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6001CEM/LA Dataset Machine Learning.ipynb

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{
"cells": [
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"'''numpy'''\n",
"import numpy as np\n",
"\n",
"'''pandas'''\n",
"import pandas as pd \n",
"\n",
"'''time'''\n",
"import time\n",
"\n",
"'''seaborn'''\n",
"import seaborn as sn\n",
"\n",
"'''sklearn'''\n",
"from sklearn import neighbors, metrics \n",
"from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV, RandomizedSearchCV, RepeatedStratifiedKFold\n",
"from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"from sklearn.metrics import classification_report, confusion_matrix\n",
"from sklearn.impute import SimpleImputer\n",
"from sklearn.svm import SVC\n",
"\n",
"'''matplotlib'''\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"'''Remove warnings'''\n",
"import warnings\n",
"warnings.filterwarnings('ignore')"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Report ID Arrest Date Time Area ID Area Name \\\n",
"0 5666847 2019-06-22T00:00:00.000 1630.0 14 Pacific \n",
"1 5666688 2019-06-22T00:00:00.000 1010.0 10 West Valley \n",
"2 5666570 2019-06-22T00:00:00.000 400.0 15 N Hollywood \n",
"3 5666529 2019-06-22T00:00:00.000 302.0 17 Devonshire \n",
"4 5666742 2019-06-22T00:00:00.000 1240.0 14 Pacific \n",
"... ... ... ... ... ... \n",
"1276155 100504416 2010-01-01T00:00:00.000 1430.0 5 Harbor \n",
"1276156 101104731 2010-01-01T00:00:00.000 2215.0 11 Northeast \n",
"1276157 101104211 2010-01-01T00:00:00.000 1310.0 11 Northeast \n",
"1276158 2179817 2010-01-01T00:00:00.000 319.0 14 Pacific \n",
"1276159 2180332 2010-01-01T00:00:00.000 1815.0 19 Mission \n",
"\n",
" Reporting District Age Sex Code Descent Code Charge Group Code \\\n",
"0 1457 44 M W 24.0 \n",
"1 1061 8 M O NaN \n",
"2 1543 31 F O 22.0 \n",
"3 1738 23 F W 22.0 \n",
"4 1472 28 M W 8.0 \n",
"... ... ... ... ... ... \n",
"1276155 521 17 M H 24.0 \n",
"1276156 1118 12 M H 24.0 \n",
"1276157 1128 52 M H 18.0 \n",
"1276158 1408 24 M H 22.0 \n",
"1276159 1994 25 M W 16.0 \n",
"\n",
" ... Charge Description \\\n",
"0 ... VANDALISM \n",
"1 ... NaN \n",
"2 ... DRUNK DRIVING ALCOHOL/DRUGS \n",
"3 ... DRUNK DRIVING ALCOHOL/DRUGS \n",
"4 ... OBSTRUCT/RESIST EXECUTIVE OFFICER \n",
"... ... ... \n",
"1276155 ... MINOR BUY/ETC TOBACCO/ETC \n",
"1276156 ... CURFEW - JUV ONLY \n",
"1276157 ... DRINKING IN PUBLIC \n",
"1276158 ... DRUNK DRIVING ALCOHOL/DRUGS \n",
"1276159 ... POSSESSION CONTROLLED SUBSTANCE \n",
"\n",
" Address \\\n",
"0 12300 CULVER BL \n",
"1 19000 VANOWEN ST \n",
"2 MAGNOLIA AV \n",
"3 HAYVENHURST ST \n",
"4 6600 ESPLANADE ST \n",
"... ... \n",
"1276155 4TH \n",
"1276156 AVENUE 58 \n",
"1276157 YORK BL \n",
"1276158 NATIONAL BL \n",
"1276159 ROSCOE \n",
"\n",
" Cross Street \\\n",
"0 NaN \n",
"1 NaN \n",
"2 LAUREL CANYON BL \n",
"3 N REGAN FY \n",
"4 NaN \n",
"... ... \n",
"1276155 GAFFEY \n",
"1276156 FIGUEROA ST \n",
"1276157 AVENUE 63 \n",
"1276158 MANNING AV \n",
"1276159 WILLIS \n",
"\n",
" Location Zip Codes \\\n",
"0 {'latitude': '33.992', 'human_address': '{\"add... 24031.0 \n",
"1 {'latitude': '34.1687', 'human_address': '{\"ad... 19339.0 \n",
"2 {'latitude': '34.1649', 'human_address': '{\"ad... 8890.0 \n",
"3 {'latitude': '34.2692', 'human_address': '{\"ad... 19329.0 \n",
"4 {'latitude': '33.9609', 'human_address': '{\"ad... 25075.0 \n",
"... ... ... \n",
"1276155 {'latitude': '33.7406', 'human_address': '{\"ad... 3342.0 \n",
"1276156 {'latitude': '34.1101', 'human_address': '{\"ad... 23673.0 \n",
"1276157 {'latitude': '34.1148', 'human_address': '{\"ad... 23673.0 \n",
"1276158 {'latitude': '34.0301', 'human_address': '{\"ad... 23451.0 \n",
"1276159 {'latitude': '34.2215', 'human_address': '{\"ad... 19730.0 \n",
"\n",
" Census Tracts Precinct Boundaries LA Specific Plans \\\n",
"0 918.0 1137.0 10.0 \n",
"1 321.0 1494.0 NaN \n",
"2 205.0 1332.0 17.0 \n",
"3 69.0 388.0 NaN \n",
"4 937.0 241.0 10.0 \n",
"... ... ... ... \n",
"1276155 975.0 1205.0 NaN \n",
"1276156 370.0 477.0 28.0 \n",
"1276157 359.0 575.0 NaN \n",
"1276158 872.0 1124.0 9.0 \n",
"1276159 147.0 418.0 NaN \n",
"\n",
" Council Districts Neighborhood Councils (Certified) \n",
"0 10.0 85.0 \n",
"1 4.0 10.0 \n",
"2 5.0 39.0 \n",
"3 2.0 78.0 \n",
"4 10.0 16.0 \n",
"... ... ... \n",
"1276155 15.0 36.0 \n",
"1276156 11.0 93.0 \n",
"1276157 9.0 93.0 \n",
"1276158 6.0 75.0 \n",
"1276159 3.0 59.0 \n",
"\n",
"[1276160 rows x 23 columns]\n"
]
}
],
"source": [
"'''Read and display data'''\n",
"data = pd.read_csv(\"Datasets/LA Crime Data/arrest-data-from-2010-to-present.csv\") #Load in the arrest data\n",
"print(data) #Display the data"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"'''Assigning attributes to use'''\n",
"data = data.head(n=100000)\n",
"\n",
"selectedData = data[[ #Here I am assigning all the values to see if any make a signifciant difference\n",
" 'Sex Code',\n",
" 'Descent Code',\n",
" 'Charge Description'\n",
"]].values"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Sex Code Decent Code Charge Description\n",
"0 M W VANDALISM\n",
"2 F O DRUNK DRIVING ALCOHOL/DRUGS\n",
"3 F W DRUNK DRIVING ALCOHOL/DRUGS\n",
"4 M W OBSTRUCT/RESIST EXECUTIVE OFFICER\n",
"5 M H PARENT IN CUSTODY, NO CARETAKER AVAILABLE\n",
"... ... ... ...\n",
"99993 M H POSSESSION CNTL SUBSTANCE PARAPHERNALIA\n",
"99994 M H CORPORAL INJURY ON SPOUSE/COHABITANT/ETC\n",
"99995 M B BATTERY TRANSPORTATION PERSONNEL/PASSENGR\n",
"99996 M W FTA AFTER WRITTEN PROMISE\n",
"99997 M B TERRORIZE CAUSING FEAR\n",
"\n",
"[87854 rows x 3 columns]\n"
]
}
],
"source": [
"selectedData = pd.DataFrame(selectedData, columns = ['Sex Code','Decent Code','Charge Description'])\n",
"selectedData = selectedData.dropna()\n",
"print(selectedData)\n"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(87854, 1) (87854, 1)\n"
]
}
],
"source": [
"X = selectedData[[ #Here I am assigning all the values to see if any make a signifciant difference\n",
" #'Sex Code',\n",
" 'Charge Description'\n",
"]].values\n",
"\n",
"\n",
"#Set the class\n",
"y = selectedData[[\n",
" 'Decent Code'\n",
"]]\n",
"\n",
"#X = selectedData.to_numpy()\n",
"\n",
"print(X.shape, y.shape)\n",
"\n",
"y = np.ravel(y)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'W', 'S', 'G', 'V', 'H', 'X', 'J', 'U', 'K', 'I', 'A', 'F', 'O', 'P', 'C', 'B'}\n",
"[[1010]\n",
" [295]\n",
" [295]\n",
" ...\n",
" [138]\n",
" [436]\n",
" [925]] [14 9 14 ... 1 14 1]\n"
]
}
],
"source": [
"'''Translate classification values into numerical values'''\n",
"Le = LabelEncoder() #Use the LabelEncoder library\n",
"for i in range(len(X[0])): #Iterate over instances of the data\n",
" X[:, i] = Le.fit_transform(X[:, i]) #Use fit_transform to convert values \n",
"\n",
"print(set(y))\n",
"yRealValues, y = np.unique(y, return_inverse=True)\n",
"\n",
"y = np.ravel(y) #Put the classification into a single 1D array to avoid future warning and error messages\n",
"print(X, y) #Ensure that the transformations have taken place correctly"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
"'''Simple function to train the models from the data'''\n",
"def trainModel(model):\n",
" model.fit(X_train, y_train) #Fit the model\n",
" prediction = model.predict(X_test) #Give the predictions for the y values\n",
" return round(metrics.accuracy_score(y_test, prediction), 3), classification_report(y_test, prediction) #Return the accuracy value and the report for the data"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"knn = neighbors.KNeighborsClassifier(n_neighbors=11, weights='uniform') #Define the KNN algorithm"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) #Split the dataset"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"def trainData(model):\n",
" start_time = time.time() #Set the starting execution time\n",
" acc, rep = trainModel(model) #set knn accuracy and report values on uncleaned data\n",
" finish_time = round(time.time() - start_time, 3)\n",
" print(\"{} seconds to run for {}\".format(finish_time, model)) #Display the runtime\n",
" return acc, rep"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.918 seconds to run for KNeighborsClassifier(n_neighbors=11)\n"
]
}
],
"source": [
"knn_acc, knn_rep = trainData(knn)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"6.491 seconds to run for LogisticRegression()\n"
]
}
],
"source": [
"log_acc, log_rep = trainData(LogisticRegression())"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"149.223 seconds to run for SVC()\n"
]
}
],
"source": [
"svc_acc, svc_rep = trainData(SVC())"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy = 0.473\n",
" precision recall f1-score support\n",
"\n",
" 0 0.62 0.20 0.30 40\n",
" 1 0.38 0.37 0.38 5292\n",
" 2 0.00 0.00 0.00 10\n",
" 3 0.00 0.00 0.00 11\n",
" 4 0.00 0.00 0.00 3\n",
" 5 0.53 0.72 0.61 8389\n",
" 6 0.00 0.00 0.00 3\n",
" 7 0.00 0.00 0.00 1\n",
" 8 0.00 0.00 0.00 6\n",
" 9 0.20 0.02 0.03 912\n",
" 10 0.00 0.00 0.00 3\n",
" 11 0.00 0.00 0.00 1\n",
" 12 0.00 0.00 0.00 1\n",
" 14 0.30 0.09 0.14 2899\n",
"\n",
" accuracy 0.47 17571\n",
" macro avg 0.14 0.10 0.10 17571\n",
"weighted avg 0.43 0.47 0.43 17571\n",
"\n",
"Accuracy = 0.477\n",
" precision recall f1-score support\n",
"\n",
" 0 0.00 0.00 0.00 40\n",
" 1 0.00 0.00 0.00 5292\n",
" 2 0.00 0.00 0.00 10\n",
" 3 0.00 0.00 0.00 11\n",
" 4 0.00 0.00 0.00 3\n",
" 5 0.48 1.00 0.65 8389\n",
" 6 0.00 0.00 0.00 3\n",
" 7 0.00 0.00 0.00 1\n",
" 8 0.00 0.00 0.00 6\n",
" 9 0.00 0.00 0.00 912\n",
" 10 0.00 0.00 0.00 3\n",
" 11 0.00 0.00 0.00 1\n",
" 12 0.00 0.00 0.00 1\n",
" 14 0.00 0.00 0.00 2899\n",
"\n",
" accuracy 0.48 17571\n",
" macro avg 0.03 0.07 0.05 17571\n",
"weighted avg 0.23 0.48 0.31 17571\n",
"\n",
"Accuracy = 0.477\n",
" precision recall f1-score support\n",
"\n",
" 0 0.00 0.00 0.00 40\n",
" 1 0.00 0.00 0.00 5292\n",
" 2 0.00 0.00 0.00 10\n",
" 3 0.00 0.00 0.00 11\n",
" 4 0.00 0.00 0.00 3\n",
" 5 0.48 1.00 0.65 8389\n",
" 6 0.00 0.00 0.00 3\n",
" 7 0.00 0.00 0.00 1\n",
" 8 0.00 0.00 0.00 6\n",
" 9 0.00 0.00 0.00 912\n",
" 10 0.00 0.00 0.00 3\n",
" 11 0.00 0.00 0.00 1\n",
" 12 0.00 0.00 0.00 1\n",
" 14 0.00 0.00 0.00 2899\n",
"\n",
" accuracy 0.48 17571\n",
" macro avg 0.03 0.07 0.05 17571\n",
"weighted avg 0.23 0.48 0.31 17571\n",
"\n"
]
}
],
"source": [
"print(\"Accuracy = {}\\n{}\".format(knn_acc, knn_rep)) #Display the accuracy and report for the entire dataset for knn\n",
"print(\"Accuracy = {}\\n{}\".format(log_acc, log_rep)) #Display the accuracy and report for the entire dataset for logistic regression\n",
"print(\"Accuracy = {}\\n{}\".format(svc_acc, svc_rep)) #Display the accuracy and report for the entire dataset for logistic regression"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"KNN Accuracy = 0.473\n",
"Logistic Regression Accuracy = 0.477\n",
"SVC Accuracy = 0.477\n"
]
}
],
"source": [
"print(\"KNN Accuracy = {}\".format(knn_acc)) #Display the accuracy and report for the entire dataset for knn\n",
"print(\"Logistic Regression Accuracy = {}\".format(log_acc)) #Display the accuracy and report for the entire dataset for logistic regression\n",
"print(\"SVC Accuracy = {}\".format(svc_acc)) #Display the accuracy and report for the entire dataset for logistic regression"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [],
"source": [
"scaler = StandardScaler() #Define which scaler to use\n",
"scale_X = scaler.fit_transform(X) #Scale the entire dataset\n",
"X_train, X_test, y_train, y_test = train_test_split(scale_X, y, test_size=0.2) #Split the dataset"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.877 seconds to run for KNeighborsClassifier(n_neighbors=11)\n"
]
}
],
"source": [
"knn_acc, knn_rep = trainData(knn)"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"5.836 seconds to run for LogisticRegression()\n"
]
}
],
"source": [
"log_acc, log_rep = trainData(LogisticRegression())"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy = 0.465\n",
" precision recall f1-score support\n",
"\n",
" 0 0.58 0.20 0.29 56\n",
" 1 0.39 0.39 0.39 5218\n",
" 2 0.00 0.00 0.00 8\n",
" 3 0.00 0.00 0.00 4\n",
" 4 0.00 0.00 0.00 2\n",
" 5 0.53 0.70 0.60 8458\n",
" 6 0.00 0.00 0.00 6\n",
" 7 0.00 0.00 0.00 1\n",
" 8 0.00 0.00 0.00 5\n",
" 9 0.18 0.01 0.01 858\n",
" 10 0.00 0.00 0.00 2\n",
" 13 0.00 0.00 0.00 1\n",
" 14 0.19 0.08 0.11 2950\n",
" 15 0.00 0.00 0.00 2\n",
"\n",
" accuracy 0.47 17571\n",
" macro avg 0.13 0.10 0.10 17571\n",
"weighted avg 0.41 0.47 0.43 17571\n",
"\n",
"Accuracy = 0.481\n",
" precision recall f1-score support\n",
"\n",
" 0 0.00 0.00 0.00 56\n",
" 1 0.00 0.00 0.00 5218\n",
" 2 0.00 0.00 0.00 8\n",
" 3 0.00 0.00 0.00 4\n",
" 4 0.00 0.00 0.00 2\n",
" 5 0.48 1.00 0.65 8458\n",
" 6 0.00 0.00 0.00 6\n",
" 7 0.00 0.00 0.00 1\n",
" 8 0.00 0.00 0.00 5\n",
" 9 0.00 0.00 0.00 858\n",
" 10 0.00 0.00 0.00 2\n",
" 13 0.00 0.00 0.00 1\n",
" 14 0.00 0.00 0.00 2950\n",
" 15 0.00 0.00 0.00 2\n",
"\n",
" accuracy 0.48 17571\n",
" macro avg 0.03 0.07 0.05 17571\n",
"weighted avg 0.23 0.48 0.31 17571\n",
"\n"
]
}
],
"source": [
"print(\"Accuracy = {}\\n{}\".format(knn_acc, knn_rep)) #Display the accuracy and report for the entire dataset for knn\n",
"print(\"Accuracy = {}\\n{}\".format(log_acc, log_rep)) #Display the accuracy and report for the entire dataset for logistic regression"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [],
"source": [
"'''Plot a confusion matrix'''\n",
"def c_m():\n",
" start_time = time.time() #Set the starting execution time\n",
" y_pred = accpred() #Get the predicted values for y\n",
" matrix = confusion_matrix(y_test, y_pred) #Build a confusion matrix for the predicted and actual values\n",
" #real_vals = ['K', 'P', 'S', 'D', 'F', 'G', 'C', 'H', 'L', 'A', 'I', 'X', 'W', 'Z', 'U', 'B', 'J', 'V', 'O'] #Set the prediction values back to diagnosis\n",
" real_vals = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N']\n",
" df_cm = pd.DataFrame(matrix, columns=np.unique(real_vals), index = np.unique(real_vals)) #Set the columns as the real values\n",
" df_cm.index.name = 'Actual' #Label the x axis as Actual\n",
" df_cm.columns.name = 'Predicted' #Label the y axis as Predicted\n",
" plt.figure(figsize = (10,7)) #Set the size\n",
" sn.set(font_scale=1.4) #Label size\n",
" sn.heatmap(df_cm, cmap=\"Blues\", annot=True,annot_kws={\"size\": 16}) #Font size\n",
" finish_time = round(time.time() - start_time, 3)\n",
" print(\"{} seconds to run\".format(finish_time)) #Display the runtime"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [],
"source": [
"def accpred():\n",
" model.fit(X_train, y_train) #Fit the model\n",
" return model.predict(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"KNN\n",
"1.137 seconds to run\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x504 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"'''Display the confusion matrix for knn'''\n",
"model = knn #Set the model as knn\n",
"print(\"KNN\")\n",
"c_m()"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Logistic Regression\n",
"6.558 seconds to run\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x504 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"'''Display the confusion matrix for logistic regession'''\n",
"model = LogisticRegression() #Set the model as logistic regression\n",
"print(\"Logistic Regression\")\n",
"c_m()"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SVC\n",
"149.112 seconds to run\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x504 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"model = SVC() #Set the model as logistic regression\n",
"print(\"SVC\")\n",
"c_m()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
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