{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "taruma_udemy_som.ipynb",
"version": "0.3.2",
"provenance": [],
"collapsed_sections": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "ZaMSaVWtMeEm",
"colab_type": "text"
},
"source": [
"# Self Organizing Map (SOM)\n",
"\n",
"Notebook ini berdasarkan kursus __Deep Learning A-Z™: Hands-On Artificial Neural Networks__ di Udemy. [Lihat Kursus](https://www.udemy.com/deeplearning/).\n",
"\n",
"## Informasi Notebook\n",
"- __notebook name__: `taruma_udemy_som`\n",
"- __notebook version/date__: `1.0.1`/`20190729`\n",
"- __notebook server__: Google Colab\n",
"- __python version__: `3.6`"
]
},
{
"cell_type": "code",
"metadata": {
"id": "7VFXA8oJMeD8",
"colab_type": "code",
"outputId": "baddca08-e62e-46e9-bc2b-0bbd1e05d524",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 51
}
},
"source": [
"#### NOTEBOOK DESCRIPTION\n",
"\n",
"from datetime import datetime\n",
"\n",
"NOTEBOOK_TITLE = 'taruma_udemy_som'\n",
"NOTEBOOK_VERSION = '1.0.0'\n",
"NOTEBOOK_DATE = 1 # Set 1, if you want add date classifier\n",
"\n",
"NOTEBOOK_NAME = \"{}_{}\".format(\n",
" NOTEBOOK_TITLE, \n",
" NOTEBOOK_VERSION.replace('.','_')\n",
")\n",
"PROJECT_NAME = \"{}_{}{}\".format(\n",
" NOTEBOOK_TITLE, \n",
" NOTEBOOK_VERSION.replace('.','_'), \n",
" \"_\" + datetime.utcnow().strftime(\"%Y%m%d_%H%M\") if NOTEBOOK_DATE else \"\"\n",
")\n",
"\n",
"print(f\"Nama Notebook: {NOTEBOOK_NAME}\")\n",
"print(f\"Nama Proyek: {PROJECT_NAME}\")"
],
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"text": [
"Nama Notebook: taruma_udemy_som_1_0_0\n",
"Nama Proyek: taruma_udemy_som_1_0_0_20190729_1028\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "2ISqUL3XNGIH",
"colab_type": "code",
"outputId": "d6815c85-77f0-4b91-dcf8-c25c306995f0",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 51
}
},
"source": [
"#### System Version\n",
"import sys\n",
"print(\"Versi python: {}\".format(sys.version))"
],
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"text": [
"Versi python: 3.6.8 (default, Jan 14 2019, 11:02:34) \n",
"[GCC 8.0.1 20180414 (experimental) [trunk revision 259383]]\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "fIWH2KDNOtrj",
"colab_type": "code",
"colab": {}
},
"source": [
"#### Load Notebook Extensions\n",
"%load_ext google.colab.data_table"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "VG2h0H_oNxWa",
"colab_type": "code",
"outputId": "fad4982d-81c7-4f19-d5de-212497b731d4",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 357
}
},
"source": [
"#### Download dataset\n",
"!wget -O SOM.zip \"https://sds-platform-private.s3-us-east-2.amazonaws.com/uploads/P16-Self-Organizing-Maps.zip\"\n",
"!unzip SOM.zip"
],
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"text": [
"--2019-07-29 10:28:35-- https://sds-platform-private.s3-us-east-2.amazonaws.com/uploads/P16-Self-Organizing-Maps.zip\n",
"Resolving sds-platform-private.s3-us-east-2.amazonaws.com (sds-platform-private.s3-us-east-2.amazonaws.com)... 52.219.96.64\n",
"Connecting to sds-platform-private.s3-us-east-2.amazonaws.com (sds-platform-private.s3-us-east-2.amazonaws.com)|52.219.96.64|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 17004 (17K) [application/zip]\n",
"Saving to: ‘SOM.zip’\n",
"\n",
"SOM.zip 100%[===================>] 16.61K --.-KB/s in 0s \n",
"\n",
"2019-07-29 10:28:36 (124 MB/s) - ‘SOM.zip’ saved [17004/17004]\n",
"\n",
"Archive: SOM.zip\n",
" creating: Self_Organizing_Maps/\n",
" inflating: Self_Organizing_Maps/Credit_Card_Applications.csv \n",
" creating: __MACOSX/\n",
" creating: __MACOSX/Self_Organizing_Maps/\n",
" inflating: __MACOSX/Self_Organizing_Maps/._Credit_Card_Applications.csv \n",
" inflating: Self_Organizing_Maps/minisom.py \n",
" inflating: Self_Organizing_Maps/som.py \n",
" inflating: __MACOSX/Self_Organizing_Maps/._som.py \n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "pJg0aH99NxVl",
"colab_type": "code",
"colab": {}
},
"source": [
"#### Atur dataset path\n",
"DATASET_DIRECTORY = 'Self_Organizing_Maps/'"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "qBpzPqOXOTLN",
"colab_type": "text"
},
"source": [
"# STEP 1"
]
},
{
"cell_type": "code",
"metadata": {
"id": "zZjimNJLOPDO",
"colab_type": "code",
"colab": {}
},
"source": [
"# Importing the libraries\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "8jWXCPBNOaqw",
"colab_type": "code",
"outputId": "5543eefa-994e-40fa-b7af-0b98c55e51aa",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 301
}
},
"source": [
"# Importing the dataset\n",
"# Dataset http://archive.ics.uci.edu/ml/datasets/statlog+(australian+credit+approval)\n",
"dataset = pd.read_csv(DATASET_DIRECTORY + 'Credit_Card_Applications.csv')\n",
"dataset"
],
"execution_count": 9,
"outputs": [
{
"output_type": "execute_result",
"data": {
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" 2 | \n",
" 4 | \n",
" 0.500 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 2 | \n",
" 144 | \n",
" 1 | \n",
" 0 | \n",
"
\n",
" \n",
" | 665 | \n",
" 15679622 | \n",
" 1 | \n",
" 31.57 | \n",
" 4.000 | \n",
" 1 | \n",
" 3 | \n",
" 4 | \n",
" 0.085 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 1 | \n",
" 2 | \n",
" 411 | \n",
" 1 | \n",
" 0 | \n",
"
\n",
" \n",
" | 666 | \n",
" 15730150 | \n",
" 1 | \n",
" 26.75 | \n",
" 4.500 | \n",
" 1 | \n",
" 8 | \n",
" 5 | \n",
" 2.500 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 2 | \n",
" 200 | \n",
" 1211 | \n",
" 0 | \n",
"
\n",
" \n",
" | 667 | \n",
" 15813192 | \n",
" 0 | \n",
" 24.92 | \n",
" 1.250 | \n",
" 2 | \n",
" 1 | \n",
" 1 | \n",
" 0.000 | \n",
" 1 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 2 | \n",
" 80 | \n",
" 1 | \n",
" 0 | \n",
"
\n",
" \n",
" | 668 | \n",
" 15606554 | \n",
" 0 | \n",
" 32.25 | \n",
" 1.500 | \n",
" 2 | \n",
" 8 | \n",
" 4 | \n",
" 0.250 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 1 | \n",
" 2 | \n",
" 372 | \n",
" 123 | \n",
" 0 | \n",
"
\n",
" \n",
" | 669 | \n",
" 15611794 | \n",
" 1 | \n",
" 17.67 | \n",
" 4.460 | \n",
" 2 | \n",
" 8 | \n",
" 4 | \n",
" 0.250 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 1 | \n",
" 80 | \n",
" 1 | \n",
" 0 | \n",
"
\n",
" \n",
" | 670 | \n",
" 15672357 | \n",
" 0 | \n",
" 37.75 | \n",
" 5.500 | \n",
" 2 | \n",
" 11 | \n",
" 4 | \n",
" 0.125 | \n",
" 1 | \n",
" 0 | \n",
" 0 | \n",
" 1 | \n",
" 2 | \n",
" 228 | \n",
" 1 | \n",
" 1 | \n",
"
\n",
" \n",
" | 671 | \n",
" 15711759 | \n",
" 1 | \n",
" 22.67 | \n",
" 2.540 | \n",
" 1 | \n",
" 8 | \n",
" 8 | \n",
" 2.585 | \n",
" 1 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 2 | \n",
" 0 | \n",
" 1 | \n",
" 1 | \n",
"
\n",
" \n",
" | 672 | \n",
" 15615296 | \n",
" 0 | \n",
" 17.92 | \n",
" 10.210 | \n",
" 2 | \n",
" 1 | \n",
" 1 | \n",
" 0.000 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 2 | \n",
" 0 | \n",
" 51 | \n",
" 0 | \n",
"
\n",
" \n",
" | 673 | \n",
" 15699294 | \n",
" 1 | \n",
" 24.42 | \n",
" 12.335 | \n",
" 2 | \n",
" 11 | \n",
" 8 | \n",
" 1.585 | \n",
" 1 | \n",
" 0 | \n",
" 0 | \n",
" 1 | \n",
" 2 | \n",
" 120 | \n",
" 1 | \n",
" 1 | \n",
"
\n",
" \n",
" | 674 | \n",
" 15788634 | \n",
" 0 | \n",
" 25.75 | \n",
" 0.500 | \n",
" 2 | \n",
" 8 | \n",
" 8 | \n",
" 0.875 | \n",
" 1 | \n",
" 0 | \n",
" 0 | \n",
" 1 | \n",
" 2 | \n",
" 491 | \n",
" 1 | \n",
" 1 | \n",
"
\n",
" \n",
" | 675 | \n",
" 15660871 | \n",
" 1 | \n",
" 26.17 | \n",
" 12.500 | \n",
" 1 | \n",
" 4 | \n",
" 8 | \n",
" 1.250 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 1 | \n",
" 2 | \n",
" 0 | \n",
" 18 | \n",
" 0 | \n",
"
\n",
" \n",
" | 676 | \n",
" 15618258 | \n",
" 0 | \n",
" 22.75 | \n",
" 6.165 | \n",
" 2 | \n",
" 6 | \n",
" 4 | \n",
" 0.165 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 2 | \n",
" 220 | \n",
" 1001 | \n",
" 0 | \n",
"
\n",
" \n",
" | 677 | \n",
" 15722535 | \n",
" 1 | \n",
" 23.00 | \n",
" 0.750 | \n",
" 2 | \n",
" 7 | \n",
" 4 | \n",
" 0.500 | \n",
" 1 | \n",
" 0 | \n",
" 0 | \n",
" 1 | \n",
" 1 | \n",
" 320 | \n",
" 1 | \n",
" 0 | \n",
"
\n",
" \n",
" | 678 | \n",
" 15711977 | \n",
" 1 | \n",
" 25.67 | \n",
" 0.290 | \n",
" 1 | \n",
" 8 | \n",
" 4 | \n",
" 1.500 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 1 | \n",
" 2 | \n",
" 160 | \n",
" 1 | \n",
" 0 | \n",
"
\n",
" \n",
" | 679 | \n",
" 15690169 | \n",
" 1 | \n",
" 48.58 | \n",
" 0.205 | \n",
" 1 | \n",
" 4 | \n",
" 4 | \n",
" 0.250 | \n",
" 1 | \n",
" 1 | \n",
" 11 | \n",
" 0 | \n",
" 2 | \n",
" 380 | \n",
" 2733 | \n",
" 1 | \n",
"
\n",
" \n",
" | 680 | \n",
" 15790689 | \n",
" 1 | \n",
" 21.17 | \n",
" 0.000 | \n",
" 2 | \n",
" 8 | \n",
" 4 | \n",
" 0.500 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 1 | \n",
" 1 | \n",
" 0 | \n",
" 1 | \n",
" 0 | \n",
"
\n",
" \n",
" | 681 | \n",
" 15665181 | \n",
" 1 | \n",
" 35.25 | \n",
" 16.500 | \n",
" 1 | \n",
" 8 | \n",
" 4 | \n",
" 4.000 | \n",
" 1 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 2 | \n",
" 80 | \n",
" 1 | \n",
" 0 | \n",
"
\n",
" \n",
" | 682 | \n",
" 15633608 | \n",
" 0 | \n",
" 22.92 | \n",
" 11.585 | \n",
" 2 | \n",
" 13 | \n",
" 4 | \n",
" 0.040 | \n",
" 1 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 2 | \n",
" 80 | \n",
" 1350 | \n",
" 1 | \n",
"
\n",
" \n",
" | 683 | \n",
" 15805261 | \n",
" 0 | \n",
" 48.17 | \n",
" 1.335 | \n",
" 2 | \n",
" 3 | \n",
" 7 | \n",
" 0.335 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 2 | \n",
" 0 | \n",
" 121 | \n",
" 0 | \n",
"
\n",
" \n",
" | 684 | \n",
" 15740356 | \n",
" 1 | \n",
" 43.00 | \n",
" 0.290 | \n",
" 1 | \n",
" 13 | \n",
" 8 | \n",
" 1.750 | \n",
" 1 | \n",
" 1 | \n",
" 8 | \n",
" 0 | \n",
" 2 | \n",
" 100 | \n",
" 376 | \n",
" 1 | \n",
"
\n",
" \n",
" | 685 | \n",
" 15808223 | \n",
" 1 | \n",
" 31.57 | \n",
" 10.500 | \n",
" 2 | \n",
" 14 | \n",
" 4 | \n",
" 6.500 | \n",
" 1 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 2 | \n",
" 0 | \n",
" 1 | \n",
" 1 | \n",
"
\n",
" \n",
" | 686 | \n",
" 15769980 | \n",
" 1 | \n",
" 20.67 | \n",
" 0.415 | \n",
" 2 | \n",
" 8 | \n",
" 4 | \n",
" 0.125 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 2 | \n",
" 0 | \n",
" 45 | \n",
" 0 | \n",
"
\n",
" \n",
" | 687 | \n",
" 15675450 | \n",
" 0 | \n",
" 18.83 | \n",
" 9.540 | \n",
" 2 | \n",
" 6 | \n",
" 4 | \n",
" 0.085 | \n",
" 1 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 2 | \n",
" 100 | \n",
" 1 | \n",
" 1 | \n",
"
\n",
" \n",
" | 688 | \n",
" 15776494 | \n",
" 0 | \n",
" 27.42 | \n",
" 14.500 | \n",
" 2 | \n",
" 14 | \n",
" 8 | \n",
" 3.085 | \n",
" 1 | \n",
" 1 | \n",
" 1 | \n",
" 0 | \n",
" 2 | \n",
" 120 | \n",
" 12 | \n",
" 1 | \n",
"
\n",
" \n",
" | 689 | \n",
" 15592412 | \n",
" 1 | \n",
" 41.00 | \n",
" 0.040 | \n",
" 2 | \n",
" 10 | \n",
" 4 | \n",
" 0.040 | \n",
" 0 | \n",
" 1 | \n",
" 1 | \n",
" 0 | \n",
" 1 | \n",
" 560 | \n",
" 1 | \n",
" 1 | \n",
"
\n",
" \n",
"
\n",
"
690 rows × 16 columns
\n",
"
"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "_-y8U7V_XqFp",
"colab_type": "text"
},
"source": [
"# STEP 4"
]
},
{
"cell_type": "code",
"metadata": {
"id": "m94FIHOYUXHM",
"colab_type": "code",
"colab": {}
},
"source": [
"# Finding the frauds\n",
"mappings = som.win_map(X)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "qA07M9R4X0UV",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 301
},
"outputId": "2de6b4e5-f085-45c5-ead6-3190a9c0d7eb"
},
"source": [
"frauds = np.concatenate((mappings[(3, 3)], mappings[(8, 8)]), axis=0)\n",
"pd.DataFrame(frauds)"
],
"execution_count": 37,
"outputs": [
{
"output_type": "execute_result",
"data": {
"application/vnd.google.colaboratory.module+javascript": "\n import \"https://ssl.gstatic.com/colaboratory/data_table/81868506e94e6988/data_table.js\";\n\n window.createDataTable({\n data: [[{\n 'v': 0,\n 'f': \"0\",\n },\n{\n 'v': 15683168,\n 'f': \"15683168\",\n },\n{\n 'v': 1,\n 'f': \"1\",\n },\n{\n 'v': 29.58,\n 'f': \"29.58\",\n },\n{\n 'v': 4.5,\n 'f': \"4.5\",\n },\n{\n 'v': 2,\n 'f': \"2\",\n },\n{\n 'v': 9,\n 'f': \"9\",\n },\n{\n 'v': 4,\n 'f': \"4\",\n },\n{\n 'v': 7.5,\n 'f': \"7.5\",\n },\n{\n 'v': 1,\n 'f': \"1\",\n },\n{\n 'v': 1,\n 'f': \"1\",\n },\n{\n 'v': 2,\n 'f': \"2\",\n },\n{\n 'v': 1,\n 'f': \"1\",\n },\n{\n 'v': 2,\n 'f': \"2\",\n },\n{\n 'v': 330,\n 'f': \"330\",\n },\n{\n 'v': 1,\n 'f': \"1\",\n }],\n [{\n 'v': 1,\n 'f': \"1\",\n },\n{\n 'v': 15682860,\n 'f': \"15682860\",\n },\n{\n 'v': 1,\n 'f': \"1\",\n },\n{\n 'v': 27.83,\n 'f': \"27.83\",\n },\n{\n 'v': 1.5,\n 'f': \"1.5\",\n },\n{\n 'v': 2,\n 'f': \"2\",\n },\n{\n 'v': 9,\n 'f': \"9\",\n },\n{\n 'v': 4,\n 'f': \"4\",\n },\n{\n 'v': 2,\n 'f': \"2\",\n },\n{\n 'v': 1,\n 'f': \"1\",\n },\n{\n 'v': 1,\n 'f': \"1\",\n },\n{\n 'v': 11,\n 'f': \"11\",\n },\n{\n 'v': 1,\n 'f': \"1\",\n },\n{\n 'v': 2,\n 'f': \"2\",\n },\n{\n 'v': 434,\n 'f': \"434\",\n },\n{\n 'v': 36,\n 'f': \"36\",\n }],\n [{\n 'v': 2,\n 'f': \"2\",\n },\n{\n 'v': 15684512,\n 'f': \"15684512\",\n },\n{\n 'v': 1,\n 'f': \"1\",\n },\n{\n 'v': 42.5,\n 'f': \"42.5\",\n },\n{\n 'v': 4.915,\n 'f': \"4.915\",\n },\n{\n 'v': 1,\n 'f': \"1\",\n },\n{\n 'v': 9,\n 'f': \"9\",\n },\n{\n 'v': 4,\n 'f': \"4\",\n },\n{\n 'v': 3.165,\n 'f': \"3.165\",\n },\n{\n 'v': 1,\n 'f': \"1\",\n },\n{\n 'v': 0,\n 'f': \"0\",\n },\n{\n 'v': 0,\n 'f': \"0\",\n },\n{\n 'v': 1,\n 'f': \"1\",\n },\n{\n 'v': 2,\n 'f': \"2\",\n },\n{\n 'v': 52,\n 'f': \"52\",\n },\n{\n 'v': 1443,\n 'f': \"1443\",\n }],\n [{\n 'v': 3,\n 'f': \"3\",\n },\n{\n 'v': 15682576,\n 'f': \"15682576\",\n },\n{\n 'v': 0,\n 'f': \"0\",\n },\n{\n 'v': 32.17,\n 'f': \"32.17\",\n },\n{\n 'v': 1.46,\n 'f': \"1.46\",\n },\n{\n 'v': 2,\n 'f': \"2\",\n },\n{\n 'v': 9,\n 'f': \"9\",\n },\n{\n 'v': 4,\n 'f': \"4\",\n },\n{\n 'v': 1.085,\n 'f': \"1.085\",\n },\n{\n 'v': 1,\n 'f': \"1\",\n },\n{\n 'v': 1,\n 'f': \"1\",\n },\n{\n 'v': 16,\n 'f': \"16\",\n },\n{\n 'v': 0,\n 'f': \"0\",\n },\n{\n 'v': 2,\n 'f': \"2\",\n },\n{\n 'v': 120,\n 'f': \"120\",\n },\n{\n 'v': 2080,\n 'f': \"2080\",\n }],\n [{\n 'v': 4,\n 'f': \"4\",\n },\n{\n 'v': 15682686,\n 'f': \"15682686\",\n },\n{\n 'v': 0,\n 'f': \"0\",\n },\n{\n 'v': 31.25,\n 'f': \"31.25\",\n },\n{\n 'v': 3.75,\n 'f': \"3.75\",\n },\n{\n 'v': 2,\n 'f': \"2\",\n },\n{\n 'v': 13,\n 'f': \"13\",\n },\n{\n 'v': 8,\n 'f': \"8\",\n },\n{\n 'v': 0.625,\n 'f': \"0.625\",\n },\n{\n 'v': 1,\n 'f': \"1\",\n },\n{\n 'v': 1,\n 'f': \"1\",\n },\n{\n 'v': 9,\n 'f': \"9\",\n },\n{\n 'v': 1,\n 'f': \"1\",\n },\n{\n 'v': 2,\n 'f': \"2\",\n },\n{\n 'v': 181,\n 'f': \"181\",\n },\n{\n 'v': 1,\n 'f': \"1\",\n }],\n [{\n 'v': 5,\n 'f': \"5\",\n },\n{\n 'v': 15684722,\n 'f': \"15684722\",\n },\n{\n 'v': 0,\n 'f': \"0\",\n },\n{\n 'v': 27.67,\n 'f': \"27.67\",\n },\n{\n 'v': 1.5,\n 'f': \"1.5\",\n },\n{\n 'v': 2,\n 'f': \"2\",\n },\n{\n 'v': 7,\n 'f': \"7\",\n },\n{\n 'v': 4,\n 'f': \"4\",\n },\n{\n 'v': 2,\n 'f': \"2\",\n },\n{\n 'v': 1,\n 'f': \"1\",\n },\n{\n 'v': 0,\n 'f': \"0\",\n },\n{\n 'v': 0,\n 'f': \"0\",\n },\n{\n 'v': 0,\n 'f': \"0\",\n },\n{\n 'v': 1,\n 'f': \"1\",\n },\n{\n 'v': 368,\n 'f': \"368\",\n },\n{\n 'v': 1,\n 'f': \"1\",\n }],\n [{\n 'v': 6,\n 'f': \"6\",\n },\n{\n 'v': 15682540,\n 'f': \"15682540\",\n },\n{\n 'v': 1,\n 'f': \"1\",\n },\n{\n 'v': 62.5,\n 'f': \"62.5\",\n },\n{\n 'v': 12.75,\n 'f': \"12.75\",\n },\n{\n 'v': 1,\n 'f': \"1\",\n },\n{\n 'v': 8,\n 'f': \"8\",\n },\n{\n 'v': 8,\n 'f': \"8\",\n },\n{\n 'v': 5,\n 'f': \"5\",\n },\n{\n 'v': 1,\n 'f': \"1\",\n },\n{\n 'v': 0,\n 'f': \"0\",\n },\n{\n 'v': 0,\n 'f': \"0\",\n },\n{\n 'v': 0,\n 'f': \"0\",\n },\n{\n 'v': 2,\n 'f': \"2\",\n 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