vectors using (TFIDF)</li>\n            <li><font color='red'>SET 3:</font>Review text, preprocessed one converted into vectors using (AVG W2v)</li>\n            <li><font color='red'>SET 4:</font>Review text, preprocessed one converted into vectors using (TFIDF W2v)</li>\n        </ul>\n    </li>\n    <br>\n    <li><strong>The hyper paramter tuning (best `depth` in range [1, 5, 10, 50, 100, 500, 100], and the best `min_samples_split` in range [5, 10, 100, 500])</strong>\n        <ul>\n    <li>Find the best hyper parameter which will give the maximum <a href='https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/receiver-operating-characteristic-curve-roc-curve-and-auc-1/'>AUC</a> value</li>\n    <li>Find the best hyper paramter using k-fold cross validation or simple cross validation data</li>\n    <li>Use gridsearch cv or randomsearch cv or you can also write your own for loops to do this task of hyperparameter tuning</li>          \n        </ul>\n    </li>\n    <br>\n    <li><strong>Graphviz</strong>\n        <ul>\n    <li>Visualize your decision tree with Graphviz. It helps you to understand how a decision is being made, given a new vector.</li>\n    <li>Since feature names are not obtained from word2vec related models, visualize only BOW & TFIDF decision trees using Graphviz</li>\n    <li>Make sure to print the words in each node of the decision tree instead of printing its index.</li>\n    <li>Just for visualization purpose, limit max_depth to 2 or 3 and either embed the generated images of graphviz in your notebook, or directly upload them as .png files.</li>                \n        </ul>\n    </li>\n    <br>\n    <li><strong>Feature importance</strong>\n        <ul>\n    <li>Find the top 20 important features from both feature sets <font color='red'>Set 1</font> and <font color='red'>Set 2</font> using `feature_importances_` method of <a href='https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html'>Decision Tree Classifier</a> and print their corresponding feature names</li>\n        </ul>\n    </li>\n    <br>\n    <li><strong>Feature engineering</strong>\n        <ul>\n    <li>To increase the performance of your model, you can also experiment with with feature engineering like :</li>\n            <ul>\n            <li>Taking length of reviews as another feature.</li>\n            <li>Considering some features from review summary as well.</li>\n        </ul>\n        </ul>\n    </li>\n    <br>\n    <li><strong>Representation of results</strong>\n        <ul>\n    <li>You need to plot the performance of model both on train data and cross validation data for each hyper parameter, like shown in the figure.\n    <img src='train_cv_auc.JPG' width=300px></li>\n    <li>Once after you found the best hyper parameter, you need to train your model with it, and find the AUC on test data and plot the ROC curve on both train and test.\n    <img src='train_test_auc.JPG' width=300px></li>\n    <li>Along with plotting ROC curve, you need to print the <a href='https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/confusion-matrix-tpr-fpr-fnr-tnr-1/'>confusion matrix</a> with predicted and original labels of test data points. Please visualize your confusion matrices using <a href='https://seaborn.pydata.org/generated/seaborn.heatmap.html'>seaborn heatmaps.\n    <img src='confusion_matrix.png' width=300px></li>\n        </ul>\n    </li>\n    <br>\n    <li><strong>Conclusion</strong>\n        <ul>\n    <li>You need to summarize the results at the end of the notebook, summarize it in the table format. To print out a table please refer to this prettytable library<a href='http://zetcode.com/python/prettytable/'>  link</a> \n        <img src='summary.JPG' width=400px>\n    </li>\n        </ul>\n</ol>"},{"metadata":{"colab_type":"text","id":"uqhvv_IiiWOs"},"cell_type":"markdown","source":"<h4><font color='red'>Note: Data Leakage</font></h4>\n\n1. There will be an issue of data-leakage if you vectorize the entire data and then split it into train/cv/test.\n2. To avoid the issue of data-leakag, make sure to split your data first and then vectorize it. \n3. While vectorizing your data, apply the method fit_transform() on you train data, and apply the method transform() on cv/test data.\n4. For more details please go through this <a href='https://soundcloud.com/applied-ai-course/leakage-bow-and-tfidf'>link.</a>"},{"metadata":{"colab_type":"text","id":"ayLYlocviWOs"},"cell_type":"markdown","source":"# Applying Decision Trees"},{"metadata":{"colab_type":"text","id":"fvKQwJMWiWOt"},"cell_type":"markdown","source":"## [5.1] Applying Decision Trees on BOW,<font color='red'> SET 1</font>"},{"metadata":{"colab":{},"colab_type":"code","id":"vnSi4W-iiWOt","trusted":true},"cell_type":"code","source":"# Please write all the code with proper documentation\ndef cv_DT(d_train, y_train):\n    import warnings\n    warnings.filterwarnings('ignore')\n\n    #defining max_depth and min_sampples_split for grid search\n    param= {'max_depth':[1,5,10,50,100,500,1000],'min_samples_split':[5,10,100,500]}\n\n    #Using Grid Search to get parameters\n    start = time()\n    #hinge loss for svm\n    clfDT = DecisionTreeClassifier(class_weight='balanced')\n    clf = GridSearchCV(clfDT, param, cv=5,scoring='roc_auc')\n    clf.fit(d_train, y_train)\n    print(\"Time elapsed : %.2f Seconds\" % (time() - start))\n    print(\" optimal parameters using Grid Search are \" + str(clf.best_params_))\n    optimal_max_depth=clf.best_params_.get('max_depth')\n    optimal_min_samples_split=clf.best_params_.get('min_samples_split')\n    return optimal_max_depth,optimal_min_samples_split","execution_count":40,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#plot confusion matrix and print report\ndef report(x,y,model):\n    y_true, y_pred = y, model.predict(x)\n    print(classification_report(y_true, y_pred))\n    print()\n    tn, fp, fn, tp = confusion_matrix(y_true, y_pred).ravel()\n    print(\"The value of TPR is %.3f\" %(tp/(fn+tp)) +\n          \"\\nThe value of TNR is %.3f\" %(tn/(tn+fp)) +\n         \"\\nThe value of FPR is %.3f\" %(fp/(tn+fp)) +\n         \"\\nThe value of FNR is %.3f\" %(fn/(fn+tp)) )\n         \n    cm=confusion_matrix(y_true, y_pred)\n    ax=sns.heatmap(cm,annot=True,fmt=\"d\")\n    ax.set(xlabel='Predicted', ylabel='Actual')\n    print()\n    print(\"The roc auc score is %f\" %roc_auc_score(y_true, model.predict_proba(x)[:,1]))","execution_count":41,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def plotROC(d_trai,y_trai,d_tes,y_tes,clf):\n    train_score = clf.predict_proba(d_trai)[:,1]\n    test_score = clf.predict_proba(d_tes)[:,1]\n    fpr_train, tpr_train, _ = roc_curve(y_trai, train_score)\n    fpr_test, tpr_test, _ = roc_curve(y_tes, test_score)\n    auc_train = auc(fpr_train, tpr_train)\n    auc_test = auc(fpr_test, tpr_test)\n    plt.title('Receiver Operating Characteristic Curve')\n    plt.plot(fpr_train, tpr_train, label = 'AUC Train = %0.2f' % auc_train)\n    plt.plot(fpr_test, tpr_test, label = 'AUC Test = %0.2f' % auc_test)\n    plt.legend(loc = 'lower right')\n    plt.plot([0, 1], [0, 1],'k--')\n    plt.xlim([0, 1])\n    plt.ylim([0, 1])\n    plt.ylabel('True Positive Rate')\n    plt.xlabel('False Positive Rate')\n    plt.show()","execution_count":42,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#calculating auc score for train and test dataset\nmax_depth = [1,5,10,50,100,500,1000]\nmin_samples_split = [5,10,100,500]\ndef calculateAUCScore(d_trai,y_trai,d_tes,y_tes):\n    auc_train_list=list()\n    auc_test_list=list()\n    for i in max_depth:\n        for j in min_samples_split:\n            model = DecisionTreeClassifier(max_depth= i , min_samples_split = j ,class_weight='balanced')\n            model.fit(d_train,y_train)\n            train_score = model.predict_proba(d_train)[:,1]\n            test_score = model.predict_proba(d_test)[:,1]\n            fpr_train, tpr_train, _ = roc_curve(y_train, train_score)\n            fpr_test, tpr_test, _ = roc_curve(y_test, test_score)\n            auc_train_list.append(auc(fpr_train, tpr_train))\n            auc_test_list.append(auc(fpr_test, tpr_test))\n    return auc_train_list,auc_test_list","execution_count":43,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def plotHeatMap(d_trai,y_trai,d_tes,y_tes):\n    listTemp= list(calculateAUCScore(d_trai,y_trai,d_tes,y_tes))\n    name=[\"HeatMap on Train Data\", \"HeatMap on Test Data\"]\n    for z in range(2):\n        k=0\n        #creating dataframe with empty cells\n        df=pd.DataFrame([],columns =[5,10,100,500],index=[1,5,10,50,100,500,1000])\n        #populating dataframe cells by auc scores\n        for i in max_depth:\n            for j in min_samples_split:\n                df.at[i,j]=listTemp[z][k]\n                k+=1\n        #changing datatype of cells from NaN to float\n        df = df.apply(pd.to_numeric, axis=1)\n        ax=sns.heatmap(df,annot=True,fmt=\"f\")\n        ax.set(xlabel='Max Depth', ylabel='Min Samples Split',title=name[z])\n        plt.show()\n        #print(\"\")","execution_count":44,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"omd_bow,omss_bow = cv_DT(d_train, y_train)","execution_count":45,"outputs":[{"output_type":"stream","text":"Time elapsed : 829.24 Seconds\n optimal parameters using Grid Search are {'max_depth': 50, 'min_samples_split': 500}\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#plot heatmap on train and test data\nplotHeatMap(d_train,y_train,d_test,y_test)","execution_count":46,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 2 Axes>","image/png":"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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure 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Fr4TX8NUM5y0Rw1ZeN/zEB4du6KsVuxxp8h89w1UVga4uVHtofGYGzUFu52sj9/Funt7mXVVBBfzvgDwHDQUz0FDwW7DsnUD2V/OxhRSE/+Zn2M7dQIA24E9ZM2ZDoB796vxvvFOFAqVlEjmzFdR6alVXhal/UYwm/EZ/SRuDZqA2Uzeqj/I+WkeuHvgN3kmuLsjZjN561eTM/+zCpFDqWjXyiXGZMLnwUfJeHk89sR4/KZ8iGXzOuwxx5xZsj+b5Tz2HDQUc6SxKJc9OZH0iQ+D1QJe3vi//SmWzetQyYkAuHfuATnZhZrzGfMk2Z9/gHXPDjyuHoTXkGHkfPsJeWv+JG/Nn0aX6kbiO+EVbEcPIb7++Nz9EGlPjUSlpeLzyNO4tWqPdee2ipHFyMdInzQOe2I8/m/OJm9TYVlkfVpAFtfciFuDfFmkPT3GKYuAmZ+St6mALLr0QBWRhWX7FrK//AjsNrzvGoXXTXeQ/eVsPPtdB0DaY/chAdXxe/5N0p4c5ZwZUFJd5c5Fvi/cWrbFPao7aU+MAKsF8a/uTLOfPkX6+AeK9M+Mz/1jSXv0XlR6Kt53jcJz0FByFnxWzoKg0vxG3K/shbh7kPbE/eDhif/Mz8lbuwJ7fBzpk54w6jGb8XvlXSzbNmE7uKf8ZVEaVViRmy5GIyLSXUSeEJH+FVG/uVEz7HEnsZ+OBasVy9oVeHTqVmp+j+59yFu73DixWo0bFBA3d0QKrK7n5Y3X4FvJ/v7Lwu2F18a6ZwcAlh1b8OhyVcltrFsBgCksHFtsDCrNsLSsO7fiXkKZ8sCtcXPssfmyyFu7Ao+o7qXm9+jRh9w1JcjC3R2kwO3h5Y3X9beS/d0Xhcpbd2wBu/FKaj2wB1NwCADmOvWxOB5UKjUFlZlhWOdl1FXeXOz7wnPAEMO6dJRTaSlld1CMf8TLyzj1roY9OeGcrtFVKs9vRIGXF5jMiIcnWC2o7Ewj6b+HgdkN3NyMvBcTZXc9VDIqRJGLyKYCxw8C7wF+wIsi8nR5t2cKCsGeEO88tyfFIw6FUixvSBimsHCsu/7J729wCH7TPyZgzgJyfv7GaWl4D7ufnIXzITe3UB22E0dxdyhHj669MNUILdaOR7fe5K0xFLk97iTmiLqYQmqCyYx7VPcSy5QHElQDW8IZ57k9MR5TcI0S85pCwjCHhhd6MzAFh+D/9idU/+g7cn6a55SFz/D7yfllQTFZFMSzzzVY/tkIgC36MB5R3cBkxhRaE3PDJpiCQ12uqzy42PeFKbwObs1b4ff6+/i+PANzw6b5aaE18XvrI3xfnoFb81ZGpM1G1py38Z/+CQFzf8BUpx55yxeV1+UX7lsl+Y1Y1q+GnBwC5v5AwOz55Cycj8pIdzRswm/qXKp/8jPWHVuwHdxbbtfvCsquXA6VjYqyyN0LHI8E+imlXgL6A3eUVkhERorIFhHZ8ln0qYrpWLeryVu/utBrlEqMJ/2JEaQ+fAcevQYgAYGY6zfCVLMWlk1ri9WR+f6beA4Ygt+bsxEvH5S18O7b5sbNITcX+4loo/7MDLLmTKfaEy/g98o72M/EVYrXOI/uxWVhT4wn7fH7SRl9O569BxaQRQSWjWtKrcvr5jvBZiNv9TIAcpcvwp5wBv+ps/EZMRbrvt1gt7tU16WgPO4LMZsRX3/SJ44h+4sPqTZuEmC4JlJH3Ub6kw+S/dn7VHvsefD2AbMZzwHXkzb+QVIfuAnbsSN4DS3153HRqMjfiLlRc5TdRuqDN5E6ejheg2/FFBZuFLLbSR//AKkjb8HcuDmmOpEX5Xqd2O2uh0pGRfnITSISiPGgEKVUPIBSKlNESv0OVik1B5gDkHxTL5cfe/akeEw18q0LU1AIKjG+xLwe3a4ma+6MkttPTsR2PBq35q2RgADcGjbF/4NvjR+of3V8X5pBxouPYT95nIzJTxpthdfGvUOXYm04X0sdWLasx7JlvZHe77oKuxlUUgLmAta+KTgEe2LJr+se3fuQNeftkuv5TxZXtMbkXx23Rk0JmP0tYjIjAYH4TZ5B+vOPGfX0HohHx66kvfB4fgV2WyFfvN/rs7CdOoF7i7Zl1lWeXPT7IjEey8a/ALAd2gfKjvgHoNJSURmGIrMdOYAt7hTmWnXy+3naMFosf6/Ea+jt5XLtRaksvxGPHn2wbt8ENhsqLQXrvl2YGzY1XD7/tZGVgXXXP7i3iyLXYQxdFKrwrJWKssgDgK3AFiBIRMIBRMQXh2ewPLEd2o8pvDam0Jrg5oZ796vJ2/J3sXymiLqIrx+2/budcRIUAh4exnE1X9yat8J26jh5fywk9cGbSRs9jPRnx2KPjSHjRUPZOAexRPC6+S5yC47gi+DRtZfTP+6MdpSRar54DriB3D9/L08ROLEe3FdIFh7dr8ayeV2xfIYsfLEWlEVwcVnYT54g949fSBlxE6mjhpH2zFhssSecite9XRTeQ4eT/tpEyCvweu3hCZ6G79etTUew2bDHHCuzrvLmYt8XeZvWOmd9mMJrI27uqLRUxD8ATMZPzRQWjjk8AvvpU9iTEjDXqW+kA26tO2IrMPhYlWVR2m/EnnAGt5btjTRPL9yaXIH95HHEPwDx8TXiPTxwa90R+8njFSKLUtEWeWGUUvVLSbIDQ8u9QbuNrLkz8X3+LTCZyFuxGPuJo3gNuw/bof1YHDesR7ersRRRsObadfG+d4wxm0KEnIXzsR8v2wrw6NEHz4E3AGDZuIa8FYudaW5XtMGeGF/IwgDwGTEWc72GAGR/9wX22JgLvuwSsdvI+mgGfi9ONaYfLl+E7cRRvIffj/XQPiybDVl4dr+avLVFZVEPn4Ky+Hk+tuNHymzO58FHjaljk6YBxoBn1ofTMQUE4vfiW6AU9sR4Mme+WjHXWxYX+b7IW7EInzET8H/7U5TVQua7rwPGPeE97D6U1QbKTtac6U6/cPaCz/Gb/A7KZsUef5qsd9+oAEFQaX4juUt+ptrDE/Cf8Skg5K5cjO3YEcz1GuDzyEQwmxAxkff3Sixb15e/HMqiEipoVxFVSReKORfXyuVOZRxcuVToPTs1JRH4w6oLvjGyZoxy+Yfm89jsSnUjXh7zyDUajeZCqcIWuVbkGo1GA1CF33y1ItdoNBqo0rNWtCLXaDQaQFVh18pF+URfo9FoKj125Xo4CyIyUET2i8ihkr5mF5G3RWS7IxwQkZQCabYCaQuLli0JbZFrNBoNlNsaKiJiBmYB/YAYYLOILFRKOVcAU0o9XiD/WKBdgSqylVJtz6VNbZFrNBoNlKdFHgUcUkodUUrlAd8CQ8rIPxz45kK6rhW5RqPRAFhtLoeC60I5wsgCNUUAJwqcxzjiiiEi9YBIoOBXWF6OOjeIyA2udF27VjQajQbOybVScF2oC2QY8L1SquCUmXpKqZMi0gBYISI7lVKHy6pEW+QajUYD5elaOQnUKXBe2xFXEsMo4lZRSp10/H8EWEVh/3mJVFqL3PvJ0Ze6C5UHD+9L3YNKg7h5XuouVBrEN/BSd+GyohynH24GGotIJIYCHwYUW9ZSRJoBgcD6AnGBQJZSKldEagDdgDfP1mClVeQajUZzUSmnLzuVUlYReQT4AzADnyildovIy8AWpdR/UwqHAd+qwgteNQdmi4gdw2PyRsHZLqWhFblGo9FAuX6ir5RaBCwqEvdCkfNJJZT7G2h1ru1pRa7RaDRQpT/RP+tgp4g86kqcRqPRVGUu9z077ykh7t5y7odGo9FcWsrxE/2LTamuFREZjjHSGlnke38/IKmiO6bRaDQXlSq8aFZZPvK/gVigBjCtQHw68G9Fdkqj0WguOpXQ0naVUhW5UuoYcAy48uJ1R6PRaC4Rl6MiF5G1SqnuIpIOFLxCAZRSyr/Ce6fRaDQXCWW7DF0rSqnujv/9Ll53NBqN5hJxmVrkQWUVVErpAU+NRnPZUBmnFbpKWYOdWzFcKlJCmgIaVEiPzpN1/x5kyteLsNsVQ3u2Z8R1VxVKf+vrxWzeFw1Adq6F5PRM1n7wDJv2HmHqvCXOfNGxCUwZfQtXd2jOxt2HmT5/KUopvD09mPzgUOqGBTvz/rl5N+Pem8+8SaNoERmBxWrjpU9+Ye+xU9hsdgZ3a8uIwVcRl5jKs3N+ICktE4Cbe3fkjv4VN/Swbvs+pnyxELvdztDeUYwYcnVhWXyxkM17DuXLIi2DtR9PBuDtr3/jr3/2oZSiS6vGTLhnCCLCu/MX8+tfW0nLzGbDZ68668qzWHn2/W/ZGx1DgK8Pbz56JxEhQaSkZzJuxpfsPnyC63t25Jn7hjrLjHj5A+JT0vHyMG6/DyaOJDjAt9zlsPafPUz59Efsdjs39rmSEUP7FUp/87Mf2bzrIAA5eXkkpWaw7vMpAEz/8hfWbNuNXSmubN2UCffdhIjw0Cvvk5CShs1mp33zhjwz4hbMZhNL1//DBwsWc+Tkaea9Po4WDesCYLFYeXnOfHYfPo7JJEy47yY6tWhMdm4e46d9wonTCZhNJnp2aMljd15f7jIoJI8t/zLlwy+x2e3cOLAXD9w6uFD6lNlfsfnfvYY8cvNISknj7+9nG/L4+Bv+2rTDkEe7Fjz90F2ICItWreej+QsRhNDg6rz+5GgCA/xITc9g/Ovvcep0ArXCajB14lgC/KqRnpnFxDc/IDY+EZvNzj03XcPQ/lex7/AxJr/3GZlZ2ZhMJkYOu56BPbtUqDyKcTkqcqVU5MXsyIVgs9t57YvfmP3UPYQF+XP7pNn0ateMhhGhzjxP3jHIeTxv2Qb2HYsFIKp5AxZMHgNAakYW1z01kytbNgTglc9/Y+Zjt9OgVgjzl2/io4WrmfzgjQBkZufy9dINtGpY21nvss27ybNa+eHVR8jOzePGZ95jYJdWeLi7MX74QJrXr0Vmdi7DXvyQLi0aFupfucri05+Y/cxIwoIDuP3Zd+jVoQUNa4fly+LufIUxb8la9h09BcD2A0fZfuAo37/5BAD3TprFlr1H6HRFQ3q2v4Jh/bsx+PEphdr7aeUm/Kt589uMp1n893ZmzFvEW4/eiYe7Ow/fMoBDJ+I4FBNXrJ+vPzycFg3rFIsvL2w2O699/B1znn+YsKDqDJ84lV4dW9KwTrgzz1P33ug8nrd4NfuiYwDYvv8I2/cf4fupxg5d9zw/gy17DtGpRWOmPnEfvj7eKKV4YtonLN3wD4O6daBRnXCmjx/B5DnzC/Xjh+V/A/Dj9IkkpqYz5tUP+OaN8Ua9119NVMsmWCxWHnj5Pdb8s4ce7a6oMHm8Outz5rw2gZo1ghj26Av07tyehvXyl8meMOpO5/HXvyxl3+Fjhjz2HOCfPQf54f3XALh7/GS27NxHuxZNmPLhl/w8ewqBAX5M//gbvvl1GWPuvJGPF/xK57YteODWwcxd8CsfL/iVJ0YM49tf/6RB3Qjee2kcSSlpDH7wKa7r3RUvTw9eGz+KehE1OZOYzG1jn6drh1b4+1arEHmUSNV1kbu2jK2I3Cgi00VkmqsLnV9Mdh2JoU5YELVDg3B3c2Ng51as2rav1PxLNuxkUJfiyxks27yH7q0b4+3pAYAIZGTnAJCRlUNI9fzhglk/Lue+a7vj6Z7/LBQgOzcPq81GrsWKm9mMr7cnIdX9aF6/FgDVvD1pUCuEM8lp5XHpxdh16Dh1atagdliwIYsr27Jqy+5S8y/5ezuDurZ19j/XYsVitZFnsWK12p2WcuvG9QgJLD6+vXLrbq6/qgMA/Tq3YtOugyil8PHyoH2zSDw9Ls0qELsOHaNuzRBqh9XA3d2Ngd3as3LLzlLzL167lUHdjOsQhNw8CxarlTyrFavNRnCA8bf39TFWorTa7FisVsTxwtqgdk0iI8KK1Xs4Jo6olo0BCA7ww6+aD7sPn8Db04Oolk0AcHd3o3lkHU4nphQrX17sPHCYurXCqBMeiru7G4N6dmHlhq2l5l+8ej2DejksYikgD4vFkEd1f5RSKAXZObkopYzfSFB1AFau38aQvj0AGNK3ByvXb/2vKrKyc1BKkZWTQ4BfNcxmE/Vrh1MvoiYAocGBBFX3Jzk1vcLkURLKanc5VDbO+isTkfeBRuSvmfuQiPRTSj1coT07B84kp1MzKMB5Hhrkz87DMSXmPZWQwsn4ZKIGfpjLAAAgAElEQVSuKO4ZWrJxJ3cN7Oo8n3T/EB6Z9hWeHu74envy5QsPArD36CniktK4qm1TPl+8zpm/b6cWrPxnH30ffYvsXAtP3j6IAF+fQm2cjE9m37HYQpZ8eXImOY2awdWd56HBAew8dLzEvKfikzkZn0RUy0YAtGlSn05XNKTv6JdRCoYN6EqDEpRTofaSUp3tuZnN+Pp4kZKeRaB/2ZbUC7MXYDYJfaJaMXJoX0RK8uCdP6eTUggrIIewoOrsPHisxLyn4pM4eSbJqVjbNI2kU8sm9Bn5PEophg28iga1azrzP/TK++w8dIzuba+gX5eyt1ZsWi+CVVt2Mah7B+ISUth75ARxicm0alzPmSctM4vVW3dx57U9L+SSy+RMQjI1Q/KHvcJqBPHv/pL3Kjh1OoGTcfF0btMCgLbNGxPVujlX3zEWpRTDB/ejQV3Dkn/ukXu5cfREvL08qRtRk2fHGB+CJ6akOZV6jcAAElMMw2X44H6Mfeltrr5jLJnZOUyd+AgmU2F7cuf+w1isNuqEl/8ba5lUPv3sMq5Y5FcDA5RSnyqlPgWuccSViogEiMgbIrJPRJJEJFFE9jriqpdRzrl90sc//3luV+IiSzbupG+nFpiL3DzxKekcijlNV4dSA/jyj/W8N+5Ols0Yz5Ae7Zg6bwl2u52p3yxh3LABxeredSQGs8nEshlPsmja43yxZB0xZ/LHhLNychn37rc8eccgfL29KuT6zoUl67fTN6q1UxbH4xKIPnmGpbOeY9n7z7Fp9yG27TtS7u2+9sjt/PDmOD59cQzb9kXz25rSLcOLwZJ1W+nXpS1ms0MOsfFEx8Sx7MOX+XP2ZDbtOsDWvflK78PnxrBizivkWa1s2nWgzLpvuLoLYcHVGT5hKm9+9gNtmkYWuvesNhsTZnzO7ddcRe2wGhVzgefI4tUb6Nc9Kl8ep05z5MQp/vxyJsu/eoeNO/awddd+LFYrC35fznfvvcKKr9+lSWQd5i4ovum7iDhH2tZt3UnTBnVZ8fW7fD/rVV57/3MyMrOdeeOTUnjmrQ+Z/PiDxRR8RXO5r7VyCKhb4LyOI64sFgDJQC+lVJBSKhjo7YhbUFohpdQcpVRHpVTHETf0daFrBqGBfsQlpTrPzySlEVaCGwBKd6ss3bSLq9s3x93NDEBSWiYHjsfR2uHHHdC5JTsOnSAzJ49DMWd44I1PGTRuOv8ejuHRGfPYHX2SxRt20rVVI9zdzAT7+9K2cV12Rxv+Z4vVxhPvfss1XVvTt2PF+EEBQgP9iSvwin4mMZWwwIAS8y75ezuDuuVblCs276JV47r4eHni4+VJtzbN2HGgZCvW2V5QgLM9q81GRlYO1f18yiwT5nh7qubtxTXd2rHz8Iky858PYUHVC7kqTielEBpcihzWbWNQ9/bO8+Wb/qV1k/r4eHvi4+1J93bN2XEgulAZTw93endqxcrNpbtrwHhLeereG/lu6gTemTCS9Mws6oWHONNfnv0t9cJDuOva3udzmS4TWiOQuPh8o+J0QhJhwSVvTLFk9Qau6ZU/0Lj87y20btYIH28vfLy96N6xNTv2HmT/YeNNr06tMESEAT06s32PMXgcXN2f+CRD/vFJKQQHGL/Hn5f9Rd9unRAR6tYKI6JmCNExxm8kIzObh1+Yyth7bqFN80ZcdOznECoZrihyP2CviKwSkVXAHsBfRBYWWYOlIPWVUlOUUs5RLqVUnFJqClCvlDLnTYvICI6fTiImPhmL1cqSjTvp2a5ZsXzRp+JJz8qhTaPig2yLN+xk4JX5Ct6/mhcZ2bkcjUsAYP2uw0TWCsHPx4vVs55m8bQnWDztCVo3rM3Mx26nRWQENYMD2LTH+MFn5eax83AMkeE1UEox6eOfaVArhLsHdivvyy9Ei4Z1OB6XQMyZJEMW67fTs0PxB0f0yTOkZ2bTpsArfs0a1dm69whWmw2L1cbWvUdK9PsWpFeHK1j4l2FRL9u4k6gWjcp0k1htNpIds3csVht/bdtLowJui/KiRaO6HIuNJ+Z0IhaLlSXrttGrY/EHePTJ06RlZtOmSf7YfniNQLbsOeSUw5Y9h2kQEUZWdi7xyanO61izdfdZ5ZOdm0dWTi4A63fsw2w2Owdc3/3mN9KzcgoNulYULZs04NipOGLizmCxWFm8egO9urQvlu/IiVOkZWTSpnljZ1x4SDBbdu5zyMPK1p37aFCnFqE1Ajl8/CRJDrfJ+n92OV0uvbq055c/1wDwy59r6H1le2ddG7cbYzYJyakcjYmjds1QLBYrj02eweA+3enfI6pCZVEaVdkid2Uk6oWzZynGMRF5CvhcKXUaQETCMFZNLHfzy81sZuJd1zL6rS+w2+3ccFV7GtUOZdaPy2lRP4Je7Q2lvmTjTgZ0bllM0ZyMTyYuMZWOTesXqvOF+65n3LvfYhLBv5o3L40oe5x3WJ8oXpj7M0MnvgvAkB7taFK3JtsOHOO3v3fQuHYYtz7/PgBjb+5LjzZNylEK+f2eeO8NjH79I0MWvaJoVKcms777gxaRtenV0fB7Llm/nQFd2xaSRb/Ordm0+xA3PzUdEejapim9HA+Bt7/+jUV/bycnz0K/h1/hxt5RjL65P0N7RfHs+99y3WNv4O/rw5tj73DWN2jsa2Rk52Cx2li5ZTcfTnyQ8BqBjH7jI6xWGza7McXxpj6dK0QOz4y4mdGvvo/NbueG3l1oVCecWd/+zhUN69K7k6HUF6/bysCu7QvLoUtbNu06wE3j3kCAbm2b06tjKxJT0vi/KR+RZ7FiV4qoFo25pb/xYF6+cQevf/I9yWkZPPz6bJrVj+DD58aQlJrOQ698gMkkhAYF8NrYuwCIS0zmox+XEhkRxm1PvQXAsEE9uKlPVyoCN7OZZ0bfzUPPvYXNZmdo/6toVK82733xAy2aRNLbodSXrN7AwJ5dCsujexQbd+zhxtHPGPLo2Nr5EBh9x1DufepV3MxmaoUG88o4YzP5Ebdex/jX3uOnP1YTHlqDac88AsCo22/guWlzGDp6IijFY/ffRmCAH7+uWMfWXftJSc9wPgBeeWIkzRqWu91XOpXQ0nYVKbzLUBkZRYKBq4DjSqkynZqOfeeeBoYA/41YnAYWYmxdlHy29nI2zK98j71Lhd6z04neszMfvWdnPh4Noi54tDzx2p4u65zg31eX7+j8BVKqa0VEfhORlo7jcGAXcD/wpYg8VlalSqlkpdQEpVQzh488SCnVXCk1Aah00xc1Go1G2V0PlY2yfOSRSqldjuP7gGVKqcFAZwyFfr68dAFlNRqNpmKowoOdZfnILQWO+wAfASil0h07PJeKiJS2XrkAZY8OaTQazSWgMlrarlKWIj8hImOBGKA9sARARLwB97PUGwYMwJhuWBDB2LBCo9FoKhWXqyIfAbwM9AVuU0r9Nym3C/DpWer9DfBVSm0vmuCYwqjRaDSVCmWrVOOX50RZi2adAR4qIX4lsLKsSpVSI8pIu/1cOqjRaDQXg8vVItdoNJr/GZT9MrTINRqN5n8JbZFrNBpNFUepqmuRn3WtFRF5U0T8RcRdRJaLSLyI3Hm2chqNRlOVqMofBLlikfdXSj0lIkOBo8CNwF/AVxXZsV03f3P2TP8jhIRmXOouVBrC3r3vUneh0mCq1fjsmTQuY78cZ62UkOda4DulVGp5bwKg0Wg0l5rLfbDzNxHZB2QDo0UkBMip2G5pNBrNxaUqK/Kz+siVUk8DXYGOSikLkIWxqqFGo9FcNijlejgbIjJQRPaLyCERebqE9LdFZLsjHBCRFEd8WxFZLyK7ReRfEbnNlb67smenDzAGY5egkUAtoCnG15sajUZzWVBeFrmImIFZQD+MJU42i8hCpdQeZ1tKPV4g/1igneM0C7hbKXVQRGoBW0XkjwJf1peIKzsEfQrkYVjlACeBV1y8Jo1Go6kSKCUuh7MQBRxSSh1RSuUB31K2F2M4js3tlVIHlFIHHcengDNASBllAdcUeUOl1Js4VkNUSmXh3EpVo9FoLg9sNnE5FNwo3hFGFqgqgsI7ocU44oohIvWASGBFCWlRgAdwuGhaUVwZ7MxzrHioHJU3BHJdKKfRaDRVhnP5IEgpNQeYUw7NDgO+V0rZCkY6NvP5ErhHqbPPXHdFkb+IsYRtHRH5GuiGsfemRqPRXDaU46yVk0DBHd5rO+JKYhjwcMEIEfEHfgeeVUptcKXBsypypdQyEdmGsXytAI8qpRJcqVyj0WiqCi5uX+wKm4HGIhKJocCHAcVWfRWRZkAgsL5AnAfwE/CFUup7VxssVZGLSPsiUbGO/+uKSF2l1DZXG9FoNJrKTnlZ5Eopq4g8AvwBmIFPlFK7ReRlYItSaqEj6zDgW6UKPUJuxdjkPlhE7nXE3VvS3g4FKcsin1ZWX4Gry6pYo9FoqhI2uytzP1xDKbUIWFQk7oUi55NKKPcV57H8SVkbS/Q+18ouJf692lH3pQfAbCLhm2XEzfqxUHqdF+/Hr2srAEzeHrgFV2d7izsAaPzVC1Rr15SMzXs4dO+rzjKR7z5OtdaNUBYrmdsPcuzpD1BWG35XtqThxxPJO3EGgOTF64mdsSC/MZOJKxZNJS8u0VlfvamPUK11QxAh58gpjj7+DvasivlA1qtrJ4LGjwGziYyfFpP22beF0gPHjcarYxsAxMsLc1B1TvS8AfcmDQl+5lGkmg/Y7aR+PI+spasACH5hHB5XNAERLMdiSHzxTVR2Dp7tWxE4bgwejRuQMPEVspavcbZT7bp+BDxgyDh17tdk/rYMAJ/+vQgYcTuYTGSv2UDKO3MrRA7rdh5iyrw/sCs7Q3u0Y8S13Qulv/XNH2zedxSA7DwLyWmZrJ01gU17o5n67VJnvujYBKY8dBNXt2+GUor3flzJ0i17MJtM3NKrA3f060xaZjYvfLKQmPhkPNzdeOm+62lcOxSAtKwcXvr0Vw6dPIOI8NJ9g2nTKN+F+vmS9UxfsIxVM8cT6OdTIbJYu2ELb8z4EJvdzk2DB/LAXbcWSp8yczabthlb7ebk5pKUnML6P4w3++nvf8xff28GYNS9wxnUt2ehsq+9/QE//b6UzX/+BMCW7TuZMnM2Bw5H89ZLT9O/dw8A9h04zOSp75GRmYXJbGLk3cOcdT3/+tvs3ncQpRT160Tw6rPj8PHxrhBZlEY5ulYuOq58EOSF8UFQdwxLfA3woVKq8nymbzJR95VRHLj9RSyxiTT//S1Slm4i52CMM8uJlz5xHofedy0+LSKd53Ef/IzJ25OQO/sXqjbpp7+IHvs2AJHvPUGN4f2I/3IJABmbCiv9goSNuI7sQzGYffNvxBOTPsaekQ1A7RfuI/S+a4o9bMoFk4mgCWM5M2YC1tPxhH81i+zVf2OJPu7MkjztA+ex32034NGsEQAqJ4eE56dgPXESc41gan79Ptl/b0ZlZJI07QNUZhYAgU88hN9tN5D22bdYY8+QOOlN/IsoBpO/HwEj7ybuzjGgFDW//oDs1evBZCLw0ZHE3jEae0oqwS89hVdUO3I2/VOuYrDZ7bz21WJmj7uTsCB/bn95Lr3aNqVhRP6U3CeHD3Aez/tzE/uOxwEQ1TySBS+NAiA1I5vrJr7LlS0aAvDL2h3EJaXxy6sPYzIJiWmZAMz9fS3N6tZkxtjbiI5N4LWvFvHRk3cD8Oa8JXRr1ZBpD9+CxWojOy9/X/O4pFTW7z5MeHBAuV5/IVnYbLwybRYfzXiNmqE1uO2BR+ndvTMNI+s580x4dJTz+OvvfmHvQWPG2+q/N7Fn/2G+/2wWeRYL9z3yFD2u7IhvtWoA7Np7gLT0wou6hYeF8sqz4/jsmx8KxXt5efLa8+OpVyeCM/GJ3DpiLN06d8Dfz5cJ/zfSWeeb78xh3g+/FnvYVDT2y3kZW+ALoAXwLvCe4/jLiuzUuVKtbWNyj8aSd/w0ymIl6Ze1VO/fudT8QUN6kPRLvuWYvu5f7JnZxfKlrtjqPM7afhCP8OCz9sU9PJiAPh1JmLesUPx/ShzA5OVRYY9/j5ZNscacwnoyFqxWMv9YhXevbqXm9xnYm8wlxhRW6/GTWE8Yg+u2hETsySmYA6sDOJU4gHh6Ovtviz2N5WA02AvPkPK6siM5G7diT0vHnp5BzsateHXthFtEOJYTMdhTUgHI2bQNn6t7lJ8AHOw6cpI6oYHUDg3E3c3MwM4tWLV9f6n5l2zcxaDOLYrFL9uyh+6tGuHtaew3vmDVFkZdfxUmk/GjD/Y3lM+RU/FENa8PQGR4DU4lpJKYmkF6Vg5bDxxnaA/jwz13NzP+Pl7O+t/6ZimP39K3Qj/M2Ln3AHVr16JORDju7u4M6tOTFWtKnwyx6M/VXNO3FwCHo4/TsW1L3NzM+Hh70aRRJGs3GL8Lm83GtFkfM25M4Z0dI8LDaNooElORxfXq161NvTrGdOrQkGCCAquT7LgP/lPiSilycnO5FOvyleMHQRcdVxR5S6XUCKXUSkd4EEOZVxo8woPIi82fSJMXl4hHeFDJeSNC8KgTStq6nS7XL25mgm7qReqqfKvRt0NTrlj6No2/fB6vJvmvyXUmjSDm1c9LVNT1p42lzT+f4dWoNmc++d3l9s8Ft5AaWOPOOM9tZ+Ixh5b8ADKHh+JWqyY5m4uPo3i0aIq4u2GNOeWMC540ntrLvsO9fh3S5/9cdj9Ca2CLi8/vx+l43EJrYD1xEvd6dTCHh4HZhHevbphrnvXDtXPmTEo6NYPyrdzQQH9OJ6eXmPdUQgonE1KIah5ZLG3Jpt0MjGrpPI85k8wfm3Yz/KWPGDP9a46dTgSgSZ0wlm/dB8DOIyeJTUzhdHIaJxNSCPTz4YVPFnLrpDlM+vRXsnLzAFj5z35CA/1oWrdmuV13SZyJT6BmaL6Mw0JrcCY+scS8p+JOczI2js4dDNdb00aRrN24leycHJJTUtm87V/izhh/13k//Erv7l0IqVHyb60sdu7Zj8VipU5EuDPuuVen03Pw7UQfi+H2m68/5zovlPJca+Vi44oi3yYiXf47EZHOwJayCohIgIi8ISL7RCRJRBJFZK8jrnoZ5ZxfS/2YedTlizgXgoZ0J3nR+mIWZFnUfW0UGRv3kLHJWCohc+dh/u08kj39H+fMp4to9PFEAAL6dMSakErWzpI/xDo67l12dLifnIMxBF7fvcQ8F5Nq/XsbPu0isjDXCKLG5KdJmDS10F2bOGkqMQNuwxJ9HJ/+vc6rTXt6BkmvzyTkjeeo+fEMbKfiwHZpV+pfsmk3fTs2x2wq/HOIT0nnUMwZurZs6IzLs1rxcHfjmxcf5Mae7XnxE2MCwv3XdCctK4dbX5zNN8s30axuOCaTCZvNzr5jsdzSqwMLJo3E29OdT35fR3auhbm/rWHMDb0u5qWelcV/rqZ/r+6YzWYAunXuQI8rO3LnqHE8+eIU2rRohtlk4kx8IktXrjkvhRufkMTEl9/ilWcex1RA5q88+wQrf/mKBvXrsGT5X+V2Ta5iV+JyqGy4osg7AH+LyFEROYox57GTiOwUkX9LKbMASAZ6KaWClFLBQG9H3IJSyqCUmqOU6qiU6nhjtfouX0RebBIe4TWc5x41g8mLTSoxb9D1PUj62fWbJPzx23ALCijkY7dnZDsHKlNXbEXc3HAL9MO3UzOq9+9Eq/VzaDBrHH7dWhP5zmOFK7TbSVq4hsBrrnS5D+eCNT4Bt5qhznNzaAi2MyVbX9UG9CZrSeEvg6WaDyEzXyVl1ifk7dxbvJDdTubSlfj0KdsdYj2TUMjSNoeFYD1jvDVl/7WBuHvGEnfv/2E5FoPleExp1Zw3odX9iEtKdZ6fSU4jLNCvxLxLNu1mUOeWxeKXbt7D1e2b4e5mdsaFBfrTp0MzAPq0b8bBGOPtx9fbk8kjhrDgpVG8+sANJKdnUjskkLAgf8IC/WndsDYA/To2Z9/xWGLikziZkMKtL85m0JMzOZ2cxrCX5pCQWv6biISG1HBa0QCnzyQQGlLyW9riP1czqF+vQnGj7hnOD5/PYu7M11BAvToR7D14mOMxsVxz2/30v+kecnJyGXTr/WftS0ZmJmOefIH/G3UPbVo2L5ZuNpsZ1Lcny1atO6drLA9sdpPLobLhSo8GYqwF0NMRIh1x1wGDSylTXyk1RSkV91+EUipOKTUFqFdKmfMmc8dBvCLD8agTiri7ETSkOynLNhXL59UwAnOAL5lbS/eVFqTG8L4E9GzHkUemFbJM3ULyXyqqtW0MJsGanM7JN77i304PsPPKkRx5eBrp6/4l+v9mAOBZP//1uXq/KHIOlfah14WRt3s/bnUicKtVE9zcqDagF9mr/y6Wz61+HUz+vuT+u6dApBsh0yaR+fuyQrNPANzq1HIee191ZaHB05LIWb8F7y4dMPn5YvLzxbtLB3LWGy9yJoff3eTni98tg8n4aVFZVZ0XLSIjOH46iZj4ZCxWG0s27qZn2ybF8kXHJpCemU0bh6ItyOKNuxhYxG/eu11T50yXLfuPUS/MUIhpWTlYrMZX1j/+9Q/tm9TD19uTGgG+hAX5c9Th+tu4J5oGtUJoXDuMVTPHs/itR1n81qOEBfrz7YsjqRHgW55iAKBlsyYcjzlFzKk4LBYLi5evpnf3LsXyHTl2grT0DNoWULA2m42U1DQA9h+K5sChaLpGdaBn1yhW/zqPpT98ztIfPsfLy5PFCz4pVmdBLBYLj06czPUD+zhnsoDhFz/ucOEppVi5dgOR9Yr/PSoadQ6hsuHKl53HRCQQ45NTtwLxZX0QdExEngI+V0qdBhCRMIxP+0+UUe78sNk5/vxHNPn6RTCZSZz/JzkHTlBr/HAydxwidZkxdSpoSA+SFq4pVrzpD6/h1SgCczUvWm+ey9Hx75G2ejv1Xh9Nbkw8zX+ZAuRPMwy8tiuhdw1E2WzYc/I4MmZq2f0TIfLtRzH5+SBA1t6jHJv4YXlLwcBmJ2nKu4TOegNMJjIWLsFy5BgBD91D3p4DZP9lfERWbUBvMv9YVahotf498WrXGnOAP76DjRk8CS++heXgEYJfegpTtWogYDlwhMTXZwLgcUVTQqZNwuTvi/dVVxLw0D3E3vIA9rR0UuZ+Tc2vZgGQ8tFX2NMMH3XQk2Nwb2K4K1LnfIn1ePk/1NzMJibeOYjR07/Gblfc0L0tjSJCmfXTSlrUr0Wvdk0BY5BzQFQLiu56dTIhhbikNDo2rV8o/v5ru/PMnB/5aulGfLzcefHe6wCIPhXPcx//giA0jAjhpfvybZyn7xjExDk/YbHZqB0SyMv3X1z/r5ubmWceH82oJ57DZrMx9Lr+NGpQj/c++oIWzZrQu4eh1Bf/uZpBfXsWkoXVauPuMeMB8PXx4Y0XnsStwBtKSezcu5/HJk4mLT2DVes2MmvuV/zy9WyWrFjD1u27SElN5+dFfwLw6rNP0KRRJM+8Mo3MzCyUUjRtFMnzTz5SQdIoncroMnEVUWfx3IvIZAwFfJj8h5FSSpX6QZBD8T+NsXRjmKPcaWAhMEUpVbLfowBbat9QGR98lwS9Z2c+es/OfMxNK8Y9VxVxr9HggrXwupo3u6xzusV9X6m0viuLZt2KsZRtnquVKqWSgQmOgIj0wFijd6crSlyj0WguNpd2yP3CcMVHvgsodaZJSYjIpgLHDwDvAL7AiyVte6TRaDSXGoW4HCobrljkrwP/iMguCqxDrpQqy9HnXuB4FNBfKRUvIlOBDcAb59NZjUajqSisVdhH7ooi/xyYAuzE9bcPk8NPbsLww8cDKKUyRcR6Xj3VaDSaCqQyWtqu4ooiz1JKvXOO9QYAWzHWL1ciEq6UihURX/Q2cRqNphJSlX3krijyNSLyOsaMk4KulVKnHyql6peSZAeGnksHNRqN5mJwuVvk7Rz/F/yC4LzWI3ds3Bx9ruU0Go2mormsLfKqti65RqPRnA+2y9wiR0SuxVjx0Ln+plLq5YrqlEaj0Vxsym/v5YuPKxtLfAj4YCx6NRe4GSi+kIlGo9FUYeyXuUXeVSnVWkT+VUq9JCLTgMUV3TFfH5c/JL3ssduq7g1W3qiU+LNn+l/BZjl7Ho3LVOU1QVxR5P9tbZMlIrWARCC8jPwajUZT5bisBzuB3xybQbwFbMN4cH1Uob3SaDSai4z9UuwvV064MmtlsuPwBxH5DfBSSqWWVUaj0WiqGrZL3YELoNRFs0Skk4jULHB+N8buPpNF5Nw36dNoNJpKjF1cD5WNslY/nA3kAYjIVRgLXX0BpAJzKr5rGo1Gc/GwIy6HykZZrhVzgbXDbwPmKKV+wHCxFN92XaPRaKowVXnWSlkWuVlE/lP0fYCCu/S69CGRRqPRVBWqsmulLIX8DbBaRBIwpiCuARCRRhjuFY1Go7lsuCynHyqlXhWR5Rhzxpeq/M09TcDYi9E5jUajuVhU5e/uynSRKKU2lBB3oOK6o9FoNJeGqmyRu7Jnp0aj0Vz22M8hnA0RGSgi+0XkUGn7FIvIrSKyR0R2i8i8AvFTRGSXI9zmSt8vm0HLaj06EPrsKMRsIuW7P0ia812h9NCJD+LTpTUAJi8vzMEBHOx4KwC1576Md9tmZG/dQ8yoSc4yPle2IfSpEWAS7Fk5xE6YjuV4LIH3DaX6LQNQVhu25FRiJ87A+v/tnXd8FNX2wL9nd9Mb6YGEEnrvIr0jICjgUxSevaCiz/KeilgRFLE8u8+fInZRUax0pEiR3nsLLYT03rO79/fHLJuEFAJkIdH75TMfZu49t8zNzpkz57a4RABCH78T3/5XgEnIWbuNxBc/BCDk0VsJGD0Is78vBzv9w6Vt4dWzK0GTJiImE1k/LSTjk+9KxQc9dh+eV3Q02sLLA1NgHU70GYN7iyYEP/0QJl9vsNlJ/3g2OYv/MO5r+pO4t2kOVisFuw+QPO0tsBpTKIImTcS7dzdUfgFJz75G4f7DAIT/bzoe7VpRsLpwPjwAACAASURBVH03Cf961ll+yJR/4966OSJC0fFYkp59DZWXX+3tsHbfCV79eQ12u2JM91bcOahzqfjXfl7LpsOnAMgvspKalcea6Xex6dApXvtlrVPuWGI6M24ZwsB20ZxKyWTSl0vJyMmnVf1QXho/CDeLGYDF2w/z4eLNADSvF8yMW4ZUmte3q3fx9aqdnEzJZMXU2wn09ar2NjjDmg1bmPHOx9jsNv4x4iruvvn6UvGvvPsxG7ftMtoiv4DU9AzWLfgGgDc++IxV6437uvfWGxk+qA8Ak6b+lz0HDmOxmGnbqhnPP/YAbhYLn3zzI/OXGr8bm81GzPFYVv/6JQH+fnwx5xfmzluCiNCscUNefPJhPDzceXr6W2zevhtfXx8AXpr8MC2bNXZZe5RHdW3ZKSJm4H1gCBALbBKRX5VSe0vINAMmA72UUmkiEuYIHwF0BjoCHsBKEVmolMqsrMy/hiI3mQh/fiIn73iaovhkGs19i+xl6yk8ctIpkvhy8aoCgbdcg0erJs7r1FlzMXl6UOemq0tlGzHlQWInTqXwyEnqjB9ByMSbOP3kmxTsPcKx6x5G5RdQZ9zVhD1xJ3GPzMCrUyu8Orfm6DUPANDwm9fw7taO3I27yF6+gbSvfqPJko9d3hbBT/2L+HsnYU1Ipt7s98hduY6imBPF9/v6/znP/ceNwr1lUwDs+fkkPfMq1hOnMIcGU++b98n7czP2rByyFywn7yljz+zQGU/hN2Y4Wd/Pw6t3N9waRBJ7ze14tGtF8DMPcfrmhwDI+Ox7xMsD/+tHlKpiymv/h8rJBSDosXvxHzeqzMvmYrHZ7bz842r+775rCA/w4Z9vzqVfm0Y0iSiey/b46F7O829W72L/qWQArmgWyZzHjJd8Rk4+10yfTY8WUQC8NW89N/drz7BOzXjx+z/4acM+xvZqy/GkdD5Zto3P/jUGf28PUrNyz5lXx+gI+rRpyN3v/1qt916mLWw2XnzzQ2a+MZWI0GBunPAfBvTuRpNGDZwyk/51t/P867nz2HfoCAB/rNvE3kNH+GHW2xQWFXHHw0/Rp3sXfH28GTGkHzOe/TcAT0x9nbnzlnDT6Ku5c9x13DnuOgBWrt3IF3N+IcDfj4SkFL7+4Td++fJ9PD08+M/zr7Bw+WpGDx8EwH8m3sFV/Yv/JpeaanStdAMOK6ViAETkW2AUsLeEzD3A+0qpNAClVKIjvDWwSillBawishMYhjEZs0LO6VoRketE5JCIZIhIpohkiUilb4dLjWf75hQej6PoZDwUWcmcvwrfwT0qlPcb0Y/MeX84r3PX7cCek1dGTimFyccbAJOfD0WJxrD63A07UfnGrnd52/djCQ8plvdwQ9wsiLsbWCxYU9IByN9xAFtSWvXccCV4tG1B0ck4rKfiwWolZ9FKvPv3rFDeZ9gAsheuAMB6/BTWE4aFaktKwZaajimwDgB5a4pXLi7YvR9LeCgA3gN6kP3b70b4rn2Y/HwxhxjKMn/jNqfCLknJMPHwcMkA3t0nEqkfEkBUsD9uFjNDOzVl5e5jFcov3HaIYZ2alglfujOGXq0a4OXuhlKKTYdPMbi9YQRcc0ULVjjy/HH9Pm7s1QZ/bw8Agvy8K80LoGVUKJFB/hd5p+dm175DNIisS/16Ebi5uTF8UB+Wr9lQofyC31dx9aC+ABw5dpKuHdpgsZjx9vKkeeNGrNlg7PLYt0dXRAQRoV2r5iQkpZTNa9kqrh7c13lttdkpKCjEarWRl19AaHDNmSRuO49DRCaIyOYSx4QSWUUCJ0tcxzrCStIcaC4ia0VkvYgMc4TvAIaJiLeIhGAsH17/XHWvio/8VeBapVSAUspfKeWnlHL9r+88cAsPxhqf7Ly2xifjFh5crqylXhjuURHkrt9xznzjn3mb+jNfoMmqLwgYNZDUD8u+FOvcMJScVcZnZ/72/eRs2EnTtV/RdO1X5KzZUuqr4FJgDgvBFl+81KstMdn5ojkbS90wLJER5G8sO7/LvW0LxM0N68m4sxKZ8R05mNy1m4zLsBCsCYnOaFtCMuaw8ssrScjUx2iwfA5u0fXJ/ObnqtzaeZGYkUNEHR/ndXgdHxIzcsqVjUvNIi4li27Nzn7WYPG2Qwx3KPj0nHz8PN2xmI3HJjzAl8SMbACOJ6VzPCmD2975iVvemsvafScqzetSkpicQkSJv0l4aAiJ5ShdgLj4RE6dTuDKzoYbskWTaNZs2EpefgFp6Zls2raL+MTSSwkXWa38tngFvbuVdl3l5RewZsNWhvTr6Sg3mNtvGs3gG+5iwJjb8PPxoVe3Tk75d2Z+xZjb/8Ur735MYeGlX6L3fMaRK6U+Ukp1LXGc72x3C9AM6A+MA2aKSB2l1BJgAfAnxhDwdVRhGZiqKPIEpdS+86wkACISLiKdHUd4FeSdb7k5GWUfhOrAf0RfshavAfu5P6SCbh/NyXue50jfW8mYu5SwpyaUive/dgCebZuR+vEPALg1qItHk/oc7nsrh/vcgk/3Dnh1beOS+6gOfIYNIOf31WXawhwSROhLk0h+7nVQpc3lkKceIn/LLgq27b6ospOfe50Tg2+iKOYEPkP7X1ReF8vibYcZ3KExZlPpxyEpM4fDp1Pp0fKcBhE2u+JEUgYfP3AtM24ZwtTvV5KZ59yr/LzyupwsXLaaq/r3xGw2/P69unWiT/eu3DzxCR6f+hod2rQs004vvvF/dOnQhi4dSv/WV67dSKd2rQjw9wMgIyubFWs2sPi7mSz/6TPy8vP5bYnxNfjIhFv57av/8d1Hb5CRmcWs2XMvwd2Wpho7O09R2oqOcoSVJBb4VSlVpJQ6ChzEUOwopV5SSnVUSg0BxBFXKVVR5JtF5DsRGedws1wnItdVlkBEOorIemAlhkX/KsbkovUi0rmidCXfcmMDGlQkVoaihBQsEcUWhyUihKKE8i0O/7PcKhVhDvTHo2Vj8nceACBzwSq8OrVyxnv37Ejw/TcSe98LqCIrAH5DepK3/QAqNx+Vm0/2qs14dWxVbv6uwpaYjDkitPg+wkKwJiSXK+szrD85DrfKGcTHm/D3XiTt3U8p2FX6/V3n3psxBQaU8rFbE5OxhIcVlxcegi2x/PLKYLeTs2glPoN7V03+PAgL8CE+vdgCT0jPISzAp1zZRdsPM6xTszLhS7YfYUC7aNwcSq2OjydZ+YVYbcajnJCRTViALwDhAT70a9sIN7OZyGB/GobW4URSRoV5XUrCQoKJL/E3SUhKJiy0/C/WhctXMXxQ31Jh9946lrmfvM3Hb0xDoWhYv/jL5X+ffkNaegZPPHhXOXmtdrpoANZv3k5k3XCC6gTgZrEwqG8Ptu/eD0BoSBAigru7G6OvHsyufZd+lHM1KvJNQDMRiRYRd+Am4OyOkJ8xrHEcLpTmQIyImEUk2BHeHmgPLDlXgVVR5P5ALnAVcI3jGHmONJ8BDyulWimlBjuOlsAjwKdVKPO8yN91EPdG9XCLCgc3C/4j+pK9rMwQeNwbR2H29yVv27k/MGyZ2Zj8vHFrZPxofXp1crpJPFo1JmLqv4i9byq21OKHteh0Et7d2oLZBBYz3t3aUXjENV8WFVGw5wBuDSKxREaAxYLPsP7k/rGujJxbo/qY/Hwp2FGi/8ViIfzNKWT/tpTc31eXkvcdMxyvnl1JenJ6KSs9d+U6fK8ZDIBHu1ao7BxsyalUhqV+Pee5d/8eFB2tfvdTm/phnEhK51RKJkVWG4u3HaZf20Zl5I4mpJGZW0CHRmU/GBdtPcTwEgpeROjatB6/7zQ6An/bdID+jjwHtI1ms2METFp2HseT0okK9q8wr0tJ25bNOBEbR2xcPEVFRSxctpoBva4sIxdzPJbMrBw6tm3pDLPZbKRnGF1iB44c5eCRY/S8wnCH/DBvCWs3buPV5x/DdJaVnpWdw+btuxnQu7icuuGh7Nx7gLz8ApRSbNiyg8YNDcM1yfGbUUqxfPV6mkU3rN5GqALqPI5K8zE6Kh8EFgP7gDlKqT0iMlVErnWILQZSRGQvsAJ4XCmVArgBqx3hHwE3O/KrlKqsR37HuWTKwUcpVaY3RSm1XkTKN4suBpudhKkfUH/Wi2A2kfHDEgoPnyDkoZvJ332I7OVGVfxH9CNzQVlrvMHsV3FvXB+TtydNVn1B/FNvkbNmK/HPvEPku0+DsmPPyOb0U28BEDbpLkzenkS+MxmAorgkTt0/laxFa/Du3p7oef8DBTmrt5C9wugkDH38Tvyv6Y94edBk1RdkfL+Y5He/rvamwGYn5eX3iPjgZTCZyPp5MUVHjlNn4m0U7jnoVOo+w/qTs3hlqaQ+Q/vh2bkdpgB/fK8dCkDyc69ReOAIIc88jPV0AnW/eAeA3OVrSP/wK/JWb8S795VEzfvcGH743OvO/Op++gZujeoj3l7UXzKb5ClvkLduC6HTnjCGOAoUHogh+aV3qr0ZLGYTT17Xh/s/mofdrhjVrSVNI4L438KNtK4fSv+20QAs2naYYZ2aImdtKnAqNZP49By6NKlXKvyRkT2Y9MVS3l+wkRZRIYy50vji6tmyPusOnuS6V77FJMKj1/Sgjo9npXnNXrWTz1ZsJyUrl7Gvz6F3qwY8f+OA6m8Li5mnHrmXex+bgs1uZ8zVg2ka3YD3Zn1NmxZNncp24bJVDB/Yp1RbWK02bn3Q+J37+ngx45l/Y3EMt5z23/9RNzyMf97/BACD+/bg/ttvAmDZ6vX0vKIT3l7O/dpp37oFQ/r3Yuzdj2A2m2nZrDE3XGP8ziZN+y9p6ZkoFC2aRvP8fyZWezuci+pcQ0UptQDD110y7LkS5wr4t+MoKZOPMXLlvBClyn+/iMgTSqlXReRdynkJKaUeqjBTkXeAJhjL3p4xt+oDtwJHlVIPnqti+5tfXZsXI6tWPLz03oxniJg+4txCfxMsXYdf7irUGNzCW1y0Gn654c1V1jmTj39Voyb0V2aRn/E/bD7fTJVSD4nIcIyxk2ccaqcwxk0uqDilRqPRXB7stXgh28oWzfrN8f/nF5KxUmohsPAC66XRaDSXlNq81kqFilxEKp1uppS6tqI4EQnAmH46CgjHcM0kAr8AM5RS6RdUW41Go3ERtdcer9y10gPDv/0NsAHOa3+jORgbUQxQSsUDOPb/vN0Rd9WFVFaj0WhcxV/SIgciMBZ9GQeMB+YD3yil9lQh30ZKqVdKBjgU+gwRuZBRMBqNRuNSrFJ7bfIKx5ErpWxKqUVKqduA7sBhjJW4zjniBDguIk+UnM3pmOU5idJrEGg0Gk2NoLrGkV8OKh1HLiIewAgMq7wR8A7wUxXyvRF4EmM255lpfwkYs5tuuNDKajQajav4S7pWROQLoC3GoPYXlFJVXlzDsTTjJMdxdr534ILZnRqNRnMx1Obhh5VN0b8ZYxGXh4E/HUvYVscyti9cRFqNRqNxCX9J14pS6oK3gXMshl5uFMZwRI1Go6lR/CVdKxdJODAUOHsnBcFYZ/ecnMioUUueX1YaUKP28bisqGOHL3cVagy2oK2Xuwo1BrfwFhedh61G2tpVw1WKfB7gq5Qqs2OBiKx0UZkajUZzwWiL/CyUUmUXJy6OG++KMjUajeZiUNoi12g0mtqNtsg1Go2mllObhx9qRa7RaDTUzGGFVUUrco1GowGstViVa0Wu0Wg06M5OjUajqfXozk6NRqOp5WiLXKPRaGo52iLXaDSaWo5NaYv8shM8oAMtX7wNMZuI/Xo5x94tveVoi6m3EtirNQBmLw/cQ/xZ0dyYgFpvbF+iHx0DwNE3fyJuzioAuv74HB7hdbDlFwKw9cbpFCZnIu4W2r33AP7toylKy2bHhLfJP5lExD960WjiNc4y/Vo3YP3gyWTtOY64mWn18p0E9mwNdjuHXv6OxPkbq70dfPp0IezpexGzifTvF5P60fel4sMm34N39/YAmDw9MQcHcKjrWACiPp6KV8eW5G3ZS+y9U5xpGsx+FZOPl9F2QXXI33WQUxOnYfL3pe7Lj+BWvy6qsJDTk9+i8NBxAJos/xRbTh7YbSirneP/eBiAem89iXt0pJGXny+2rGyOjfpXtbcDwNpjyby26gB2pRjdJpI7u0aXin991QE2xaYCkG+1k5pbyOr7BgDQ5d2lNA32BSDCz5O3r+kEwJ0/bCKn0ApAal4hbcMDeHNkRzLzi5jy+x5iM/Jwt5iYMriNM/2U3/ew6mgSQV7u/HBzz1J1+GbHCebsPIlJhD6NQnikd3OXtAXA2p2HeGX2Iux2O2P6duaukX1Kxb82exGb9h0FIK+wiLTMHNZ8MJmN+47y+uxFTrmjp5N55f7rGdilFRv2xvDGd0tQdoWXpzvT7h5Ng/BgCousPD3zJ/YdiyPA15tX77+eyNBAdsXEMu3T3wBjuN99o/szqEurKtXP1ehx5Jcbk9Bqxp1sGfsS+XEpdF88naTFW8g5eMopcuC5L5zn9e8ain+7RgBY6vjQ+LF/sP6qp0BB96XTSVy8BWtGDgC7Jr5H5o6YUsVFjR9AUXo2a7o/QsToHjR/djw7J7xN/Ny1xM9dC4Bvq/p0/OwxsvYYiq3xI2MoTM5gbc9HQQS3QF8XtIOJ8OcncvKOpymKT6bR3LfIXraewiPFmzIlvjzTeR54yzV4tGrivE6dNReTpwd1brq6VLYnxj/hPI9892mylq0DIPi+seTvi+HUAy/i3jjKKPu2p5yyJ299Elta6QW/4h6Z4TwPe/JubFk5F3nT5WOzK2as3M8HYzoT7uvJP7/bQL/oUJoEF7f7Y32LF1r6ZscJDiRlOa89LGa+G9+jTL6fXH+F8/w/83fQv3EoALM2H6VFqB9vjOzI0dQcZqzcz4fXdQHgmlb1uLF9fZ5dUnpJ/00nU1kZk8R343rgbjGRmltYPTdfDja7nelfLuDDx28hPMif8S/MpH+nFjSJDHPKPD5+mPN89tIN7D9xGoBuraKZM+1+ADKycxk56R16tDV+Ny9+Po+3Hx5H43qhfLdsIzN/XcW0e8bw06qt+Ht7Mu/Vh1m4fhdvff87r028gaaRYcyeMgGL2UxSehY3PPsB/To2R0TOWT9XU5t95Be8VG1NIqBzU3KPxpN3PBFVZCP+5z8JG9a1Qvm6Y3px+kdjEcaQAR1I+WMX1vQcrBk5pPyxi5CBHSotL3RYV6fVnvDbBoJ6tykjEzGmF/E/Fy/0GDluAEff+cW4UIqi1KwyaS4Wz/bNKTweR9HJeCiykjl/Fb6DyyqjM/iN6EfmvD+c17nrdmDPyatQ3uTjhXf39mQvNRS5R9MG5K7fAUBhTCxukeGYg+tUub5+w/uUKr862Z2QQf063kQFeONmNjG0WQQrY5IqlF90IJ5hzSOqnH92gZVNsakMaGwompjUHK6ICgIgOsiHuMw8UnILAOgSGUiAp1uZPL7fFcsdXRrhbjEewyBv9yqXf77sjjlF/fAgosKCcLNYGHZlW1ZuO1Ch/KINuxh+Zbsy4Us376V3u2Z4eRh1FRGy84z7zM4rIDTQD4AV2w5wbe+OAAy5ojUb98aglMLLwx2L2QxAQZEVEbmg+rkC+3kcNQ2XWuSOPTsjHZenlFIJrijHMyKI/LgU53V+XCoBnZuWLxsVgleDUFLXGNaRx1lpC+JS8YgIcl63efs+lM1O4ryNxLz5o5FH3SDyTxlplM2ONSsPtyC/Uso5YlQPtt/2GgAWf28AmkwaS1DP1uQeS2D/U59SmJRRHbfvxC08GGt8svPaGp+MV4fyl/e01AvDPSrCqYirgu+QHuSUUPb5+4/iN6QneZv34Nm+OW71wrBEhGBLSUcpRf1PXgSlSPtuIRnfLSqVl1fXtliT0yk6HncBd3puErMLCPf1cF6H+3qwO6H85YDjMvOIy8xzKmKAQqud8d+uxyIm7ujaiAFNSluGK2IS6RYVhK+H8Qg1D/Fl+ZFEOkcGsjs+g9NZ+SRkFxDs7UFFHE/PYVtcOu+vO4y72cS/+zSnTXjAxdx2hSSmZRIRVLw0dFigP7tiYsuVjUtO51RSOt1aR5eJW7RhN7cMLTYOptxxLQ++8TUe7hZ8vTz48tm7y5RnMZvx9fIkPTuXQD8fdh6J5flZv3A6JZ2XJlyHxWw+r/q5itrsWnGJRS4iHUVkPbASeNVx/CEi60WkcyXpJojIZhHZvCDviCuqRsToniTM2wD2c//Rdk18l3X9n2DTtVOo070ldW+oms8uoHNTbHkFZO83fohiMeMZGUzGpoOsHzKZjM0Haf78zRd1HxeL/4i+ZC1eA/aq2xf+I/uTVcKCTv1wDiZ/Xxr98i6Bt1xL/r4jzvxOjH+cY2Me4uTdzxH4z5F4dW17Vl79yJq/slru5WJZfDCeQU3DMZvEGbbgjt7Mvqk704e15bVVBziZnlsqzaKD8QxrUWzB39ElmqwCKzfOXse3O07SItQPswiVYbMrMvKL+GJsNx7t3ZwnFu5E1YAOt0UbdjO4a2vMptLqISk9i8OxifRsW2wkfblkHe/9+58sffM/jOrdide/WXzO/Ns3ieKn6Q8w+/kJzJq3moLComq/hwtBnce/moarXCufAQ8rpVoppQY7jpbAI1SyX6dS6iOlVFelVNervZpUJFaG/PhUPOsFO6896wVREJ9armzE6B5OtwpAwVlpPUqkLYg39sWw5eQT/+NaAjoZP+D806l4RhppxGzC4udV2hof3ZP4n4rLKErNwpabT4KjczP+tw1OH311UpSQgiUixHltiQihKCGlXFn/s9wq58Ic6I9Xu+ZkryzuoLXn5BE/+U2OjfoXpx9/HUtgAEUOv6rVUa4tNYPspevwal+iE89swu+qnmTOX3U+t3dehPl6kJBd4LxOyC4g1Kd863jxwYRSStlI7wlAVIA3XaMC2V/Cf56WV8iehEz6NCpua18PCy8MacN343sw7ao2pOUVEunvVWkdw309GdQkDBGhbUQAJoS0PNcotbBAf+JTi79IEtMyCQ8sf/OWRRt2M7x72zLhSzbuYWDnlrhZDNdIamYOB08k0L5JFABDr2zDjsMny5RntdnIzsunjq93qfwa1wvF29Odw6cSz6t+rsKmVJWPmoarFLmPUmrD2YFKqfWAT3UXlrntCN6NI/BqEIq4mYkY3ZPExVvKyHk3rYdbgC8Zmw86w5JX7CCkf3ssAT5YAnwI6d+e5BU7ELMJtyDD3ycWM6FDOpO93/iRJi3eQr2xfQEIv+ZKUtfsKS5EhPBru5fyjwMkLtlKkGPUTHCftmSX6IitLvJ3HcS9UT3cosLBzYL/iL5kL1tfRs69cRRmf1/ytu2rct5+Q3uTvXIjqoT1ZPLzATfDtRAwdii5m3djz8lDvDyco1zEywPvXp0ocIxmAfDp2YnCmFinsncFbcL9OZGey6mMPIpsdhYfind2TJbkaGoOmQVFdIgodmlk5hdRaDW+LNLyCtkel07joOKf7e+HE+jTKAQPh0IDyCoooshmpPlpzyk6RwY63S4V0b9JKJtOGUbD8bQciux2Ar3K+tKrgzbR9TiRkEJsUhpFViuLNuymX6eybrejcUlk5eTRoWn9MnEL1+9iWPdiv7m/jyfZefkcc7jz1u2OIbqu0cb9O7bg1zXGvjJLN+2lW6toRITYpDSsNhtguHCOnU6mXkidKtfPldhRVT5qGq7ykS8UkfnAF8CZIRP1gVuBRRWmukCUzc7+yZ/S+dunELOJU9+sIOdALE2euIHMHTEkOZR63dE9if+ltIK1pudw5I0f6b74JQCO/Hcu1vQczN4edPl2MuJmRkwmUlbvJvarZQCcmr2Ctu89QO/1b1GUns3Oe99x5hfYoxX5cSnkHU8sVc6habNp994DtJh2K4UpWex5+IPqbgaw2UmY+gH1Z70IZhMZPyyh8PAJQh66mfzdh8hebrxb/Uf0I3NBWWu8wexXcW9cH5O3J01WfUH8U2+Rs2arI01fUs4ayujepD71XvkPSikKDx/n9FNvA2AJCSTy/WcAELOZzN9WkrO6+MXqP6Kvyzo5z2AxmZjUvwUTf9mK3a4Y1aYeTYJ9+d/6w7QO86e/o5Ny8cF4hjaPcHa6AcSk5fDS8n2IgFJwR9foUqNdFh9M4I4ujUqVF5Oaw3NL9yBAk2Bfnh/U2hn35KKdbIlNIz2/iKGzVnFf9yaMaRPJ6NaRTPl9D9d/9SduZhNTh7QtVY9qbQ+zmck3X839r3+J3a4Y3acTTSPDeP/H5bSJrkf/Ti0BwxofemXZepxKSiM+NZOuLRqWyvO5O67lP+/NwSSCv7cnL9w1CoAxfTvx9Ec/MfKJt/H38eLV+68HYNvBE3wyfw1uZhNiEp66ZQSBfsZLsrz6XUpqYidmVRFX+eREZDgwihKdncCvSqkFVUm/JPymmvfau0w0CNB7dp6hwcONL3cVagymzr0udxVqDJ49xl30G3BkgxFV1jnzTsyvtDwRGQa8DZiBj5VSM8qRGQtMwRhSv+PM7mki0gD4GMP4VcDVSqljlZXnslErSqmFwEJX5a/RaDTVSXW5TETEDLwPDAFigU0i8qtSam8JmWbAZKCXUipNREp+fnwBvKSUWioivlThY8FVo1YCRGSGiOwTkVQRSXGczxCRqg801mg0mkuEUqrKxznoBhxWSsUopQqBbzG8EyW5B3hfKZXmKDsRQERaAxal1FJHeLZSKpdz4KrOzjlAGjBAKRWklAoGBgDpjjiNRqOpUdhQVT5KDpV2HBNKZBVJcd8gGFZ5ZOnSaA40F5G1jmHZw0qEp4vIjyKyTURec1j4leIq10ojpdQrJQOUUvHADBG5w0VlajQazQVzPq4VpdRHwEcXUZwFaAb0B6KAVSLSzhHeB+gEnAC+A24HZlWWmass8uMi8oRjZidgzPIUkUmUflNpNBpNjaAaXSunMDoqzxDlCCtJLMbgjyKl1FHgIIZijwW2O9wyVuBnoMJJlGdwlSK/EQjGmM2ZvVpzrAAAFtdJREFUJiKpGLM8g4CxLipTo9FoLphqHEe+CWgmItEi4g7cBPx6lszPGNY4IhKC4VKJcaStIyJnJj0MBPZyDlziWnE48Cc5DkSkD0YHwC6lVPlTLjUajeYyUl1T75VSVhF5EFiMMfzwE6XUHhGZCmxWSv3qiLtKRPYCNuBxpVQKgIg8BiwTYzD/FmBmuQWVwCWKXEQ2KqW6Oc7vBh7AeAM9LyKdyxtTqdFoNJeT6px675gvs+CssOdKnCvg347j7LRLgfbnU56rOjtLzjO+F7hKKZUkIq8D6wGtyDUaTY2iJk69ryquUuQmEQnE8MGLUioJQCmVIyJWF5Wp0Wg0F4xW5GUJwPDtCKBEpK5S6rRjllKVptJaavXKB9VLDVxs7fKRl3+5a6D5i1ITlhC+UFzV2dmogig7MMYVZWo0Gs3FoC3yKuKYanr0Upap0Wg0VaEmbhhRVf4amy9rNBrNRWJTtdedqxW5RqPRoH3kGo1GU+vRPnKNRqOp5WgfuUaj0dRy7Nq1otFoNLUbbZFrNBpNLUePWtFoNJpajnataDQaTS1Hu1YuM0EDOtDsxTsQs4nTXy/j+Lu/lIpvOvU2Anu1AcDs5Y5bSACrmxs7zkWM7UejR68D4NibPxI/5w8AxM1M85fvIrBna5RdEfPytyTN34BnVAgt37of92B/itKy2fvAuxScNpZYb/LMPwke0snI6425JP6yDoBWb0+kTs/WWDONPVT3PfQ+2XuOu6QtfPp0IfyZexGzifQ5i0n56PtS8WFP3YNPd2OFTPH0xBIcwMEuxl4f9WdNxatjS3K37CV2whRnmoazX8Xk6wWAOagO+TsPEjtxGgDe3doR/swExGLBmpbJiX9OAiDo9tHUGTsUpRQFB49xetKbqMKiSvOqbkyN2uA+cByICeuu1Vg3LiwV79b/RswNWhgXFnfE25+89x4CwOvfH6GSYwGwZ6ZS+PN7pdMOHIelbS/y3nnQuJc2PXHvdwMqOw2Aom0rsO1ajal+C9wH3OhMJ0F1KZz3IbbD27F0GoCl8xBMgWHkvv8I5GW7pB0A1u48xCuzF2G32xnTtzN3jexTKv612YvYtM+YdJ1XWERaZg5rPpjMxn1HeX32Iqfc0dPJvHL/9Qzs0orbp39Cbl4BAKlZObSNjuSth8eRmZPHc7N+ITYxFXc3Cy/cNYpmUcZmYc/N+plV2w8S5O/Djy894Mw3IzuXJz74gbjkdOqF1OG1iTfg7+PlsvYoD22RX05MQosZd7Ft7IsUxKXQdfHLJC3eTO7B4p2VDj/3ufM86q5h+LaLBsBSx4fox65n01VPgoIrls4gefFmrBk5NHrkOoqSM1jf8xEQwS3QF4Cmz99C/JxVxM/5g8DebWjy9Hj2PvgewYM74dc+mk0Dn0A83Oj84/OkLNuOLTvPqMMLX5I0b4OL28JExJSJnLj9aYrik4me+xZZy9dTeLh4d73E6cVr1Afecg2erZs4r1M+novJy4M6N11dKtvj459wnke+9zTZvxsvKJOfDxEvPMCJO5/FejoJc1AAAJbwYAJvvZaY4fehCgqJfHsy/iP7kfHj7xXmVe2I4D74nxR8/wYqKw3Pm5/BdmQ7KuW0U6Ro5XcUOc4tnQZiCmtQnN5aSP4XU8vN2hTeEPHwLhNuPbCJomWzS4XZTx4ozsfTB6+7pmM7Zmz4Yjt1GNuRnXjc+PiF32cVsNntTP9yAR8+fgvhQf6Mf2Em/Tu1oElkmFPm8fHDnOezl25g/wmjnbq1imbOtPsBQ9mOnPQOPdoav5nPnrrTmebf737HgM7GS/Hj31bTskEEbz10E0fjkpj+5QJmTroNgFG9OzJuUDeenvlTqTp+Mn8N3VpFc9fIPsyat5pZ89fw6NghLmiNiqnNFrmrtnq7ZPh3bkru0Xjyjyeiimwk/vwnocOuqFA+fEwvEn5cA0DwgI6k/rETa3oO1owcUv/YSfDAjgDUHTeAY+/8bCRSiqLULAC8m0eRtmY3AGlr9hAyrCsAPs2jSF+3D2WzY88tIHvfCWdelwqv9s0pPB5H0cl4KLKSOX8VfoN6VCjvP7IfmfP+cF7nrtuB3fHiKQ+Trxc+3duT5VC+Adf0J2vJn1hPJwFgS81wyorFjHi6g9mEeHlgTUypNK/qxhQRjUpLRGUkg92Gdf9GzE0q/nuYW3bDun/juTMWwa3fDRSu+uG862Ru3gXb0V1gLQRAJZ5EZaacI9XFszvmFPXDg4gKC8LNYmHYlW1Zue1AhfKLNuxi+JXtyoQv3byX3u2a4eXhXio8Oy+fjfuOMqBzSwBi4pLo1sowlqLrhRKXnE5KhvG10aVFo3It7RXbDnBtb+Pvc23vjqzYuv/CbvYisClblY+aRq1X5B4RQRTEFT8MBXEpeEQElSvrGRWCZ4MwpyJ2L5M2FfeIICz+hrXVeNKNXLF0Bm1nPopbqGFtZu89TuiIbgCEXt0Ni583lkBfsvccJ2hgB0xe7rgF+RHYqw0e9YKdeTeePI5uK16j6dTbEHfXfAhZIoKxnk52XhfFJ2MJDy5ftl4Y7lER5KzbUeX8/Qb3IKeEsnePjsTk70uDr2bQ6Ke3CRg9EABrQgops36k2R+f0+zPr7Fn5ZCzZluleVU34heIykpzXqvsNMQvsHxZ/yBMASHYT+wrDrS44XHzM3iMn4y5afELwNJpILYjOyAno0w+lmad8bxtCu7X3lduWZaWV1TtZVHNJKZlEhHk77wOC/QnIS2zXNm45HROJaXTrXV0mbhFG3YzrHvbMuErtu7nytbR+Hp5AtC8QTjLthhtuSsmltMp6RWWd4bUjGxC6/gBEBLgS2qG69xMFVGNmy9fclymyEUkQERuFJF/O44bRaTOOdJMEJHNIrJ5Xl5MtdcpbHQvEuetB3vlfwixmPGMDCFj0wE2DXmSjM0Hafb8LQAcnvIldXq05orfX6FOz9bkx6WAzU7qHztJWbaNLvNepM3/PUzG5oMouzGc6chLs9nQ6xE2DZ2MWx1fGj44qtrv7XwJGNmXzEVrwF71IVf+I/uXsuAxm/Fq25ST9zzPyTufJeSBcbg3MpS736DuHB54B4d63YzJyxP/awdUntdlxNyyG9aDW0ot/J7/0SQKvnqRwvkzcRtwExIQivgEYG7eFevWZWXysB3ZQd7MJ8n/fAr2Y3txH35naQGfAEwhUdiP7XH17VwUizbsZnDX1phNpVVDUnoWh2MT6dm2aZk0C9fvLmXB3zmiN5m5+Yx99gO+WbqRlg3rYpIqbUMAgIjAechXF9W4+fIlxyWKXERuBbZi7BLt7TgGAFscceWilPpIKdVVKdV1pFfjKpVVEJ9ayvL1qBdMQXz5+zuHj+5Jwo9rndeFZdIGURifSlFqFrbcfJLmG9ZT4m/rnX71woQ0dt/5XzYNnkTM9G8AnJ2Yx9/6iU2DnmD72BdBhLwjhp+xMDHduL9CK6e/XYF/57IPQ3VgjU/BUjfEee0WEYI1ofxPd/8R/c5LkZoD/fFs35zsFcUWpTU+mezVW1B5BdjSMsndtBuPltH49OxIUWw8ttRMsNrIWrIW786tKs2rulFZpS1w8S1toZfE0qIbtrMsZZXt+JtlJGM/eQBTeAMkvAGmwDA8756O5z0zwM0dz7umGwnyc8BmbH5l3bUaU3jDs8roiu3QVrBf+s/ysEB/4lOLLeLEtEzCA/3LlV20YTfDy7G6l2zcw8DOLXGzmEuFp2XlsDvmFH06NHOG+Xp5Mu3u0cyZdj8vTRhDWmYOUWHlfw2dISjAl6R0w32ZlJ5FkL9Ple+vutAWeVmeBroope5XSr3oOO4DugLPVGdBWduO4N24Lp4NQhE3M2Gje5K8eHMZOe+m9bAE+JC5+aAzLGXFdoL6d8AS4IMlwIeg/h1IWbEdgOQlWwjs1RqAwD5tyT1ojGBwC/JzWgsNHx7D6W9WGJmZBIujQ9SndQN8WzcgdaXhtnAPK/4QCR1+BTn7izsfq5O8XQdxb1QPt6hwcLPgP6IvWcvWl5FzbxyFyd+XvG37ysmlfPyG9SZ7xUZUYZEzLGvZery7tDH84J4eeHZoQeGRkxSdTsKrY0vE0wMA7x4dKThystK8qht7/DEkMBwJCAGTGUvLboZL5CwkKAI8vbHHHSkO9PAGs8P95eWLKbIp9pQ47DG7yPvgP+TPfJL8mU9CUSH5s54y5HwCnMnNTTpiL9GpCufhg3cBbaLrcSIhhdikNIqsVhZt2E2/Ti3KyB2NSyIrJ48OTeuXiVu4fhfDupfjN9+0l74dm+PhXrxNb2ZOHkVW46X24x9b6dyiodPtUhH9O7bg1zXGs/frmu0MKKd+rsauVJWPmoarRq0IlPv9YaeKW71VFWWzc3DyJ3T89mnEbCLumxXkHIgl+omxZO04QvLiLQCEj+5F4i9/lkprTc/h2Btz6br4ZQCO/fcHrOk5AByZ9jWt33uQZtNupzAlk30P/w+AOj1b0+Tp8aAU6ev3ceDJWQCY3Cx0+cUYnWDNzmXvxHdRNsNt0eaDh3AL9geB7N3HOfD4R9XZBMXY7MS/8AH1P3nRGH74wxIKD58g5OGbyd91iOzlxqgZ/xH9yJxf1hpvOPtV3JvUx+TtSdPVX3B68lvkrNnqSNOXlA9LD2UsPHKS7NVbaDzvfyi7nfTvF1NwyBhWmbloDdE/v4Oy2SjYG0P6d8VD/8rLq9pRdgqXzcbjH4+AyYR111pUShxuvUZhjz/mVOqWlt2w7d9UKqkpuC7uQ24xXC0iFG1YWGq0S3m4dR6EuUkHsNtR+TkULvrUGSf+wYhfEPaTB0ulsXQahKXbUMQnAM/bpmCP2UXhks/PzvqisZjNTL75au5//UvsdsXoPp1oGhnG+z8up010Pfp3MjopF23YzdAr2xqujRKcSkojPjWTri0alsl78Ybd3Dmid6mwo6eTeWbmT4gITSJDeeHOYlfipA9+YPP+Y6Rn5zLk0f9y/+gBXNevM3eO7M3j73/Pz6u3UTc4gNcm3lDt7XAuavOoFXHFZ4KI3AY8BywBzphiDYAhwDSl1GfnymN5+Nja26rVTF3/S9/xU1NpeG+9y12FGoOp16DLXYUag2ePcRdtIIYGtKiyzknKOHDpnfiV4BLXilLqcww3yh9AgeNYCXStihLXaDSaS01t9pG7bEKQUipNRFYAkY6gU0qp8nubNBqN5jJTE33fVcUlilxEOgL/BwQAsRh+8SgRSQcmKqW2uqJcjUajuVBqoqVdVVxlkX8G3KuUKjUnXUS6A58CHVxUrkaj0VwQNXF8eFVxlSL3OVuJAyil1ovIpR8gqtFoNOdAW+RlWSgi84EvKB61Uh+4FVhUYSqNRqO5TOiNJc5CKfWQiAwHRlGisxN4Xym1wBVlajQazcWgOzvLQSm1EFh4TkGNRqOpAdRm14qr1loJEJEZIrJPRFJFJMVxPuNcC2dpNBrN5UCdx79zISLDROSAiBwWkScrkBkrIntFZI+IzHaENRSRrSKy3RF+X1Xq7iqLfA6wHBiglIp3VDACuN0Rd5WLytVoNJoLoroschExA+9jzGSPBTaJyK9Kqb0lZJoBk4Fejjk3Z3b5OA30UEoViIgvsNuRNq6yMl21aFYjpdQrZ5Q4gFIqXik1Ayi7YINGo9FcZqpx0axuwGGlVIxSqhD4FqO/sCT3YPQZpgEopRId/xcqpQocMh5UUUe7yiI/LiJPAJ8rpRIARCQcwyKv0tJ/AxPm1Ii1DERkglLKRatc1S50WxSj26KYv0pbWAtPVVnniMgEYEKJoI9KtEEkpfVcLHDlWVk0d+SzFjADU5RSixxh9YH5QFPg8XNZ4+A6i/xGIBj4w+EjT8VYayUIuPTLml0cE84t8rdBt0Uxui2K+du1Rcm9ExzH+b7ILEAzjD0bxgEzz/QfKqVOKqXaYyjy2xxGcKW4atGsNKXUJKVUS6VUkONopZSaBIx2RZkajUZTQziFMW/mDFGOsJLEAr8qpYqUUkeBgxiK3YnDEt8N9DlXgZdjz84XLkOZGo1Gc6nYBDQTkWgRcQduAn49S+ZnDGscEQnBcLXEiEiUiHg5wgOB3kDFO2U7cNWiWTsrigLO+ZlQw6j1vr9qRLdFMbotitFtUQKllFVEHgQWY/i/P1FK7RGRqcBmpdSvjrirRGQvYMPwhaeIyBDgvyKiMPTl60qpXecq01UbSyQAQ4Gzl60V4E+llN4dQKPRaKoJV41amQf4KqW2nx0hIitdVKZGo9H8LXGJRa7RaDSaS8fl6OysFYjIMRHZ5Zgqu/ly1+dSIiKfiEiiiOwuERYkIktF5JDj/8DLWUdXcj73LwbvOKZi7xSRzpev5q6hvGfh79weNRGtyCtngFKqo1Kq6+WuyCXmM2DYWWFPAsuUUs2AZY7rvyqfUfX7H44xbKwZxnjqDy5RHS81Zz8Lf/f2qFFoRa4pg1JqFZB6VvAo4HPH+ef8hecDnOf9jwK+UAbrgToiUvfS1PSyotujBqEVecUoYImIbHFMx/27E66UOu04j6f2DSO9WCq6//KmY0fy16K8Z+Hv3B41DpetR/4XoLdS6pRjVbKlIrLfYan97VFKKcc4178lf8P7L/MslIz8G7ZHjUNb5BWglDrl+D8R+AljRbO/MwlnPpEd/yde5vpcaiq6/6pMx67VVPAs/G3boyaiFXk5iIiPiPidOcdYP3135an+8vwK3OY4vw345TLW5XJQ0f3/CtzqGK3RHcgo4XKo9VTyLPwt26OmoseRl4OINMawPMBwP81WSr10Gat0SRGRbzDWgQgBEoDnMdaGmAM0AI4DY5VSZ3cI/iU4n/sXEQHewxjlkgvcoZT6ywxXrehZEJFg/obtUVPRilyj0WhqOdq1otFoNLUcrcg1Go2mlqMVuUaj0dRytCLXaDSaWo5W5BqNRlPL0YpcUy2IiBKRr0pcW0QkSUTmVUPe/UUkQ0S2icgBEVklIiMvIr9GIjK+xPXtIvLexdZTo7lcaEWuqS5ygLZn9hsEhlC9M/pWK6U6KaVaAA8B74nIoAvMqxEw/lxCGk1tQStyTXWyABjhOB8HfHMmQkS6icg6h1X9p4i0cIQ/KiKfOM7bichuEfGurBDHzlNTgQcd6UJFZK6IbHIcvRzhU0TkS0e5h0TkHkcWM4A+jvW1H3WE1RORRQ65V6unOTSaS4NW5Jrq5FvgJhHxBNoDG0rE7Qf6KKU6Ac8B0x3hbwNNRWQM8Clwr1IqtwplbQValsjjTaXUFcA/gI9LyLUHBgI9gOdEpB7G2tmrHetrv+mQ6wjcCLQDbhSRkuuFaDQ1Gr36oabaUErtFJFGGNb4grOiA4DPRaQZxrKobo40dhG5HdgJfKiUWlvF4qTE+WCgtTE7HAB/EfF1nP+ilMoD8kRkBcaCT+nl5LdMKZUBIMbO5g0pvRyrRlNj0YpcU938CryOsVZJcInwacAKpdQYh7JfWSKuGZAN1DuPcjoB+xznJqC7Uiq/pIBDsZ+9BkVFa1IUlDi3oZ8NTS1Cu1Y01c0nwAtKqV1nhQdQ3Pl5+5lAEQkA3gH6AsEicv25ChCR9sCzwPuOoCXAv0rEdywhPkpEPB2LPPUHNgFZgF/Vb0mjqdloRa6pVpRSsUqpd8qJehV4WUS2UdrafRN4Xyl1ELgLmOHYwOBs+pwZfoihwB9SSi1zxD0EdHVs9rsXuK9Eup3ACmA9ME0pFecIs4nIjhKdnRpNrUWvfqj5yyIiU4BspdTrl7suGo0r0Ra5RqPR1HK0Ra7RaDS1HG2RazQaTS1HK3KNRqOp5WhFrtFoNLUcrcg1Go2mlqMVuUaj0dRy/h+PTHJDwlvhtQAAAABJRU5ErkJggg==\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#model with optimal max_depth and optimal min_samples_split\ndt_bow=DecisionTreeClassifier(max_depth= omd_bow , min_samples_split = omss_bow,class_weight='balanced')\ndt_bow.fit(d_train,y_train)","execution_count":47,"outputs":[{"output_type":"execute_result","execution_count":47,"data":{"text/plain":"DecisionTreeClassifier(class_weight='balanced', criterion='gini',\n            max_depth=50, max_features=None, max_leaf_nodes=None,\n            min_impurity_decrease=0.0, min_impurity_split=None,\n            min_samples_leaf=1, min_samples_split=500,\n            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n            splitter='best')"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#report on train data\nreport(d_train,y_train,dt_bow)","execution_count":48,"outputs":[{"output_type":"stream","text":"              precision    recall  f1-score   support\n\n           0       0.42      0.87      0.57      5314\n           1       0.97      0.76      0.85     26935\n\n   micro avg       0.78      0.78      0.78     32249\n   macro avg       0.69      0.82      0.71     32249\nweighted avg       0.88      0.78      0.81     32249\n\n\nThe value of TPR is 0.762\nThe value of TNR is 0.872\nThe value of FPR is 0.128\nThe value of FNR is 0.238\n\nThe roc auc score is 0.904307\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 2 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#report on test data\nreport(d_test,y_test,dt_bow)","execution_count":49,"outputs":[{"output_type":"stream","text":"              precision    recall  f1-score   support\n\n           0       0.36      0.74      0.48      2278\n           1       0.94      0.74      0.82     11544\n\n   micro avg       0.74      0.74      0.74     13822\n   macro avg       0.65      0.74      0.65     13822\nweighted avg       0.84      0.74      0.77     13822\n\n\nThe value of TPR is 0.735\nThe value of TNR is 0.742\nThe value of FPR is 0.258\nThe value of FNR is 0.265\n\nThe roc auc score is 0.799304\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 2 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#plot roc curve for train and test data\nplotROC(d_train,y_train,d_test,y_test,dt_bow)","execution_count":50,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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\n"},"metadata":{}}]},{"metadata":{"colab_type":"text","id":"jm4vkPwMiWOu"},"cell_type":"markdown","source":"### [5.1.1] Top 20 important features from<font color='red'> SET 1</font>"},{"metadata":{"colab":{},"colab_type":"code","id":"puCcjOAkiWOv","trusted":true},"cell_type":"code","source":"# Please write all the code with proper documentation\nfeature_names = count_vect.get_feature_names() #getting feature names\n#getting feature importance\narr=dt_bow.feature_importances_\n#binding feature importance and names\npos = sorted(zip(arr, feature_names))\nwords=list()\nfor i in pos:\n    words.append(i[1])\nprint(\"Top 20 important features are : \\n\",words[-20:])","execution_count":51,"outputs":[{"output_type":"stream","text":"Top 20 important features are : \n ['disappointing', 'highly', 'terrible', 'worst', 'thought', 'wonderful', 'excellent', 'horrible', 'nice', 'favorite', 'bad', 'loves', 'good', 'perfect', 'disappointed', 'love', 'delicious', 'best', 'great', 'not']\n","name":"stdout"}]},{"metadata":{"colab_type":"text","id":"7JyitDTTiWOx"},"cell_type":"markdown","source":"### [5.1.2] Graphviz visualization of Decision Tree on BOW,<font color='red'> SET 1</font>"},{"metadata":{"colab":{},"colab_type":"code","id":"-jxU7zrAiWOy","trusted":true},"cell_type":"code","source":"# Please write all the code with proper documentation\n#creating graph of the tree\n#reference from this blog : https://pythonprogramminglanguage.com/decision-tree-visual-example/ to save as .png image\nexpo_graph = export_graphviz(dt_bow,feature_names=feature_names,max_depth = 2, filled=True,rounded=True) #creating graph using graphviz\ngraph = pydotplus.graph_from_dot_data(expo_graph)\n\ncolors = ('magenta', 'yellow')\nedges = collections.defaultdict(list)\n\nfor edge in graph.get_edge_list():\n    edges[edge.get_source()].append(int(edge.get_destination()))\n\nfor edge in edges:\n    edges[edge].sort()    \n    for i in range(2):\n        dest = graph.get_node(str(edges[edge][i]))[0]\n        dest.set_fillcolor(colors[i])\n\ngraph.write_png('Bow_Tree.png') #creating and saving graph as image ","execution_count":52,"outputs":[{"output_type":"execute_result","execution_count":52,"data":{"text/plain":"True"},"metadata":{}}]},{"metadata":{"colab_type":"text","id":"5przjMTbiWOz"},"cell_type":"markdown","source":"## [5.2] Applying Decision Trees on TFIDF,<font color='red'> SET 2</font>"},{"metadata":{"colab":{},"colab_type":"code","id":"Bm3mfI7miWOz","trusted":true},"cell_type":"code","source":"# Please write all the code with proper documentation\nomd_tf,omss_tf = cv_DT(d_train_tf, y_train)","execution_count":53,"outputs":[{"output_type":"stream","text":"Time elapsed : 1040.50 Seconds\n optimal parameters using Grid Search are {'max_depth': 50, 'min_samples_split': 500}\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#plot heatmap on train and test data\nplotHeatMap(d_train_tf,y_train,d_test_tf,y_test)","execution_count":54,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 2 Axes>","image/png":"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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#model with optimal max_depth and optimal min_samples_split\ndt_tf=DecisionTreeClassifier(max_depth= omd_tf , min_samples_split = omss_tf,class_weight='balanced')\ndt_tf.fit(d_train_tf,y_train)","execution_count":55,"outputs":[{"output_type":"execute_result","execution_count":55,"data":{"text/plain":"DecisionTreeClassifier(class_weight='balanced', criterion='gini',\n            max_depth=50, max_features=None, max_leaf_nodes=None,\n            min_impurity_decrease=0.0, min_impurity_split=None,\n            min_samples_leaf=1, min_samples_split=500,\n            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n            splitter='best')"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#report on train data\nreport(d_train_tf,y_train,dt_tf)","execution_count":56,"outputs":[{"output_type":"stream","text":"              precision    recall  f1-score   support\n\n           0       0.43      0.90      0.58      5314\n           1       0.97      0.76      0.86     26935\n\n   micro avg       0.78      0.78      0.78     32249\n   macro avg       0.70      0.83      0.72     32249\nweighted avg       0.88      0.78      0.81     32249\n\n\nThe value of TPR is 0.762\nThe value of TNR is 0.899\nThe value of FPR is 0.101\nThe value of FNR is 0.238\n\nThe roc auc score is 0.914059\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 2 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#report on test data\nreport(d_test_tf,y_test,dt_tf)","execution_count":57,"outputs":[{"output_type":"stream","text":"              precision    recall  f1-score   support\n\n           0       0.36      0.75      0.49      2278\n           1       0.94      0.74      0.82     11544\n\n   micro avg       0.74      0.74      0.74     13822\n   macro avg       0.65      0.74      0.65     13822\nweighted avg       0.84      0.74      0.77     13822\n\n\nThe value of TPR is 0.735\nThe value of TNR is 0.751\nThe value of FPR is 0.249\nThe value of FNR is 0.265\n\nThe roc auc score is 0.802764\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 2 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#plot roc curve for train and test data\nplotROC(d_train_tf,y_train,d_test_tf,y_test,dt_tf)","execution_count":58,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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\n"},"metadata":{}}]},{"metadata":{"colab_type":"text","id":"PIKUCldjiWO0"},"cell_type":"markdown","source":"### [5.2.1] Top 20 important features from<font color='red'> SET 2</font>"},{"metadata":{"colab":{},"colab_type":"code","id":"p2P0iHQciWO1","trusted":true},"cell_type":"code","source":"# Please write all the code with proper documentation\nfeature_names_tf = tf_idf_vect.get_feature_names() #getting feature names\n#getting feature importance\narr=dt_bow.feature_importances_\n#binding feature importance and names\npos = sorted(zip(arr, feature_names_tf))\nwords=list()\nfor i in pos:\n    words.append(i[1])\nprint(\"Top 20 important features are : \\n\",words[-20:])","execution_count":59,"outputs":[{"output_type":"stream","text":"Top 20 important features are : \n ['whatever want', 'never found', 'eat eat', 'like sour', 'brush teeth', 'buddy', 'baby formula', 'italy', 'rate', 'mash', 'recommend anybody', 'much else', 'buy bags', 'think tea', 'pay attention', 'issues not', 'think one', 'health benefits', 'chipotle', 'plan use']\n","name":"stdout"}]},{"metadata":{"colab_type":"text","id":"RTZB9ipUiWO3"},"cell_type":"markdown","source":"### [5.2.2] Graphviz visualization of Decision Tree on TFIDF,<font color='red'> SET 2</font>"},{"metadata":{"colab":{},"colab_type":"code","id":"DMbZoPoTiWO4","trusted":true},"cell_type":"code","source":"# Please write all the code with proper documentation\n#creating graph of the tree\n#reference from this blog : https://pythonprogramminglanguage.com/decision-tree-visual-example/ for saving the image\nexpo_graph_tf = export_graphviz(dt_tf,feature_names=feature_names_tf,max_depth = 2, filled=True,rounded=True) #creating graph using graphviz\ngraph_tf = pydotplus.graph_from_dot_data(expo_graph_tf)\n\ncolors = ('green', 'yellow')\nedges = collections.defaultdict(list)\n\nfor edge in graph_tf.get_edge_list():\n    edges[edge.get_source()].append(int(edge.get_destination()))\n\nfor edge in edges:\n    edges[edge].sort()    \n    for i in range(2):\n        dest = graph_tf.get_node(str(edges[edge][i]))[0]\n        dest.set_fillcolor(colors[i])\n\ngraph_tf.write_png('TFIdf_Tree.png') #creating and saving graph as image ","execution_count":60,"outputs":[{"output_type":"execute_result","execution_count":60,"data":{"text/plain":"True"},"metadata":{}}]},{"metadata":{"colab_type":"text","id":"NVhl_V6MiWO5"},"cell_type":"markdown","source":"## [5.3] Applying Decision Trees on AVG W2V,<font color='red'> SET 3</font>"},{"metadata":{"colab":{},"colab_type":"code","id":"uYDXnzMmiWO5","trusted":true},"cell_type":"code","source":"# Please write all the code with proper documentation\nomd_w2,omss_w2 = cv_DT(d_train_w2, y_train)","execution_count":61,"outputs":[{"output_type":"stream","text":"Time elapsed : 215.71 Seconds\n optimal parameters using Grid Search are {'max_depth': 10, 'min_samples_split': 500}\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#plot heatmap on train and test data\nplotHeatMap(d_train_w2,y_train,d_test_w2,y_test)","execution_count":62,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 2 Axes>","image/png":"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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#model with optimal max_depth and optimal min_samples_split\ndt_w2=DecisionTreeClassifier(max_depth= omd_w2 , min_samples_split = omss_w2,class_weight='balanced')\ndt_w2.fit(d_train_w2,y_train)","execution_count":63,"outputs":[{"output_type":"execute_result","execution_count":63,"data":{"text/plain":"DecisionTreeClassifier(class_weight='balanced', criterion='gini',\n            max_depth=10, max_features=None, max_leaf_nodes=None,\n            min_impurity_decrease=0.0, min_impurity_split=None,\n            min_samples_leaf=1, min_samples_split=500,\n            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n            splitter='best')"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#report on train data\nreport(d_train_w2,y_train,dt_w2)","execution_count":64,"outputs":[{"output_type":"stream","text":"              precision    recall  f1-score   support\n\n           0       0.38      0.85      0.52      5314\n           1       0.96      0.72      0.82     26935\n\n   micro avg       0.74      0.74      0.74     32249\n   macro avg       0.67      0.78      0.67     32249\nweighted avg       0.86      0.74      0.77     32249\n\n\nThe value of TPR is 0.723\nThe value of TNR is 0.846\nThe value of FPR is 0.154\nThe value of FNR is 0.277\n\nThe roc auc score is 0.866228\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 2 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#report on test data\nreport(d_test_w2,y_test,dt_w2)","execution_count":65,"outputs":[{"output_type":"stream","text":"              precision    recall  f1-score   support\n\n           0       0.35      0.80      0.49      2278\n           1       0.95      0.71      0.81     11544\n\n   micro avg       0.73      0.73      0.73     13822\n   macro avg       0.65      0.76      0.65     13822\nweighted avg       0.85      0.73      0.76     13822\n\n\nThe value of TPR is 0.713\nThe value of TNR is 0.799\nThe value of FPR is 0.201\nThe value of FNR is 0.287\n\nThe roc auc score is 0.832856\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 2 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#plot roc curve for train and test data\nplotROC(d_train_w2,y_train,d_test_w2,y_test,dt_w2)","execution_count":66,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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\n"},"metadata":{}}]},{"metadata":{"colab_type":"text","id":"PnwVzXytiWO7"},"cell_type":"markdown","source":"## [5.4] Applying Decision Trees on TFIDF W2V,<font color='red'> SET 4</font>"},{"metadata":{"colab":{},"colab_type":"code","id":"YqODkVCgiWO7","trusted":true},"cell_type":"code","source":"# Please write all the code with proper documentation\nomd_tf_wt,omss_tf_wt = cv_DT(d_train_tf_wt, y_train)","execution_count":67,"outputs":[{"output_type":"stream","text":"Time elapsed : 208.35 Seconds\n optimal parameters using Grid Search are {'max_depth': 10, 'min_samples_split': 500}\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#plot heatmap on train and test data\nplotHeatMap(d_train_tf_wt,y_train,d_test_tf_wt,y_test)","execution_count":68,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 2 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x2Lx9mT38q3Mf3EWAG2u6sqAR2+kRnQU0695nmPb9herKzgqjMd/f5ul7/zAyo9+AcA70JcbXx9DRLM6oOD7Jz/k8Oa9WhaVKIumvdtxjVMO60uRw5BS5DCp7WiCa4cz6sPHEZNg8vBg9azfWPv1UgDaDe5GvweuQ8wmdv6xmUWvf1tmWScoKVOLtye3/u9RwurXxGFX7Fy2iUVvfFfhMrgQsmjYpTlDJ95OZPN6fPPQNLYtWg9ArZb1uf7lu/Dy90XZHfwxYy4xC9cCcOMbY6jTthGCcPxAPHPGv09hbkGlyaNUtGvlwiIm4drJd/Lxra+SkZDCg/NfYcfvm0iKPXm60sKXvnR97jHqCqJaNShWxsBxN7F//a5iYde9fBc/TfiIw1tiuevzp2jWpx27V8SQuPsIX4ydyvWvjqY0Bj93G7tXbC0WNnTSKHb/GcNX97+D2WLG4uN1jr0uHS0LAzEJ102+k4+ccnioFDksKCGH2k45ZCWlMf36idgLbXj6evH4krfY8fsmbIVWrppwC9OGPENOahbDptxHdI9WxK7+97RlneCKcTdxoIRMV360kH1rdmC2mBnz9XMumVZ3WaTHJTN7/Af0vufqYu2w5hUw+/H3ST6YQGDNEB5e+Aq7V/5DfmYuC176koLsPAAGP3crPUZdccrDptKpxor8vJwQJCI9ReRxERlYGeXXbR9NyqEEUo8kYbfaiVmwhpYDO502ffuhPYiZv9p1X7t1QwLCg9j71z+usIAawXgF+HB4SywAm376i1bOMpP2xZG8P77UslsO7ETqkSQS9x51hXkH+NCwS3M2zF4OgN1qJz8z9+w7XAZaFgZ120eTXEIOrcqRw1anHOxWO/ZCY06xh6cFEeMMgdB6NUk+mEBOahYAsau20XpQ1zLLAkOm/uFB7CkiU2t+IfvW7HDVd+zfAwRFhp1jr0vnfMsi7WgyCbsOU/LQmuQDCSQfTAAgMymN7JRM/EMDAVxKHMDi7XlhpgJW3FFv551KUeQisr7I53uA6UAAMElEnq7o+oIiQkiPS3HdZ8SnEBQRUmra4NrhhNStQezq7Sfax9XP3covr3xdLF1gZCgZ8anFygyMCC2zHZ6+XvQZO4Sl7/5YLDykbk1yUjK56e2xPPzLa9zw+j2VZpFrWRgERYSQUUIOgWXIIbSIHACCaoXy2KI3eGbNdFZ8MJ/MpDRSDiZSo1EtQuqEYzKbaDWwE8G1QsssS0QYXIpMi+Id6EuL/h2J/Xv7adOcCxdKFmVRt11jzBYPUg4lusJueutent/wATUbR/H357+dRU/PDeVQbl9VjcqyyC1FPo8BBiilXgQGArecLpOIjBGRjSKycWtWbKU0rN2Q7mz7db3ry+h22wB2L99KRkJqOTnLZ8CjN7Lqk0Wn+PZMZjNRrRuy9qvfmXb1BArzCuh739DTlHL+0LIwaF9CDgAZ8an836CneLP3Y1xyQy/8w4PIy8xh7nOfcsv0R7jv+0mkHU3GUeJ1vGRZ3W8bwK4yZGoym7h52kP8/flvpB5JqrxOuklFyuJ0BNQIZsTU+/n+iQ+KWe3fP/EhL3e9j8TYONoN6V7hfSuXCjqz80JQWT5yk4iEYDwoRCl1HEAplSMip10Hq5SaCcwEeKrBSLcfexmJaQRHnXwtDaoVRkZiWqlp2w3pwbznP3Xd1+/YhIadm9PttgF4+XpjtpgpyM3n708XE1TEwgiqFUZmYtkKrm77aFpf1ZVBE27GJ9AX5VBYC6xsW7SOjIRUjmzdB8C2X9fR575r3O3eGaFlYZCRmEZQCTlkliGHn4vIoSiZSWkk7jlKw87N2LZoPTuXbWbnss0AdB3ZD4e9+I+6ZFn1OzahQefmdC8i08LcfNfA5g2v3UPygQRWfbronPpbFhdKFqXh5e/DXZ89yeK3Z7tcdUVRDkXMgtX0uXcIG7//053uVRx61sopBAGbME6AViJSSykVLyL+zrAK5WjMPsIaRBJSpwaZiam0G9Kd7x6efkq6Go2j8Any41CRGRLfPTrD9fmSG3tRp00jFjt/ZAVZedTrEM3hLbFccv1l5b7ufTDsRdfnyx+9gcKcfNZ8sQSAjLgUwhvVInl/PNGXtiapiN+4ItGyMDgas4/wEnL41k05BEWGkpOWha3Aik+gHw06NeOvT34FwC8skJyUTHwC/eh+2wC+euDdMsv6thSZnlDiV4wbhneADz88NbPC+1+UCyGL0jBbzNz+4eNs+ukv10yWE4TVj3C5WVpefglJ++LOtdtnThW0tN2lUhS5UqrBaaIcwHUVXZ/D7mDexM+5+4sJmMwmNsxZQeLeowx47EaObjvAzqWbAMOVELNgdTmlnWTu858x7MSUuxVbXbMvWl3RiWteuAO/0EDu/PRJ4nce5JPbXy+zrHkvfM7Idx7EbPEg9Ugi34//8Ow7XAZaFgYn5DC6hBwGOuWwwymH9qXIoWZ0bQY/eysKhSCs/GghCbuPAHDNpFHUalEPgKXTfiL5QIIrX2llnY6gyFD6P3QdibHHeOSXVwFYPWsJ652DwBXJ+ZZFnbaNuP3Dx/EN8qNF/44MeOwmpg58grZXd6dRl+b4hfjT6UbjKMfZ4z8gYedhhk+5Dy9/H0SE+J2H+Om50t8KKpVqrMil5MhyVeFMXCua/w76n0JTGm8e/Pac3/Rz37nX7X8v30c/rHDPwrlwUcwj12g0mnOmGlvkWpFrNBoNQBWcVuguWpFrNBoN6FkrGo1GU91R2rWi0Wg01RztWtFoNJpqThXcQ8VdtCLXaDQa0Ba5RqPRVHtserBTo9FoqjfataLRaDTVHO1aqXju9il9dzbNf5uCfEv5if4j5BRoWVQkevqhRqPRVHe0Ra7RaDTVHK3INRqNpppTjZfol3vUm4g84k6YRqPRVGcu9jM7R5USdkcFt0Oj0WguLA7l/lUOInKliOwWkdjSDpwXkXoislxEtojIPyJylTPcIiKzRGSbiOwUkQnuNP20rhURGQncDDQUkflFogKAcz+dV6PRaKoSFTRrRUTMwAxgAHAU2CAi85VSO4okew6Yo5R6X0RaAr8CDYCbAC+lVBsR8QV2iMi3SqmDZdVZlo98NRAPhANTioRnAf+cUc80Go2mqlNxLpMuQKxSaj+AiHwHXAMUVeQKCHR+DgLiioT7iYgH4AMUApnlVXhaRa6UOgQcArqfWR80Go2mGnIGilxExgBjigTNVEqdOEW7NnCkSNxRoGuJIl4AlojIQ4AfcLkz/AcMpR8P+AKPKaXK9YCU5VpZpZTqKSJZFD8qUQCllAo8TVaNRqOpdii7+64Vp9KeWW7C0zMS+FwpNUVEugNfikhrDGveDkQBIcBfIrL0hHV/OsqyyHs6/wacQ2M1Go2melBxrpVjQN0i93WcYUW5G7gSQCm1RkS8MdzYNwOLlVJWIElE/gY6AWUq8tPOWhGR0LKuM+6aRqPRVGEqcPrhBqCJiDQUEU9gBDC/RJrDQH8AEWkBeAPHneH9nOF+QDdgV3kVljXYuQnDpSKlxCmgUXmFn098e15CzWfuA5OJjB8Wk/bxnGLxHrVqEPnaeEwBfojZTPLUT8lZuQHvNk2p+aIxLV5ESJnxFdlLVwMQ8fJj+PXpij01nUNDx7rKCh8/Gv++XVFWG9YjcSQ8MxVHVk6xuhosmEnKjK9I++xHAEwBfkS89CheTRqglCLxuf8jf+vOKiUL3x4dCH/8LsTigbLaOP7Wx+StiwHAf1Avwu4dCWYTOSvWkTzlU6OsqJpEvvwY5tBg7BlZJDz5JrbEZAACr7mc0PtGApD6/rdkzltqNMDiQc3n7se3S1twKJLf+Zzs3/+ucDn49+pI1KR7wGQibfbvHP/gh2Lxlqga1Hn7UcyBfmA2kfjGLLJWbMK/Z3sinxzlkkP8a5+Rs8YY348YfxvB1/XFHOTPjtbDXGXVem40ft3bAGDy8cIjLIgd7Yy+B1/fj5oPDgcgafps0n/6A/H2ot6Mp/CsXwvsDjKXrSfxzVkVLoOiBPXpQIOX7kJMJpK+XUrc9LnF4j1rh9P4nYfwCPIDk4kjr35F+h+b8Qjxp8nMJ/BvH83xOcs5+OzHrjx+bRrR+J2HMHl7kvbHZg49/wkAvq0a0PD1sZi8LSibnQMTZpKzNRZzgC/R0x/BM6oG4mEi/oP5HJ/9BwDNv34e/45NyVq/k92jXq1UWZRKBVnkSimbiDwI/AaYgU+VUv+KyGRgo1JqPjAO+EhEHsPQp3copZSIzAA+E5F/MXTvZ0qpcieXlOVaaVgBfTo/mEzUfP4Bjt39DNbEZOrPmUbO8rUU7jvsShI6diRZi1eS8d0veDauR+0PX+LA5aMo2HuIwzc9BHYH5hqh1J/7P7KXrzV+XD//Tvo3C4h8fXyx6nJXbyb5/z4Fu4PwcXcROma4S7EB1HhqDDl/bSyWp8YzY8lZtYn4R18Biwcmb68qJwt7WibH7puE/Xgqnk3qU+ejV9jf51ZMwQHUGD+awzc+hD0tg4jXxuHTrT15a7dS44l7yJy3jMx5S/Hp2o7wx+8k4am3MAX5E/rALYZsFdT74T2yl6/FkZlN2L0jsKdmcHDQaBDBFFQJ3juTiajJYzlw2/PYElJoPG8qmUvXURB7cgyq5oPDyPhlFalfL8Irui4NPpvE7stGY0vN5ODol7AlpeLVtB4NZ01mV/c7AMhcup6UWQtpuvzDYtXFv3xSuYWNGox3S8POMQf5E/HISGKHPoZSiiYL3iFz6TpUoY3kj+aSs3YbYvGg4dcv49/7ErL/3FTxsnDKo+Gr97BzxIsUxqfQ+tc3SfttA3l7j7qS1H7kRlIXrCbxi9/waVKH5l89x5auY3HkWzn61rf4NKuHb/N6xYpt+Pq97H/ifbI376H5V88R3LcD6cu3UO+52zk2dTbpy7cQ3K8j9Z+7nR03TiTijkHk7TnK7lGv4REaSPu/3iP5p5Uoq42493/G5ONFxK0DK0cG5VGBe2YppX7FmFJYNGxikc87gEtLyZeNMQXxjHBnQRAicr2ITBWRKSJy7ZlWUtl4t22G9XA81qMJYLWR+euf+PUrMdlGgcnfFzCsY1tSihGcXwDOQQ7xtIA6+VTO27gde3rWKfXlrt7sypMfswuPiHBXnF//7liPJlIYe8gVZvL3xbdTGzJ/WGwEWG3FLPiK5FxkUbBzH/bjxgB54d5DiJcXYrFgqVML66Fj2NMyjP6v2UrAQON/0DO6HrnrtgKQty4Gv37dDDlc2onc1VtwZGTjyMwmd/UW/Hp2AiDw+itInfmdsy0KR3q5s6vOGN92TSg8FI/1SCLKaiNjwUoCBxSfOKCKyMEc4Is10eh7/o792JKMzwV7DiPenoinYfPkbd2N7XjZO3MGDelFxoKVgPFWkLVqK/aMbByZOWSt2kpA70tQ+QXkrN1mtMNqI2/7Piy1wipOACXw7xBN/sF4Cg4b8kiZt4qQK7oUT6QMOQCYA30pdMrDkVdA1vpdqAJrseSWmiGYA3zI3rwHgOM/rCDkSqeMlSq1LJTC5OdjhPt5Y0vPRjkPdMhctQ1Hdl6F991dlM3h9lXVKHevFRH5HxANfOsMGisiA5RSD1Rqy84Aj5ph2BKOu+5ticn4tG1WLE3KjK+o8/ErBN8yFJOPN0fvOrlgyrttMyJeeRxLrZokPP2WS0m7Q+D1A8laZPxoxdeb0NHDOHr3BELvvNGVxlInEntqBhGvjsOrWUMKdsSS9Or7qLyCs+3yaTlXWZzAf2BP8nfGoqxWrIfjsDSsg0dUBLbE4/j3745YjH+dgl378R9wKelfzsN/wKWY/f0wBQfgEXFqOzwiwjAF+AEQ/vAofLq0xXo4nqSXZ2BPSa9YOUSGYY1Pdt1bE1Lwbd+0WJqkd76h4ReTCR81GJOvN/tvfe6UcgIH9SB/+z5Uoc2tei21a+BZN4Ls1cbbsCUyDGt8ETkkJGOJLK6wTQF+BPbvQvJnJd2oFYdnZBiFcSmu+8L4FPw7NimW5uiU2bT4diIRd16F2deLncNfKKfMUArji5QZl4JnpDF8dnDip7T4diL1Jo5CRNg+9BkAEj77lWafT6Djlk8w+3uzd+zUYsbTBaXq6We3ccci7wdcoZT6TCn1GXCVM+y0iEiQiLwuIrtEJFVEUpzLTV+/fg3NAAAgAElEQVQXkeAy8o0RkY0isnF2+pHTJTsrAq7qQ+bc3znQ9zaOjZ1I5BtPgBju//x/dnNoyL0cHvYwofcMNyxzNwi9dwTY7WQtMHx8YQ/cStqsn1C5+cUTms14tYwm47uFHL7hQRy5+YTeM7xC+3cmlCULAM/o+oSPu4ukSdMAcGRmk/TidGpNnUDdr6ZgPZboetgdf/MjfDq3pd6P0/Hp1AZrwvGyH4RmM5ZaNcjbsoPDNzxI3tad1Hjynkrt7+kIHtqLtB+XsavHnRy88wXqTn28mBy8mtQj8qk7OPbsDLfLDBrci4xFf7u/StBsot60J0j+fAHWI4ln2oUKJezanhyfs5wtne5h120v0/i9R4rJ40yIGHUlhyZ9xpZOYzj4wmc0nno/AMF9OpD770E2d7ibfwaMo8ErozH7+1RkN86ai32vlVigqGOsrjOsLOYAaUAfpVSoUioM6OsMm3O6TEqpmUqpTkqpTsOD654u2SnYklLwiKzhuveICMeamFIsTdCNV5C12LCc87fuRLw8MYcUnwpfuP8Ijtw8PJs0KLfOwGsH4NenK/FPvOkK827bnBrjR9Nw6SyCb7+W0DEjCL55CLbEZGyJyeT/sxuA7CV/4dUy2u3+nQnnKguPiHCi3nuehKffxnok3pUnZ8U6jox4lCMjH8N64CiFh4zZVPbjqcQ//BKHb3iQ5Hc/B8CRlYMt8dR22BJTcKRn4sjNdw1uZv+2slJkYUtIwVLrpMvLEhmGNaG4HEKGDSTjl1UA5G7ZjcnLE3OoUw6RYdT/8BmOjvs/Cg8nuF1v8JDLyJi/0nVvTUjBUquIHCLDi7Wj9qsPUnAwjpRKtMYBChNS8Iw6+SbgWSuMwvji60xqjuxPygLn97JpDyYvCx6hp18uUpiQimcRd5BnVBiFCUaZNW7qQ+qvawFIXbAav/aG9V9jeD9XeMHBBAoOJ+EdXbsCelgBOM7gqmK4o8gDgJ0iskJEVmAsMw0Ukfkl9mApSgOl1BtKKdcvQCmVoJR6A6h/zq0uQf623VjqR+FROwIsHgRe1Zuc5WuLpbHFJeHbrQMAno3qYvLyxJ6aYeQxG2LwiKqJZ6O6hsVZBr49LyHk7huJu/8Fw8fu5Oht4zlw+SgOXD6K9C9+JnXmd6R/swB7chrW+ONYGtQx8nfrQGHs4dMVf06ciyxMAX7U/mAyyVM/I3/LjmJ5zKFBAJgC/QkaOZgMp7/fFBzostpC7xlO5k9LAMj5eyN+l3bEFOiPKdAfv0s7kvO3MQCcvWItPl3aVqoscv/Zi1eDKCx1IhCLB0FDepG5dH2xNNa44/j1aAeAV+M6iJcFe4ohhwafTiLhjVnkbnJ/ZpFXozqYg/zJ3Xxytlj2ys0EXNYBU6AfpkA/Ai7rQPbKzQBEjLsVc4Af8ZM/qoAel0321li8G9bCq25NxOJB2DU9SVuyoViawmPJBPU0vhfv6NqYvDyxpWSctkxrUhr2rDz8Oxouqxo39iHtN0PG1sQ0Aru3AiCwZxvyD8Q76zhO0GVGHZbwIHwaR1Fw+MK+iZygOlvkosrxT4lI77LilVJ/lpJnCbAUmKWUSnSGRWDsmjhAKXV5yTwl2dPiyjOSll+vztSYcC+YTGT+tITUD78j7KHbyN++l5zla/FsXI+IyY9g8vVBKUXy25+Qu3ozAUP7E3rPMJTVBkqR8r+vyVm2BoDIt5/Gt0tbzMGB2FLSSJn+FZk//kaDxZ8inhbszkG6/JhdJL34XrH2hD1wK47cPNf0Q6/mjYh46VHEYsF6JJ6EZ6fiyMw+ky5WuixCx44k9J7hLmsb4NjoZ7CnZhD59tN4NTMmMqW+/w1Zvxpfu//AnoQ/ficoRd7G7SRNnoGyGoNigdcPJHTMCCPPh9+SOfd3wDll8Y0nMAf4Y09NJ+HZqdiK+JHL4kyOegvocwm1JjqnH36/lOMz5lDzsVvI27aXrKXr8YquS+3XHjQG35Qi4fXPyf5rCzUeHEbN+26i4GCcq6wDt0/EnpJB5NN3EDy0Nx4RodgSU0mdvYSkd43ho5qPjES8PE+ZRhhy0+XUuN+YiHB8xhzSfliGR2QYLdZ8Tn7sEVShIa+UL34hbfYSt/t3pke9BffrSP0X70LMJpK+W0bctB+p88QIcmL2kbZkAz5N6tDo7fsx+XoDisMvf0HGn8b00w7rPsDs74N4emDLyGXXyBfJ23sUv7aNXdMP05dvdk1NDOjSnPqT70bMZlRBoTH9cNt+LBEhNH7nITxrhoAIcdN/Ivkn4w2m5dyX8YmujdnXG2taNvvHzSDjz61u9a1b3E9n5wMqQuo1vd3WOaHz/jzn+iqSchW5K6FIGNALOKyUKnOOlIiEAE9j7BlQ0xmciDEp/nWlVLkHcp6pItf8N9Bndp5En9l5kopQ5ClXu6/Iw36pWoq8rJWdC51r/xGRWsB24C6MPQEeLatQpVSaUuoppVRzp488VCnVQin1FFDlpi9qNBqNcrh/VTXK8pE3VEptd36+E/hdKTUEYxevu86hzhfPIa9Go9FUDtV4sLOseeRFZ//3Bz4CUEpliUiZXRGR0y0pFSDijFqo0Wg054GqaGm7S1mK/Ihzr9yjQEdgMYCI+ADlOecigCswphsWRTAOrNBoNJoqxcWqyO8GJmNseD5cKXVi6V034LNyyl0I+CulThlydk5h1Gg0miqFslep8cszoqxNs5KAsaWELweWl1WoUuruMuJuPpMGajQazfngYrXINRqN5j+DclyEFrlGo9H8l9AWuUaj0VRzlKq+Fnm5e62IyJsiEigiFhFZJiLHReTW89E4jUajOV9U5wVB7ljkA5VST4rIdcBB4HpgJfBVZTas/vyJ5Sf6r1B44Tbbr2o4YivpBJ1qiGP79vITadzGcTHOWiklzdXA90qpDDnLPYo1Go2mqnKxD3YuFJFdQB5wn4jUAPLLyaPRaDTViuqsyMv1kSulngZ6AJ2UUlYgF2NXQ41Go7loUMr9q6rhzmCnL3A/8L4zKAroVJmN0mg0mvONcojbV1XDnROCPgMKMaxygGPAy5XWIo1Go7kAKCVuX1UNd3zkjZVSw0VkJIBSKlf0aKdGo7nIsF/ks1YKnTseKgARaQwUlJ1Fo9FoqhdV0dJ2F3cU+SSMLWzrisjXwKUYZ29qNBrNRUNV9H27S7mKXCn1u4hsxti+VoBHlFLJld4yjUajOY9Uxdko7nJaRS4iHUsExTv/1hORekqpzZXXLI1Gozm/XKwW+ZQy4hTQr4LbotFoNBcMu8OdSXxVk7IOluh7PhtyrqzatI03Zn6Dw+Hg+oG9uPumq4vFxyUlM/GdT0nLzCLI349Xx48hMjwUgKmfzuGvjTE4HIruHVrx1JibERHGTpxCcmoGdoedji2b8sx9t2E2m8jIyuaJN94nLjGZqIhw3n76fgL9/fjsx0X8umINADa7gwNH4/jz62kEBfgz8Z1P+HNDDKFBgcz9X+XO3ly1+V/e+PR7HA7F9Zf34O7rryghixQmzvjqpCweuYPI8BDWb9vNW5/96Ep34FgCbz5+F/26tmfUs1PIzTPGuFMzsmjdpD7vPj2W5etjmP7tAkxiwmw28eRdN9KxRTQA85av5aMfFgFwz42DuKZvNwCsVhuvfjybjdv3IibhoZuHMqB7h0qRxd+7j/LmvLU4lIPrujTjrr7tissiLYsXvv+LtOx8An29eHVEHyKC/Vzx2fmFXD/lR/q2qs+Ea3uQV2jjia+WcTQlC5NJ6N2iHo9c1RmA+LRsnp+9kqz8AhwOxcODOnNZi7psO3ycl35c5Spz7IAO9GvdAIAvV25n7obdCNAkMpQXh12Gl6VyNiU1N2qD58BbQUzYtv6Jdc3CYvESGIbX4NGIbwAqP4eCeR+gsozTGj3a9MTScygA1lXzsW1bVSyv102PYgquSd5HzxgB3n54X/cAEhyOSk8mf+50yM8FwHPgrZgbtwNrAQULP8KRcAgAS7/heES3AzFhP7CdwiWVup3TKVyUrpUTiIg3xoKgnhiW+F/AB0qpKrNM32538Or7XzLz5fFEhIUy8rHJ9Onansb1arvSTPlkNkP69+Ca/j1ZF7ODabN+4NVxY9i6cy9bd+7lh/deAmDUk6+ycdtuOrdtzttP34+/rw9KKR5/bQZLVm1gUO+ufPL9r3Rt15K7b7qaT77/hU++/4XH7hzGnTcM4s4bBgGwYt1Wvpz3G0EB/gAMvbwnIwb359mpH1e+LD6azcxJDxMRFszIJ9+gT+e2NK5b66QsZv3EkD5duaZvN9Zt2820r+fx6iN30KVNM76favwQM7JyuPqBSXRv3xKAWa+Mc+V/7M2Z9O3cFoCubZrRp3NbRIQ9B48yfsonzH9vEhlZOXww5xe+e/NpRIThT7xG385tCfT3ZeaPiwkNCmDBjBdwOBxkZOdWjiwcDl6bu5oP7rmSiCA/bnlvPr1b1qNxRIgrzdSF6xncsQlDOzVhfWwc0xZv4JURfVzxM37bRMeGkcXKHdWrDZ2jo7Da7IyZuYhVu47Qs3ldPlq2lYHtGjKsewv2Jabx4KdLWNRiONGRIXzz8DV4mE0cz8xl2P/NpVeLeqRk5/Ht3//y0/gb8LZ48MRXf7A4Zj/XdGpa8cIQwfPK28n/5k1UZired72Ibe9mVHKcK4nn5SOxbfsb27ZVmOq3wLPvMArmfwjeflguu5a8TycBCp+7JmPbu9mlmM3NOkFh8Ylslh6DsR/cgXXNQizdB2PpPhjr8jmYG7dFQiPIe/8JTFGN8bzyDvI/fxFT7WjMdZqQ99GzAHjf/jymes1xHN5V8bI4DY5qPGvFnXeJL4BWwHvAdOfnLyuzUWfK9j37qVerJnUia2KxeHBlry4sX7ulWJr9R+Lo2rYFAF3atnDFC0JBoRWrzUah1YrNbicsJBAAf18fAGx2O1arjROz55ev28LQ/pcCMLT/pfxRoi6ARSvXMqhXN9d9p9bNXEq9Mtkee5B6tWpQJzLckEXPS1i+PqZYmv1HE+jaxlAWXVo3Zfn6f04p5/c1W+jZoRU+Xp7FwrNz81i/bTf9uhqWra+PNyeWFeQVFHLip/D31h10b9uCoAA/Av196d62Bau2/AvAz8tWu94STCYTIYGVI5ftR45TNzyQOmGBWDzMXNGuESv+PVwszf6kdLpEGw+5zo1rFYvfcTSZ1Ow8ujc9aRD4eHrQOToKAIuHmea1w0jMyAFABHLyCwHDkq8R6OvK42E2fmqFNjtFV2HYHYoCqx2b3UF+oc2Vp6IxRTXGkZqESj8ODjv2HWvxaFp8GMwUHoX94A4AHId2YnbGmxu1wX5gO+TnQH4u9gPbMTcyHuRYvLB0vZLCv+cVK8ujaUds2/4CwLbtLzyaXWKU1bQjtn/+NuqI24d4+yL+Qc5MFjB7gNkCZjMqJ7NSZHE6LvYFQa2VUi2L3C8XkR2V1aCzITEljYgaoa77iPBQtu3eVyxN04Z1Wbp6E7deM5BlazaRk5dPemY27VpE07ltc/rf/ihKwYjB/WlUN8qVb+zzb7NtzwF6dmrDgEuNV+jU9AxqhAYDEB4SRGp6RrG68vIL+HvTdp4Ze/63bU9MSSci7KTFGREWwra9B4uladqgNkvXbuXWwf1Ytm6rIYusbIKLPGgWrdrI7UP7n1L+H+ti6NqmueshB7Bs7Vbe/XoeqRlZzHj2fgCSUtKJDC/ajmCSUtLJzDGsuBnfLmDD9r3UjQznmXuGExYcWCH9L0pSRi6RQSfdJBFBvmw7crxYmqa1Qlm2/SC39GzNH9sPkVNgJT0nn0AfL6YsXMerI/qwNvZYqeVn5hWwcucRbunZCoCxAzpy38eL+Xb1DvIKbXx4zyBX2m2Hk5j0/V/Ep2XzyojeeJhNRAT5cXvv1lz56nd4Wzzo1qQ2PZrWqXA5AEhACCorxXWvMlMx1W5cLI0j8Qjm5p2wbViCuVknxMsHfPyNvJmpJ/NmpSEBxnfr2fsGrOsWgbWweH1+gahs43ehsjMQv0BnO0KLl5WZigSE4jgWi+PQTnwfmQYI1k1LUSlxnE+qs2vFHYt8s4i4TEsR6QpsLCuDiASJyOsisktEUkUkRUR2OsOCy8g3RkQ2isjGj7+bd7pkZ8W4u4azaftuhj08iY3bdlMzLASTycThuEQOHInn98+nsnTWVNbH7GTT9j2ufB+8NJ4/vnyHQquN9f/sLK3NQPEn9J/rt9K+RfR5scDPhnGjrmfTv3sZNu5VNv67l5qhwZhMJ/8VjqdmEHs4jh7tW56Sd9GqjQy6rPhWO/27tWf+e5N456l7mf7tgjLrttsdJKak065ZI+ZMmUC7Zo2YMuuniunYWfD41V3YtD+B4e/MZeP+eGoG+WIyCXPW7KRn87rF/OVFsdkdTPhmBSMvbUmdMENJLd66j6GXNGHJsyOZftdAnvvuTxwOQzu0qVeTn8bdwNcPXcMny2MosNrIzC1gxb+H+eXpYSx5biR5Viu/bI49X10/hcJl32Ku1xzvu1/CXK8ZjsxUcJz+FAVTRD0kpCb23W7sEV+OkpSQmkh4FLnTHiV32iOY67fEVLcSXExl4FDi9lXVcMcivwRYLSIn3jnrAbtFZBuglFJtS8kzB/gD6KOUSgAQkUhglDNuYGkVKaVmAjMBCvaudvv5GBEWQuLxk0/5xORUahaxSgFqhoXwf88+BEBuXj5LV28i0N+XH3/7k7bNGuPr4w1Az05tiNkVyyWtT/4TeXla6Nu1A8vXbqZ7h1aEBgdxPDWdGqHBHE9NJ7SENbl45XoG9e7qbvMrlIiwYBJT0lz3iSlp1AwNKpamZmgw//fUvYBTFmu2Euh38pX+t9Wb6Ne1HRYPc7F8aZnZbN97iHeceUvSqVUTnp+eTFpmNjXDgtm4fW+RdqTTqXUTggP88Pby5PJu7QEY2KMDc5etPrdOn4aaQb4kON0eAIkZudQM9CuRxo+pt18OQG6BlWXbDxLo40XMoSS2HExgzpqd5BVYsdod+HpaXAObL/24inrhgdx6WWtXWXM37OF/dxsuo3b1Iyiw2UnPzSfU/+TbS6OIYHw9LcQmpHEsLZvaoQGu+P6tG7D1UCJXd4yucFkYVnSY614CQ10Dma402ekU/DjNuLF44dO8MxTkorLSMNVvfjJvQAiOQ7sw1Y7GVKshPg9MAZMZ8QvE+9YJ5H/1GionE/EPMqxx/yBUbqazHalIYGiJdqTi0fpSHMf2gdXwtdv3xWCuHY3jyEmjqrKpzrNW3Gn5lUBDoLfzaugMGwwMOU2eBkqpN04ocQClVIJS6g2g/rk1+VRaNW3IobgkjiYcx2q1sXjlevp0LT4LIi0jC4fTuvj4+1+4bsBlANSqEcrG7bsNP7jNxsZtu2lUN4rcvHyOp6YDho/8r40xNKxj+FL7dG3P/GWGn2/+sr/pW6SurJxcNm7fTd9uJafhnx9aRdfnUHwSRxOTDVms2kSfzsWftWmZ2Sdl8dNvXNe/e7H4RX9tZFDPUze4/H3NZnp1ao2Xp8UVdjg+CeV8J92x7zBWq43gAD8ubd+S1TE7yczOJTM7l9UxO7m0fUtEhD6d2rDhX0PJr/tnN43qRJ5SV0XQqk4NDidnciw1C6vNzm8x++ndsl6xNGk5+S6r+ZPlMVzrHGh87eY+LH5mBIsmDOexwV0YfEm0S4lPX7yR7HwrTwzpVqysWsH+rIs13AH7E9MptNoJ8fPmWGoWNrsh77i0LA4mZRAVGkCtYD/+OZxEXqENpRTrYuNoVPO0L6znhCNuP6bQCCQoHExmzC27YdtTYmzHx58Tb5eWS4dgi1kJgH3/NsyN2oC3L3j7Gj7z/duwbf6DvGmPkDdjHPlfvIxKSSD/q9cAsO3Zgkcb4zfm0eYybHuMZSf2vVvwaGuML5miGqMKclHZGTgyUjDXaw5iMtpXrzmO8+1aOYOrquHOys5DIhIC1C2avpwFQYdE5ElgllIqEUBEIjCW9h85pxaXgofZzDNjb+G+iVOwOxxcO+AyouvXZsZXc2nZpAF9u3Zgw7ZdTJv1AyJCx9ZNefa+2wAYcGln1v+zkxseeB4R4dKOrenTtT0paRk8/NK7FFptOByKLm2bc9NVxozMu2+8mvGv/4+5S1ZSq2Y4bz99n6stf6zZTI8OrfD19irWxiff/ICN23aRnpnN5aMe5/5bruX6gb0qWhSGLEYP577J0w1Z9O9OdL0oZny7gJaN69O3S1s2bN/DtK/nIQgdW0bz7JjhrvzHklJITEmjU6smp5S9eNUm7rqu+MvU0jVbWfDnOjzMZrw8Lbw57m5EhKAAP+69aRAjn3wDgLE3XUVQgGENP3rbtTwzbRZvfvo9IYEBvPTgbRUuB0MWJp6+pjv3fbwYh0NxTeemREeG8L/fNtGyTjh9WtVn4754pi3aiAhc0jCSCdf1KLPMxPQcPv4jhoY1gxjx7s8AjOjRkuu7NuPxwV2Y/MMqvv7LGNR9cfhliAhbDiTw6Yp/8DCZMIkw4bruhPh5E+LnzeVtGjLy3Z8xm4TmtcO4oWvzsqo/e5SDwt++wHvkk2ASbDErUcnHsPS6Hkf8Aex7t2Cu3wLPvjeBAvuRXRQu/sLIm5+DddU8fO58EQDrXz8bA59lYF2zEO/rHsCjfS9URgr5P00HwB4bg7lxO3zufwushRQsNGZx2Xetx9ygBT5jXgWlsO/fhn3v1sqRxWmoii4TdxFVjodfRF7CUMD7OPkwUkqp0y4Icir+pzEOoIhw5ksE5gNvKKVST5f3BGfiWrno0Wd2utBndp5En9l5Er9nvzhnLfx35I1u65xLE36oUlrfHdfKMIytbPsopfo6rzJXdSql0pRSTymlmiulQjAU+sfAMneUuEaj0ZxvHGdwlYeIXCkiu0UkVkSeLiW+nogsF5EtIvKPiFxVJG6CM99uEbmiZN7ScEeRbwfOyHEnIuuLfB4NTAP8gUmldUqj0WguNApx+yoLETEDM4BBQEtgpIiUnAL2HDBHKdUBGAH8z5m3pfO+FcZY5P+c5ZWJO7NWXgO2iMh2iuxDrpQaWkYeS5HP9wIDlVLHReRtYC3wuhv1ajQazXnDVnE+8i5ArFJqP4CIfIfhlSi6/kYBJ6a7BQEnRnavAb5TShUAB0Qk1lnemrIqdEeRzwLeALbh3lsFgMnpJzdh+OGPAyilckTE5mYZGo1Gc94oz9IuioiMAcYUCZrpnD4NUJvikzqOAiXnI78ALBGRhwA/4PIiedeWyFubcnBHkecqpaa5ka4oQcAmjLlMSkRqKaXiReTk/CaNRqOpQrhrpULxNS9nyUjgc6XUFBHpDnwpIq3Ly3Q63FHkf4nIaxgzToq6Vk47/VAp1eA0UQ7gujNpoEaj0ZwPzsQiL4djGNO1T1DHGVaUuzF84Cil1jg3Jwx3M+8puKPIT6x2Kbr64az2I1dK5QIHzjSfRqPRVDZnYpGXwwagiYg0xFDCI4CbS6Q5DPQHPheRFoA3cBzDYP5GRKYCUUATYD3l4M6CoGq1L7lGo9GcDfYKssiVUjYReRD4DTADnyql/hWRycBGpdR8YBzwkYg8hmEY36GMRT3/isgcjIFRG/CAUspeXp1u7WAvIldjTIfxLtLYyWfWPY1Go6m6VORJb0qpX4FfS4RNLPJ5B8ZB9qXlfQV45Uzqc+dgiQ8AX6AvxqKeG3HD1NdoNJrqhKMaz8NwxyLvoZRqKyL/KKVeFJEpwKLKbpjJP6T8RP8RlK30rVT/i0h4uTOx/jNIaIVvW/SfpjrvCeKOIj+x0UeuiEQBKUCtMtJrNBpNtaMCBzvPO+4o8oXOwyDeAjZjPLg+qtRWaTQazXnGIRexa0Up9ZLz448ishDwVkpllJVHo9FoqhvlTg2pwpx20ywR6ew81efE/e0Yp/u8JCKhp8un0Wg01RGHuH9VNcra/fBDoBBARHphbHT1BZDBuS1N1Wg0miqHA3H7qmqU5VoxF9k7fDjGpjA/YrhYzu/RHRqNRlPJVOdZK2VZ5GYROaHo+2McpnwCtxYSaTQaTXWhOrtWylLI3wJ/ikgyxhTEvwBEJBrDvaLRaDQXDRfl9EOl1CsisgxjzvgSdfJwTxPw0PlonEaj0Zwv7FXQ0naXMl0kSqm1pYTtqbzmaDQazYXhorTINRqN5r+EVuRVgFXrNvP69I+x2x3ccPUARt9yQ7H4uIQknn/zPVLTMwkK8Of1Zx8jsmY4AFM/nMXKNZsAuPf2YQzq17NY3lenfcTcX5exYfF3ABQWWpnw2jvs2L2P4KAA3p44ntq1IgDYve8gk6e8T3ZuLiYRvvvgbby8PLn3iRc5npqG3W6nY5uWPPfoGMzmcs9UPTtZrN/KG//7DLvDwfWD+jN65LXFZZF4nIlvv++SxWsTHiKyRpghi4++4q91WwxZ3HIDV/btAcDR+CSefOWd/2/vPMOjKroA/E4KkE566AlJaKETQi+RIhEFAQXxQ1BEERUEKSIK0nsXkKqIil2shN5b6NJ7M0AKqaSQZHfn+3GXTTa0AFlIYF6e+7A7fU72nnvumUZi8nWqBJZnwtC+2Npm/3zWbtnFh6On88PcCQRV9Adg8fIV/LZqA9ZWVgx97w0a1a1JVMw1hk2aS1xCIkIIXmrbkm4dn8MSbD98hknLV2OQBjo0qcWbbc3/rleuJfLZV3+ScD0NFwc7xr/VAW837RjFq3FJjFz6F9HxyQhgzoBXKeVRnO/X7+a7tRH8F5PAplmDcHWyB+D81WuM+PIPjl+Mom/HUHq00eR24eo1hsz/1VRnZGwC777YnG6ts7f3/3rVTqb/tNasPIvI48I1pmw+gcEgebFqaXrW9TOXR3I6o9YeJSE9E+eitoxrUw1vJ23D0/dW7OPQ1SRqlSrO7Pa1TXkiLsUxc+spDBLsi1gzqnVVyhbP7sO609EM/udfvu1ajyBvF7L0BkavO8qJmOvoDZK2lUvwZkh5U3q9QfK/71ZhBW0AACAASURBVHfh5VjUrJ5HQf4d2fnoeSIUuV6vZ+ysBSyaOgofT3e6vDOY0EYh+PtmH7Qx9YultGsdSvs2zxCx/xAzF33DxE8GsHnnXo6dOscvi2eQmZXFG/0/pUm92jg6aD/GIyfOkHw9xay+31auxdnRkfDl81m5fivTFy5j2meD0en0DB03gwnD+lMpwI/EpGRsbDRlPW3kYBwd7JFSMuCzSazetIPnWjSxgCwMjPt8CQsnfYqPpzuvvPcxoQ2D8S9XOlsWC77hhVZNad+6OREHjjBryXImDO3Lll37OX76PD8vmExmZhY9B46icUhNHB3smbHoW17r1Jaw0EaMnrmQ38I30KVdawBS09L5dkU41SsFmuo4ezGS8E07+H3xdGLiEnhryBj+XjoLa2trBr3zGlUCy5Oalk6XPkNpUKe6WfvyRQ4GA+O/DWfBwG54uznz6ujFNK9ZEf9SnqY0039aywsNa9CuUQ0ijp9n1q/rGf+WdoDVp4t/p9fzjWkQ5E/ajUyEcfl2zYAyNK1RgV6Tvjarz9nBjo9ebcPG/SfNwn1LePDTqN6mNrX6cAbP1K5kio+KT2Ln0bOUcHfJ1/7nRm+QTNx4nC861sHbsRj/+34Xzcp74u/uaEozY+sp2lYuQbsqpdj9Xxyfbz/N2DbVAOhex5cbOj2/Ho40K3f8huPMaFeT8m6O/PTvJRZHnGP0s9qJZamZOpYfvEg1n+y+rTsdTaZe8vNrDUnP0tNp2XbCKpagpIsdAMsPXsTPzYHUzEd/tG9htsjvNv0QACFERyHEaSFEkhAiWQhxXQiR/Cgal1cOnzhN2VIlKFPSB1tbW8KeacyG7RFmac5e/I+Q2tqPMqRWNTZu320KD65RBRsba+ztilHBvxzbdmun2On1eqbNX8rAd3qYlbVh+27at9HO22jdrCER+w4hpWTH3gNUKO9LpQDN0inu4myyum8+GHR6PVlZOpNiyHdZnDxD2ZI+lCnpja2tDWHNG7Jx+x6zNOcuRlKvpnazhdQMYuOOvUZZRFKnemVsrI2yKF+WbXsOIqVk98GjtGqqWZHtWjdnQ44y5yz9kTe7tKdIEVtT2Mbtewhr3pAiRWwpXcKLsiV9OHzyDJ7urlQJ1CwwB3s7/MqWIvpaPPnNkXOXKePlSmkvV2xtrGlTL4hNB82V7Nkr1wip7KvJoZIvmw5o8Wcvx6LTG2gQpL1Z2Bcrgl1RrW+Vy5WglEfxW+pzd3agql8pbKzvfEtFHDtPGS9XSubIP+X7NQx4uaXFl5gciUqijIs9pV3ssbW24tkKPmw6G2OW5lxcCiFltDezuqXd2HQuO75eWXccbG+1+4SA1AxN6V7P0OHpWNQUN2/HGd4I9qNILpncyNKhMxjI0OmxtbbCoahWbvT1G2w7f40OVR/PDpf6+7gKGvdU5MBkoJ2U0kVK6SyldJJSOlu6YfdDTGw8Pp4epu/enu7ExJorh4r+vqzboo3drtu6i9S0dBKTkqno78u23QdIv5FBQmIyew4cISrmGgDLV6wktFEInu7mOxLkrM/GxhpHR3sSk65z8b8rCAFvDx7Jy299yJff/2aW7+3BI2n2Yg8c7O1o3axBvssBIOZaPD5e7qbv3p7uRMeZy6JC+XKs26Y9yNZv222UxXUq+pdj+55/NVkkJbP74FGiY+NITL6Ok6M9NsaHko+HGzHGMo+dPkdUzDWa1jd/DY6Oi8fbrB1uxORS2JejYjhx5jzVKwXknwCMxCRex8ct2xL0cnUmOuG6WZqKZbxZv+8EAOv3nyD1RiaJKWlcjI7Dyb4YA+b8ROeRC5n+01r0hoe311btPkqbetnn6248cBIvVycqlvW5S678ISb1hslNAuDtVIzY1AyzNBU8ndhwJhqADWdjSM3Uk5ieeddyR7QMou8fB3h28Wb+OXGVN4I1I+Z4TDJRKTdo4udplr5loDfFbG1otWgzYUu20L2OLy7FtIfklM0n+KBxBawe08rJwjyPPC+KPFpKefxBChdCeAshahsv7zykf1sIsVcIsXfxtz89SJV3ZFCfN9j771Fe6jWAvf8exdvDHSsrKxrVrUWTenXo9t5HDB4zjRpBFbG2siLmWjxrNu3g1Q5t81yHTm/gwOHjTPrkQ5Z9PoH1WyPYte9fU/zCKSPZ+OtXZGZlEXHgcL72734Y1Ps19h46xsu9h7D30DG8PNywsraiYXANmoTU4rUPPmXIuFnUqFIBK6s7/0QMBgNTvljGoHe633cb0tJvMGDUND5693XT28qj5sPOrdh78iKdRy5k38mLeLk6YWVlhd5g4MDpSwzs3Irlw3sRGZvAH9v+vXeBdyFLp2fzwZO0Dq4CQHpGFov/3sq7LzbPh57kDwOaVGBfZAKvfLeTfZEJeDkWxfoeb47f7b/I5+1rsbpXM9pXKcm0LScxSMm0zScZ2KTiLemPRidhLWBNr2b807MJ3+y/QGRSGlvOxeJmX4Qq3o/PRjTcx1XQyIuPfK8Q4kfgd8D0CJdS/nanDEKImsB8wIXsE6BLCyESgXellPtvl09KuRDjPi5ZV4/necWsl6cbUbHXTN+jY+Pw8jS3or083Jg1ZigAaWnprNu8E2cnzT/Y+7WX6f3aywAMGTONcmVKcvz0OS5dvspz/3sHgBsZGYS9+g7hy+eb6vPx8kCn05OSkkZxFye8Pd2pUyMI1+Laj7FJ/docO32O+nVqmNpRtGgRQhvVY+O23TQMrpnXLuYZLw83omLizGTh7X6rLGaOHKTJIv0Ga7dG4OyoHV7x9v868vb/OmqyGDeLcqVLUNzZiespaej0emysrYm6Fo+XuxupaTc4c+E/eg4cBcC1+ET6jpjM56OH4O3uRrRZO+Lx8tDakaXTMWDkNNq2aELLJvXyXQYAXsWdiIrPXrcWk5CMt6uTeRpXJ2a831mTw41M1u07jrN9MbxdnalYxpvSXtrhJqG1KnH4bCTZ55DfP9sOn6FSuRK4u2i/ucjYeC5fS6TzZwsAiE5I5pVRC/lueC88XBzvVtQD4eVQjOjrN0zfo6/fwNOhqHkax2JMe0H7TaZl6lh/JhqnYrbcifi0TE5du061EpqrqHUFH977fT+pmTrOxqXQ6xfN/RaXlkn/Pw8ys11Nwk9E0dDXA1trK9zsi1KzRHGORSdzIuY6m8/Fsu38FjL1BlIzdXyy6jDjjD76R0FBVNB5JS8WuTOQBrQGXjBez98jz1LgAyllZSllS+NVCegPfPUQ7b0tVSsGcinyKpFXo8nKyiJ8wzZCG4aYpUlITMZgfD1etPxXOjzXAtD84IlJmsv/5NkLnDp7kYbBtWjWIJjNK5ay5sdFrPlxEcWKFiV8+XwAQhuG8MeqjQCs2byDerWrIYSgUUgtTp+7SPqNDHQ6PXsPHsW/XBnS0tKJNboidDo9W3btxa+sZfyAVSv6c/HyVSKvxpCVpSN80w6aNww2l0VStiwWf7+CDkZ/v15vIDFJcz+cPHeR0+cv0TC4BkII6tYMYq3RNfXnmk2ENgzGydGerb8tYfV3c1n93VyqVw7k89FDCKroT/OGwYRv2kFmZhaRV2O4ePkq1SoGIKXks6nzKV+uFD1eutfP6MEJ8ivFpeh4ImMTyNLpWRVxlGY1K5jL4XoaBoNmLyz5ZxsvNq5pzFuS62kZxCenArD7+HnKlzR3Edwv4RFHCAvJdqsElvZm06xBhE/5gPApH+Dt6swPn71tESUOEOTjzKXENC4npZGlN7D6VBTN/b3M0iSkZ2Iwrvv7cs952gfd/TfqXMyGlAwdFxM0Oe26FIefmwNORW3Z+E4oK99syso3m1LNx4WZ7WoS5O2Cj1Mx9vyn3QvpWToORSXh6+pAv8aBrO7VjJVvNmViWHXqlnF7pEoctL1W8noVNPKyH/kbD1Cug5QyIneglHKXECLfzy2zsbFm2Adv0XvwKPQGPR3CWhLgV5Y5Xy4nqGIAoY1C2HPwCDMXfYMQgjrVq/Bpf20mgU6np3u/YQA42tsz8ZP+ppkmd6Ljcy35ePxMwl59BxdnJ6aMGAiAi5Mj3V9uxyvvDEIgaFK/Ns0aBHMtPpH3h40nMysLaZCE1KpK53Zt8lsMmiysrRnWtyfvDB2H3mCgQ5tQAnzLMGfpjwRV8Ce0YTB7/j3GrCXLEQjqVK/MJ33f1GSh19FjwAiTLCYM7Wvyiw/o9T+GjJvJ51/9QKUAPzqGPXPXdgT4luHZZg1o/+aH2Fhb8Um/N7G2tmL/4RP8tW4LgX5lean3YAD69exK03r5O9XMxtqKj7uF0Wf6d9p0u8Y1CSjlxdwVGwnyLUnzWhXZe/ICs3/ZAALqVCjHsG5hAFhbWfFhl5a8PfUbpIQqviXo1Exr33drI1i6agdxSSm8PGI+jasHMvKNF7iWlELX0YtITc/ASgi+XRvBirHv4mhXlLSMTHYdPcfw7nl30+U3NlZWfBRaiXdX7McgJe2DSuHv7si8nWeo4uVMc38v9kbG8/n2MwigdilXPg6tbMrf86fdnE9IJT1Tz7OLN/NZyyAa+nowvGUVBv39L0KAc1FbRrYOums7utQow2drj9Jp2XYk0L5KSSp4Ot01z6OiIPq+84rIXnmfK0KIIVLKyUKIz7nNQ0hK2e+OhQoxG/BH2/b25sGCZYDuwHkp5fv3atj9uFaedKTu7gNOTxOGS0cedxMKDIZDtyy8fmqx7/P5Q6vhCeW65VnnfHzx2wKl9u9mkd8c4Nx7v4VKKfsJIcKA9sDN97PLwFwp5cr7LU+hUCgsjaFAOk3yxt02zfrL+P/Xd0pzN6SU4UD4A7ZLoVAoHimFebDzjopcCPHn3TJKKdvdJa8L8DGaRe6N5pqJAf4AJkopEx+otQqFQmEhCq89fnfXSgM0//b3QATc1yz9n9AOogiVUkYBGM//fN0Y1/pBGqtQKBSW4om0yAEfoBXQFXgV+Af4Xkp5NA/l+kopJ+UMMCr0iUKIB5kFo1AoFBZFJwqvTX7HeeRSSr2UcpWUsgdQHzgDbBJC3HPGCXBRCDEk52pO4yrPj8iexaJQKBQFhid2HrkQoijQFs0q9wVmAyvyUG4XYCjaUXE3Vx1EA38CLz9oYxUKhcJSPJGuFSHEMqAqsBIYJaXM8wReKWUC8JHxyl3uG1hgdadCoVA8DIV5+uHdluh3AwKBD4Adxi1s82Mb21EPkVehUCgswhPpWpFS5mUfltsihDh0pyi06YgKhUJRoHgiXSsPiTfwLJCQK1wAO/JUgm3Re6d5ShDWT8RBTvmCcPK4d6KnBOGmZJGf6AukrZ03LKUh/gYcpZQHc0cIITZZqE6FQqF4YJRFngsp5Zt3iXvVEnUqFArFwyCVRa5QKBSFG2WRKxQKRSGnME8/VIpcoVAoKJjTCvPKA08xVCgUiicJHTLP170QQrQRQpwUQpwRQgy9TfwMIcRB43XKeJ4xQoiaQoidQoijQohDQogueWm7ssgVCoWC/BvsFEJYA3PRNh2MBPYIIf6UUh4z1SXlgBzp+5J9snca0F1KeVoIURLYJ4RYfa+tv5VFrlAoFGiDnXm97kEIcEZKeU5KmQn8gHY2w53oirZdOFLKU1LK08bPV9DOcbjnyd9KkSsUCgWaRZ7Xf0KIt4UQe3Ncb+coqhTmu7xGkn3kpRlCiHKAH9r5DbnjQoAiwNl7tV25VhQKhYL7m34opVwILMyHal8BfpFS6nMGCiFKAN8APaSU92yaUuQKhUIB6GW+zVu5DJTJ8b20Mex2vAK8lzNACOGMdpDPJ1LKXXmp8IlR5Nt27WXizPnoDQY6vdCGXq91Nou/EhXN8PEziE9MwsXZiYkjBuPjpbmeps9bwpYdewDo/XpXwlo2AyBi30GmzllMVpaOKhUDGP3xAGxsrAHYvf8Qk2YtQKfT4VrcmaVzp5CRkUmP9waTmZWFXqenVWhj3u/1GgC79h5g2twlGAwSe/tijPtkIGVLl8x/OUTsY+KshZocnm9Nr27m279fiYph+ISZxCcm4+LsyMThg/Dx0vbsmP7FV2zZaZRDj1cIa9EUgO7vDSE1LR2A+IQkqlWuwOwJn5J0PYXhE2by3+Uoiha1ZczQDwgs7wvApxNmsmXHHtxcXfh92TxT/SfOnGPM1Lmkpd+gpI8Xk0YMxtHBPt/lALDtwDEmffUbBoOBji0a8GaHVuayiI1nxLzlJCSn4OJoz/h+r+Hj7sruI6eYsjR72/3zV6KZ3P91ngmpbgqb+OUvrNiwi4hvpwKQmZXFJ59/y7Fz/+Hi5MCUAa9Tysudf7buYekf2W/Npy5d4cdJg6nkV5rZy//mry27SU5JM5VjSbafvsLklfswSEmH2v70bBpkFn8lMZWRK3aRkJaBs10RxndqiLeLPVcSU/nw+y0YpESnl3StX4GX6waa5f3gu81EJqTw6/ttAfhiwyF+23cWVwdtz6S+LWvQpEIpsvQGRv0RwYkr8egNkudr+vFmjnboDQZenb8aL2c7Pu/W3LICyUU+ziPfAwQKIfzQFPgraKesmSGEqAS4AjtzhBVBO/NhmZTyl7xW+EQocr1ez9hpc1k0czw+Xh506fUBoY3r4e9XzpRm6pzFtGvTgvbPtSJi30Fmzl/KxBGD2bxjN8dOnuWXpXPJzMrijfeH0KRBMPZ2dgwbO40lsybgW7Y0cxYt44/wdXR64VmSr6cwdtocFkwbSwkfL+IStAHlIkVs+XL2ROzt7cjS6ejeZxBN6gdTo2plxkydy+yJI/D3LcsPv/3NgqXfM+7Tgfkvh+lfsGjGWHw83eny1gBCG9XD369sthzmLtHkENaCiH3/MnPB10wcPpDNO/Zw7NRZfvnyc00O/T6mSf1gHB3sWTZ3sil//0/HE9q4HgCLlv1EpcDyzB7/Kecu/se46V+wZNZ4AF4Ma8mrHZ9n2LjpZm38bNLnDHq3J3VrVeO3f9bw1fe/0tf4sMtfWRgYv+RnFg5/D2+34nT9eCrNg6viX6aEKc20Zb/zQrO6tG9ej4jDp5j93V+M79edkKoV+HmqtpV+0vVU2vYdQ4MalUz5jp69RHJKmll9v23YhbOjPf/MGUH49n3M/PZPpnz4Bm2b1KVtk7oAnLp4hf5TFlHJrzQAzYKD6BrWhOf7jsn3/udGbzAw4e+9zO/xDN7OdvxvwWqaVSqNv5eLKc301ft5vqYf7WqVZ/e5KGavO8i4Tg3xdCzGsrdaU8TGmrSMLDrNXUmziqXwctYewOuP/YddkVtVSbcGlejRuLJZ2Nqjl8jS6fnl/bakZ+roOOcf2lQrRylXRwCW7zyJn6czqRlZFpTG7cmvWStSSp3xJLXVgDXwpZTyqBBiNLBXSnnzYPtXgB+kNHsV6Aw0BdyFEK8bw16/3b5VOXkiBjsPHz9F2dIlKVOqBLa2toS1aMaGreZvJGfPXyKkTk0AQmrXYOPWnabw4JpVsbGxxt6uGBUC/Ni2ax+JScnY2tjgW1a76RrUrc26TdsAWLl2Ey2bNaKEj3b4kbtrcQCEENjb2wGg0+nQ6XQIoZ1ZLYDUVO3mv56SiqeHu2XkUKoEZUr6GOXQlA3bcsnhwn+E1K5ulEN1Nhrjz164RHCNoGw5+PuyLWKfWd6U1DR27/uXFk0amPLUM5ZVvlwZLkfFcC1e2/AyuGZVXJydbmnjxf8uE1yzKgANgmuxdlPeNsO8X46cuUhZH09Ke3tga2tDm0a12bj3sFmac5FR1KtaAYCQqoG3xAOs3XWQxrUqY1e0CKA9IKZ/8zsDXjOfhLBpz2HaNQsBoFX9mkQcOYXM9aoevn0fbRrWMX2vUcEPT1cXHgVHIuMo4+ZIaTdHbG2sebZaOTadiDRLcy4mmZDy2i7Tdf28TfG2NtYUMb6JZuoNZv1Ky8jimx0neKtZ1Ty1QwDpmTp0egMZOj221lY4FrUFIDopja2nrtCxjv/DdveByMdZK0gpV0opK0gp/aWU44xhI3IocaSUI6WUQ3Pl+1ZKaSulrJnjuqsSBwsrcuM5nbWNl8X2IY+JvWZykwB4e3kQExtnlqZiYHnWbd4OwLrNO0hNSycxKZmKAX5si9hH+o0bJCQmsWf/IaJiYnEt7oJeb+DI8VMArNm0jaiYawBcuBRJ8vUUXn9/CJ179uWP8HWmevR6PZ16vEfT57vSoG4tqgdpltyoof3pM2gELV7sxl+r19Prtfw/8S4mNs5cDp4exFzLJYcAP9Zt0ZTnui07c8lh/y1yyMn6rTupV6eGyRVSMcCPdZu1B+LhYye5Gh1DdC6558bfr6zpIbtmY7ZM85vo+ES83Yubvnu7FScmLsksTYVypVgX8S8A63cfIjU9g8TrqWZpwrfvJ6xxtvL9ftUWmgdXu0UBR8cn4e2h1WdjbY2jfbFbylq9Yz9hjWs/fOcegJjr6fi4OJi+ezvbE5Ns/lZRwac4649pky02HI8kNUNHYloGAFFJqbw8dyVtpv3O642rmKzxuRsO0b1hJYrZWt9S5w+7T/Hy3JV8tmIXyemZALQMKotdERtaTVlBm2m/071RZVzsNffLlPB99H+2lsn4edQYkHm+ChoWUeTG1Um7gE3AZOO1WQixSwhxx19yzik9i5d9n69tGvReL/YeOMxLr7/H3oOH8fZ0x8rKikb16tCkQTDdeg9k8GeTqBFUCWsrK4QQTBk9lMmzF/JKrw9wsLfDykoTl15v4NiJ08ybMpoF08eyYOn3XLikWS/W1tb8+vVc1q/4hsPHTnH63AUAlv24gi+mjmb979/y4nOtmTx7Ub72L+9y6Mneg0d4qWc/czmE1Nbk0Gcwg0dNoUbVSlhbmd+c4eu28Jxx/ACgV7eXuZ6SSqc3+vLdr39TKdAfa6u7/6TGDP2AH35fSec3PyA1PR1b28fn3RvY/UX2HTtD58GT2Hv0DF5uLlhZZSuR2IQkzly6QsMamnsgJj6JtTsP0jWs6X3Xdej0BYoVKUJg2fwfF8kvPny2FvsuxNBlXjh7L0Tj5WyHlVGp+rg48PN7z/HnBy/w18FzxKWkc+JqApHxKTxTpcwtZXUOCeTv/i/wY58wPJzsmLZqP6C9GVhZCdYM7sDKAe35ZvtxIuNT2HLyMq4OxahS0u2R9jkn9zP9sKBhqbtoKdBbShmRM1AIUR/tvM4at8uUc0pP1rVzeZaWl6eHmfUYHXMNL0/3XGncmTVhOABpaems27QNZyfNL9e7R1d69+gKwJCRkyhXRpvyWbNqZZZ9oQ1CbY/Yx8X/tIFnby8PXFycsLcrhr1dMerUrMrJM+dNbhgAZydHQmpXZ9uuvbi7FufkmXMm6zysRVN6D/w0r93LM16e7uZyiL2GVy4XjpeHO7PGfZIth807suXQvQu9u2srgoeMmkK5MtlKJyExicPHT5nyAjg62DN2WH8ApJQ82/lNSpf0uWsby5crw6Lpmk/4wqXLpsHV/MbbrTjRcdmL4aLjE/FyN7eivdxcmDG4FwBp6RmsiziIc46B19U7DvBMSA1sjW6FE+cjuRQVa/Jp38jMou37o/lnzgi83VyIvpaIj7srOr2elLQbFHfKtoBX5bLsHzVeTnZEJWW/IUQnp5msalMaZ3umd9UeUmkZWaw/9h/OdkVuSRPgVZz9F2NJSM3g2JV4wqb/gd5gID41gze/XMeSni1xd7Qz5elYx59+320GIPzwBRoFlMTW2go3x2LULOvJ0StxnLyawOaTkWw7fYVMnZ7UjCyG/bKD8S81tJRIbiEfZ608cizlWnHIrcQBjFNpHG6T/qGoWqkClyKvEHkliqysLMLXbya0cX2zNAmJSRgMmndr0Tc/0qFta0BzhSQmaUeQnjxznlNnztMwRLvhbg5iZmZm8uV3P9P5xecACG1SnwOHjqLT6Um/cYPDR09S3rcM8QmJJF9PAeBGRgY79xzAr1wZnJ2cSElNM1ntO/YcoHy57AFIy8lhi2lg8rZy+PZnOjzX6vZyOHuehnWzX57WbNpOs4Z1KVo0+8ZOvp5CVpY2KPXrX6upUyPonjNQbsrUYDCwYNkPdG4f9pC9vj1BAWW5eDWWyOg4srJ0rNq+n+bB1czSJCSnmGSxeMVaOoSa/2bCt+8zc4U0rRPExsXjWDVvJKvmjaRYEVv+mTMCgObBVflz825A86uHVA00uQgMBgNrdhwgrNHjcasABJVy51L8dS4npJCl07P68EWaVTJfo5KQegODQVNmS7Ye48Vamq86OimNG1k6AJLTMzlwKRZfD2c6hwSydnAHwj9sz1dvtqKcuxNLerYEIPZ6uqncDccjCTAOqpZwcWD3+WhA85UfjryGn4cz/VrVZM0grayJLzeirp/3I1XiULhdK5ayyMOFEP8Ay8he4VQG6A6syu/KbGysGTagD70//BS9Xk+H51sTUL4ccxYtI6hSBUKb1GfPgUPMnL8UIQR1alTl04HvAqDT6en+7iAAHO3tmThisGmK4Vff/cLmHbuRBgNdOrSlnnGw1N+3LI3qBdOxRx+shBWdXniWwPK+nDxznk/GTkVvMCANkmefaULzRpoiHflRPwZ8Mg5hJXB2cmTMxwNu05P8kMM79B44Ar3BQIe2rQjwK8ecxd8SVCmQ0Mb12HPgMDMXfo3AKIcP+2TL4T1tpoajgz0Thw8yyQEgfP2WW6Yynrv4H5+Mm4EQAn+/sowe+oEpbvDIyew5cJjEpGRadOzBuz3/R6fnW7Ny3WZ++O0fAFo2a2h6kOS7LKytGfbmS/QZNw+9wcCLofUJKFOCuT/8QxX/soTWrcaeo6eZvfxvhIDalf35pFd2/y7HxBF9LZHgKgF5qq/DMw0Y9vk3tH1/NC6O9kwe8Lopbt/xs3h7FKe0t/nRbNO/+YOV2/ZyIzOLlr2H07FFA97t/Fy+9D83NtZWDG0bTJ9lGzEYJO1rlyfAqzjz1h+iSik3mlcqzd4LMcxee1C7R8p58fHzwQCci01i+uoDCLQdArs3qkygd/G71jdzccsgsgAAGUtJREFUzQFOXk1ACEHJ4g582k4bCO4SEsiI33fR8fN/AEm7WuWp4ONqkT7fL4V5P3KRe2Q93woWIgxtf4Gbj/3LwJ9SypV5yX8/rpUnHoP+3mmeEgxR5x53EwoMhuN5WivyVGDX5bOHHiF9vmzbPOucvy/983hGZO+AxUaapJThQLilylcoFIr8pCC6TPKKpWatuAghJgohjgsh4oUQccbPE4UQd38nUygUiseAlDLPV0HDUoOdPwEJQKiU0k1K6Q6EAonGOIVCoShQ6JF5vgoallLkvlLKSVLKqJsBUsooKeVEoNxd8ikUCsVjoTDPWrGUIr8ohBiSczWncZXnR5jv06tQKBQFAuVauZUugDvaas4EIUQ82ipPN7RNYRQKhaJAUZgtcovMWpFSJgAfGS+EEE3Qjj86LKWMt0SdCoVC8TAUxKX3ecVSs1Z25/jcC5gNOAKf3e5EaYVCoXjc6KXM81XQsNQ8ctscn3sDraWUsUKIqcAuYKKF6lUoFIoHoiC6TPKKpRS5lRDCFc3iF1LKWAApZaoQQmehOhUKheKBUYr8VlyAfWj7yEshRAkp5VUhhKMxTKFQPCyGwrw7SMGjIM5GySuWGuz0vUOUAehgiToVCoXiYVAWeR6RUqYB5x9lnQqFQpEXCvOslSfi8GWFQqF4WPSy8LqqlCJXKBQKlI9coVAoCj3KR65QKBSFHOUjVygUikKOQblWFAqFonCjLHKFQqEo5KhZKwqFQlHIUa4VhUKhKOQo18pjZtuuvUycOR+9wUCnF9rQ6zXzsyuuREUzfPwM4hOTcHF2YuKIwfh4eQIwfd4StuzYA0Dv17sS1rIZABH7DjJ1zmKysnRUqRjA6I8HYGNjDcDu/YeYNGsBOp0O1+LOLJ07hYyMTHq8N5jMrCz0Oj2tQhvzfq/XAOjeZxCpaekAxCckUq1KRWZPHGE5eUTsY+KshZo8nm9Nr24v55JHDMMnzCQ+MRkXZ0cmDh+Ej5eHJo8vvmLLTqM8erxCWIumAOzae5Bp877CIA3Y29kxblh/ypYuSWZmFh+Pm86xk2co7uzE1FEfUaqEN4lJyQwYPoEjJ07zYlgLPhnQx1T/632Hci0ugaJFiwCwcPoY3F3z/0zubQeOMemr3zAYDHRs0YA3O7Qyl0NsPCPmLSchOQUXR3vG93sNH3dXdh85xZSlK0zpzl+JZnL/13kmpLopbOKXv7Biwy4ivp0KwN5jZ5i89DdOX7zCpP49aN2gllldKWnpvDhgPM/Urc6wXtrfo+dns4lNSKZYEW2z0PnD38XdxSnf5QCw/fRVJq/aj8Eg6VC7PD2bVDGXRWIqI/+IICE1A2e7Iozv2ABvF3uuJKby4Q/bMEiJzmCga0gFXq4bAMC732ziWsoNdAYDtct68nHbOlhbZe+MvWzHCaavOcjGwR1wdShKcnomn/0RQWR8CkVsrBnVPoQAb+3vHjbjTxyK2mIlBDZWguW9n7WIHO6GssgfI3q9nrHT5rJo5nh8vDzo0usDQhvXw98v+2jQqXMW065NC9o/14qIfQeZOX8pE0cMZvOO3Rw7eZZfls4lMyuLN94fQpMGwdjb2TFs7DSWzJqAb9nSzFm0jD/C19HphWdJvp7C2GlzWDBtLCV8vIhLSASgSBFbvpw9EXt7O7J0Orr3GUST+sHUqFqZZV9MNbWl/7CxhDapb1l5TP+CRTPG4uPpTpe3BhDaqB7+fmWz5TF3iSaPsBZE7PuXmQu+ZuLwgWzesYdjp87yy5efa/Lo9zFN6gfj6GDPmGnzmD1hOP6+ZfhhxT8s+PpHxn0ygN/+WYOzkwPhPyxi5brNTJ+/lGmjPqJIkSL07dWN0+cucub8xVvaOXHEIKpWCrSgHAyMX/IzC4e/h7dbcbp+PJXmwVXxL1PClGbast95oVld2jevR8ThU8z+7i/G9+tOSNUK/Dz1IwCSrqfStu8YGtSoZMp39OwlklPSzOor4eHK2Pf+x9I/N9y2PXN+WEmdygG3hE/8oDtB/mVvkyP/0BsMTFi5l/mvheLtbMf/Fq2lWcVS+Hu5mNJMX3OQ52v40a6mH7vPRTN7/b+M69gAT8diLOvVkiI21qRlZNFpXjjNKpbCy9mOyS83wrGYLVJKBv20nbVH/6NNNe2+i0pKZefZKEq42JvqWLz1GBV9XJnxShPOxyYzYeVeFvZ4xhS/qMczuDoUtags7kZhtsgtddTbI+Pw8VOULV2SMqVKYGtrS1iLZmzYussszdnzlwipUxOAkNo12Lh1pyk8uGZVbGyssbcrRoUAP7bt2kdiUjK2Njb4li0NQIO6tVm3aRsAK9duomWzRpTw8QIwWZJCCOzt7QDQ6XTodDqEMN/oMSU1ld37/6VF0wYWkoZRHqVKUKakj1EeTdmwLZc8LvxHSG3NugypXZ2NxvizFy4RXCMoWx7+vmyL2GfqX2qqpryup6Ti6eEGwIatu2jfpgUArZs3JmLfv0gpsbcrRu3qQRQtUsRifb0bR85cpKyPJ6W9PbC1taFNo9ps3HvYLM25yCjqVa0AQEjVwFviAdbuOkjjWpWxM7496PUGpn/zOwNea2+WrpSXOxXKlcJK3Lq557Gzl4hPuk7DHA+DR8mRy/GUcXOitJsjtjbWPFu1LJtOXjZLcy42iRA/7Tdd18+LTSe0eFsba4oY30Qz9QZyGq2OxbQ3CZ1BkqU3mP3ep646QP9WNe5Yh5+nM1cSU4lLuZG/nX0I9FKf56ugUegVeUzsNZObBMDby4OY2DizNBUDy7Nu83YA1m3eQWpaOolJyVQM8GNbxD7Sb9wgITGJPfsPERUTi2txF/R6A0eOnwJgzaZtRMVcA+DCpUiSr6fw+vtD6NyzL3+ErzPVo9fr6dTjPZo+35UGdWtRPcj8xl2/ZSf16tTA0cHBIrIAiImNM5eHpwcx13LJI8CPdVt2ALBuy85c8th/izwARn3Ulz5DRtKiYw/+Wr3R5K6JuZZdn42NNY4O9iQmJd+zncMnzKTTG32Zv/R7iyyNjo5PxNs9213j7VacmLgkszQVypViXcS/AKzffYjU9AwSr6eapQnfvp+wxnVM379ftYXmwdXwdHUhLxgMBqYu+50Pu7e/bfzwud/x8qBJLPhllcWWiMckp+PjnG0ZezvbEZOcbpamgndx1h+PBGDD8UhSM3UkpmUAmnX98rxw2kz/k9cbV8bL2c6Ur883m3hmygrsi9jQsopm+Gw8EYmnsz0VfVzvWMfhyDiuJqYRnawZB0II+nyzia4LVvPL3jP5LIG8UZgPX7aYa0UI4QK0AUoZgy4Dq6WUiXfJ8zbwNsC8aWPp1b1rvrRl0Hu9GDd9Hn+sXEudmtXw9nTHysqKRvXqcOTEKbr1HohrcRdqBFXC2soKIQRTRg9l8uyFZGZl0TCkNlZG359eb+DYidMsnj2RjIwM/tf7Q2oEVcK3bGmsra359eu5JF9P4YOPx3D63AUCy/ua2hG+bjOdnn/0vr/cDHqvJ+NmzOeP8PXUqRGULY+Q2hw5cZpufQZr8qhaCWsrzRpb9tMffDF5JNWDKvLl8l+Z/PliRg/t90D1TxoxCG9PD1LT0uj/6QT+XL3BZNU/SgZ2f5EJS37mz00R1K4cgJebC1ZW2VZlbEISZy5doWGNygDExCexdudBlozqm+c6fly9jca1q+Dj7npL3IR+3fF2L05q+g0+nLqEv7bsoV2zkIfv2APwYetaTFy5jz8Pnqd2OS+8nOxMbxc+Lg78/G4YMcnpDPhhK62qlMHdsRgAX7zWnIwsPcN+28nu8zHULOPBkq3H+OK15rfU0bNxFSav2k/nL1YR6O1CxRKupjq+6tkCb2d74lNu8M43m/DzcKaOr9cj6z+oJfq3IIToDnwGrEFT4AChwHghxCgp5bLb5ZNSLgQWAmRdO5cnqXp5episRoDomGt4ebrnSuPOrAnDAUhLS2fdpm04OzkC0LtHV3r30B4YQ0ZOolwZ7blTM4dve3vEPi7+p3XD28sDFxcn7O2KYW9XjDo1q3LyzHmTGwbA2cmRkNrV2bZrr0mRJyQmcfjYSWaNH56Xbj0wXp7u5vKIvYaXRy55eLgza9wngFEem3dky6N7F3p37wLAkFFTKFemJPEJSZw8c57qQRUBCGvRhN4DPzOVFRUTi4+XBzqdnpTUNIq7ON+1jd6e2sCqg709bVs248jxU/muyL3dihMdl20zRMcn4uVubkV7ubkwY3AvANLSM1gXcRBnh2zLdfWOAzwTUgNbo2vhxPlILkXF8nzfMQDcyMyi7fuj+WfOnQeu/z11nv3Hz/HT6m2k3cggS6fDvlhR+ndrZ3pjcLArxnONgzly+qJFFLmXsx1Rydk+/ejkdDOr+maa6a80BiAtI4v1x/7D2a7ILWkCvFzYfzGWVkFlTOFFba1pXrEUm05cxsOxGJcTUun8xSpAexvoumA1377VCg8nO0a/WA/QrN/nZv5FaVftd+dtfGNwcyxGaKVSHLkc/8gVeUG0tPOKpVwrnwB1pJR9pJRjjdc7QDDwaX5WVLVSBS5FXiHyShRZWVmEr99MaGPzwcSExCQMxtNUFn3zIx3atgY0V8hNN8DJM+c5deY8DUO01+ibg5iZmZl8+d3PdH7xOQBCm9TnwKGj6HR60m/c4PDRk5T3LUN8QiLJ11MAuJGRwc49B/Arl/1jX7NxG80ahphmaliKW+WxhdDG9czSmMnj25/p8Jw2m+MWeZw9T8O6tXF2ciQlNY0Ll7SH2Y49Bynvq/UttHE9/li1Xuvjpm3Uq139lrGBnOh0ehISNRdHlk7H5h27CcgxMJ1fBAWU5eLVWCKj48jK0rFq+36aB1czS5OQnGKSw+IVa+kQav67Cd++j7DGtU3fm9YJYuPicayaN5JV80ZSrIjtXZU4wMQPerBm/ihWzRvJwNde5IWmIfTv1g6dXk9CsvZ7ydLp2bzvCAFlS9y1rAclqKQbl+KuczkhhSydntVHLtGsYimzNAmpGRgMmiJbsu04L9YqD0B0Uho3srTTGZPTMzlw6Rq+Hk6kZWQRe11zz+j0BraevoKfhxOB3sXZOKQD4QPaET6gHV7Odnzf+1k8nOxITs8kS6f5l3/bf4465bxwLGZLeqaO1IwsANIzdew8G0WAV95cV/mJQco8XwUNS7lWBNz2PcVAPh/1ZmNjzbABfej94afo9Xo6PN+agPLlmLNoGUGVKhDapD57Dhxi5vylCCGoU6Mqnw58F9CUSvd3BwHgaG/PxBGDTVMMv/ruFzbv2I00GOjSoS31jIOl/r5laVQvmI49+mAlrOj0wrMElvfl5JnzfDJ2KnqDAWmQPPtME5o3ylag4es306tbZyyNJo936D1wBHqDgQ5tWxHgV445i78lqFIgoY3rsefAYWYu/BqBUR4f9smWx3vabA1HB3smDh9kksfIIe8zYPh4hBA4Ozky5uP+AHRs25qPx04j7JW3cHF2ZMrIj0xtaf1yT1JS08jS6diwdRcLp42hhI8XvQeOIEunx2AwUD+4Bi+9kP/uJhtra4a9+RJ9xs1DbzDwYmh9AsqUYO4P/1DFvyyhdaux5+hpZi//GyGgdmV/PumVPU3zckwc0dcSCa5y60yT23HkzEX6T1lMcmo6m/cd4YufwlkxY9gd02dm6Xhn7Dx0egMGg4F61SrSqUXDh+737bCxtmLoc3Xo881mDNJA+1rlCfByYd6Gw1Qp6UbzSqXYeyGG2ev/1X4T5bSphADnriUzffUBhBBIKenesCKB3sWJS7nBB99v1f6OUhsgfSn47rI6fy2Z4SsiEAL8PV0Y2V57+4hLucGHP2qTCXQGA2HVytEo0DIPtbtRmGetCEu8TgghegAj0Fwr/xmDywKtgDFSyqX3KiOvrpWnAkPBGyV/XBiizj3uJhQYDEd3PO4mFBjsuo56aAPR06VinnVObNLJAnX2sEVcK1LKr9HcKJuBDOO1CQjOixJXKBSKR01hnrVisemHUsoEYGPOyximUCgUBY789JELIdoIIU4KIc4IIYbeIU1nIcQxIcRRIcTyXHHOQohIIcScvLTdUrNWagLzARcgEs0vXloIkQi8K6Xcb4l6FQqF4kHJL0tbCGENzEVzJUcCe4QQf0opj+VIEwh8DDSSUiYIIXJP0RkDbMlrnZYa7FwK9JZSRuQMFELUB74Catwuk0KhUDwu8nEeeQhwRkp5DkAI8QPQHjiWI81bwNybXgopZczNCCFEHcAbWIXmor4nlnKtOORW4gBSyl2A5ZY1KhQKxQNyPz5yIcTbQoi9Oa63cxRViuxJHqBZ5ebzPaECUEEIsV0IsUsI0QZACGEFTAMG3U/bLWWRhwsh/gGWkd2hMkB3tKeMQqFQFCju52CJnIsXHxAbIBBoDpQGtgghqgHdgJVSysi7rce4XWH5jpSynxAiDO11IucS/blSypWWqFOhUCgehnxc6HMZzXC9SWmyV7jfJBKIkFJmAeeFEKfQFHsDoIkQ4l3AESgihEiRUt52wPQmFttrRUoZDoRbqnyFQqHIT/JxWuEeIFAI4YemwF8BXs2V5negK/CVEMIDzdVyTkr5v5sJhBCvo03ZvqsSBwv5yIUQLkKIiUKI40KIeCFEnPHzRCFE/p8goFAoFA+JvI9/dy1HSh3wPrAaOA78JKU8KoQYLYRoZ0y2GogTQhxDm549WEoZd/sS742lVnauBjYAX0spo4xhPsDrwDNSytb3KkOt7MyBWtlpQq3szEat7MwmP1Z2FilaOs86JzMjskCt7LSUa8VXSjkpZ4BRoU8UQrxhoToVCoXigSmIm2HlFUtZ5GuAdWgWebQxzBvNIm8lpWyZ75VaCCHE28YR6qceJYtslCyyUbJ4/FhqHnkXwB3YbPSRx6PtteIGvHy3jAWQt++d5KlBySIbJYtslCweM5aafpgAfGS8zDC6Vr6yRL0KhULxNPI4zuwc9RjqVCgUiicWS22adehOUWh7CBQmlO8vGyWLbJQsslGyeMxYarAzGngWyL1trQB2SClL5nulCoVC8ZRiqemHfwOOUsqDuSOEEJssVKdCoVA8lVjEIlcoFArFo+NxDHYWCoQQF4QQh4UQB4UQex93ex4lQogvhRAxQogjOcLchBBrhRCnjf+7Ps42WpL76b/QmG08CeaQEKL242u5ZbjdvfA0y6MgohT53QmVUtaUUuZpc/cniKVAm1xhQ4H1UspAYL3x+5PKUvLe/zC0XesC0eZTf/GI2vioyX0vPO3yKFAoRa64BSnlFiA+V3B74Gvj56+BFx9pox4h99n/9sAyqbELKC6EKPFoWvpYUfIoQChFfmcksEYIsS/X6R9PK95SyqvGz1EUvmmkD8ud+p+X02AKO7e7F55meRQ4LLYf+RNAYynlZeOhqGuFECeMltpTj5RSCiGe2lHyp7D/t9wLOSOfQnkUOJRFfgeklJeN/8cAK9AOVH2aib75imz8P+Ye6Z807tT/vJwGU6i5w73w1MqjIKIU+W0QQjgIIZxufgZaA0funuuJ50+gh/FzD+CPx9iWx8Gd+v8n0N04W6M+kJTD5VDoucu98FTKo6Ci5pHfBiFEeTTLAzT303Ip5bjH2KRHihDie7RDYT2AaOAztKOpfgLKAheBzlLK3AOCTwT303+hnZA7B22WSxrwhpTyiZmueqd7QQjhzlMoj4KKUuQKhUJRyFGuFYVCoSjkKEWuUCgUhRylyBUKhaKQoxS5QqFQFHKUIlcoFIpCjlLkinxBCCGFEN/m+G4jhIgVQvydD2U3F0IkCSEOCCFOCiG2CCGef4jyfIUQr+b4/roQYs7DtlOheFwoRa7IL1KBqkIIO+P3VuTvir6tUspaUsqKQD9gjhCixQOW5Qu8eq9ECkVhQSlyRX6yEmhr/NwV+P5mhBAiRAix02hV7xBCVDSGDxBCfGn8XE0IcUQIYX+3SownT40G3jfm8xRC/CqE2GO8GhnDRwohvjHWe1oI8ZaxiIlAE+P+2gOMYSWFEKuM6SbnjzgUikeDUuSK/OQH4BUhRDGgOhCRI+4E0ERKWQsYAYw3hs8CAoQQHYCvgN5SyrQ81LUfqJSjjBlSyrpAJ2BxjnTVgWeABsAIIURJtL2ztxr3155hTFcT6AJUA7oIIXLuF6JQFGjU7oeKfENKeUgI4Ytmja/MFe0CfC2ECETbFtXWmMcghHgdOAQskFJuz2N1IsfnlkAVbXU4AM5CCEfj5z+klOlAuhBiI9qGT4m3KW+9lDIJQAhxDCiH+XasCkWBRSlyRX7zJzAVba8S9xzhY4CNUsoORmW/KUdcIJAClLyPemoBx42frYD6UsobORMYFXvuPSjutCdFRo7PetS9oShEKNeKIr/5EhglpTycK9yF7MHP128GCiFcgNlAU8BdCPHSvSoQQlQHhgNzjUFrgL454mvmSN5eCFHMuMlTc2APcB1wynuXFIqCjVLkinxFShkppZx9m6jJwAQhxAHMrd0ZwFwp5SngTWCi8QCD3DS5Of0QTYH3k1KuN8b1A4KNh/0eA97Jke8QsBHYBYyRUl4xhumFEP/mGOxUKAotavdDxROLEGIkkCKlnPq426JQWBJlkSsUCkUhR1nkCoVCUchRFrlCoVAUcpQiVygUikKOUuQKhUJRyFGKXKFQKAo5SpErFApFIef/hf2asNKbWJkAAAAASUVORK5CYII=\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#model with optimal max_depth and optimal min_samples_split\ndt_tf_wt=DecisionTreeClassifier(max_depth= omd_tf_wt , min_samples_split = omss_tf_wt,class_weight='balanced')\ndt_tf_wt.fit(d_train_tf_wt,y_train)","execution_count":69,"outputs":[{"output_type":"execute_result","execution_count":69,"data":{"text/plain":"DecisionTreeClassifier(class_weight='balanced', criterion='gini',\n            max_depth=10, max_features=None, max_leaf_nodes=None,\n            min_impurity_decrease=0.0, min_impurity_split=None,\n            min_samples_leaf=1, min_samples_split=500,\n            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n            splitter='best')"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#report on train data\nreport(d_train_tf_wt,y_train,dt_tf_wt)","execution_count":70,"outputs":[{"output_type":"stream","text":"              precision    recall  f1-score   support\n\n           0       0.36      0.81      0.50      5314\n           1       0.95      0.72      0.82     26935\n\n   micro avg       0.73      0.73      0.73     32249\n   macro avg       0.66      0.76      0.66     32249\nweighted avg       0.85      0.73      0.77     32249\n\n\nThe value of TPR is 0.718\nThe value of TNR is 0.807\nThe value of FPR is 0.193\nThe value of FNR is 0.282\n\nThe roc auc score is 0.848314\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 2 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#report on test data\nreport(d_test_tf_wt,y_test,dt_tf_wt)","execution_count":71,"outputs":[{"output_type":"stream","text":"              precision    recall  f1-score   support\n\n           0       0.34      0.75      0.47      2278\n           1       0.93      0.71      0.81     11544\n\n   micro avg       0.72      0.72      0.72     13822\n   macro avg       0.64      0.73      0.64     13822\nweighted avg       0.84      0.72      0.75     13822\n\n\nThe value of TPR is 0.711\nThe value of TNR is 0.746\nThe value of FPR is 0.254\nThe value of FNR is 0.289\n\nThe roc auc score is 0.802955\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 2 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#plot roc curve for train and test data\nplotROC(d_train_tf_wt,y_train,d_test_tf_wt,y_test,dt_tf_wt)","execution_count":72,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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w6RJkwgPD6d+/frFv7AUdKDQNK3MLBbF5ZR0IhPSiEy4RmS8EQyMx2lcTEwj2/JnO78INHJ3potHCqNrRdOuSRTNcyJpnHkOr9SzOGcl/1l4JpDl+OeB36svePmApy94+f55383bKLiay8zM5MUXX+TFF1+kcePG3H777bi6upZbkAAdKDRNs1JGdg7nrlzj7JVUzsVd43zcVSLijcBwMTGdzJy/Du9s5FEL33q16eHnyZy2mbR1uEjznAs0SD+PW1IoDnFnIOHany9wbwgN20OrKVAvwAwCvkZw8GgC5dQJXJXs3buXhQsXcvz4cWbNmsXrr7+Oq6truW9XBwpN0/4iLTOHsNhUQmNSOROTwpnLxv1zcVfJc1JAfXcX/Lzd6OzjxejOTfHzro1vPTd867riq6KpFXsEzv8EIRshNc+kfp6+0LAdBAww/jZoZ/x18674na1CoqKiGDRoEI0bN+aHH36w6aim4uhAoWk1VHaOhXNxVzkZncKp6BRORqdw+nIKF+KvkZsCzslBCGjgTtvGHoy7oSmtG9WhZYM6+Ddww9PVGbIzIDECLh02biGH4dIRyEgyC3CFNjdB29HQqAM0aAu1rJoLSTOdPn2atm3b4uPjwxdffMHw4cPx9LTmigrbqXLzUegrszWtZJRSXEpK51R0Cqcu/xkUwmJSrzcXOQi0aOBO+yaetGlch7aNPWhT3wV/lxRcrl6EpChINm9571+N/XNDji7m6KFu0Kw7NOsGDTuAk4ud9rxqS0xM5NFHH2X16tXs2LGDwYMHF/+iIojIAaVUYGleq88oNK0aSbqWZQaD5OtnCKeiU0hO/3OYaBNPV9o18WBwmwa0a+xOl9qx+KedxCV2NyRegPAoOHwRUmP4nymva3kZncpePtC0q9m57AtNOuugYEMbNmzgrrvuIjo6mkceeYRevUoyI7Ht6UChaVVQZraFMzFGEMg9QzgVnUJ0cvr1dTxcnWjfxIObuzWjXWMP2jX2oL17Kp7xRyDqV4g6AMGHIcMcYeTsBl5+RhBo3NkMAj5/Xmvg5aObjSrAokWLWLNmDV26dOH7778nMLBUJwE2pQOFplVyKelZhFxK4fjFJI5fTObExWTOxKSQlWP82ndxdKBVozr0a1Wfdk08jFtjD5q6ZiFRQRD1G5w7BL8d+LNT2cHJaCbqchv49DRuDdrqkUV2ktsFICIEBgbi7+/PY489hotL5ThD04FC0yqR+KuZHI1K4lhUEicuJnP8YhLn4v4cQlrf3YWOzTwZ3LYlHZt50rGpBwH13XFydICMFLiwF879Crt2w8VDoHLMF7aGlkOgWQ8jKDTpAs7lP6xSK15ERAR33nkn06ZNY/bs2dx55532rtL/0IFC0+wkNSObo5FJHIlMJDgykSORSUQm/DnLanNvNzo182RyD186+XjSqZkXjTxq/ZmeQSk4uxOO7IBzuyHqoBEYHJyNYDDwAfDvDz49jIykWqVisVhYuXIljz32GDk5Odxyyy32rlKhdKDQtAqQmW3hVHQKhyMTCY5I5EhkImdiUq8PQ/Xzrk1Xv7rM6uvPDT5edPLxwqt2AenmstKNM4azOyH0F4g5YRbQBwbeDwGDwK83uLhX3M5pJXbmzBkWLVrErl27GDFiBO+//z4tWrSwd7UKpQOFptmYUorzcdcIjkzkcIRxO34xmcxsYyhqfXcXuvrVZVyXZtzg50VX37p4uxfRFn31CpzZAqc2Qeg2yLoKjrWM5qOb34EOE6B23QraO80WTpw4wZEjR1i7di3z5s2zeRI/W9OBQtPKKC41wwwKSQRHGM1IiWaCu9rOjnTx8WJe/wC6+tblBl8vfOvVLv7AEBcGJ380bhF7AQUezaDrNGg3FgIG6j6GKiY4OJjDhw8zd+5cJk6cSHh4OPXqVY0mQR0oNK0ELBbFmZhU9p+LJ+hcPAcuJBARb/QrOAi0bezB6E5N6OZXl65+dWnTqI7R0Vx8wUbn8ykzOMSeNJ5v0gWGPAbtxhjXLVTyX57a/8rIyOD555/npZdeomnTpkydOhVXV9cqEyRABwpNK1JGdg5HI5PYdy6eoHMJBJ2Lv37xWkOPWgT612N2X3+6+tali68Xbi4l+Jey5ED4dji5yWhWSrkE4mjkQApcYASHus3Lac+0ivDHH3+wcOFCQkJCmDNnDsuWLauQJH62pgOFpuWRmpHNgfMJ7D8bz75z8RyOSLzet9C6UR3GdmlKrwBvAgPq0dzbrWRty5YcIxjEhcLpLbB/FeRkGhe6tR4B7ccbeZF0crxqISoqiiFDhtCkSRM2bdrEmDFj7F2lUtOBQqvR4lIz2H8unr1n49l/Lp4TF5OxKHB0EDo382ROX396tfCmV4B30R3OBcnOhHO7jDOG8O1GegyLmUrD0QVaDoO2I6HbLN3fUI2EhITQoUMHfHx8+PLLLxk+fDgeHlX7inYdKLQa5VJSGvvOGoFh39l4QmNSAajl5ED35nW5d1hrerXwpkfzerjXKsW/R1oCnNlq9DWc2QqZKcYZQ8th0HES1PUzmpP8+uh0GNVMQkICDz30EB988AG7du1i0KBBTJo0yd7VsgkdKLRqSynFhfhr7A03A8O5uOsdz3VqOREYUI9be/jQp4U3nX28qOVUwvQVlhyICYHI/RAVBJFBEHsKUMYkPJ1vgXbjjCuinWvbfge1SuO7777j7rvvJjY2lieeeMLuSfxsTQcKrVo5e+Uqv4VeMc8Y4ricnAGAt7sLvQLqMa9/C/q08KZDU08cHUo4gijlshkQ9htBIeqgcU0DQG1v8O0FnSdDy6HgE1ht5mbWirZgwQI++OADunXrxo8//kiPHj3sXSWb04FCq9KycywcvJDI1pDLbD1xmfArxoG7sWct+rSoT+8W3vRt6U2rhnVK3vEcsdcICBcPQuQBSLpgLHNwMoatdp9pBAefnuDdUg9drUHyJvHr27cvbdq04eGHH8bZuYCr6asBPXGRVuWkZmTz6+lYfg65zPaTMSRcy8LZUejXqgHD2zdicNuGBNQv4YikXDnZcGI9bH8B4sON57z8jGDg28u4Nb1BNyXVYOfPn+eOO+5gxowZzJkzx97VsZqeuEir9mKS09l8wjhr+CMsjswcC3XdnLmxXSNGdGzMoDYN8HAtw6+5tAQ4+B/YtwqSIox5nCevgRZDoE5D2+2IVmVZLBaWL1/O448/jlKKKVOm2LtKFUYHCq3Sik3J4IcjF/nv0Wj2n49HKfCv78acfv6M6NiYQP961l31XORGTsPeFRD8GWRdM5LqjX7JuNhNz82gmU6dOsWiRYvYvXs3I0eOZOXKlQQEBNi7WhVGBwqtUknPymFryGW+PRjFztOx5FgU7Zt4cP/wtozp0oQ2jUrY11DgRpKNJHvBn0HoViPBXpcp0PdOo+9B0/I5deoUx48f58MPP2TOnDmVPomfrelAoVUKJ6OTWff7eX44cpGU9Gyaerlyx+CW3NrDh9aNbHC9gVJw5mcIWgNh24wroj2awrAnoed83byk/Y9Dhw5x+PBh5s+fz80330x4eDh169bMLL06UGh2Y7Eodp6OZc3us+wOvYKrswNjOzdlck9f+rasX/LhqwVRCsJ3GJ3TkfuNDKy9FkGHm42L3vQQVi2f9PR0nnvuOV555RV8fHyYPn06rq6uNTZIgA4Umh2kZebw7aFI1u4+S1jsVRp71uLR0e2Y0bs5dd1sNEdwdiYc/RIOfGgECE8fGP8GdJsJTpVjHmKt8vntt99YuHAhp06dYv78+bz22mtVMomfrelAoVWYmJR0PvrjPB/vOU/CtSy6+HjxxtRujO3SFBcnG/2yz7xmjF76/W1IjoS6/jDiWehzp86npBUpKiqKYcOG4ePjw+bNmxk5cqS9q1Rp6EChlbvQmBTe3xXO+kMXybJYGNGhMYsHtaRXQD3bdQqmJcL+1bBnOVy7As37w4Q3ofVwfSGcVqQTJ07QsWNHfHx8+Oabbxg2bBh16tSxd7UqFR0otHJz4HwCK3aG8fOJy7g6OzC1lx8LBragRQMbzuecnmScPexdCRnJ0PomGPQg+Pe33Ta0aik+Pp4HH3yQdevWsXPnTgYPHsyECRPsXa1KSQcKzaaUUuw4FcvyHWHsOxdPXTdnlg5vw9x+/tSvU8s2G8nOMPodwrZD0FpIi4eOE2HQQ8YscJpWjG+++YZ77rmHuLg4nnzySXr37m3vKlVqOlBoNpGdY+HHo5dYviOMk9EpNPNy5f/Gd2RaL7/SpevOLybEuPYhfCdc+MO4OE4cjAR8w5+GZt3Lvg2tRpg3bx7r1q2jR48e/PTTT3Tr1s3eVar0dKDQyiQtM4evDkTw/q5wIhPSaNOoDq9N6crN3ZrhXNarppOi4NjXcORLuHzMeK5he+g+20jd7T8AatfcIYua9fIm8evfvz8dOnTgoYcewslJHwKtUa7vkoiMBt4EHIHVSqmX8i1vDqwD6prrPK6U2lSeddJsIykti4/+OMcHv50j7momPZrX5ZkJnRjevhEOZbn+IT0JTmyAI1/Aud2AMhLxjfk3dJgAnk1ttQtaDXH27FmWLFnCrFmzmDt3LkuWLLF3laqccgsUIuIIvAvcBEQC+0Vkg1LqRJ7VngK+VEotF5GOwCYgoLzqpJVdTEo6a3af5ZM9F0jNyGZou4bcNaQVvVt4l20EU1oibP47HP0acjLAuxUMfQK63Ab1W9luB7QaIycnh3fffZcnnngCBwcHZs6cae8qVVnleUbRGwhVSoUDiMjnwEQgb6BQgKd53wu4WI710crgQtw1Vu4K46sDkWTnWBjbpSl3DW1Fp2ZeZS88Yj98vQBSLhrpNLpNh2Y99LBWrdRCQkJYuHAhf/zxB2PGjGHFihU0b97c3tWqssozUPgAEXkeRwJ98q3zD2CLiPwNcAdGFFSQiCwBlgD6w65gpy+nsHxHGBuCL+IowuSevtwxuCUBthrieukIfDgO3BvAgs3gW6p0+Zr2F6GhoZw6dYqPPvqImTNn1rgkfrZm756c6cCHSqnXRKQf8JGIdFZKWfKupJR6H3gfjImL7FDPGudoZBLvbD/D5uOXcXNxZMGAABYNakljTxtd3RwXBjv+ZTQ11fKEqR8ZkwNpWikdOHCA4OBgFixYwIQJEzh79iyenp7Fv1ArVnkGiijAL89jX/O5vBYCowGUUn+IiCvQAIgpx3ppRdh3Np53toey63Qsnq5OLB3ehvn9A6jnbqP8SNfiYefLxlXUDs4w8H7ovxTcvG1TvlbjpKWl8eyzz/Lqq6/i5+fHjBkzcHV11UHChsozUOwH2ohIC4wAMQ2YkW+dC8Bw4EMR6QC4ArHlWCetEMERibyy+SS/hcbRoI4Lj41uz6y+zcs2a1xeOdlw4AMji2t6EvSYa3RWezS2TflajbRr1y4WLVrEmTNnWLhwIa+++qpO4lcOyi1QKKWyReReYDPG0Ne1SqnjIvIcEKSU2gA8BKwSkQcwOrbnqao2iXcVFxqTymtbTvHfY9F4u7vw1LgOzOzjT20XG83ulpMFJ76HX1+DmBN/ziDXpLNtytdqrKioKIYPH46fnx9bt25l+PDh9q5StSVV7bgcGBiogoKC7F2NKi8mOZ3Xt57hi/0XcHNxYtGgFiwc2MJ2ZxCXT8DhT4yL5a7GQO16RpK+Djfr0UxamRw9epQuXYyZCH/44QeGDRuGu7sN84dVUyJyQClVqtEi9u7M1ipYWmYOK3eFsXJnONkWC3P6BfC3G1vbJg9TXBicWA/H10P0EXBwgrajofssaD0CHG0UhLQa6cqVKzzwwAN8/PHH15P4jR8/3t7VqhF0oKghlFL8cOQS/9oUwsWkdMZ1acqjo9vhX98Gv8QOfQx7VsDlo8Zj315G81KXKcawV00rA6UUX331Fffeey8JCQk888wz9OmTf6S9Vp50oKgBjl9M4tkNJ9h3Lp6OTT15Y1p3erco4ygji8WYe3rXvyFiDzRoC6P+BR1vBi9f21Rc04C5c+fy0UcfERgYyC+//HK92UmrODpQVGPxVzN5dcspPt93gbpuLrx4Sxem9vIr21zUlhxj9NKe5RAXCnUaw9C/w8AH9BSjms3kTeI3ZMgQbrjhBu6//36dxM9O9LteDWXnWPh4z3mW/Xyaq5k5zO0fwP0JyYz+AAAgAElEQVTD2+LlVsY+gtRY+HYRhO8An0C4dbUxD4QOEJoNhYeHs3jxYmbNmsX8+fNZuHChvatU4+lAUc0EnYvnqfXHOBmdwsDWDXh6QkfaNvYoe8HnfjPyMaUnws1vG6m+9eglzYZycnJ4++23efLJJ3F0dGTOnDn2rpJm0oGimki6lsVLP53ks30XaOblyopZPRjVqUnZc9xcCoaQjfDrMqgXALO+hia6jVizrRMnTrBgwQL27t3LuHHjWLFiBb6+uq+rstCBoopTSvHj0Uv8Y8MJEq5lsnhQC+4f0bbss8plZ8Cmh+Hgf4zHHScZZxKuOi2CZntnz54lLCyMTz/9lGnTpukkfpWMDhRVWFRiGv+3/hjbTsbQxceLD+f3orOPDdJ+J1+CL2ZBVJDRSd1jLni3KHu5mpbH/v37OXz4MIsXL2bcuHGEh4fj4WGDZlLN5nSgqIIsFsXHe8/z8n9PooD/G9+Ruf38cSrr1KMAEfuMIJGRCrd/ZAx31TQbunbtGk8//TSvv/46/v7+zJ49G1dXVx0kKjEdKKqY0JhUHv/mCEHnExjUpgEv3tIFP2+3shdssUDQGmOWOc9mMHs9NO5Y9nI1LY8dO3awaNEiwsLCuOOOO3j55Zd1Er8qQAeKKiI7x8LKXeG8ufUMtV0ceW1KV27t4WObttyEc/D9vXDuVyPVxq2rdNpvzeYiIyO56aab8Pf3Z9u2bQwbNszeVdKspANFFRByKZlHvg7mWFQyY7s04dmbO9PQwwa5mbLSjHkhtv8LxMFI2tdjrh72qtlUcHAwXbt2xdfXl++//56hQ4fi5maDs2CtwuhAUYllZlt4b0co72wLpa6bM+/N7MHYLk3LXnBWOhz4EHa/DqnR0PomGP861PUr9qWaZq3Y2Fjuu+8+PvvsM3bs2MGQIUMYO3asvaullYIOFJXUyehkHvwimBOXkpnYrRnPTOiEd1lnmcvOMIa7/roMUi6C/0C4bS0EDLBNpTUNY8j2559/ztKlS0lKSuLZZ5+lX79+9q6WVgZWBQoRcQGaK6VCy7k+NV5uX8QbW0/jVduZFbN6Mrpzk7IXHH8WPpkCcWegeX+4dSW0GFz2cjUtn9mzZ/PJJ5/Qp08f1qxZQ6dOnexdJa2Mig0UIjIOWAa4AC1EpBvwjFLqlvKuXE0TGpPKQ18FExyRyLguTfnnpM5lP4sAuHjICBKWbJj5tdFhrfshNBuyWCyICCLCsGHD6NmzJ0uXLsXR0UYzJWp2Zc0ZxXNAH2A7gFLqsIi0Ltda1TAWi2Ltb2f59+ZT1HZx5O3p3ZnQtZltCj+5Cb5ZBG71Yfa30KCNbcrVNFNoaCiLFy9m9uzZLFiwQCfxq4asuUIrSymVmO+5qjV/aiV2Ie4a01bt4fkfQxjUpgFbHhhsmyBhsRijmT6fDg3bwqKfdZDQbCo7O5tXX32VLl26cOjQIVxcdBbh6sqaM4oQEbkdcBCRFsBSYE/5Vqv6U0rxZVAEz208gYMIr07pymRbXReRngTfLoHTP0G3mTBuGTjri5o02zl27Bjz588nKCiIiRMn8t5779GsmY3OgrVKx5pAcS/wNGABvgU2A38vz0pVd1dSM3j8m6NsDblM/1b1+feUrvjUrW2bwmNPweczjIvoxr4KvRbp/gjN5i5cuMD58+f5/PPPuf3223USv2pOcmeSKnQFkVuVUt8W91xFCQwMVEFBQfbYtE3sPB3LQ18eJjk9m0dHtWPBgBY4lGXGubxCNsJ3d4JzbZiyTg971Wxq7969BAcHs2TJEgBSU1OpU6eOnWulWUtEDiilAkvzWmv6KJ4q4LknS7Oxmiw7x8IrP51k7tp91HevxcZ7B7JoUEvbBInsDNj6rJHMr2E7WLJTBwnNZq5evcqDDz5Iv379eOWVV8jIyADQQaIGKbTpSURGAaMBHxFZlmeRJ0YzlGaly8np/O3TQ+w7F8+0Xn48M6ETtV1sNGzw3G+wcakxf3WPOUZzk5MN0ntoGrBt2zYWL15MeHg4d911Fy+99BK1aunvV01TVB9FDHAMSAeO53k+BXi8PCtVnfwWeoWlnx0iLSuH16d25ZbuNpy1K/Y0fHo7uNaFmd9AmxG2K1ur8SIjIxk1ahQtWrRg586dDB6sL9CsqQoNFEqpQ8AhEflEKZVegXWqFiwWxbvbQ1m29TStG9Zh+awetG5kw3z7MSdh3QRwdoO5G6B+K9uVrdVohw4donv37vj6+rJx40aGDBlC7do2GmyhVUnW9FH4iMjnInJERE7n3sq9ZlVYwtVM5n+4n9d+Ps2kbj58f+8A2waJiP2wbrwxmmnejzpIaDZx+fJlpk6dSo8ePdi5cycAo0eP1kFCs2p47IfA88CrwBhgPvqCu0IdjUzizo8PEJuSwfOTOjOzT3PbDh2M2Acf3WJcaT3za+NiOk0rA6UUn3zyCffddx+pqak8//zz9O/f397V0ioRa84o3JRSmwGUUmFKqacwAoaWz/eHo5i84neUUnx1Zz9m9fW3XZDIyYaDH8FHt0KdRrBgsw4Smk3MmDGD2bNn065dOw4fPsyTTz6Js7OzvaulVSLWnFFkiIgDECYidwJRgJ7cNg+LRbHs59O8sz2U3i28WT6zB/Xr2HBkSNh2+O9jcOUU+ATC7f8BTxvMS6HVWHmT+I0cOZJ+/fpxzz336CR+WoGsCRQPAO4YqTteALyABeVZqaokPSuHB788zKaj0Uzr5cdzEzvj4mTNiZoVEs7Bz8/AifXg6QO3fwQdJugrrbUyOX36NIsXL2bOnDksXLiQ+fPn27tKWiVXbKBQSu0176YAswFExKc8K1VVxKZksPg/QQRHJvLUuA4sHNjCdk1NpzfDF7ONKUqHPA4DloKLu23K1mqk7Oxsli1bxjPPPIOrq6vupNasVmSgEJFegA+wWyl1RUQ6AY8BNwI2vCCg6gmLTWXeB/uITclgxayejOpkg8mFwEjod3ITbH8B6gXA7O/AS8dlrWyOHDnCggULOHDgALfccgvvvvsuTZvq5kvNOkVdmf0vYDIQDDwlIj8AdwMvA3dWTPUqp+CIROZ+sA9HET5f0o9ufnVtU/DelbDlKcjJBC8/mPCmDhKaTURGRhIREcFXX33F5MmTdRI/rUSKOqOYCHRVSqWJiDcQAXRRSoVbW7iIjAbeBByB1UqplwpY53bgHxhDboOVUjNKUP8KF3Qunnkf7KeeuzMfL+yDf30bNgcd/gScXGHeJvAN1H0RWpn8/vvvHDlyhDvvvJOxY8cSHh6Ou7tuvtRKrqhe13SlVBqAUioeOF3CIOEIvIsxlLYjMF1EOuZbpw3wBDBAKdUJuL+E9a9Q+87GM2ftPhp61OLLO/rZNkhs/xdcCoZ+94JfLx0ktFJLTU3lvvvuY+DAgbz22mvXk/jpIKGVVlFnFC1FJDeVuGDMl309tbhS6tZiyu4NhOYGFxH5HOMs5USedRYD7yqlEswyY0pY/wpz8EIC8z7YR1MvVz5b3JdGnjacCCjxAux8CdqMhIGVOlZqldyWLVtYsmQJFy5c4J577uHFF1/USfy0MisqUEzO9/idEpbtg9FclSsSY+7tvNoCiMhvGM1T/1BK/ZS/IBFZAiwBaN68eQmrUXYR8ddYvC6Ihh61bB8klDIupAMY+KDO/KqVWkREBOPGjaNVq1bs2rWLgQMH2rtKWjVRVFLAXypo+22AoRijqHaJSJf8c3Qrpd4H3gdj4qIKqNd1KelZLFy3n6wcC2vn9bJ9kPj5/+D3t+GGqeCXP45qWvEOHDhAz5498fPzY9OmTQwaNAhXVz31rWY7NroyrEBRgF+ex77mc3lFAhuUUllKqbPAaYzAUSnkWBRLPztEWOxVls/qSauGNpyoxZIDG/5mBIlei2HSCnAoz49Dq26io6OZMmUKgYGB15P43XTTTTpIaDZXnkem/UAbEWkhIi7ANGBDvnXWY5xNICINMJqirO4wL2//2hTC9lOxPDexEwNaN7BdwTlZ8PUCOPQRDH4Exv5bBwnNakop1q1bR8eOHdm4cSMvvviiTuKnlStrUngAICK1lFIZ1q6vlMoWkXuBzRj9D2uVUsdF5DkgSCm1wVw2UkROADnAI0qpuJLtQvnYGHyR1bvPMqefPzP7+Nu28GPfGGk5bnzKCBSaVgLTpk3jyy+/ZMCAAaxevZr27dvbu0paNSdKFd3kLyK9gTWAl1KquYh0BRYppf5WERXMLzAwUAUFBZXrNiLirzHmzV9p27gOX9zRD2dHG/7aVwr+czNcPg4PnQZHq2O1VoPlTeK3bt06UlJSuPvuu3HQZ6KalUTkgFIqsDSvteZb9hYwHogDUEoFA8NKs7GqIMeieOjLYADenNbdtkECYNe/4ewuGPSwDhKaVU6ePMngwYNZs2YNAHPnzuXee+/VQUKrMNZ80xyUUufzPZdTHpWpDFb9Gs6+c/E8e3Mn/LzdbFv4ie+NHE5dp0Pfu2xbtlbtZGVl8eKLL9K1a1dOnDhBnTo2HEyhaSVgzU/aCLP5SZlXW/8NY3RStXPiYjKvbTnFmM5NuLWHjXMsXToC390Jvr1h/Bv6ymutSIcPH2b+/PkcPnyY2267jbfffpsmTWyUeFLTSsiaQHEXRvNTc+AysNV8rlrJyDbmlajr5sILt3SxbdK0rDT4ZhG4esHUj8FZD1/UihYdHU10dDTffPMNt95aXBIETStf1gSKbKXUtHKviZ29sfUMJ6NTWDsvEG93F9sWvu15Y3a6Wd+CR2Pblq1VG7t37+bIkSPcfffdjB49mrCwMNzcbNz8qWmlYE0fxX4R2SQic0WkWk6BeuB8Ait3hjGtlx83trfxgfzcb/DHuxC4EFoPt23ZWrWQkpLCvffey6BBg3jjjTeuJ/HTQUKrLIoNFEqpVsDzQE/gqIisF5Fqc4aRnpXDI18H09SrNk+N71j8C0oi8gB8PR/q+cNNz9m2bK1a2Lx5M507d+a9997jvvvu4+DBgzqJn1bpWDW+Tin1u1JqKdADSAY+KddaVaBlP58mPPYqr9x2A3Vq2XC46tGv4cOxRpK/6V9ALT1iRfuriIgIxo8fj5ubG7t37+aNN97QI5u0SqnYQCEidURkpohsBPYBsUC1yBdw8EICq38NZ0af5rZL0WGxGH0S3ywEn56weDs00lfOagalFPv27QPAz8+P//73vxw6dEin4NAqNWvOKI4BfYFXlFKtlVIPKaX2lnO9yl1Gdg6Pfn2EJp6uPDHGhgfy3143LqrrPhtmrwd3G+aI0qq0S5cuMXnyZPr06XM9id+IESN0Ej+t0rOmraWlUspS7jWpYGt3nyM0JpUP5vXCw9XZNoUqBfvXQsuhcPPb+loJDTDOIj788EMefPBB0tPTefnllxkwYIC9q6VpVis0UIjIa0qph4BvROR/EkJZMcNdpZWcnsWKnWHc2L4Rw9o3sl3BB9dBciQM+7sOEtp1t99+O19//TWDBg1i9erVtG3b1t5V0rQSKeqM4gvzb0lntqv01u4+S1JaFg/eZMN/2IRzsPlJCBhkpOjQarScnBxEBAcHByZMmMCNN97IHXfcofMzaVVSod9apdQ+824HpdQveW9Ah4qpnu0lXstkza9nGdWpMZ19vGxTqMUC6+8BBCa9p+eWqOFCQkIYNGjQ9SR+c+bM4a677tJBQquyrPnmLijguYW2rkhFWf3rWVIzs3nAlmcTe5fD+d0w5iWoW/FzemuVQ1ZWFs8//zzdunXj1KlTeHnZ6IeIptlZUX0UUzFmpWshIt/mWeQBJBb8qsot/momH/x2lnFdmtK+iadtCo09BVufhbZjoNtM25SpVTmHDh1i3rx5HDlyhKlTp/LWW2/RqJEN+780zY6K6qPYhzEHhS/wbp7nU4BD5Vmp8rJyZxhpWTncP8JGZxM52fDdHeDiDhPe1B3YNdjly5e5cuUK69evZ+LEifaujqbZVKGBQil1FjiLkS22you/msl//jjPzV2b0bqRDa5+jQmBz6YZndiT1+hkfzXQrl27OHr0KPfccw+jR48mNDSU2rVr27tammZzhfZRiMhO82+CiMTnuSWISHzFVdE21v1+jrSsHO4Z1to2Bf7yHKQlwMjnofNk25SpVQnJycncfffdDBkyhLfeeut6Ej8dJLTqqqjO7NzpThsADfPcch9XGVczsln3xzlu6tiYNo1tkAA38yqEbYMbpkH/v+kmpxpk06ZNdOrUiZUrV/Lggw/qJH5ajVDU8Njcq7H9AEelVA7QD7gDcK+AutnMF/sjSLyWxZ1DWtmmwDM/Q3Y6dBhvm/K0KiEiIoKJEyfi5eXF77//zmuvvYa7e5X6V9C0UrFmeOx6jGlQWwEfAG2AT8u1VjaUnWNh7W9n6RVQj57+9cpeoFLw2xvGMNjmOpFbdaeUYs+ePYCRxG/Lli0cPHiQPn362LlmmlZxrAkUFqVUFnAr8LZS6gHAxhNKl5+tIZeJTEhj4cCWtinw5I9w8RAMeRwcbZiWXKt0Ll68yKRJk+jXr9/1JH7Dhg3DxcXGMyBqWiVnTaDIFpEpwGzgB/M5G2XRK3+f7ougqZcrN3W0waikjFTY/AQ0aAs3TC17eVqlpJRi9erVdOzYkS1btvDqq6/qJH5ajWbNT+IFwN0YacbDRaQF8Fn5Vss2ohLT+PVMLH+7sQ2ODjbocN72T0iMgPn/1WcT1dhtt93Gt99+y5AhQ1i9ejWtW9topJymVVHFHu2UUsdEZCnQWkTaA6FKqRfKv2pl91VQBABTevqWvbALe2DvSui9BPz7lb08rVLJm8Rv0qRJjBw5ksWLF+v8TJqGdTPcDQJCgTXAWuC0iFT683CLRfFVUCQDWzfAz7uMk9SnJ8P390BdPxj+tG0qqFUax44dY8CAAdeT+M2ePVtnetW0PKz5T3gdGKuUGqCU6g+MA94s32qV3Z7wOKIS07itrGcTFgusvwviz8Kk5Xru62okMzOTZ599lh49ehAWFka9ejYYFadp1ZA1De0uSqkTuQ+UUiEiUumHfXx9MBKPWk6M6tSkbAUdXAcnf4BRL0LAQNtUTrO7AwcOMG/ePI4dO8aMGTN44403aNiwSl1HqmkVxppAcVBEVgAfm49nUsmTAl7LzOanY9Hc3LUZrs6OpS8o5TJs/Qc07wd977ZZ/TT7i4uLIzExkY0bNzJ+vL5wUtOKYk2guBNYCjxqPv4VeLvcamQDW45f5lpmDrd0L8PlHkrBhnshKw0mvKXTdFQD27dv5+jRoyxdupSRI0dy5swZXF1d7V0tTav0iuyjEJEuwGjgO6XUzebt30qp9IqpXul8eygKn7q16RXgXfpC9q6EM1tg1AvQUM9xXJUlJSVxxx13cOONN7J8+fLrSfx0kNA06xSVPfbvGOk7ZgI/i0hBM91VOjEp6ew+E8uk7s1wKO21E0e+hC1PGZMR9Vpk2wpqFWrjxo107NiR1atX8/DDD3PgwAGdxE/TSqiopqeZwA1Kqasi0hDYhDE8tlLbGHwJi6J0zU5KwfYXYdcrEDAIblmum5yqsIiICCZPnkz79u1Zv349vXr1sneVNK1KKqrpKUMpdRVAKRVbzLqVxvpDUXT28aR1o1KkE9/8pBEkus2CWd9CbT1csqpRSvH7778DfybxCwoK0kFC08qgqIN/SxH51rx9B7TK8/jbIl53nYiMFpFTIhIqIo8Xsd5kEVEiEljSHcjr7JWrHI1KYmLXUpxNJF+Efe8bQWLiO+BU6UcAa/lERkZy8803M2DAgOtJ/IYOHaqT+GlaGRXV9JR/2rZ3SlKwiDhizLV9ExAJ7BeRDXmvyTDX8wDuA/aWpPyC/BB8EYDxXZuW/MV7loPKgSGP6OamKsZisbBq1SoeeeQRsrOzWbZsGQMH6mteNM1Wipoz+5cylt0bIy9UOICIfA5MBE7kW++fwMvAI2XZmFKKDcEX6R3gTVOvEk5JmZ4EBz6EjpOgXkBZqqHZweTJk1m/fj033ngjq1atomVLG6WU1zQNKN9+Bx8gIs/jSPLNYyEiPQA/pdSPRRUkIktEJEhEgmJjYwtc59TlFM7EpDKhNGcTBz6EjGQYsLTkr9XsIjs7G4vFmIRx8uTJrFq1iq1bt+ogoWnlwG4d1CLiACwDHipuXaXU+0qpQKVUYGFpFjYGX8TRQRjTpYSBIjsT9qwwRjk1616y12p2ceTIEfr168eqVasAmDVrFosWLUJ0k6GmlQurA4WIlHTweRTGfNu5fM3ncnkAnYEdInIO6AtsKE2HtlKKH49con+r+jSoU8Jqnv4JUi5Cf302UdllZGTwzDPP0LNnT86fP69zM2laBbEmzXhvETkKnDEfdxURa1J47AfaiEgLM4ngNGBD7kKlVJJSqoFSKkApFQDsAW5WSgWVdCfOxKRyLu5a6RIAXj4GCLQYXPLXahVm//799OjRg+eee47p06cTEhLCrbfeau9qaVqNYE2up7eA8RhXaaOUChaRYcW9SCmVLSL3ApsBR2CtUuq4iDwHBCmlNhRdgvW2HI8GKN10p3GhULc5OOt0DpVZQkICqampbNq0iTFjxti7OppWo1gTKByUUufztf/mWFO4UmoTxhXdeZ8rcOYfpdRQa8osyLaTMdzg60VjzxIe7LMzIWI/NGpf2k1r5Wjbtm0cPXqU++67j5EjR3L69GmdfkPT7MCaPooIEekNKBFxFJH7gdPlXC+rJV7L5HBEIkPbNSr5iw98AEkXdD6nSiYxMZHFixczfPhwVq5ceT2Jnw4SmmYf1gSKu4AHgebAZYxO57vKs1Il8euZK1gUDGlbwo7NtETY8ZLRN9FmZPlUTiux77//no4dO7J27VoeffRRncRP0yqBYpuelFIxGB3RldKOU7F41Xamm1/dkr1w9zJIS4CRz+srsSuJCxcuMGXKFDp06MCGDRsIDCxTRhdN02yk2EAhIqsAlf95pdSScqlRCSil2HUmloFtGuBYkpTiV0KNlB1dp0HTruVXQa1YSil2797NoEGDaN68OVu3bqVv3746P5OmVSLWND1tBX4xb78BjYCM8qyUtUIupRCbksHQkjQ7/f42vNsLHF1g+DPlVzmtWBcuXGDcuHEMHjz4ehK/wYMH6yChaZWMNU1PX+R9LCIfAbvLrUYlcDgiEYA+Lepb94L0JNjxMgQMhDH/Bs9SpPvQysxisbBixQoee+wxlFK89dZbOomfplVi1gyPza8FUIoLFmzvZHQydWo54VvPyiSAB9ZBZgrc9E89JNaObr31Vr7//ntuuukm3n//fQICAuxdJU3TimBNH0UCf/ZROADxQKFzS1Skk5dSaNfEw7opT7MzjX6JFoOhWbfyr5z2F9nZ2Tg4OODg4MDUqVOZOHEi8+bN0/mZNK0KKLKPQoz/4q5AQ/NWTynVUin1ZUVUrihKKUKik+nQ1MqZ7I5/p3M62UlwcDB9+vTh/fffB2D69OnMnz9fBwlNqyKKDBRKKQVsUkrlmLf/Gf1kLxeT0klJz6Z9E8/iV1bK6MRu2B5ajyj/ymkApKen89RTTxEYGEhkZCRNmpQiF5emaXZnzainwyJS6fJvh1xMBrDujCJ8B1w+Cv3/pq+ZqCD79u2je/fuvPDCC8ycOZOQkBAmTZpk72ppmlYKhfZRiIiTUiob6I4xjWkYcBUQjJONHhVUxwKdjDYCRTtrzih+fxvqNIYuU8q5Vlqu5ORk0tLS+Omnnxg1apS9q6NpWhkU1Zm9D+gB3FxBdSmRkOgUmnu7UadWMf3xl49D2C9w4/+Bk04FUZ62bNnC8ePHeeCBBxgxYgSnTp3S6Tc0rRooqulJAJRSYQXdKqh+hTpzOYW2ja1odtq3CpxqQ+CC8q9UDZWQkMD8+fMZNWoUa9as0Un8NK2aKerneEMRebCwhUqpZeVQH6vkWBTn4q4xrLiMsZnX4Ng30HEiuHlXTOVqmG+//ZZ77rmH2NhYnnjiCZ5++mkdIDStmikqUDgCdTDPLCqTi4lpZGZbaNHAvegVT3wPGcnQY3bFVKyGuXDhAtOmTaNz585s2rSJ7t0r3ZgHTdNsoKhAcUkp9VyF1aQEzl65ClB8oDj0MXi3BP8BFVCrmkEpxa5duxgyZAjNmzdn27Zt9OnTB2dnZ3tXTdO0clJsH0VldD7OCBQBRQWK1Fg4/xvcMFUPibWR8+fPM2bMGIYOHXo9id/AgQN1kNC0aq6oQDG8wmpRQpeS0nFyEBrWKaItPPRnQEHb0RVWr+rKYrHwzjvv0KlTJ3bv3s3bb7/NoEGD7F0tTdMqSKFNT0qp+IqsSElcTs6gkUetonM8nf4JPJrq+SZsYNKkSWzcuJFRo0axcuVK/P397V0lTdMqUGmyx9rd5eR0Gnu5Fr5CThaEbYdOt+hmp1LKysrC0dERBwcHpk+fzm233cbs2bN1fiZNq4GsSeFR6VxOTqexRxGB4tyvxmintvqK4NI4ePAgvXv3ZsWKFYCRxG/OnDk6SGhaDVVlA0UjzyL6J45+A7U8odWNFVepaiAtLY0nnniC3r17Ex0djZ+fn72rpGlaJVDlmp6UguT0bBoU1pGdlQ4hG6DDBHC2ckIjjT179jB37lxOnz7NggULePXVV6lXr569q6VpWiVQ5QJFtsXIdF5ooDiz2Wh20gkAS+Tq1atkZWXx888/M2KETsWuadqfqmCgsADg7e5S8ApBa43RTi0GV2CtqqaffvqJ48eP89BDDzF8+HBOnjyJi0sh76umaTVWleujyM7JPaMo4IB28ZAx90SfO8HBsWIrVoXExcUxd+5cxowZw7p168jMzATQQULTtAJVuUCRYzY91SvojOLgf8DZHQLnV3CtqgalFF9//TUdO3bk008/5amnnmL//v06QGiaVqQq1/SUY87G6ulaQNqIi4fBtye4elVwraqGCxcuMGPGDG644Qa2bNlC1676YkRN04pXZc8oPFzzxbicbIg5AU1usEOtKi+lFNu2bSDM+zoAABnDSURBVAPA39+fHTt2sGfPHh0kNE2zWpULFBaLwsXJAVfnfH0QcaGQnQ5NutinYpXQ2bNnGTlyJMOHD7+exK9///44OVW5E0lN0+yoygWKHKXwzH82ARB91PirAwU5OTm8+eabdO7cmb1797J8+XKdxE/TtFKrcj8tcywKj4L6J6KPgGMtaNC24itVyUycOJEff/yRsWPHsmLFCn2FtaZpZVLlAoXFUsQZRaMO4Fgz50bIm8Rv9uzZTJ8+nRkzZuj8TJqmlVm5Nj2JyGgROSUioSLyeAHLHxSREyJyRER+EZFi81fnqALOKJQyziiadLZd5auQoKAgAgMDWb58OQBTp05l5syZOkhommYT5RYoRMQReBcYA//f3r2HR1Vejx7/LkMIolxaEJEEASEgSQiRmxqgIqCAgBS5KBUEETjUWgIqjx4rVfkV8cD5tU9boQqkgAUjhSNIlRQUYoBoJCDXAALKLZGfhUi4CAES1vljT0JuJJPLzGTC+jzPPJnZsy9r3kxm5X3f2WsTBowQkbBCq20HOqlqJLACmFXafnOuKrcGFepRZB6DCxnQpENlhO43Ll68yEsvvcS9997LyZMn7ToRxhiP8GSPogtwSFW/U9XLwAfAoPwrqGqCql5wPUwGQkrb6VWFWwsPPaVvc34Gd6xw0P7iyy+/pH379syaNYuxY8eyd+9eBgwY4OuwjDHVkCfnKIKB4/kepwH3lrD+M0B8cU+IyARgAkBQ41ZFexTff+1MZN8eXpF4/crFixe5evUqn332Gb16Vdmr1hpjqoEqMZktIiOBTsADxT2vqvOAeQBBd4RqkUSR/jXcEVntJ7LXrFlDamoqU6dOpWfPnuzbt4/AwOr9mo0xvufJoad0IP/3MkNcywoQkd7A74BHVfWSOzu+pXCi+GFPtb429qlTpxg5ciT9+/dn6dKleUX8LEkYY7zBk4kiBQgVkRYiUhN4AlidfwURuQd4FydJ/MfdHdcKzBf21RzIOgO1G1ZK0FWJqvLBBx/Qtm1b/vnPf/Laa6+xZcsWK+JnjPEqjw09qWq2iDwHrAUCgL+raqqITAe2qupqYDZwK7Dc9VXOY6r6aGn7DqqRr3zHpXPOz1p1K/kV+N6xY8cYPXo07du3JzY2lnbt7KxzY4z3eXSOQlXXAGsKLft9vvvlupRazRr5ehS5iSKoTnl2VeWoKuvXr6d37940a9aMxMREOnfuTECAXV/DGOMbVWIyu6wKJoqzzs8g/+9RfPvtt4wfP56EhAQ+//xzHnjgAe677z5fh2WqiStXrpCWlkZWVpavQzEeVKtWLUJCQip1DtMvE0VQcT0KPx56yi3i9+qrrxIYGMi7775rRfxMpUtLS6NOnTo0b97cztqvplSVjIwM0tLSaNGiRaXt1y8TRYEeRZb/9ygGDhxIfHw8AwYM4G9/+xshIaWed2hMmWVlZVmSqOZEhAYNGnDy5MlK3a9/JooA/x96unz5MjVq1OCmm25izJgxjBo1iieeeML+iI1H2fur+vPE79jvrkcBUOOmfA2Rlyj8ZzJ7y5YtdOzYkblz5wIwfPhwRowYYX/ExpgqyT8TRf4eRdYZ5+fN9X0TTBlcuHCBF154gfvvv5/Tp0/TsmVLX4dkjNetWrUKEWH//v15yz7//PMitcrGjBnDihUrAGci/uWXXyY0NJQOHTpw//33Ex9fsOLP4MGDiYqKolWrVtSrV4+oqCiioqL44osv3I5tzpw5LF26tAKvrmQZGRn06tWL0NBQ+vTpw5kzZ4pd7/nnnyc8PJy2bdsyZcoUVJ1LQHfr1o02bdrkvbaMjAyPxZqffyaK/D2KrDMQUBNq1PJdQG7YvHkz7dq1449//CPjx48nNTWVfv36+TosY7wuLi6Obt26ERcX5/Y206ZN48SJE+zZs4evv/6aVatWce7cuQLrrFy5kh07drBgwQK6d+/Ojh072LFjB9HR0QXWy87Ovu5xfvOb3/Dkk0+W7QWVwYwZM+jXrx8HDx6ke/fuzJpVtGD2xo0b2bp1K7t372b37t0kJSWRlJSU9/yyZcvyXluDBg08Fmt+fjlHEVA4UdSqB1V82Cb3wkIJCQn06NHD1+GYG9wb/0pl7/dnK3WfYU3q8trAkgtznj9/ns2bN5OQkMDAgQN54403St3vhQsXmD9/PocPHyYoKAiA22+/neHDh7sdW0hICCNHjmTt2rW88sorZGRkEBsby+XLl2ndujXvvfceN998M6+++ioNGzZk8uTJdOvWjW7durFhwwbOnDnDwoULiySdsvroo49ITk4GYPTo0fTt25cZM2YUWEdEyMrK4vLly+Tk5JCdnU2jRo0qdNyK8sseRWDhoada9XwXTAn+9a9/5f3H8OCDD7J3715LEuaG9tFHH9G3b19at25NgwYN2LZtW6nbHDp0iDvvvJO6dSv2hZVGjRqxfft2hg0bxrBhw0hJSWHnzp20bNmSRYsWFbuNqrJlyxZmz57N9OnTizyfmZmZNwxU+PbNN98UWT8jI4PbbrsNgODgYE6cOFFkne7duxMdHU3jxo1p0qQJAwcOpHXra5d4HjVqFFFRUbz55pvlbImyqz49iirk5MmTxMTEEBcXR1RUFJMnT6ZmzZrUqOGXzW2qodL+8/eUuLg4YmJiAHjiiSeIi4ujY8eO1/0iR2V+wePxxx/Pu79r1y5+//vfk5mZyblz5657LZfHHnsMgI4dO3LkyJEiz9evX58dO3ZUWowA33zzDd9++y3p6enk5OTQu3dv+vTpQ3R0NMuWLSM4OJizZ88yePBgmjdvzq9+9atKPX5x/PKTKzAg35vnYmaVSRSqSlxcHJMmTeLs2bNMnz6dl156yYr4GQP8+OOPbNiwgd27dyMi5OTkICLMnj2bBg0acPr06SLrN2zYkFatWnHs2DHOnj1boV7FLbfcknf/qaeeIj4+noiICBYsWJA3HFRY7lBXQEBAsXMbmZmZ1x0lWLZsGW3atCmwLPcch9tuu4309HTuuOOOItt9+OGHREdH58Xbt29fkpOTiY6OJjg4GIC6desyYsQItmzZ4pVE4ZdDT1W1R3Hs2DGefvppWrVqxfbt25k2bZolCWNcVqxYwahRozh69ChHjhzh+PHjtGjRgk2bNhEaGsr333/Pvn37ADh69Cg7d+4kKiqK2rVr88wzzxATE5NXYv/kyZMsX7683LH89NNPNG7cmCtXrvD++++Xez+5PYriboWTBMCjjz7K4sWLAVi8eDGDBg0qss6dd95JYmIi2dnZXLlyhcTERNq2bcuVK1c4deoU4Mx5fvLJJ0RERJQ79rLwy0RRleYorl69ytq1awFo1qwZmzZtIikpifDwG+dqe8a4Iy4ujsGDBxdYNmTIEOLi4ggKCmLJkiU8/fTTREVFMXToUBYsWEC9es7f9h/+8Aduu+02wsLCiIiIYMCAARXqXUyfPp3OnTvTtWtXwsLCKvS6yuKVV17hk08+ITQ0lI0bNzJ16lQAvvrqKyZOnAg4Q3JNmzYlMjKS9u3b06VLF/r160dWVhZ9+vTJW968eXPGjh3rlbgl9/u5/iLojlBNP7iHhrc6XUL+qxHcNxEeKjrR5GkHDx5k/PjxJCYmkpiYyC9+8Quvx2CMu/bt20fbtm19HYbxguJ+1yKyTVU7lWd/ftmjyDuP4koW5Fzyeo8iOzub2bNnExkZyY4dO4iNjbUifsaYassvJ7PzzszOPSvby4liwIABrF27lkGDBjF37lyaNGni1eMbY4w3+WeiyO1R5CUKz5fvuHTpEoGBgdx0002MGzeOsWPHMmzYMKvPZIyp9vx76MlLPYrk5GQ6dOjAnDlzABg6dCjDhw+3JGGMuSH4ZaII8FKi+Omnn5gyZQrR0dGcO3eO0NBQjxzHGGOqMr8behLyna2Zlen89ECi2LRpE6NHj+bw4cM8++yzzJw5s8IlBIwxxh/5ZY8ijwfnKLKzswkMDCQxMZE5c+ZYkjCmklTlMuMAGzZsuO6Z2mWVkpJCREQErVq1YsqUKcWuc/r0afr370/79u0JDw/nvffey3suNjaW0NBQQkNDWbJkSaXEVB5+lygKzAtU8tDTqlWrmDlzJuAU8UtNTbVzI4ypZL4uM16aykwUEydOZOHChRw8eJDU1FQ+/fTTIuv89a9/JSoqip07d7JhwwZiYmLIzs7m1KlTzJgxg5SUFJKTk5k2bdp1r1/haX459JQn6wwEBEFgxa5F8cMPP/Db3/6W5cuX06FDB1544QUr4meqt/iX4X92V+4+G7eDfm+VuIqvyoynpKTw4osvcv78eRo1asSiRYu4/fbb+dOf/sT8+fOpUaMGkZGRvPHGGyxYsICAgAAWLVrE3Llzy11a/Pjx42RlZdG5c2fAqfq6atUqHnrooQLriUhe0jt//jwNGzYkICCA+Ph4+vXrR/36zohJz549WbduHcOGDStXPBXhd5+EBb5odPZ7uPX2cu9LVVmyZAmTJ0/m/PnzzJgxg6lTpxIYGFjxQI0xRRRXZrxjx44lblPRMuOXLl0iJiaG1atX07BhQ5YuXcq0adOYN28es2bN4ujRo9SsWZPMzEzq16/PuHHj8q5JUdhnn33Giy++WGR5nTp12LRpU4Fl6enpNG3aNO9xSEgI6enpRbaNiYlhwIABNGnShLNnz7JixQpExO3tvcHvEkUBP34LDe4q9+bHjh1j3LhxdOrUidjYWO6+++5KDM6YKqyU//w9xRdlxvft20dqaiq9e/cGICcnh5CQEADCw8MZOXIkgwYN4pe//GWp++rdu3ellxVfs2YNXbp0ITExkQMHDtC3b192767k3l4F+V2iKPDG+fE7CB98/ZWLkVvEr1+/fjRr1oykpCTuueceAgICKjlSY0x+viozrqpERkYW+Y8fYO3atSQmJrJ69WrefPNNdu3aVeK+ytKjCA4O5vjx43mP09LS8sqE57dw4UJef/11RIQ2bdrQtGlTDhw4QHBwcIG5krS0NK9Viy3M/yazc+9c+BEunoafu9+jOHDgAD169OCRRx4hMTERgE6dOlmSMMYLfFVmPCwsjPT0dLZs2QLA5cuXSU1NJScnh7S0NHr27MmsWbM4deoUFy5coE6dOkUmynPl9igK34pLQk2bNiUoKIiUlBRUlX/84x/XLSu+fv16AE6cOMGhQ4do0aIFffv2JT4+nszMTDIyMli/fj0PP/ywW6+50qmqX93qhLRWVVVN26r6Wl3VfZ9oaa5cuaJvvfWWBgUFaf369XXhwoV69erVUrczpjrZu3evT4/fo0cPjY+PL7Dsz3/+s06cOFFVVTdv3qz33nuvtm/fXjt16qTr1q3LW+/SpUs6depUbdmypYaHh2uXLl303//+d7HHSUhI0P79+xdYtm3bNu3atau2a9dOw8LCNDY2VrOysjQ6OlrbtWun4eHhOmvWLFVV3bdvn0ZERGhUVJQmJSVV6DUnJydrWFiY3nXXXTpp0qS85W+//bbOnz9fVVXT0tK0d+/eeXEsXbo0b7158+Zpy5YttWXLlrp48WK3j1vc7xrYquX83PW7MuN1m7bRs8e/gV3L4cNx8OxX0KjkuYU+ffqwbt06HnvsMebMmUPjxo29FK0xVYeVGb9xVHaZcf+bo8gdfDqyEQJrw8+aF7teVlYWgYGBBAQEMGHCBCZMmMCQIUO8F6gxxlQTfjdHAcDlC7BnJYT9sthzKJKSkoiKisor4jdkyBBLEsYYU07+mSj2fwyXz0FUwYuKnz9/nkmTJtG9e3eysrKsm21MIf421GzKzhO/Y/9MFNuXQP1m0Kxr3qLExEQiIiJ4++23ee6559izZ0+RMyCNuZHVqlWLjIwMSxbVmKqSkZFBrVoVq1ZRmN/NUdTgChzeCD1ehpsK5rnatWuzadMmunbtep2tjblxhYSEkJaWxsmTJ30divGgWrVq5Z1QWFn87ltPLUIa6eFxlyBmFx8mbGP//v288sorgHPGpZ0TYYwxRVXkW08eHXoSkb4i8o2IHBKRl4t5PkhElrme/0pEmpe2z7p6lv/5WWeGjn+BIUOGsHLlyryTcCxJGGNM5fNYohCRAGAO0A8IA0aISFih1Z4BTqtqK+BPwP8pbb9nf7pM22nJfPzxx8ycOZMvvviCmjVrVnb4xhhjXDzZo+gCHFLV71T1MvABUPj89UHAYtf9FUAvKaUK2NEzSkRkJDt37uTll1+2Sq/GGONhnpzMDgaO53ucBtx7vXVUNVtEzgANgFP5VxKRCcAE18NLm5O+3GOVXgFoSKG2uoFZW1xjbXGNtcU1bcq7oV9860lV5wHzAERka3knZKoba4trrC2usba4xtriGhHZWt5tPTn0lA40zfc4xLWs2HVEpAZQD8jwYEzGGGPKyJOJIgUIFZEWIlITeAJYXWid1cBo1/2hwAb1t+/rGmNMNeexoSfXnMNzwFogAPi7qqaKyHSccrergVjgHyJyCPgRJ5mUZp6nYvZD1hbXWFtcY21xjbXFNeVuC7874c4YY4x3+WetJ2OMMV5jicIYY0yJqmyi8ET5D3/lRls8LyJ7RWSXiKwXkWa+iNMbSmuLfOsNEREVkWr71Uh32kJEhrveG6ki8r63Y/QWN/5G7hSRBBHZ7vo7ecQXcXqaiPxdRP4jInuu87yIyF9c7bRLRDq4tePyXkPVkzecye9vgbuAmsBOIKzQOs8C77juPwEs83XcPmyLB4Harvu/vpHbwrVeHWAjkAx08nXcPnxfhALbgZ+5Hjfyddw+bIt5wK9d98OAI76O20Nt8QugA7DnOs8/AsQDAtwHfOXOfqtqj8Ij5T/8VKltoaoJqnrB9TAZ55yV6sid9wXAf+HUDcvyZnBe5k5bjAfmqOppAFX9j5dj9BZ32kKBuq779YDvvRif16jqRpxvkF7PIOA9dSQD9UXkjtL2W1UTRXHlP4Kvt46qZgO55T+qG3faIr9ncP5jqI5KbQtXV7qpqn7izcB8wJ33RWugtYgkiUiyiPT1WnTe5U5bvA6MFJE0YA3wW++EVuWU9fME8JMSHsY9IjIS6AQ84OtYfEFEbgL+CIzxcShVRQ2c4aceOL3MjSLSTlUzfRqVb4wAFqnqf4vI/Tjnb0Wo6lVfB+YPqmqPwsp/XONOWyAivYHfAY+q6iUvxeZtpbVFHSAC+FxEjuCMwa6uphPa7rwv0oDVqnpFVQ8DB3ASR3XjTls8A/wTQFW/BGrhFAy80bj1eVJYVU0UVv7jmlLbQkTuAd7FSRLVdRwaSmkLVT2jqg1VtbmqNseZr3lUVctdDK0Kc+dvZBVObwIRaYgzFPWdN4P0Enfa4hjQC0BE2uIkihvxmrCrgadc3366DzijqidK26hKDj2p58p/+B0322I2cCuw3DWff0xVH/VZ0B7iZlvcENxsi7XAwyKyF8gBpqpqtet1u9kWLwDzRWQKzsT2mOr4j6WIxOH8c9DQNR/zGhAIoKrv4MzPPAIcAi4AT7u132rYVsYYYypRVR16MsYYU0VYojDGGFMiSxTGGGNKZInCGGNMiSxRGGOMKZElClPliEiOiOzId2tewrrNr1cps4zH/NxVfXSnq+RFm3LsY6KIPOW6P0ZEmuR7boGIhFVynCkiEuXGNpNFpHZFj21uXJYoTFV0UVWj8t2OeOm4T6pqe5xik7PLurGqvqOq77kejgGa5HtunKrurZQor8U5F/finAxYojDlZonC+AVXz2GTiHztukUXs064iGxx9UJ2iUioa/nIfMvfFZGAUg63EWjl2raX6xoGu121/oNcy9+Sa9cA+b+uZa+LyIsiMhSn5tZS1zFvdvUEOrl6HXkf7q6ex9vljPNL8hV0E5G/ichWca498YZr2SSchJUgIgmuZQ+LyJeudlwuIreWchxzg7NEYaqim/MNO610LfsP8JCqdgAeB/5SzHYTgT+rahTOB3Waq1zD40BX1/Ic4MlSjj8Q2C0itYBFwOOq2g6nksGvRaQBMBgIV9VI4A/5N1bVFcBWnP/8o1T1Yr6n/59r21yPAx+UM86+OGU6cv1OVTsBkcADIhKpqn/BKan9oKo+6Crl8SrQ29WWW4HnSzmOucFVyRIe5oZ30fVhmV8g8LZrTD4Hp25RYV8CvxOREOBDVT0oIr2AjkCKq7zJzThJpzhLReQicASnDHUb4LCqHnA9vxj4DfA2zrUuYkXkY+Bjd1+Yqp4Uke9cdXYOAncDSa79liXOmjhlW/K303ARmYDzd30HzgV6dhXa9j7X8iTXcWritJsx12WJwviLKcAPQHucnnCRixKp6vsi8hXQH1gjIv8L50pei1X1f7txjCfzFxAUkZ8Xt5KrtlAXnCJzQ4HngJ5leC0fAMOB/cBKVVVxPrXdjhPYhjM/8VfgMRFpAbwIdFbV0yKyCKfwXWECfKqqI8oQr7nB2dCT8Rf1gBOu6weMwin+VoCI3AV85xpu+QhnCGY9MFREGrnW+bm4f03xb4DmItLK9XgUkOga06+nqmtwElj7YrY9h1P2vDgrca40NgInaVDWOF0F7aYB94nI3ThXb/sJOCMitwP9rhNLMtA19zWJyC0iUlzvzJg8liiMv5gLjBaRnTjDNT8Vs85wYI+I7MC5LsV7rm8avQqsE5FdwKc4wzKlUtUsnOqay0VkN3AVeAfnQ/dj1/42U/wY/yLgndzJ7EL7PQ3sA5qp6hbXsjLH6Zr7+G+cqrA7ca6PvR94H2c4K9c84N8ikqCqJ3G+kRXnOs6XOO1pzHVZ9VhjjDElsh6FMcaYElmiMMYYUyJLFMYYY0pkicIYY0yJLFEYY4wpkSUKY4wxJbJEYYwxpkT/H0lS0HJkauGxAAAAAElFTkSuQmCC\n"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"## Adding Review Summary To the Reviews as a Feature"},{"metadata":{"trusted":true},"cell_type":"code","source":"listA = [preprocessed_summary[i] +\" \" + preprocessed_reviews[i] for i in range(len(preprocessed_reviews))]\ndwA_train,dwA_test,y_train,y_test=ms.train_test_split(listA, y, stratify=y, test_size=0.3, random_state=0)","execution_count":73,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"trainA_list=[]\nfor sent in dwA_train:\n    trainA_list.append(sent.split())\nw2vA_model=Word2Vec(trainA_list,min_count=5,size=50, workers=4)\nprint(w2vA_model.wv.most_similar('great'))\nprint('='*50)\nprint(w2vA_model.wv.most_similar('worst'))\nw2vA_words = list(w2vA_model.wv.vocab)","execution_count":74,"outputs":[{"output_type":"stream","text":"[('awesome', 0.8339515328407288), ('terrific', 0.7918261885643005), ('excellent', 0.7878096699714661), ('fantastic', 0.7748837471008301), ('wonderful', 0.754327654838562), ('good', 0.7311663627624512), ('amazing', 0.6998000144958496), ('perfect', 0.696079432964325), ('nice', 0.6359057426452637), ('decent', 0.6150010824203491)]\n==================================================\n[('greatest', 0.7539382576942444), ('nastiest', 0.7430515289306641), ('best', 0.7420390248298645), ('closest', 0.6962352395057678), ('tastiest', 0.6634582281112671), ('experienced', 0.6421462297439575), ('ive', 0.6411201357841492), ('awful', 0.6400278210639954), ('tasted', 0.6336954832077026), ('ever', 0.6260101199150085)]\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# average Word2Vec\n# compute average word2vec for each review in train dataset.\n#train dataset\nd_trainA_w2 = [];\nfor sent in tqdm(trainA_list): # for each review/sentence\n    sent_vec = np.zeros(50) # as word vectors are of zero length 50, you might need to change this to 300 if you use google's w2v\n    cnt_words =0; # num of words with a valid vector in the sentence/review\n    for word in sent: # for each word in a review/sentence\n        if word in w2vA_words:\n            vec = w2vA_model.wv[word]\n            sent_vec += vec\n            cnt_words += 1\n        else:\n            sent_vec+=0\n    if cnt_words != 0:\n        sent_vec /= cnt_words\n    d_trainA_w2.append(sent_vec)\nprint(len(d_trainA_w2))\nprint(len(d_trainA_w2[0]))","execution_count":75,"outputs":[{"output_type":"stream","text":"100%|██████████| 32249/32249 [01:04<00:00, 497.30it/s]","name":"stderr"},{"output_type":"stream","text":"32249\n50\n","name":"stdout"},{"output_type":"stream","text":"\n","name":"stderr"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# average Word2Vec\n# compute average word2vec for each review in train dataset.\n#test dataset\ntestA_list=[]\nfor sent in dwA_test:\n    testA_list.append(sent.split())\nd_testA_w2 = [];\nfor sent in tqdm(testA_list): # for each review/sentence\n    sent_vec = np.zeros(50) # as word vectors are of zero length 50, you might need to change this to 300 if you use google's w2v\n    cnt_words =0; # num of words with a valid vector in the sentence/review\n    for word in sent: # for each word in a review/sentence\n        if word in w2vA_words:\n            vec = w2vA_model.wv[word]\n            sent_vec += vec\n            cnt_words += 1\n        else:\n            sent_vec+=0\n    if cnt_words != 0:\n        sent_vec /= cnt_words\n    d_testA_w2.append(sent_vec)\nprint(len(d_testA_w2))\nprint(len(d_testA_w2[0]))","execution_count":76,"outputs":[{"output_type":"stream","text":"100%|██████████| 13822/13822 [00:28<00:00, 484.09it/s]","name":"stderr"},{"output_type":"stream","text":"13822\n50\n","name":"stdout"},{"output_type":"stream","text":"\n","name":"stderr"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Please write all the code with proper documentation\nomdA_w2,omssA_w2 = cv_DT(d_trainA_w2, y_train)","execution_count":77,"outputs":[{"output_type":"stream","text":"Time elapsed : 214.72 Seconds\n optimal parameters using Grid Search are {'max_depth': 100, 'min_samples_split': 500}\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#plot heatmap on train and test data\nplotHeatMap(d_trainA_w2,y_train,d_testA_w2,y_test)","execution_count":78,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 2 Axes>","image/png":"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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#model with optimal max_depth and optimal min_samples_split\ndt_A_w2=DecisionTreeClassifier(max_depth= omdA_w2 , min_samples_split = omssA_w2,class_weight='balanced')\ndt_A_w2.fit(d_trainA_w2,y_train)","execution_count":79,"outputs":[{"output_type":"execute_result","execution_count":79,"data":{"text/plain":"DecisionTreeClassifier(class_weight='balanced', criterion='gini',\n            max_depth=100, max_features=None, max_leaf_nodes=None,\n            min_impurity_decrease=0.0, min_impurity_split=None,\n            min_samples_leaf=1, min_samples_split=500,\n            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n            splitter='best')"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#report on train data\nreport(d_trainA_w2,y_train,dt_A_w2)","execution_count":80,"outputs":[{"output_type":"stream","text":"              precision    recall  f1-score   support\n\n           0       0.42      0.85      0.56      5314\n           1       0.96      0.77      0.85     26935\n\n   micro avg       0.78      0.78      0.78     32249\n   macro avg       0.69      0.81      0.71     32249\nweighted avg       0.87      0.78      0.81     32249\n\n\nThe value of TPR is 0.767\nThe value of TNR is 0.846\nThe value of FPR is 0.154\nThe value of FNR is 0.233\n\nThe roc auc score is 0.896223\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 2 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#report on test data\nreport(d_testA_w2,y_test,dt_A_w2)","execution_count":81,"outputs":[{"output_type":"stream","text":"              precision    recall  f1-score   support\n\n           0       0.40      0.81      0.53      2278\n           1       0.95      0.76      0.84     11544\n\n   micro avg       0.77      0.77      0.77     13822\n   macro avg       0.67      0.78      0.69     13822\nweighted avg       0.86      0.77      0.79     13822\n\n\nThe value of TPR is 0.758\nThe value of TNR is 0.806\nThe value of FPR is 0.194\nThe value of FNR is 0.242\n\nThe roc auc score is 0.855170\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 2 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAXUAAAEKCAYAAADticXcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAG9VJREFUeJzt3XucVVXdx/HPd0YFBBQQIwSfuIgSphIaKngDFfCKmRX2+IiXRPOS9wQ1b2mpaaavjMJEMU1Cy0RD0BAvqAheEAVRJy/JKJByU242Z37PH2czHogZzsicOYft9+1rvTh77bX3XtvX8JvFb6+ztiICMzNLh7Jid8DMzBqOg7qZWYo4qJuZpYiDuplZijiom5mliIO6mVmKOKibmaWIg7qZWYo4qJuZpchmxe5Abfp1PNhfdbX/8vTCOcXugpWgqs8qtbHn+M9Hb+cdczZv22Wjr1coHqmbmaVIyY7UzcwaVXWm2D1oEA7qZmYAmapi96BBOKibmQER1cXuQoNwUDczA6h2UDczSw+P1M3MUsQPSs3MUsQjdTOz9AjPfjEzSxE/KDUzSxGnX8zMUsQPSs3MUsQjdTOzFPGDUjOzFPGDUjOz9IhwTt3MLD2cUzczSxGnX8zMUsQjdTOzFMn8p9g9aBAO6mZm4PSLmVmqOP1iZpYiHqmbmaWIg7qZWXqEH5SamaWIc+pmZini9IuZWYqkZKReVuwOmJmVhOrq/EsdJO0kaWZOWSbpHElXSKrMqT8055gRkiokvSFpYE79oKSuQtLwfG7DI3UzM2iwkXpEvAH0BJBUDlQCDwAnAjdFxA257SX1AIYAOwPbAf+QtGOy+1bgYGAeMEPS+IiYU9f1HdTNzACqCvKSjAOBf0bEe5JqazMYGBsRq4F3JFUAvZN9FRHxNoCksUnbOoO60y9mZpAdqedb8jcEuDdn+0xJsySNltQ6qesAvJ/TZl5SV1t9nRzUzcygXjl1ScMkvZBThq17OklbAEcC9yVVI4GuZFMzHwI3FuI2nH4xM4N6jcAjYhQwagPNDgFeiogFyTEL1uyQdBvwcLJZCWyfc1zHpI466mvlkbqZGTTY7Jccx5KTepHUPmfft4HXks/jgSGSmkjqDHQDpgMzgG6SOiej/iFJ2zp5pG5mBg06T11Sc7KzVk7Nqb5eUk8ggHfX7IuI2ZLGkX0AWgWcEckLUyWdCUwCyoHRETF7Q9d2UDczgwad/RIRy4Ft1qn7vzraXwNcs576CcCE+lzbQd3MDCCi2D1oEA7qZmbgtV/MzFLFQd3MLEVSsqCXg7qZGUAmU+weNAgHdTMzcPrFzCxVHNTNzFLEOXUzs/SIas9TNzNLD6dfzMxSxLNfzMxSJCUjdS+9WyQ/ueF8/jpzHKP/8fmSzF17dOXW8bdw26Tf8bu/30r3njsB0HfA3vzhsd/X1H/jWzsD0LPPbtw26Xc1ZVLF3+k7sE9R7scKo6ysjBnTJ/HgA2PWqr/pV1exZNGbNdv77rMn05+fyKoV73H00Yc1djfToeGX3i0Kj9SLZOJ9j/LAnQ8y4tc/qak79ZJTGHPTH5k+ZQZ79u/NqZecwrnfvYAXp77MM48+B0CXr3fm8pGXMvSAk5n57CucMvA0AFq2asndU+/khSdfLMr9WGH8+KwfMnfuW2zVsmVN3e69dqV161ZrtfvX+5Wc/MNzOe/c0xq7i+mRkgW9CjZSl9Rd0kWSbknKRZK+XqjrbWpmPf8qy5Z8snZlBM1bbAlA85bN+XjBxwCsWrGqpknTZk3X+7O3/2H7Mn3KDFavWl2wPlvj6tChPYceciCjR3/+isuysjKuu/anDB9x9Vpt33tvHq+++jrVJT6KLGkeqddO0kVk3/oxluwbPCD7KqZ7JY2NiGsLcd1N3W+uGMn19/yC0346DJWVcdbgs2v27TOoL6cMP4lWbVsx4vhL/+vYfkcewH2j/tKY3bUC+9WNVzJ8xNW0bNmipu6M00/koYcfZf78hUXsWUp5SmOdTgZ2joj/5FZK+hUwG3BQX4/Bxx/Ob68cyVMTpnLA4ftx4Q3nc8GxFwEwdeIzTJ34DLvuuQsnXXhCTT1Am6+0oUv3zsx48oVidd0a2GGHHsTChR/x0suvsv9+ewPQvn07jvnO4fQ/6Jgi9y6lUjL7pVDpl2pgu/XUt0/2rVfuG7o/WD6vQF0rXQOOGcBTE6YC8MTDT9U8KM016/lXaf8/7dmq9VY1df2O2J+pE58hU5WOH0qDPn324IjDB1Dx5jTuufu39OvXl1kzH6dr10688fozVLw5jS23bMbcOVOL3dXUiOrqvEspK1RQPweYLOkRSaOSMhGYDJxd20ERMSoi9oiIPbZr3rFAXStdHy/4mN323hWAXn2/SeU72ReHb9fp89+P3b6xA5s32Zxli5fV1PUf3I/JD05p3M5aQV1y6bV06rIHO+y4F/973OlMmfIM27bbmY7/80122HEvdthxL1asWEn3HvsUu6vpUR35lxJWkPRLREyUtCPQG+iQVFcCM9a8UPXL7tLfXEzPvXdl6zZbM27Gn7jzxru44Se/4qwrT6d8s3I+W/0ZN170awD2O3RfBn7nIKqqMqxetZqrfvT5Q7J2Hdux7Xbb8spzs4p1K1YC9th9N+6/73Zat96aww87mMsvO5/devYvdrc2LSlZ+0VRotN4+nU8uDQ7ZkX19MI5xe6ClaCqzyq1sedYftX/5h1zml92z0Zfr1A8T93MDCAlz6Qc1M3MIDXpFwd1MzMo+Qeg+XJQNzODkp+qmC8HdTMz8EjdzCxVHNTNzFIkJcsEOKibmeF3lJqZpYuDuplZiqRk9otfZ2dmBg26oJekVpLulzRX0uuS9pbURtJjkt5K/mydtFXyIqEKSbMk9co5z9Ck/VuShuZzGw7qZmbQ0Ks03gxMjIjuwG7A68BwYHJEdCO7Yu3wpO0hQLekDANGAkhqA1wO7El2ccTL1/wiqIuDupkZEJnqvEtdJG0N7AfcDhARn0XEEmAwsOYN4mOAo5LPg4G7Imsa0EpSe2Ag8FhELIqIxcBjwKAN3YeDupkZ1GuknvtCn6QMyzlTZ+DfwB2SXpb0B0nNgXYR8WHSZj7QLvncAXg/5/h5SV1t9XXyg1IzM+o3pTEiRgGjatm9GdALOCsinpd0M5+nWtYcH5IKMt3GI3UzM2jInPo8YF5EPJ9s3082yC9I0iokf655e3glsH3O8R2Tutrq6+SgbmYG2bcn51vqEBHzgfclrXnJ8IHAHGA8sGYGy1DgweTzeOD4ZBbMXsDSJE0zCRggqXXygHRAUlcnp1/MzICoatB56mcB90jaAngbOJHsIHqcpJOB94DvJW0nAIcCFcCKpC0RsUjSz4AZSburImLRhi7soG5mBhscgddHRMwE9ljPrgPX0zaAM2o5z2hgdH2u7aBuZobXfjEzS5d0rBLgoG5mBh6pm5mli0fqZmbpEVXF7kHDcFA3MwPCI3UzsxRxUDczSw+P1M3MUsRB3cwsRSKjYnehQTiom5nhkbqZWapEtUfqZmap4ZG6mVmKRHikbmaWGh6pm5mlSLVnv5iZpYcflJqZpUjqg7qkh4BaFxiOiCML0iMzsyKIdCynXudI/YZG64WZWZGlfqQeEU82ZkfMzIrpSzOlUVI34BdAD6DpmvqI6FLAfpmZNapMSma/lOXR5g5gJFAF9APuAu4uZKfMzBpbhPIupSyfoN4sIiYDioj3IuIK4LDCdsvMrHFFtfIupSyfKY2rJZUBb0k6E6gEWhS2W2ZmjSsts1/yGamfDWwJ/BjYHfg/YGghO2Vm1ti+NCP1iJiRfPwUOLGw3TEzK45MdT5j3NKXz+yXKaznS0gR0b8gPTIzK4K0pF/yyalfkPO5KfAdsjNhzMxSo7rEZ7XkK5/0y4vrVD0jaXqB+mNmVhSlPlUxXxtMIklqk1PaShoIbN0IfTMzazQR+Zd8SCqX9LKkh5PtOyW9I2lmUnom9ZJ0i6QKSbMk9co5x1BJbyUlrwkq+aRfXiSbUxfZtMs7wMn53dYXt+tm2xT6ErYJevSDp4vdBUupAqRfzgZeB7bKqbswIu5fp90hQLek7En2y557SmoDXA7sQTYGvyhpfEQsruui+QT1r0fEqtwKSU3yOM7MbJPRkLNfJHUk+yXNa4DzNtB8MHBXRAQwTVIrSe2BA4DHImJRcs7HgEHAvXWdLJ+7eHY9dc/lcZyZ2SYj6lHy8GvgJ8C6L8m7Jkmx3JQzOO4AvJ/TZl5SV1t9nWoN6pK+Kml3oJmkb0rqlZQDyH4ZycwsNapDeRdJwyS9kFOGrTmPpMOBheuZZDIC6A58C2gDXFSI+6gr/TIQOAHoCNxINqcOsAy4uBCdMTMrlvrMfomIUcCoWnb3BY6UdCjZaeBbSbo7Io5L9q+WdAefTxevBLbPOb5jUldJNgWTW//EhvpW60g9IsZERD/ghIjoHxH9kjI4Iv66oRObmW1KqutR6hIRIyKiY0R0AoYAj0fEcUmeHEkCjgJeSw4ZDxyfzILZC1gaER8Ck4ABklpLag0MSOrqlM+D0t0lTY6IJUmHWgPnR8SleRxrZrZJCAo+T/0eSduSzXrMBE5L6icAhwIVwAqS5VgiYpGknwFrlmq5as1D07rkE9QPiYiadEtELE7+WeGgbmapUVWALx9FxBMkKZPallZJZr2cUcu+0cDo+lwzn6BeLqlJRKwGkNQM8JRGM0uVRhipN4p8gvo9wOQksS+yD0/HFLJTZmaNbUO58k1FPmu/XCfpFeAgslM0JwFfK3THzMwa05dppA6wgGxA/y7ZZQL+UrAemZkVQepH6pJ2BI5NykfAn8m+p7RfI/XNzKzRZL4EI/W5wNPA4RFRASDp3EbplZlZIyvxt9Tlra61X44GPgSmSLpN0oGQkl9lZmbrqEZ5l1JW1zdK/xYRQ8iuVTAFOAf4iqSRkgY0VgfNzBpDAy/oVTQbXKUxIpZHxJ8i4giyaw+8TIEWojEzK5aGWiag2PKd/QJkv01KdhGb2hayMTPbJFWrtNMq+apXUDczS6tMsTvQQBzUzcxIz+wXB3UzMyj5WS35clA3M6P0Z7Xky0HdzAynX8zMUqXUpyrmy0HdzAzIeKRuZpYeHqmbmaWIg7qZWYoU4BWlReGgbmaGR+pmZqniZQLMzFLE89TNzFLE6RczsxRxUDczSxGv/WJmliLOqZuZpYhnv5iZpUh1ShIwDupmZqTnQWlZsTtgZlYKoh6lLpKaSpou6RVJsyVdmdR3lvS8pApJf5a0RVLfJNmuSPZ3yjnXiKT+DUkD87kPB3UzM7Ij9XzLBqwG+kfEbkBPYJCkvYDrgJsiYgdgMXBy0v5kYHFSf1PSDkk9gCHAzsAg4LeSyjd0cQd1MzOgSpF3qUtkfZpsbp6UAPoD9yf1Y4Cjks+Dk22S/QdKUlI/NiJWR8Q7QAXQe0P34aBuZkbDpV8AJJVLmgksBB4D/gksiYiqpMk8oEPyuQPwPkCyfymwTW79eo6plYO6mRn1S79IGibphZwyLPdcEZGJiJ5AR7Kj6+6NdR+e/WJmRv2mNEbEKGBUHu2WSJoC7A20krRZMhrvCFQmzSqB7YF5kjYDtgY+zqlfI/eYWnmkbmZGg85+2VZSq+RzM+Bg4HVgCnBM0mwo8GDyeXyyTbL/8YiIpH5IMjumM9ANmL6h+/BI3cyMBp2n3h4Yk8xUKQPGRcTDkuYAYyVdDbwM3J60vx34o6QKYBHZGS9ExGxJ44A5QBVwRkRs8IuvDupmZkCmgb5RGhGzgG+up/5t1jN7JSJWAd+t5VzXANfU5/oO6mZmpOcbpQ7qZmZAeO0XM7P08EjdvrBW7bfhuF+dTsu2WxMRPHfv4zx5xyMM/c3ZfKVLewCabdWclcuW88tDh1O+eTnf//kpbL9LFyKCv145hoppcwDodWQfDj79KIhg6cLF/PGcW1m++JNi3p5thLvGPsBfHpqIJLp17cTVF5/HKedczPIVKwFYtHgJu/TYiVuuvazmmFdff4PjTj2PX145nAH99gXgw/kLuezaXzN/4UdIMPKGn9Ghfbui3NOmwqs02hdWXZXhb1f/kXmz36VJ86Zc8NAvmPv0LMaceXNNm6MuOY6Vn6wAYO8hBwJw3aCf0GKbrTjtzuHceOQlqEwcfdlQfnHwBSxf/AlHDv8B+w4dyMRf37/e61ppW/Dvj7jn/gd58J7f07RJE87/6c955B9PctfIG2ranHPx1fTbd6+a7Uwmw02/vYM+3+q11rlGXH0Dw44fQp/evVixYiUqS8kbIAooHSHd89SLYtm/lzBv9rsArF6+igX/rKTVV9us1abnYXvz0vhnAfhqtw68+exsAD79eBkrl61g+127gIQkttiyCQBNWzZj6YLFjXcj1uCqMhlWr/6MqqoMK1etZtu2n/9cfLp8OdNfeoUD99u7pu5P94/n4AP60qZ1q5q6f77zHplMhj69s4F+yy2b0axp08a7iU1UFZF3KWWNHtQlndjY1yxlbTpuS8cenXh3ZkVNXdfe3fnkoyX8+935AFS+/i++cdDulJWXZdvv0pnW7behuirDuEtvZ/jE67lq+kja7dCRaX9+vFi3Yhup3bZtOeHY73DQ0cfTb/APaNl8S/ruuXvN/slPPceeu+9Gi+bNgezIfvJTz/L9bx+21nnefb+Sli1acPaIn3HMCWdww2/+QCaTlvf6FE7U479SVoyR+pW17chdT+G1T/7ZmH0qii22bMJJI8/lr1eNYfWnK2vqex3Zt2aUDvD8uCksnb+I8x/6OUdfPpR3X3yT6upqyjYrZ5/jDub6w0ZwWe8f8cHcf2Xz67ZJWrrsE6Y8PY1J993B4w/ew8pVq3lo0ue/pB/5x5McetABNdvX3fx7zv3RSZSVrf3XOJPJ8NIrr3HBmT9k7B9uYd4H8/nbhH801m1sshpw6d2iKkhOXdKs2nYBtT6tyV1P4exOQ0r71+FGKtusnJN+dx4v/G0qsybN+Ly+vIzdBn6LXx5xcU1ddaaaB352V832OX+5ioVvf0jHHl8D4ON/LQBg5t+f46AfDW6kO7CGNu2FmXTYrl1NKuXA/fsw89U5HDGwP4uXLOXVOW9w889/WtN+9ty3uPDyawFYvHQZTz83g/Lyctpt25bu3bqwfYfsQ/f+++3NrNlzgbzesfClVeoj8HwV6kFpO7I/QesmeAU8+9/Nv3yOve5UFlRU8sTtE9aq33GfXVjw9gcsnb+opm7zplsgic9WrmanfXYhU5VhQUUlW32lNe26daB5m5YsX/QJO+2zKwsqNrjej5Wo9u22ZdZrc1m5ahVNmzTh+RdmsnP3bgA8OmUq+/fpTZMmW9S0n3T/nTWfL7n6Rvbv25sD9+tDJpNh2afLWbR4CW1at2L6i6/UnMdqV+oj8HwVKqg/DLSIiJnr7pD0RIGuucnossdO9P7Ofnzw+ntcOCE70vr79WOZ88RMeh3RZ63UC0DLtltz2pgRRARL5y/i7vNuBWDZwsVMuvkv/HjcFVT/p4pFlR9xzwUjG/1+rGHsunN3Du63D9878SzKy8vpvmNXvjv4EAAemfwkPzzue3mdp7y8nAvO+CEnnz0CAnrstAPHHDmokF1PhUykY6SuKNEbSXv6xb6YG174ebG7YCVo87ZdNnrO5g++9u28Y86f3nugZOeIep66mRnOqZuZpYpz6mZmKeJlAszMUsTpFzOzFEnL7BcHdTMznH4xM0sVPyg1M0sR59TNzFLE6RczsxQp1W/X15eDupkZkPFI3cwsPZx+MTNLEadfzMxSxCN1M7MU8ZRGM7MU8TIBZmYp4vSLmVmKpCWolxW7A2ZmpSAi8i4bImm0pIWSXsupu0JSpaSZSTk0Z98ISRWS3pA0MKd+UFJXIWl4PvfhoG5mRnaknm/Jw53A+t72fVNE9EzKBABJPYAhwM7JMb+VVC6pHLgVOAToARybtK2T0y9mZjTs7JeIeEpSpzybDwbGRsRq4B1JFUDvZF9FRLwNIGls0nZOXSfzSN3MDMhEdd5lI5wpaVaSnmmd1HUA3s9pMy+pq62+Tg7qZmbUL6cuaZikF3LKsDwuMRLoCvQEPgRuLMR9OP1iZkb9Zr9ExChgVH3OHxEL1nyWdBvwcLJZCWyf07RjUkcd9bXySN3MjGxOPd//vghJ7XM2vw2smRkzHhgiqYmkzkA3YDowA+gmqbOkLcg+TB2/oet4pG5mBlQ34DdKJd0LHAC0lTQPuBw4QFJPIIB3gVMBImK2pHFkH4BWAWdERCY5z5nAJKAcGB0Rszd0bQd1MzMafPbLseupvr2O9tcA16ynfgIwoT7XdlA3M4ONndVSMhzUzcxo2PRLMTmom5nhpXfNzFLFI3UzsxTxSN3MLEUy2VmEmzwHdTMz/OJpM7NUSctLMhzUzczwSN3MLFU8+8XMLEU8+8XMLEW8TICZWYo4p25mliLOqZuZpYhH6mZmKeJ56mZmKeKRuplZinj2i5lZivhBqZlZijj9YmaWIv5GqZlZinikbmaWImnJqSstv53STNKwiBhV7H5YafHPha1PWbE7YHkZVuwOWEnyz4X9Fwd1M7MUcVA3M0sRB/VNg/Omtj7+ubD/4gelZmYp4pG6mVmKOKiXOEmDJL0hqULS8GL3x4pP0mhJCyW9Vuy+WOlxUC9hksqBW4FDgB7AsZJ6FLdXVgLuBAYVuxNWmhzUS1tvoCIi3o6Iz4CxwOAi98mKLCKeAhYVux9WmhzUS1sH4P2c7XlJnZnZejmom5mliIN6aasEts/Z7pjUmZmtl4N6aZsBdJPUWdIWwBBgfJH7ZGYlzEG9hEVEFXAmMAl4HRgXEbOL2ysrNkn3As8BO0maJ+nkYvfJSoe/UWpmliIeqZuZpYiDuplZijiom5mliIO6mVmKOKibmaWIg7o1OEkZSTMlvSbpPklbbsS5DpD0cPL5yLpWqpTUStLpX+AaV0i64Iv20ayUOKhbIayMiJ4R8Q3gM+C03J3KqvfPXkSMj4hr62jSCqh3UDdLEwd1K7SngR0kdUrWhb8LeA3YXtIASc9JeikZ0beAmjXk50p6CTh6zYkknSDpN8nndpIekPRKUvoA1wJdk38l/DJpd6GkGZJmSboy51yXSHpT0lRgp0b7v2FWYJsVuwOWXpI2I7sW/MSkqhswNCKmSWoLXAocFBHLJV0EnCfpeuA2oD9QAfy5ltPfAjwZEd9O1p1vAQwHvhERPZPrD0iu2RsQMF7SfsBysksu9CT7d+Al4MWGvXuz4nBQt0JoJmlm8vlp4HZgO+C9iJiW1O9F9sUfz0gC2ILsV9+7A+9ExFsAku4Ghq3nGv2B4wEiIgMsldR6nTYDkvJyst2CbJBvCTwQESuSa3g9HUsNB3UrhJVrRstrJIF7eW4V8FhEHLtOu7WO20gCfhERv1/nGuc04DXMSopz6lYs04C+knYAkNRc0o7AXKCTpK5Ju2NrOX4y8KPk2HJJWwOfkB2FrzEJOCknV99B0leAp4CjJDWT1BI4ooHvzaxoHNStKCLi38AJwL2SZpGkXiJiFdl0y9+TB6ULaznF2UA/Sa+SzYf3iIiPyaZzXpP0y4h4FPgT8FzS7n6gZUS8RDZX/wrwCNkljs1Swas0mpmliEfqZmYp4qBuZpYiDupmZinioG5mliIO6mZmKeKgbmaWIg7qZmYp4qBuZpYi/w9SEX0YFibV1gAAAABJRU5ErkJggg==\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#plot roc curve for train and test data\nplotROC(d_trainA_w2,y_train,d_testA_w2,y_test,dt_A_w2)","execution_count":82,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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40FtHw9q+XjQoVENavl44G8mAX8fD2p5ZFOXOPxckvElGe+sBCQt0vhhT4uD1FjjdiUWUmPMBBANlry7+yYXd/CpA961jT37Bl3Mx7WN6b4NjCRQvZ6RENw9HfjNVUxHjx5l//79vPvuu8yfPx+x89FQhSvhoTuzNQ0S0zIJi0klPDaF8NhUwmNTCct9nEJiWtZflq/m5kKAnxeN/Lxy7+vVqEYtq6RQ26caNTxdkeQoiA+DuNDr93FhkBBh3FJjCw7MtZqxJ5/TrJPTxJObCGobTUA5ScG7NlTzhQrWzFMeHDt2jAMHDjBz5kwAoqOjqV27doHLi8h+pVRQSd6rQox60rSqJjk9y/jht0oGYVZJIT71r3vn3h6uBPp70djfm17N/An09ybA30gKAf5e1Pbx+OteplJGErh6BCJOwlXzdu0MZKb8NRjPmlCzCdRsDE36QI1Gxp69l7/RDOTlbyQHTz+js1b/6DtURkYGzz33HM899xz169fnzjvvxNPTs9AkUVo6UWiaE6RmZBMRl5J7VBAWa5UQYlKITflrIvB0dyHQ35vG/l70aOJPoL+X8byWce/v7Z5/c4NSkBgJwScg6hRcPQFXT0HUaciwKmjr2xDqtoOes8G/Ofg1Ab/GRnLwrPH39WpOsXv3bubNm8fx48eZPn06r776Kp6ejm+K04lC0xzAYlFcik8lJDqFkOgUwmJTCIu53jR0LSnjL8t7uLkQ6OdFYC1vOgXUpLG/t5kMvGhcy/vvRwR5KQUJl4yjgqjTRlKIOg1RJyEt/vpy3nWgXnvoNsVIDPU6QL12xlGBVq5FREQwYMAA6tevz/fff2/XUU1F0YlC00oh26IIiU7mzJVETkcmceZKImeuJBISk0KG1dBQd1ehkZ/RNDSsff3cBJDTXFSnejVcXGxoslEK4sOvJ4HchHAa0hOuL+dVy0gInSZC3fbG43rtjb4CrUI5c+YMbdq0ISAggC+++IKhQ4dSo0bZHuXpRKFpNlBKcSUhnVORCZy5ksipSCMhnL2SlHuugAg0qeVN63q+DGlXj2a1fWhWx5umtX1oUMMT16ISQXYmxFwwEkDCJUhPNI4G0hONJBAbYvQhZCRdf41PXePIoMskqNvWeFy3HVQvUaUGrRyJi4vj4YcfZvXq1WzdupWBAwdy6623OiUWnSg0LY/EtEzOXEnk5OVETkeatyuJf+lArl+jGm3q+zKzb1Pa1PelbQNfWtWrjreHDf9SKTEQfQ6unYXosxB1xkgAsRfA8tfRSrh7G6OCqvlCjQDoPv16QqjTFnwc14GpOc+GDRtYtGgRkZGRPPTQQ9xwQ3GuSGx/OlFoVZbFogiJSeHU5QRORiZy8nICpyITCIu5frkJ32putG3gy9guDWnXwJe29X1pU98Xfx+PwleelQ4x560Swrnrj1Otiiq7uEPtlkY/QYfxUKcN1GlldChX8zVq+mhVyvz581mzZg2dO3fmu+++IyioRCNa7UonCq1KSErP4tTlBE5cTuDk5YTco4XUzGwAXASa1/GhS6Afk29oYiSFBr4E+HkV3olssUDcRYg8at6OGU1HcaFGNdEc1RtAndbQ4Rao3dp4XLsV+DUFV/1vWNXlnM8mIgQFBdG0aVOWLVuGh0cROyRlRJ9wp1U6MckZHL8Uz7GIBI5diufEpQQuXEvOnV/Ty532DX1p37AG7RvUoF1D4yjB09218BVnphrDS3MSQuRRuHLsep+BuBoJoF4HMxG0No4WarfSQ0y1AoWFhXH33XczefJkZsyY4bD30SfcaVWSUorIhDSOmwnhWEQCJy7Fcyk+LXeZQH8vOjWqyW3dA+jQqAYdGtWgQQ1P20scxF6E7a9CyE6jPyHnKMHDFxp0gm5ToX4naNDZGFXk7mX/D6pVShaLhZUrV7Js2TKys7Od1lFtC50otAojNSObw+FxHAiN5UBILIfC4nLPRxCBlnWrc0PzWnRqVJOOZlLw8y7GoXt6Elw+BBEHIGK/cR8fCm6e0GKI0YfQoLNx82sKLg67QKRWyZ09e5b58+ezbds2hg0bxrvvvkvz5s2dHVaBdKLQyi2lFOevJfP7qatsPR3F7gvRZGYbTaUt6vowqE09ugTWpFNADdo1qIFPtWJsztmZcOW4kRAuHTCSQtSp60cMfk0hsCf0WmCci1AzwAGfUKuqTpw4wZEjR3jvvfeYPXu23Yv42ZtOFFq5kpaZza7z0Ww9HcWWU1cJjTHqDrWuV53Z/ZrRt2Vtujf2L3rUkTWlIDoYLv4PQnder2mUZTZRedeGgJ7Q/hbjPqCHPjFNs7vDhw9z6NAhZs2axfjx4zl//jz+/hXjjHidKDSni4hLZcvJK/x+OoodwddIy7Tg6e5Cv5Z1WDCwBYPb1KVxLe/irzjqDJz4Dna9fb3iafX6RtNR84FGQgjoaRw9lPM9Oq3iSk9P59lnn+X555+nYcOGTJo0CU9PzwqTJEAnCs0JlFKcuZLEz8cj2XwikmMRRumJJrW8mXxDEwa3rUufFrWLHoX09xUbzUknvoOTG4ymJIDAXtBiEHSZbIxC0klBKyM7d+5k3rx5nDx5kpkzZ/LKK6+USRE/e9OJQisTSikOh8ez6ehlfj4eSUi00aTUo4kfj4xux7D29WlZ16f4bbVKwaWD15NDzHkQF2jSD0b/F9qPM8pia1oZi4iIYNCgQTRo0IBNmzYxevRoZ4dUYjpRaA6jlOJIeDw/HL3MD0cuExGXirur0K9lHRYObMHw9vWpV6OEe1dZ6XB4Lex40xi2Kq5Gc1K/e6HdOF3rSHOakydP0r59ewICAvjyyy8ZOnQovr6+zg6rVHSi0OxKKcXRiOvJITzWSA43tqrD/cPbMLxDfWp6laIsRVo87P8Adr4DSZHGZTNveQvajTWumKZpThIbG8vSpUt5//332bZtGwMGDGDChAnODssudKLQ7OLc1SS+ORDOxiOXCItJxc1FuLF1He4b2poRHRpQ07sUySE9Cc78BMe/hbO/QHY6tBgMt64w7nWfg+Zk3377Lffccw9RUVE8+uijTi/iZ286UWgllpaZzU/HIvlsTyh7LsTgItC/VR3+MaQ1IzrWL97JbnllJMPZzUZyOLMZslKNeklBc6DrFGjUzX4fRNNKYe7cubz//vt069aNH374gR49ejg7JLvTiUIrtvNRSXy2O5SvD4QTl5JJ09rePDK6Hbf1CKCebylGdGSmWiWHn41rN/vUgx4zoOOt0LiPPhtaKxesi/j16dOH1q1b8+CDD+LuXjmr/epEodkkM9vCryeu8MnuEP48F42bizCyYwOm9m5C3xa1bbs6W0FSY+HAx7DzbaPfwbuOcdTQ8VZo2g9cijlMVtMcKCQkhLvuuoupU6cyc+ZMFi5c6OyQHE4nCq1QVxPS+GxPKJ/tDuVqYjoBfl48OKINd97QuHRHD/HhcGoTnPoeLm4HlQ2u1WD6N9B8kC69rZU7FouF5cuX88gjj6CU4o477nB2SGVG/zdqf6OU4mBYHO//eZEfj14my6IY1KYuz93alCHt6hV9Sc+CJFyC4+uNpqXwPca0Om2hvzmktVEP3bSklUunT59m/vz5bN++nREjRrBy5UqaNWvm7LDKjE4UWq6MLAubjl7m/T8vcDg8Ht9qbszs24wZfZvSvI5PyVaaGAknNsDxb4w6SwD1O8NN/2de0a21/T6ApjnI6dOnOX78OB988AEzZ84s90X87E0nCo1rSel8uiuUT3aHEJWYTos6PjwzviO39QikenEqsubISIZj38Chz8zkoKBuexjymNHvoJODVgEcPHiQQ4cOMWfOHG655RbOnz+Pn5+fs8NyCp0oqrCTlxN4b/sFvjt0iYxsC4Pb1mV2v2YMbF23ZJ3TV0/CvveNM6bT441mpcGPQscJULet/T+ApjlAWloazzzzDC+++CIBAQFMmTIFT0/PKpskQCeKKsdiUfx++iprtl9gR3A0Xu6uTLqhMbP7N6Nl3erFX2FWutG0tO89CN0Brh7QYQIEzYUmffTJcFqF8ueffzJv3jxOnz7NnDlzePnllytkET9704miikjLzGbdgXBW/+8CF64l07CmJ8tGtWNKr8YlOzEuNRb2roE970LSFfBvDsP/Dd2mgU9t+38ATXOwiIgIhgwZQkBAAD///DMjRoxwdkjlhk4UlVxCWiaf7Arhve0XuZaUTpfAmrwxpTujOzXA3bUEI4xiQ2DXO8Z5D5nJ0HIoTFhuXCpUj1jSKqATJ07QoUMHAgICWLduHUOGDKF69RIcXVdiOlFUUtFJ6bz/50U+3HmRxLQsBrapy6JBLenTolbJRmxcOwe//wdOrDfKeHe+A/ougQad7B67ppWFmJgYHnjgAT788EP++OMPBg4cyM033+zssMolnSgqmcvxqby77Tyf7wklPcvCqI4NuGdwKzoH1iz5SqOD4cObISUa+v0Det2lryGtVWjr1q1j8eLFREdH89hjj9GrVy9nh1Su6URRSYRGp7D8j3N8vT8ci4IJ3QJYNLgFreqVsA5+ehKc+wVOfm9UbnX1gDk/QmBP+wauaWVs9uzZfPjhh/To0YOffvqJbt10gcmi6ERRwYVGp/D6b2dZfygCVxdh0g2NuWtgy5JdYzolxkgKJzfCud+Mct7etY3hrQOWQq0W9v8AmlYGrIv49evXj/bt27N06VLc3PRPoC0c+i2JyCjgdcAVWK2Uej7P/CbAh4CfucwjSqlNjoypsoiMT+PNLWf5Ym8Yri7C7H7NWDiwBfVLcsW4tAT4bjGc+sGouVQj0Bje2n6cUbFV113SKrALFy6wcOFCpk+fzqxZs6pEET97c9gvgIi4Am8Dw4FwYK+IbFBKnbBa7HHgS6XUchHpAGwCmjkqpsogOimd5VuD+WhXCEoppvRqwpKbWpUsQQAkXIa1UyDyKPRbYpwD0ai7Pv9Bq/Cys7N5++23efTRR3FxcWHatGnODqnCcuSuYi/gnFLqPICIrAXGA9aJQgE1zMc1gUsOjKdCS0zLZNX/LrDmf+dJzcxmYo9A7h3aumRNTGAcRexeAf97GbLSYPLn0G6MfYPWNCc5efIk8+bNY+fOnYwePZoVK1bQpEkTZ4dVYTkyUQQAYVbPw4HeeZZ5CtgsIv8AfIBh+a1IRBYCC4Eq98dOy8zmo50XeWdrMHEpmYzp3IAHhrelVb0SjvNOTzQSxI63IC0O2o6FIY9Cg852jVvTnOncuXOcPn2ajz/+mGnTplW5In725uzG5ynAB0qpl0WkL/CxiHRSSlmsF1JKvQu8CxAUFKScEGeZy8q28NX+cF7/9SyRCWkMbFOXh0a0LdkwV4vFKM536DPjPIiMJGgzGgY/oi8pqlUa+/fv5/Dhw8ydO5ebb76ZCxcuUKNGjaJfqBXJkYkiAmhs9TzQnGZtHjAKQCm1U0Q8gTrAVQfGVa5ZLIpNxy7zyuYznL+WTPcmfrw6qRt9W5agLEZsCBz+3EgQcSHgUd0YwRQ0DwIq33V9taopNTWVp59+mpdeeonGjRszdepUPD09dZKwI0cmir1AaxFpjpEgJgNT8ywTCgwFPhCR9oAnEOXAmMq1/52N4oWfTnEsIoE29auzamYQw9rXK95hc3YmnP4R9r8Pwb8b01oMMkp8tx8HHiW8roSmlUPbtm1j/vz5nD17lnnz5vHSSy/pIn4O4LBEoZTKEpElwM8YQ1/fU0odF5FngH1KqQ3AUmCViNyP0bE9W+UMeK5CLsWl8u/vT/DjsUgC/b145c6ujO8WULwrycVehAMfwcFPjCJ9NQKMpqVu08CvcZEv17SKJiIigqFDh9K4cWN+/fVXhg4d6uyQKi2paL/LQUFBat++fc4Owy4ysy28/+cFXvv1LNkWxb1DWzN/QHOqubnavpLgLUbHdPAWY0hr65HQcza0Hg4uxViPplUQR48epXNnY/DF999/z5AhQ/Dx0UfKRRGR/UqpoJK81tmd2VXWkfA4Hv76CKciExnarh5P3dKx+ENd970P398Pvg1h0DLoMQNqBjomYE1zsmvXrnH//ffzySef5BbxGzdunLPDqhJ0oihjmdkW3tpyjrd+P0ed6h6snNGTER3qF3/43q7l8NMj0Go4TPoY3L0cE7CmOZlSiq+++oolS5YQGxvLk08+Se8WqU2tAAAgAElEQVTeeUfaa46kE0UZCo5K4v4vDnEkPJ7bugfw5C0dqenlbvsKrp6E35+DmPNw5Ri0Gwe3vwdu1RwXtKY52axZs/j4448JCgrit99+y2120sqOThRlQCnFp7tDefaHE3i6u/L21B6M7dLQ9hVkZ8G2F2Hbf8HFHWqYTU0DH9Z1mLRKybqI36BBg+jSpQv//Oc/dRE/J9HfuoPFp2Ty8LrD/Hz8CgNa1+GlO7oWry5T7EVYtwDC90DXKTDyOfCu5bB4Nc3Zzp8/z4IFC5g+fTpz5sxh3rx5zg6pytPXrnSgA6GxjHnjf/x28iqPjWnPh3N6FS9JnN8KKwdC1CmYuAZuXaGThFZpZWdn89prr9G5c2f27t2Li760brmhjygcwGJRrN5+nhd/Ok2Dmp58vagf3Rr72b6C5Guw+f/g8GdQpw1M/RJqNXdcwJrmZCdOnGDu3Lns3r2bsWPHsmLFCgID9Qi+8kInCjuLTc5g6VeH2XLqKqM7NeD5iV1s77C2WODAB/Dr00Y9phvvh4EP6bOptUrvwoULBAcH89lnnzF58mRdxK+c0YnCjg6FxbH40wNEJabzzPiOzOjT1PYNPi4Mvp5r9EU0GwBjXoJ67RwbsKY50d69ezl06BALFixg7NixnD9/Hl/fEl66V3MonSjsQCnFRztDePaHE9Tz9eTrRX3pEmhjU1NqHOxbAzveNEY3TVgBXSfrCwdplVZKSgpPPPEEr776Kk2bNmXGjBl4enrqJFGO6URRSsnpWTzyzVE2Hr7E0Hb1ePnOrvh5exT9woTLsOsd4+zqjERoORRGvwB1Wjs+aE1zkq1btzJ//nyCg4O56667eOGFF3QRvwpAJ4pSuHgtmbs+3s/Zq4k8NLItiwa1xKWoQn7RwfDn60b5b0sWdLwV+t8HDbuWTdCa5iTh4eEMHz6cpk2bsmXLFoYMGeLskDQb6URRQn+cieIfnx3AxUX4cG4vBrSuW/gLLNnw879g90pw9YDuM4xrVNdqUTYBa5qTHD58mK5duxIYGMh3333H4MGD8fYu4SV8NafQA5WLSSnFu9uCmfP+Hhr5ebFxyY1FJwmADf8wLkF6wzy4/xiMe0UnCa1Si4qKYurUqXTr1o0//vgDgDFjxugkUQHpI4piSMvM5pF1R1h/6BJjOjfgpTu64u1hw1eYEgNHvoAeM2Hsy44PVNOcSCnF2rVruffee4mPj+fpp5+mb9++zg5LKwWbEoWIeABNlFLnHBxPuRUZn8bCj/dxNCKeB0e0YfGQVrYPfT30qdEf0esuxwapaeXAjBkz+PTTT+nduzdr1qyhY8eOzg5JK6UiE4WIjAVeATyA5iLSDXhSKXWro4MrL46ExzH/w30kp2fx7owghneob/uLLRbYuwYa94EGnRwXpKY5kcViQUQQEYYMGULPnj259957cXXVF8+qDGzpo3gG6A3EASilDgGtHBlUefLTsUjuXLkTd1cX1t3Tr3hJQinY8gzEXoAb5jsuSE1zonPnzjF06FDef/99AObNm8f999+vk0QlYkuiyFRKxeWZVrGun1pC7/95gUWf7qddgxqsX9yfdg1qFG8FG5bA9leNvolOtzkmSE1zkqysLF566SU6d+7MwYMH8fCw4fwhrUKypY/ipIjcCbiISHPgXmCXY8NyLqUUb245xyu/nGFkx/q8Prk7nu7F3DuKvQgHP4Hu0+HmN/SZ1lqlcuzYMebMmcO+ffsYP34877zzDo0aNXJ2WJqD2HJEsQToCViAb4B04D5HBuVMSime/+kUr/xyhok9Anl7ao/iJ4nMNPjxEUBg0CM6SWiVTmhoKCEhIaxdu5Zvv/1WJ4lKzpYjipFKqWXAspwJInIbRtKoVCwWxVMbj/PRzhBm9GnK07d0LPpM67wuHYJv74aokzD0CfBr7JhgNa2M7d69m8OHD7Nw4ULGjBnD+fPnqV69urPD0sqALUcUj+cz7TF7B+JsFovikW+O8NHOEO4a2IJnxhczSWRnwR//hdVDIS0Opq2DAUsdF7CmlZHk5GQeeOAB+vbty4svvkh6ejqAThJVSIFHFCIyEhgFBIjIK1azamA0Q1Ua2RbFw18fYd2BcO4d2pr7h7UuXj38mAuwbj5E7INOt8OY/+or0WmVwpYtW1iwYAHnz59n0aJFPP/881SrVs3ZYWllrLCmp6vAMSANOG41PRF4xJFBlaWsbAsPfnWY9Ycu8cDwNtw7tJjVW5UyriMREwy3vwedJjomUE0rY+Hh4YwcOZLmzZvzxx9/MHDgQGeHpDlJgYlCKXUQOCginyql0sowpjKTlW3hgS8Ps+HwJR4a2ZbFQ0pwesjxb+HSAZiwXCcJrVI4ePAg3bt3JzAwkI0bNzJo0CC8vLycHZbmRLb0UQSIyFoROSIiZ3JuDo/MwZRS/N93x9hw+BLLRrUrWZLISoffnob6naDLJPsHqWll6MqVK0yaNIkePXrkFvEbNWqUThKaTYniA+B9QIDRwJfAFw6MqUx8vieMz/eEcc/gliwa3LL4K8hIgS9mGOdLDHsaXPRZqFrFpJTik08+oUOHDqxfv55nn32Wfv36OTssrRyxJVF4K6V+BlBKBSulHsdIGBXWsYh4ntpwnIFt6rJ0RNuSreTEejj7MzQfCK2H2TdATStDU6dOZcaMGbRt25ZDhw7x2GOP4e7u7uywtHLElvMo0kXEBQgWkbuBCKDCXtw2KT2LJZ8doHZ1D16f1A3X4p4nkePqCeP+zo/sF5ymlRHrIn4jRoygb9++LF68WNdn0vJlyxHF/YAPRumO/sACYK4jg3KkZ78/QUhMCq9P7o6/Twlr02RlwLFvofUI8PK3b4Ca5mBnzpxhyJAhvPfeewDMmTNHV3rVClVkolBK7VZKJSqlQpVSM5RStwAXHR+a/W09fZW1e8O4e1BLejUvxXkOO9+ChHDotdB+wWmag2VlZfHiiy/StWtXjhw5ojupNZsVmihE5AYRmSAidcznHUXkI2B3mURnR4lpmfzrm6O0qledfw4r5rkS1iKPwh8vQOPe0Hq4/QLUNAc6cuQIffr0YdmyZYwePZoTJ04wdepUZ4elVRAFJgoR+X/Ap8A04CcReQr4HTgMtCmT6OzoxZ9OczkhjRcmdqGaWykOsX99GjyqGxVhNa2CCA8PJywsjK+++op169bRsGFDZ4ekVSCFdWaPB7oqpVJFpBYQBnRWSp23deUiMgp4HXAFViulns9nmTuBpzCucXFYKWX33Zx9F2P4eFcIc/s3p2fTUvQpZCTDhW0QNBfqtbNfgJrmADt27ODIkSPcfffduUX8fHx8nB2WVgEV1vSUppRKBVBKxQBnipkkXIG3MYbSdgCmiEiHPMu0Bh4F+iulOgL/LGb8RVJK8ewPJ2lY05MHR5byQOj8VshOh7aj7BKbpjlCUlIS9913HzfeeCMvv/xybhE/nSS0kirsiKKFiOSUEheM62XnlhZXShV1ybZewLmc5CIiazGOUk5YLbMAeFspFWuu82ox4y/SzvPRHAqL49kJnfD2sGU0cCGOf2s0OzXRJyNp5dPmzZtZuHAhoaGhLF68mOeee04X8dNKrbBfzryFi94q5roDMJqrcoRjXHvbWhsAEfkTo3nqKaXUT3lXJCILgYUATZo0KVYQq7adp051D27vGVis1/2NUnDxT2jQGdz0JR+18icsLIyxY8fSsmVLtm3bxo033ujskLRKorCigL+V0fu3BgYDgcA2Eemc9xrdSql3gXcBgoKCbL5ed3BUEr+fjuKfw1oX/yp1ef3vJUi8BP2WlG49mmZn+/fvp2fPnjRu3JhNmzYxYMAAPD09nR2WVonYcsJdSUUA1pd3CzSnWQsHNiilMpVSF4AzGInDLj748yIeri5M69209Cs7+Ak06Qt97in9ujTNDiIjI7njjjsICgrKLeI3fPhwnSQ0u3NkotgLtBaR5iLiAUwGNuRZZj3G0QTmuRptAJs7zAsTn5rJugPh3Ny1EXV9S9lGm3DZKP7Xbqy+/rXmdEopPvzwQzp06MDGjRt57rnndBE/zaFs7t0VkWpKqXRbl1dKZYnIEuBnjP6H95RSx0XkGWCfUmqDOW+EiJwAsoGHlFLRxfsI+ftqXxgpGdnM6d+s9Cvb8SYg0EoX/9Ocb/LkyXz55Zf079+f1atX066dHqqtOVaRiUJEegFrgJpAExHpCsxXSv2jqNcqpTYBm/JMe8LqsQIeMG92Y7EoPtkVQlBTfzoF1CzdyiKPwu4V0HM21Gtvl/g0rbisi/iNGTOGAQMGcM899+Di4shGAU0z2LKVvQGMA6IBlFKHgSGODKq0/gy+xsXoFGb0LWXfRHYmfP+AUfhv6BNFL69pDnDq1CkGDhzImjVrAJg1axZLlizRSUIrM7ZsaS5KqZA807IdEYy9fL4nFH9vd0Z1alDylVgs8N0SCN8Do18A71IUEdS0EsjMzOS5556ja9eunDhxgurVqzs7JK2KsqWPIsxsflLm2db/wBidVC5FJ6Xzy4krzOzbrHQ1nX75PziyFoY8Dp1vt1+AmmaDQ4cOMWfOHA4dOsTtt9/Om2++SYMGpdjx0bRSsCVRLMJofmoCXAF+NaeVS98ejCAzWzHphsZFL1yQiANGKfHu02Hgg/YLTtNsFBkZSWRkJOvWreO224oqgqBpjmVLoshSSk12eCR2oJRi7d4wujfxo039El6ETyn4+THwrgMjn9PDYbUys337do4cOcI999zDqFGjCA4Oxtvb29lhaZpNfRR7RWSTiMwSkXJ9CdTD4fGcu5rEpKBSHE0Eb4HQHTDkX+BZyhFTmmaDxMRElixZwoABA3jttddyi/jpJKGVF7Zc4a4l8CzQEzgqIutFpFweYXxzIJxqbi6M6VKKWvtxocZ929H2CUrTCvHzzz/TqVMn3nnnHe677z4OHDigi/hp5Y5N4+uUUjuUUvcCPYAEjAsalSsZWRY2HL7EiI4NqOHpXvIVZaUZ9266DILmWGFhYYwbNw5vb2+2b9/Oa6+9pkc2aeVSkYlCRKqLyDQR2QjsAaKAclcvYPu5KOJSMpnQrVHpVpSeaNxXK9etbFoFpZRiz549ADRu3Jgff/yRgwcP6hIcWrlmyxHFMaAP8KJSqpVSaqlSqtxdM/unY5H4erpxY+s6pVtReqJxNOFaiqMSTcvH5cuXmThxIr17984t4jds2DBdxE8r92wZ9dRCKWVxeCSlkG1R/HbyKje1q1e6cycAYi+Ap599AtM0jKOIDz74gAceeIC0tDReeOEF+vfv7+ywNM1mBSYKEXlZKbUUWCcif7sGhA1XuCszB0JjiU7OYHiH+qVfWfI1qGO3Sueaxp133snXX3/NgAEDWL16NW3alPKSvJpWxgo7ovjCvC/ule3K3K8nruDuKgxsU7d0K0qMhKhT0HKofQLTqqzs7GxEBBcXF26++WZuuukm7rrrLl2fSauQCtxqlVJ7zIftlVK/Wd+AclVG9bdTV+ndvHYpRzulwxfTISsDBti1mK1WxZw8eZIBAwbkFvGbOXMmixYt0klCq7Bs2XLn5jNtnr0DKanw2BTOXU1icNtSHk2svwfC98Kty6F+R/sEp1UpmZmZPPvss3Tr1o3Tp09Ts6Y+YVOrHArro5iEcVW65iLyjdUsXyAu/1eVve1nrwEwqDTNTvHhcOxr6PcP6DDeTpFpVcnBgweZPXs2R44cYdKkSbzxxhvUq1fP2WFpml0U1kexB+MaFIHA21bTE4GDjgyqOP539hr1a1SjVb1SnKh08nvjvucc+wSlVTlXrlzh2rVrrF+/nvHj9c6GVrkUmCiUUheACxjVYssli0Wx83w0g9vWRUpTvC/4N6jdGmq3tF9wWqW3bds2jh49yuLFixk1ahTnzp3Dy8vL2WFpmt0V2EchIn+Y97EiEmN1ixWRmLILsWCnryQSk5xBv5alPMnu8hEIDLJPUFqll5CQwD333MOgQYN44403cov46SShVVaFdWbnXO60DlDX6pbz3Ol2BkcD0Ldl7ZKvJCUGkiKhrr5AvVa0TZs20bFjR1auXMkDDzygi/hpVUJhw2NzzsZuDLgqpbKBvsBdgE8ZxFakfSExBPh5EeBXij258H3GfUAP+wSlVVphYWGMHz+emjVrsmPHDl5++WV8fMrFv4KmOZQtw2PXY1wGtSXwPtAa+MyhUdlAKcW+i7Hc0My/dCsK3wPiAo10otD+TinFrl27AKOI3+bNmzlw4AC9e/d2cmSaVnZsSRQWpVQmcBvwplLqfiDAsWEVLTw2lauJ6fRsVqvkK1EKzv0GddpANV3eWfurS5cuMWHCBPr27ZtbxG/IkCF4eHg4OTJNK1u2JIosEbkDmAGY40hxemnVfSFGf3pQ01IcURxeC5cOQK+FdopKqwyUUqxevZoOHTqwefNmXnrpJV3ET6vSbKkeOxe4B6PM+HkRaQ587tiwirY/JBbfam4lvzZ2RgpsfhwCb9DnT2h/cfvtt/PNN98waNAgVq9eTatWrZwdkqY5VZGJQil1TETuBVqJSDvgnFLqP44PrXD7Q+Lo1sQPV5cSnj9x+HNIuQaTPgZdg6fKsy7iN2HCBEaMGMGCBQt0fSZNw7Yr3A0AzgFrgPeAMyLi1OPw5PQsTkcm0L1JCZudLBbYtRwadoMmfe0bnFbhHDt2jP79++cW8ZsxY4au9KppVmz5T3gVGKOU6q+U6geMBV53bFiFOxoRj0VB9yYlvMDQ6U0QfRb6LobSnNGtVWgZGRk8/fTT9OjRg+DgYPz9SzmCTtMqKVv6KDyUUidyniilToqIU4d9HA2PB6BLQAmrc/7xPHjVgg4T7BiVVpHs37+f2bNnc+zYMaZOncprr71G3brl4jxSTSt3bEkUB0RkBfCJ+XwaTi4KeDQinkY1PaldvQRnxCoFsSHQaSK46WGOVVV0dDRxcXFs3LiRcePGOTscTSvXbEkUdwP3Ag+bz/8HvOmwiGxwLCKeTiU9mogLhfQEaNDZvkFp5d7vv//O0aNHuffeexkxYgRnz57F09PT2WFpWrlXaB+FiHQGRgHfKqVuMW//VUqllU14f2dRivPXkulc0kRxepNx37Cr/YLSyrX4+HjuuusubrrpJpYvX55bxE8nCU2zTWHVY/+FUb5jGvCLiOR3pbsyl5qZDUDHgBrFf3FmKmx/FZoN0NViq4iNGzfSoUMHVq9ezYMPPsj+/ft1ET9NK6bCmp6mAV2UUskiUhfYhDE81qnSMo1ahe0bliBR7HsPkq7A7e/bOSqtPAoLC2PixIm0a9eO9evXc8MNNzg7JE2rkAprekpXSiUDKKWiili2zKRlZuPn7U6DGsVsNkhPNI4mmg+CZrocQ2WllGLHjh3A9SJ++/bt00lC00qhsB//FiLyjXn7Fmhp9fybQl6XS0RGichpETknIo8UstxEEVEiUmR7UHqmhdb1qhf/ina//z9IvgZDnyze67QKIzw8nFtuuYX+/fvnFvEbPHiwLuKnaaVUWNPTxDzP3yrOikXEFeNa28OBcGCviGywPifDXM4XuA/Ybct6M7ItNK7lXZxQ4PJh2L0cguZAYM/ivVYr9ywWC6tWreKhhx4iKyuLV155hRtvvNHZYWlapVHYNbN/K+W6e2HUhToPICJrgfHAiTzL/Rt4AXjIlpVmZlsILM6FijJSYP094F0bhj5h++u0CmPixImsX7+em266iVWrVtGiRQtnh6RplYoj+x0CgDCr5+HkuY6FiPQAGiulfihsRSKyUET2icg+gAD/YiSKg5/AlWMwYTl46RINlUVWVhYWizGwYeLEiaxatYpff/1VJwlNcwCndVCLiAvwCrC0qGWVUu8qpYKUUkEAjYpzRBEfCm5e0GpYSUPVypkjR47Qt29fVq1aBcD06dOZP39+8futNE2zic2JQkSKO/g8AuN62zkCzWk5fIFOwFYRuQj0ATbY0qFdrEQRthfqtNbF/yqB9PR0nnzySXr27ElISIiuzaRpZcSWMuO9ROQocNZ83lVEbCnhsRdoLSLNzSKCk4ENOTOVUvFKqTpKqWZKqWbALuAWpdS+olYcYGuiSIyEsN3Q/mbbltfKrb1799KjRw+eeeYZpkyZwsmTJ7ntttucHZamVQm21Hp6AxiHcZY2SqnDIjKkqBcppbJEZAnwM+AKvKeUOi4izwD7lFIbCl9DAQG7CJ7urrYtfOp7QEH7W0ryVlo5EhsbS1JSEps2bWL06NHODkfTqhRbEoWLUiokT/tvti0rV0ptwjij23pavkOPlFKDbVlnsa5od+xbqNMG6rWz/TVaubFlyxaOHj3Kfffdx4gRIzhz5owuv6FpTmBLH0WYiPQClIi4isg/gTMOjqtAgo2JIiMZQv6EDuMdG5Bmd3FxcSxYsIChQ4eycuXK3CJ+OklomnPYkigWAQ8ATYArGJ3OixwZlF1kpAAKfHSHZ0Xy3Xff0aFDB9577z0efvhhXcRP08qBIpuelFJXMTqiK5bYi8a9X1OnhqHZLjQ0lDvuuIP27duzYcMGgoJ0hV9NKw+KTBQisgpQeacrpRY6JCJ7iT5n3Ndu6dw4tEIppdi+fTsDBgygSZMm/Prrr/Tp00fXZ9K0csSWpqdfgd/M259APSDdkUHZRUwwiKs+oijHQkNDGTt2LAMHDswt4jdw4ECdJDStnLGl6ekL6+ci8jGw3WER2Uv0OfBroq+LXQ5ZLBZWrFjBsmXLUErxxhtv6CJ+mlaO2TI8Nq/mQH17B2J30cFQu5Wzo9Dycdttt/Hdd98xfPhw3n33XZo1a+bskDRNK4QtfRSxXO+jcAFigAKvLVEuKGUkiqb9nB2JZsrKysLFxQUXFxcmTZrE+PHjmT17tq7PpGkVQKGJQoz/4q5cr9FkUUr9rWO73Em6ApnJ+oiinDh8+DBz585lwYIF3H333UyZMsXZIWmaVgyFdmabSWGTUirbvJX/JAHXRzzV0iWnnSktLY3HH3+coKAgwsPDadCggbND0jStBGzpozgkIt2VUgcdHo29RAcb9/qIwmn27NnDrFmzOHXqFLNmzeKVV16hVq1azg5L07QSKDBRiIibUioL6I5xGdNgIBkQjIONHmUUY/FFnwNXD6gZ6OxIqqyEhARSU1P56aefGDlypLPD0TStFAo7otgD9AAqXunVmPPg3xxcbKwyq9nF5s2bOX78OPfffz/Dhg3j9OnTuvyGplUChfVRCIBSKji/WxnFVzLR53SzUxmKjY1lzpw5jBw5kjVr1ugifppWyRR2RFFXRB4oaKZS6hUHxFN6lmyIuQCthzs7kirhm2++YfHixURFRfHoo4/yxBNP6AShaZVMYYnCFagOttb1LifiwyE7XR9RlIHQ0FAmT55Mp06d2LRpE927d3d2SJqmOUBhieKyUuqZMovEXmLMVrFauhigIyil2LZtG4MGDaJJkyZs2bKF3r174+7u7uzQNE1zkCL7KCocPTTWYUJCQhg9ejSDBw/OLeJ344036iShaZVcYYliaJlFYU/RweDuA7765C57sVgsvPXWW3Ts2JHt27fz5ptvMmDAAGeHpWlaGSmw6UkpFVOWgdhN9Dmo3QJ0DSG7mTBhAhs3bmTkyJGsXLmSpk116XZNq0pKUj22fIsJhgZdnB1FhZeZmYmrqysuLi5MmTKF22+/nRkzZugifppWBdly4aKKIzsTYkN0/0QpHThwgF69erFixQoApkyZwsyZM3WS0LQqqnIlitgQUNn68qcllJqayqOPPkqvXr2IjIykcePGzg5J07RyoHI1PeVeJ1sfURTXrl27mDVrFmfOnGHu3Lm89NJL+Pv7OzssTdPKgcqVKPQ5FCWWnJxMZmYmv/zyC8OGDXN2OJqmlSOVK1FEnwNPP/DW5axt8dNPP3H8+HGWLl3K0KFDOXXqFB4e+hrjmqb9VeXqo4gONvondKdroaKjo5k1axajR4/mww8/JCMjA0AnCU3T8lW5EkXMed0/UQilFF9//TUdOnTgs88+4/HHH2fv3r06QWiaVqjK0/SUmQrxYbp/ohChoaFMnTqVLl26sHnzZrp27erskDRNqwAqzxFFzAXjXg+N/QulFFu2bAGgadOmbN26lV27dukkoWmazSpPosgdGqsTRY4LFy4wYsQIhg4dmlvEr1+/fri5VZ4DSU3THK/yJAo9NDZXdnY2r7/+Op06dWL37t0sX75cF/HTNK3EKs+uZfQ58KkHnjWcHYnTjR8/nh9++IExY8awYsUKfYa1pmmlUokSRdUe8WRdxG/GjBlMmTKFqVOn6vpMmqaVmkObnkRklIicFpFzIvJIPvMfEJETInJERH4TkZLXr84pL14F7du3j6CgIJYvXw7ApEmTmDZtmk4SmqbZhcMShYi4Am8Do4EOwBQR6ZBnsYNAkFKqC/A18GKJ3iwtAZKvVrkjitTUVJYtW0bv3r2JiorS14nQNM0hHHlE0Qs4p5Q6r5TKANYC460XUEr9rpRKMZ/uAgJL9E5VsCN7586ddO3alRdffJG5c+dy4sQJxo0b5+ywNE2rhBzZRxEAhFk9Dwd6F7L8PODH/GaIyEJgIYB3w3ySQRW8TnZqaioWi4Vff/2VoUMr5lVrNU2rGMpFZ7aITAeCgEH5zVdKvQu8C1CzcTv1twVyEkWt5o4KsVzYtGkTx48f56GHHuKmm27i5MmTuLu7OzssTdMqOUc2PUUA1uMyA81pfyEiw4DHgFuUUukleqeYYKjZGNy9SvTy8u7atWtMnz6dsWPH8umnn+YW8dNJQtO0suDIRLEXaC0izUXEA5gMbLBeQES6AysxksTVEr9T9DmoVflGPCmlWLt2Le3bt+fLL7/kySefZM+ePbqIn6ZpZcphiUIplQUsAX4GTgJfKqWOi8gzInKLudh/gerAVyJySEQ2FLC6wt7IHBpb+fonQkNDmTVrFs2bN2f//v089dRTOklomlbmHNpHoZTaBGzKM+0Jq3CIp4UAABaLSURBVMelv5RaSgykxVeaGk9KKX777TeGDRtG06ZN+eOPP7jhhhtwdXV1dmiaplVR5aIzu1RiKs+Ip+DgYBYsWMDvv//O1q1bGTRoEH369HF2WFolkZmZSXh4OGlpac4ORXMgT09PAgMD7dqHWfETRU7V2Ap8DkVOEb/HH38cd3d3Vq5cqYv4aXYXHh6Or68vzZo102ftV1JKKaKjowkPD6d5c/uNAq0EiSIYxBX8K+5ZyTfffDM//vgj48aNY/ny5QQGluy8Q00rTFpamk4SlZyIULt2baKiouy63kqQKM4ZScK1Yg0VzcjIwM3NDRcXF2bPns2MGTOYPHmy/ifWHEpvX5WfI/7GFf96FDHBFa7Zac+ePfTs2ZN33nkHgDvvvJMpU6bof2JN08qlip0olKpQ5cVTUlJYunQpffv2JTY2lpYtK1aC0zR7WL9+PSLCqVOncqdt3br1b7XKZs+ezddffw0YHfGPPPIIrVu3pkePHvTt25cff/xrxZ9bb72Vbt260apVK2rWrEm3bt3o1q0bO3bssDm2t99+m08//fT/t3fn8VVV1wLHf8uYBJGpJYBCENAAmgEiICpDQUEFREEJqBUcgYcTgQqtjwoVnhSEV60KPMtQBsXUB08GBwQrNARqZJAEEqKAyhCkNoQxIEPCen+cQwgZb0Jyb25Y38/nfj7Jufucs+4m3HX33vescwmvrniZmZl069aN5s2bc88993D06NFC240aNYrIyEgiIyNz+wCcL7y0b9+esLAwfv3rX3P27NkKizUv/04Ux/8FZ0/4xVdj161bR1RUFK+//jpDhgwhNTWVnj17+josY7wuLi6OTp06ERcX5/E+Y8eO5cCBA6SkpPD111+zdOlSjh8/flGbJUuWkJSUxOzZs+ncuTNJSUkkJSXRoUOHi9plZ2cXeZ7nnnuORx99tHQvqBQmTpxIz5492blzJ507d2bKlIIFs5ctW0ZKSgrJyckkJiYyadIksrKyABg9ejS//e1v2bVrF9WrV2fevHkVFmte/r1G4Uf3yT5/Y6E1a9bQtWtXX4djLnPjP0pl+4/HyvWY4Q1r8Yf7Ioptk5WVxbp161izZg333Xcf48ePL/G4J0+eZNasWfzwww8EBwcD0KBBAwYMGOBxbKGhoQwcOJCVK1cyZswYMjMzmTNnDmfOnKFFixYsWLCAq666ipdffpmQkBBGjBhBp06d6NSpE6tXr+bo0aPMnTu3QNIprWXLlpGYmAjA448/To8ePZg4ceJFbbZv306XLl0ICAigRo0aREZGsmrVKvr06cPatWtZtGhR7v6TJ09myJAhlxSTJ/x7RFHJy4t/9NFHuZ8Y7rjjDrZv325JwlzWli1bRo8ePWjRogV169Zl8+bNJe6za9currvuOmrVurTbHNevX58tW7bQv39/+vfvz8aNG0lOTuaGG24o8pO5qrJhwwamTp3KhAkTCjx/5MiR3Cmu/I9vv/22QPvMzEzq1asHQKNGjThw4ECBNq1bt2bFihX8/PPPZGRkEB8fz759+8jIyCAkJCT34tvQ0FD27y9QPq9C+P+IIiAYaleur5NmZGQQGxtLXFwc0dHRjBgxgqCgIK680r+721QdJX3yryhxcXHExsYC8PDDDxMXF0fbtm2L/CJHeX7B46GHHsr9eevWrYwbN44jR45w/PjxIu/l8uCDDwLQtm1bdu/eXeD5OnXqkJSUVG4xAvTq1YtNmzZx++23U79+fW6//XafV2bw73euzO+d0uJXVI7yFqpKXFwcw4cP59ixY0yYMIHf/e53Vp/JGODQoUOsXr2abdu2ISLk5OQgIkydOpW6dety+PDhAu1DQkIICwtj7969HDt27JJGFVdffXXuz4899hgrVqwgMjKS2bNn504H5Xd+qisgIKDQtY0jR44UOUvwwQcf0LJly4u2nb/GoV69euzfv59rr7220H3HjRvHuHFOtaMBAwbQokUL6tWrx8GDB8nJySEgIID09HQaNWpU4usuD/499VTJigHu3buXJ598krCwMLZs2cLYsWMtSRjjWrx4MYMGDWLPnj3s3r2bffv20axZMxISEmjevDk//vgjaWlpAOzZs4fk5GSio6OpXr06Tz/9NLGxsbkl9jMyMnLn6svixIkTXHPNNZw9e5b333+/zMc5P6Io7JE/SQDcf//9zJ8/H4D58+fTp0+fAm2ys7M5dOgQAFu2bCEtLY1u3boREBBA586dWbJkSbH7VwT/TRTncuDwDz4vL37u3DlWrlwJQJMmTUhISGD9+vVERPhmaG9MZRUXF8cDDzxw0bZ+/foRFxdHcHAw7733Hk8++STR0dHExMQwe/ZsateuDcCrr75KvXr1CA8PJzIykt69e1/S6GLChAnccsstdOzYkfDw8Et6XaUxZswYPvnkE5o3b87atWsZPXo0AF999RXDhg0D4PTp03Tq1Inw8HCeffZZFi5cmDv1NHXqVF577TXCwsLIysriiSee8ErcolrwhnGVWe3GN+rRfd/A4d3wZmu47y1o+7hPYtm5cydDhgwhPj6e+Ph4fvWrX/kkDmM8kZaWxk033eTrMIwXFPZvLSKbVbVdWY7nvyOK3Ptke/8bT9nZ2UydOpVWrVqRlJTEnDlzrIifMabK8t/F7EzflRfv3bs3K1eupE+fPsyYMYOGDRt6PQZjjPEW/00Uh76DoBpQo4FXTnf69GkCAwO54oorGDx4ME899RT9+/e3+kzGmCrPj6ee3Ptke+GNOjExkTZt2jB9+nQAYmJiGDBggCUJY8xlwY8TxXcVvj5x4sQJRo4cSYcOHTh+/DjNmzev0PMZY0xl5J+JIvsMHNlToesTCQkJREVF8ec//5lnnnmGlJQUevToUWHnM8aYyso/E8WRPaDnKrTGU3Z2NoGBgcTHxzN9+vRLrjNjjHFU5jLjAKtXry7ySu3S2rhxI5GRkYSFhTFy5MhC2xw+fJh7772X1q1bExERwYIFC3Kf2717N927dyc8PJzw8HD27dtXLnGVln8mityqseU7oli6dCmTJk0CnCJ+qampdm2EMeXM12XGS1KeiWLYsGHMnTuXnTt3kpqayueff16gzdtvv010dDTJycmsXr2a2NjY3HIhgwYNYsyYMWzfvp0NGzYQEhJSLnGVln9+66mcr6H46aefeOGFF1i0aBFt2rThxRdftCJ+pmpb8RL8a1v5HvOaKOg5udgmviozvnHjRkaNGkVWVhb169dn3rx5NGjQgDfeeINZs2Zx5ZVX0qpVK8aPH8/s2bMJCAhg3rx5zJgxo8ylxfft28epU6e45ZZbAOdNf+nSpdx1110XtROR3KSXlZWVWyF269atBAQEcOeddwJQo0aNMsVRHvzznfDQd3DVL6D6Ly/pMKrKe++9x4gRI8jKymLixImMHj2awED/uv+2Mf6isDLjbdu2LXafSy0zfvr0aWJjY1m+fDkhISEsXLiQsWPHMnPmTKZMmcKePXsICgriyJEj1KlTh8GDB+fekyK/v//974waNarA9po1a5KQkHDRtv3799O4cePc34sqCx4bG0vv3r1p2LAhx44dY/HixYgIO3bsoFatWvTt25c9e/Zw9913M2nSJK64wvsTQf6ZKDJ3lcv6xN69exk8eDDt2rVjzpw53HjjjeUQnDF+oIRP/hXFF2XG09LSSE1NpXv37gDk5OQQGurcmiAiIoKBAwfSp08f+vbtW+KxunfvXu5lxT/99FPat29PfHw8O3bsoEePHmzbto3s7GwSEhLYsmULjRo1IiYmhnfffZfHH/d+ySI/TRTfQ9NOZdr1fBG/nj170qRJE9avX8/NN9/s83rvxlR1viozrqq0atWqwCd+gJUrVxIfH8/y5cv54x//yNatW4s9VmlGFI0aNbpo8bmosuBz587llVdeQURo2bIljRs3ZseOHYSGhtKmTRuaNm0KQN++ffn66699kij8bjFbOAfH0su0PrFjxw66du1Kr169iI+PB6Bdu3aWJIzxAl+VGQ8PD2f//v1s2LABgDNnzpCamkpOTg7p6enceeedTJkyhYMHD3Ly5Elq1qxZYKH8vPMjivyPwpJQ48aNCQ4OZuPGjagq7777bqFlwa+77jq++OILAA4cOMCuXbto1qwZt912GxkZGWRmZgLOIrs3K91eRFX96hES2lT1D7VUty1WT509e1YnT56swcHBWqdOHZ07d66eO3fO4/2NqQq2b9/u0/N37dpVV6xYcdG2N998U4cNG6aqquvWrdNbb71VW7dure3atdNVq1bltjt9+rSOHj1ab7jhBo2IiND27dvrZ599Vuh51qxZo/fee+9F2zZv3qwdO3bUqKgoDQ8P1zlz5uipU6e0Q4cOGhUVpRERETplyhRVVU1LS9PIyEiNjo7W9evXX9JrTkxM1PDwcL3++ut1+PDhudunTZums2bNUlXV9PR07d69e24cCxcuzG332WefaVRUlEZGRupTTz2lZ86c8ei8hf1bA5u0jO+7fldmvFFoqO4ffByGxkPDaI/2ueeee1i1ahUPPvgg06dP55prrqngKI2pfKzM+OWjvMuM+90aRSDO0LOkqadTp04RGBhIQEAAQ4cOZejQofTr188LERpjTNXid2sUQXrWqRgbXLPINuvXryc6Ojq3iF+/fv0sSRhjTBn5XaII5GyRV2RnZWUxfPhwOnfuzKlTp2yYbUw+/jbVbEqvIv6N/S5RBOnZQu+THR8fT2RkJNOmTeP5558nJSWlwBWQxlzOqlWrRmZmpiWLKkxVyczMpFq1auV6XL9bowggu8gRRfXq1UlISKBjx45ejsqYyi80NJT09HQyMjJ8HYqpQNWqVcu9oLC8+F2iAHIXsj/88EO++eYbxowZQ5cuXdi2bZtdE2FMEQIDA2nWrJmvwzB+qEKnnkSkh4h8KyK7ROSlQp4PFpEP3Oe/EpGmnhz3Xzm1iYmJoV+/fixZsiT3IhxLEsYYU/4qLFGISAAwHegJhAOPiEj+ywqfBg6rahjwBvBaScc9eFK5qXMfPv74YyZNmsQ///lPgoKCyjt8Y4wxroocUbQHdqnq96p6BvgbkP/69T7AfPfnxUA3KaEK2N6jSmRkJMnJybz00ktW6dUYYypYRa5RNALy3o4pHbi1qDaqmi0iR4G6wMG8jURkKDDU/fX0unXrUqzSKwAh5Oury5j1xQXWFxdYX1zQsqw7+sVitqrOBGYCiMimsl6GXtVYX1xgfXGB9cUF1hcXiMimsu5bkVNP+4HGeX4PdbcV2kZErgRqA5kVGJMxxphSqshEsRFoLiLNRCQIeBhYnq/NcuB8cfUYYLXa1UDGGFOpVNjUk7vm8DywEggA/qqqqSIyAafc7XJgDvCuiOwCDuEkk5LMrKiY/ZD1xQXWFxdYX1xgfXFBmfvC78qMG2OM8S6/q/VkjDHGuyxRGGOMKValTRQVVf7DH3nQF78Rke0islVEvhCRJr6I0xtK6os87fqJiIpIlf1qpCd9ISID3L+NVBF539sxeosH/0euE5E1IrLF/X/SyxdxVjQR+auI/FtEUop4XkTkLbeftopIG48OXNZ7qFbkA2fx+zvgeiAISAbC87V5FnjH/flh4ANfx+3DvrgDqO7+/Mzl3Bduu5rAWiARaOfruH34d9Ec2AL8wv29vq/j9mFfzASecX8OB3b7Ou4K6otfAW2AlCKe7wWsAAS4DfjKk+NW1hFFhZT/8FMl9oWqrlHVk+6viTjXrFRFnvxdAPwXTt2wU94Mzss86YshwHRVPQygqv/2coze4klfKFDL/bk28KMX4/MaVV2L8w3SovQBFqgjEagjIteWdNzKmigKK//RqKg2qpoNnC//UdV40hd5PY3ziaEqKrEv3KF0Y1X9xJuB+YAnfxctgBYisl5EEkWkh9ei8y5P+uIVYKCIpAOfAi94J7RKp7TvJ4CflPAwnhGRgUA7oIuvY/EFEbkCeB14wsehVBZX4kw/dcUZZa4VkShVPeLTqHzjEWCeqv5JRG7HuX4rUlXP+Towf1BZRxRW/uMCT/oCEekO/B64X1VPeyk2byupL2oCkcA/RGQ3zhzs8iq6oO3J30U6sFxVz6rqD8AOnMRR1XjSF08D/wugql8C1XAKBl5uPHo/ya+yJgor/3FBiX0hIjcDf8FJElV1HhpK6AtVPaqqIaraVFWb4qzX3K+qZS6GVol58n9kKc5oAhEJwZmK+t6bQXqJJ32xF+gGICI34SSKy/GesMuBx9xvP90GHFXVAyXtVCmnnrTiyn/4HQ/7YipQA1jkrufvVdX7fRZ0BfGwLy4LHvbFSuBuEdkO5ACjVbXKjbo97IsXgVkiMhJnYfuJqvjBUkTicD4chLjrMX8AAgFU9R2c9ZlewC7gJPCkR8etgn1ljDGmHFXWqSdjjDGVhCUKY4wxxbJEYYwxpliWKIwxxhTLEoUxxphiWaIwlY6I5IhIUp5H02LaNi2qUmYpz/kPt/poslvyomUZjjFMRB5zf35CRBrmeW62iISXc5wbRSTag31GiEj1Sz23uXxZojCV0c+qGp3nsdtL531UVVvjFJucWtqdVfUdVV3g/voE0DDPc4NVdXu5RHkhzhl4FucIwBKFKTNLFMYvuCOHBBH52n10KKRNhIhscEchW0Wkubt9YJ7tfxGRgBJOtxYIc/ft5t7DYJtb6z/Y3T5ZLtwD5L/dba+IyCgRicGpubXQPedV7kignTvqyH1zd0ce08oY55fkKegmIv8jIpvEuffEeHfbcJyEtUZE1rjb7haRL91+XCQiNUo4j7nMWaIwldFVeaadlrjb/g3cpaptgIeAtwrZbxjwpqpG47xRp7vlGh4COrrbc4BHSzj/fcA2EakGzAMeUtUonEoGz4hIXeABIEJVWwGv5t1ZVRcDm3A++Uer6s95nv4/d9/zHgL+VsY4e+CU6Tjv96raDmgFdBGRVqr6Fk5J7TtU9Q63lMfLQHe3LzcBvynhPOYyVylLeJjL3s/um2VegcA0d04+B6duUX5fAr8XkVDgQ1XdKSLdgLbARre8yVU4SacwC0XkZ2A3ThnqlsAPqrrDfX4+8BwwDedeF3NE5GPgY09fmKpmiMj3bp2dncCNwHr3uKWJMwinbEvefhogIkNx/l9fi3ODnq359r3N3b7ePU8QTr8ZUyRLFMZfjAR+AlrjjIQL3JRIVd8Xka+Ae4FPReQ/cO7kNV9V/9ODczyat4CgiPyysEZubaH2OEXmYoDngTtL8Vr+BgwAvgGWqKqK867tcZzAZpz1ibeBB0WkGTAKuEVVD4vIPJzCd/kJ8LmqPlKKeM1lzqaejL+oDRxw7x8wCKf420VE5Hrge3e6ZRnOFMwXQIyI1Hfb/FI8v6f4t0BTEQlzfx8ExLtz+rVV9VOcBNa6kH2P45Q9L8wSnDuNPYKTNChtnG5Bu7HAbSJyI87d204AR0WkAdCziFgSgY7nX5OIXC0ihY3OjMllicL4ixnA4yKSjDNdc6KQNgOAFBFJwrkvxQL3m0YvA6tEZCvwOc60TIlU9RROdc1FIrINOAe8g/Om+7F7vHUUPsc/D3jn/GJ2vuMeBtKAJqq6wd1W6jjdtY8/4VSFTca5P/Y3wPs401nnzQQ+E5E1qpqB842sOPc8X+L0pzFFsuqxxhhjimUjCmOMMcWyRGGMMaZYliiMMcYUyxKFMcaYYlmiMMYYUyxLFMYYY4plicIYY0yx/h9wB/C2ky2SFgAAAABJRU5ErkJggg==\n"},"metadata":{}}]},{"metadata":{"colab_type":"text","id":"Em-rF6u2iWO8"},"cell_type":"markdown","source":"# [6] Conclusions"},{"metadata":{"colab":{},"colab_type":"code","id":"3Hnu2QziiWO9","trusted":true},"cell_type":"code","source":"# Please compare all your models using Prettytable library\nfrom prettytable import PrettyTable\n    \npt = PrettyTable()\n\npt.field_names = [\"Vectorising Method\",\"Max Depth\", \"Min Samples Split\", \"AUC Score\",\"Precision\", \"Recall\", \"F1-Score\"]\n\npt.add_row([\"Bag Of Words\",50, 500 , 0.795, 0.84, 0.74, 0.77 ])\npt.add_row([\"Tf-Idf\",50, 500, 0.803, 0.84, 0.74, 0.77 ])\npt.add_row([\"AV-W2V\",10, 500, 0.831, 0.85, 0.74, 0.77 ])\npt.add_row([\"TfIdf-W2V\",10, 500, 0.802, 0.84, 0.72, 0.75 ])\npt.add_row([\"\",\"\",\"\",\"\",\"\",\"\",\"\"])\npt.add_row([\"Rev+Des with AV-W2V\",10, 500, 0.842, 0.85, 0.73, 0.77 ])\n\n\nprint(pt)\nprint(\"\\n\\n Note: Auc Score, Precision, Recall and F1- Score are on Test Data\")","execution_count":83,"outputs":[{"output_type":"stream","text":"+---------------------+-----------+-------------------+-----------+-----------+--------+----------+\n|  Vectorising Method | Max Depth | Min Samples Split | AUC Score | Precision | Recall | F1-Score |\n+---------------------+-----------+-------------------+-----------+-----------+--------+----------+\n|     Bag Of Words    |     50    |        500        |   0.795   |    0.84   |  0.74  |   0.77   |\n|        Tf-Idf       |     50    |        500        |   0.803   |    0.84   |  0.74  |   0.77   |\n|        AV-W2V       |     10    |        500        |   0.831   |    0.85   |  0.74  |   0.77   |\n|      TfIdf-W2V      |     10    |        500        |   0.802   |    0.84   |  0.72  |   0.75   |\n|                     |           |                   |           |           |        |          |\n| Rev+Des with AV-W2V |     10    |        500        |   0.842   |    0.85   |  0.73  |   0.77   |\n+---------------------+-----------+-------------------+-----------+-----------+--------+----------+\n\n\n Note: Auc Score, Precision, Recall and F1- Score are on Test Data\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"1) The highest AUC score is achieved after vectorization of reviews using *Average Word2Vec* method. The AUC score is 0.831.\n\n2) The best value of max_depth and min_samples_split, the hyperparameters, remains same from grid search and from the heatmaps on train and test datasets.\n\n3) Top 20 most important features are completely different between Bag of Words and Tf-Idf.\n\n4) The lowest ROC-AUC score is achieved after vectorization by *Bag of Words*. The score is 0.795.\n\n5) We can say that model is descent in predicting positive reviews looking at the Precision, Recall and F1-score.\n\n6) The AUC score for Average W2V featurization increased by 1.3% after adding the summary as a feature."}],"metadata":{"colab":{"collapsed_sections":["NTwQITJKiWN5","T_LFck8iiWOX","mjfrB76yiWOa","0tcbbZxSiWOd","u566lt93iWOf","mUNnof40iWOl","heHLB_FCiWOm","ylTn3LgiiWOo","NVhl_V6MiWO5","PnwVzXytiWO7"],"name":"08 Amazon Fine Food Reviews Analysis_Decision Trees.ipynb","provenance":[],"version":"0.3.2"},"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":"3.7.3"}},"nbformat":4,"nbformat_minor":1}