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{
"cells": [
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": true,
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"\n",
"df = pd.read_csv('transaction_dataset.csv')\n",
"df.columns = [x.lower() for x in df.columns]"
]
},
{
"cell_type": "code",
"execution_count": 30,
"outputs": [],
"source": [
"# Drop unnecessary columns like Token Types, Address, Index\n",
"cols_to_drop = [\n",
" ' erc20 most sent token type',\n",
" ' erc20_most_rec_token_type',\n",
" 'address',\n",
" 'index',\n",
" 'unnamed: 0'\n",
"]\n",
"\n",
"features = [x for x in df.columns if (x != 'flag' and x not in cols_to_drop)]"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 31,
"outputs": [],
"source": [
"# Get only features that have unique values greater than 1\n",
"unique_values = df.nunique()\n",
"\n",
"features = [x for x in features if x in unique_values.loc[(unique_values>1)]]"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 32,
"outputs": [],
"source": [
"from sklearn.base import BaseEstimator, TransformerMixin\n",
"from sklearn.preprocessing import StandardScaler, OneHotEncoder, FunctionTransformer\n",
"\n",
"\n",
"class BasePipeStep(BaseEstimator, TransformerMixin):\n",
"\n",
" def __init__(self, columns=[]):\n",
" self.columns = columns\n",
"\n",
" def fit(self, X, y=None):\n",
" return self\n",
"\n",
" def transform(self, X):\n",
" X = X.copy()\n",
" return X\n",
"\n",
"class SelectColumns(BasePipeStep):\n",
"\n",
" def transform(self, X):\n",
" X = X.copy()\n",
" return X[self.columns]\n",
"\n",
"class FillNumericData(BasePipeStep):\n",
"\n",
" def fit(self, X, y=None):\n",
" self.means = { col: X[col].mean() for col in self.columns}\n",
" return self\n",
"\n",
" def transform(self, X):\n",
" X = X.copy()\n",
" for col in self.columns:\n",
" X[col] = X[col].fillna(self.means[col])\n",
" return X\n",
"\n",
"\n",
"class ScaleNumeric(BasePipeStep):\n",
"\n",
" def fit(self, X, y=None):\n",
" self.scaler = StandardScaler()\n",
" self.scaler.fit(X[self.columns])\n",
" return self\n",
"\n",
" def transform(self, X):\n",
" X = X.copy()\n",
" X[self.columns] = self.scaler.transform(X[self.columns])\n",
" return X\n",
"\n",
"class GetValues(BasePipeStep):\n",
"\n",
" def transform(self, X):\n",
" X = X.copy()\n",
" return X.values"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 33,
"outputs": [],
"source": [
"from sklearn.pipeline import Pipeline\n",
"preprocessing = Pipeline([\n",
" ('feature_selection', SelectColumns(features)),\n",
" ('fill_missing', FillNumericData(features)),\n",
" ('standard_scaling', ScaleNumeric(features)),\n",
" ('returnValues', GetValues())\n",
"])"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 34,
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"\n",
"X = df[features]\n",
"y = df['flag']\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(\n",
" X, y, test_size=0.2, random_state=42)"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 35,
"outputs": [],
"source": [
"X_train = preprocessing.fit_transform(X_train)\n",
"X_test = preprocessing.transform(X_test)\n"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 36,
"outputs": [],
"source": [
"import susi\n",
"from sklearn import metrics\n",
"from imblearn.over_sampling import SMOTE\n",
"\n",
"\n",
"\n",
"\n",
"sm = SMOTE(random_state=42)\n",
"X_train_smote, Y_train_smote = sm.fit_resample(X_train,y_train)\n"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 37,
"outputs": [],
"source": [
"def train(som, name):\n",
" history = som.fit(X_train_smote, Y_train_smote)\n",
" y_pred = som.predict(X_test)\n",
"\n",
" print(\"Accuracy: {0:.1f} %\".format(som.score(X_test, y_test)*100))\n",
"\n",
" score = metrics.roc_auc_score(y_test, y_pred)\n",
"\n",
" print(f'Area under ROC of Model On Test Set - {score:,.2%}')\n",
"\n",
" plot_confusion_matrix(y_test, y_pred, name)"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 38,
"outputs": [],
"source": [
"import seaborn as sns\n",
"def plot_confusion_matrix(y_test, y_prediction, name):\n",
" disp = metrics.confusion_matrix(y_test, y_prediction)\n",
"\n",
"\n",
" df_cm = pd.DataFrame(disp, index = ['Valid', 'Fraud'],\n",
" columns = ['Valid', 'Fraud'])\n",
"\n",
"\n",
" plt.figure(figsize = (10,7))\n",
" cf_matrix = sns.heatmap(df_cm, annot=True, fmt='.1f')\n",
" cf_matrix.figure.savefig(\"confusion_matrix - %s\" % name)"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 39,
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"def plot_probability(som, name):\n",
"# calculate probabilities\n",
" proba = som.predict_proba(X_test)\n",
"\n",
"# plot probabilities\n",
" class_index = 1\n",
" plt.hist(proba[:, class_index])\n",
" plt.xlabel(f\"Probability for class {class_index} - Fraud\")\n",
" plt.ylabel(\"Number of datapoints\")\n",
" # plt.show()\n",
" plt.savefig(\"probability - %s\" % name)"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 40,
"outputs": [],
"source": [
"from susi.SOMPlots import plot_umatrix, plot_estimation_map\n",
"\n",
"def plot_u_matrix(som, name):\n",
" u_matrix = som.get_u_matrix()\n",
" plot_umatrix(u_matrix, 2, 2)\n",
" plt.savefig(\"distance matrix - %s\" % name)\n",
"# plt.show()"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 47,
"outputs": [],
"source": [
"def plot_estim_map(som, name):\n",
" estimation_map = som.get_estimation_map().squeeze()\n",
" plot_estimation_map(estimation_map)\n",
" plt.savefig(\"estimation map - %s\" % name)"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 46,
"outputs": [],
"source": [
"from susi.SOMPlots import plot_umatrix, plot_estimation_map\n",
"number_of_epochs = 100000"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 48,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy: 66.1 %\n",
"Area under ROC of Model On Test Set - 64.40%\n"
]
},
{
"data": {
"text/plain": "<Figure size 720x504 with 2 Axes>",
"image/png": 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\n"
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": "<Figure size 432x432 with 2 Axes>",
"image/png": 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AXwIXAXOAnYGTgY9K2i4iXmnQrg8Cl5N2+fkSsAJwOHCrpK0iYlonGl5l505UmhOex5Me6fLz4vPEiPg7gNL/ch8ozpuZWRd1KDwvB06JiBcqjv1I0v2kDQcOIu3e01+bRgBnAY8BO0TES8Xx64A/AycAh3Si4ZUiYnIn6m16wlARlJsCewJjI+KAitOvA07HM23NzLqqUxOGImJqVXD2ubR4b/QYr51Im67/uC84i3rvJM2R2bvMyxuzNkmIiGeBa2ocf460bMXMzLqsyxOG1iren2pQbuvi/bYa5/4IvAPYGLinTe2qSdJxTRSLiDgpp96mw1PSr0k3em+KiDtyLmJmZp0zbFj2Mz5GS6pcejghIiY0+pKk4cCxwFzg4gbF1yzen6hxru/YGDocnqTh4f4Eaau+ADoTnsCWpIlCIek5YDJwI2m5yj9yLmpmZu3TQs9zRkRs1cKlzgC2B46OiPsalF26eK+1devsqjKdtH6NY0uQZhAfQZrElL1HQdPhGRGrS9oM2IU0e2kn0iShkPQUabLQ7yLi3NxGmJlZa7q18UHxSK9DSb3UU5r4St9ayiVrnFuqqkzHRMQj/Zx6QNINwM3AASy89LKhrL5+RPw9Is6KiA8Cq5B6o18iLUD9OHBOTn1mZjZwnd5hqNjX/BjgPODTTX6tbxnKmBrn+o7VGtLtmogI0qzi/XK/29JTVSStRrrZu0vxvh5pDPz2VuozM7PBqQjO44GJwMFF4DRjSvG+PfDbqnPbATOBf7ajjQM0Esjeui9nwtCeLAjMzUg3WO8CfkG693lzsztOmJlZ+3Rq2LaYqXo8cCFwYETM76fcGqR7h49GRN9Q7GTSxjkHSzq9Yp3nm4GxwHkRMacjDW+SpK2Aw4DseTs5Pc8rgXmkNT7HApOKJSpmZtZDHdqe73PAicCjpJ7jJ6qu81RE3FD8+RTSpJudSWs4iYg5kg4jZcYtks4BlidN0nmaFModJ+nBfk6tBCxHGjU9OLfenPC8H9gI+ChpK76tJN0I3BoRs+t+08zMOqZDPc++dZrrkIZsq00Gbqhx/D8i4jJJr5Dul36XBXvbfiUiunW/81HSSOlCTSM9GPufpAlQD+dWmjPb9g2SxpCGbt8BfBI4CnhV0h9ZsGzl1txGmJlZazo12zYixgHjBlo2In4F/KpNzcoWEWM7UW/ubNsnIuLCiDggItYj9UQPB1YlLUTtyB6CZmbWPz/Ps/tanW27EQt6oDsDo4tTz7SpXU0ZNmwYI0eO7OYlB2TevHm9bkKWESPKte1k2f5RGDVqVK+bkO3f//53r5uQ5fHHH+91E5r22muvtfzdsv1/v5skbQhsGBHXVxzbljSUvBLpIScNd1eqljPbdn8WhOUY0pZGM0kLTPuGbP+a2wAzMxsYh2dd3yaF5PUAkkaTHqe5LPAKcLak6RFxVU6lOT3P84oL3Qr8kBSYU/ubumxmZp0nqZW9bYeSrYDKnuXHSbN+tyBNGJpEWq5yVU6lOeE5Frit1+tyzMxsYe551rUKC3Y7Ang3aZXI3wAk/Yz0fNIsOc/zvNnBaWY2+HjCUF0vk5453fdUmLeTbjf2eYXUE82SNWFI0jDSQtgPAK8vDj8IXAFc4CFcM7PuG4KBmOMeYD9JFwAfId3rrFyfui5p04YsOROGRgHXAjuSFpg+WZzaHXhv0bjdvWGCmVl3OTzrOhW4GphefL4DuKXi/G6kDROy5NxlPob0GLLxwCoRsXZErE1apvJd0j3R7HFjMzNrXe6Q7VAL2oj4NWmlyBmk7QZ369vcXtLKwOPA+bn15gzb7g38PCK+XNWw54GvSFqXNIvp2NxGmJlZ64ZaIOaKiJtZ+D5n3/FngA+2UmdOz3Mtig1/+zG5KGNmZl3knmf35fQ8nydtCN+fDYsyZmbWRQ7E+iStCBwEbAusyKIdx4iIXXLqzAnPG4DPSbohIn5T1bDdgM8Al+Vc3MzMBs7h2b/iluKtwJrAC6RlKc+yIERnkJazZMkJz2OAdwHXSrqDNP0XYHPgLUUDjsttgJmZtc47DDV0Mmmd5y7A3aRZt3sDfyRNcv0YaTJslpxNEh4hbXP0M2BjYN/itRFwCbB1UcbMzLrI9zzr2gU4JyJuYsFzPRURsyLia6RA/XZupU31PItdGcYAL0XEJ5V++qsUp5/um/ZrZmbdNwQDMcfKwN+KP/ftklf5SKMbgONzK2225zmCtJPQQZDurEbE9OLl4DQz6yH3POt6mvRUFYAXgdnAehXnR7JwmDalqfAsdg1q6aaqmZlZD90DvBlSxw+4HfispHUkrQccAtybW2nOXeZrgfflXsDMzDrHOww1dDWwfbHFLMDXSXN1HgIeKP58Um6lOeH5ZWANSRMlvUnSUrkXMzOz9nN49i8ifhgRG0TEK8XnG4Htge8BpwE7RsQvc+vNWaoynTRT6c3APlDzJnVERNaTWszMbGCGWiAOVERMBaYOpI6coLuABdN8zcxskHB4NkfShsBqwN8i4oWB1NV0eEbEuIFcyMzMOsPhWZ+k95GGadcrDr0TuFHSqsAfgK9GxOU5dXpbCjOzEvOEofokjQWuJG3JdyLwnx9AREwnTRr6WG69Dk8zs5IbNmxY1muIOQ64i7Qp/A9qnL8NeGtupUPup2hmtrhxz7OurYGLImJ+P+cfB1bPrdQzY83MSm4IBmKOYcCrdc6PBl5rpVIzMysp3/Ns6B/ADnXOv480rJvF4WlmVnK+51nXT4APSzqIBZkXkpaWdCZpw4QJuZV62NbMrOSGYG+yaRFxtqT/Bs4BxpP2K7iE9LSV4cB5EXFRbr11w1PSji009Obc75iZWescnvVFxD6SfkHaHW8T0nKVPwEXRMQvWqmzUc9zEs3vKqSi7PBWGmJmZvmG6H3MbBFxJWm9Z1s0M2w7G7gCuLNdFzUzs/bpRHhKOoq0/nFLYH3gkYhYL7OOh4F1+zm9SkTMGEgbe6lReJ4FfKJ4bQ6cS1ov81ynG2ZmZs3p0CSgb5J25fkL8LoB1HMv8I0ax18cQJ1ZJC1DyrGNSPc6q3/biIg4KKfOuuEZEYdJ+h9gL2AccDpwqqSrSDdZ/y/nYmZm1l4dHLbdICIeLK7xN2DZFut5KiJ+2r5m5ZH0NuCXwEp1igWQFZ4Nf12JiDkRcVlEvJfU/T6J1JW/XtKjkr4uaa2ci5qZWft0Yp1nX3C2qX1LSFq+XfVlOguYD+wJrBQRw2q8sufqZPX1I2JaRHwzIt4A7AjcB3wNODD3wmZm1h6DfJOEbYFZwAuSnpc0UdKaXbz+ZsCpEXFNRDzfrkqz13lKWhL4IHAA8A7ShKK2/YZiZmZ5WrjnOVpS5cOgJ0RE9kYBTbgH+DFpl58RwFjgYGAXSdtExLQOXLPak8CcdlfadHhK2prUw9ybdPN4CvBZ4JKImNnuhpmZWWMt9iZnRMRWnWhPpeJ2X6WfSboZuIj0eLBPdboNpPD+hKSzImJeuypttEnCqsC+pF7mZsB00ozbcyPi7+1qhJmZta5M6zwj4mJJ3wCqg7VTTgHWBG6TdDbwMLBIiOZu8NOo5/k4aRbSdaR7m79qZ3KbmdmQ9DDw31261ijS8pQtSb3Qai1t8NMoPJcgjRW/o3g1+g0nImKFnAaYmdnAlKnnWdgQeKpL1/oB8FHgKuAWoC37FDQKz5tpfns+MzPrgV6Hp6R1gKWBByJiTnFspYh4tkbZzwFrAWd3qXl7km41tvX+aqNNEsa282JmZtZekjqyw5CkfVmwtd4qwEhJxxSfH4mICyuKXwDsRNrG7+Hi2H7FY8CuL44tQZptuxfwAHB82xtdm0gTXNvKjyQzMyu5DvU8DyIFYqWTivfJwIXUN4V0u29vUvgKeAj4NvCtdq65bGASaa1pW5fi5CxVWYL00NA3ASsALwB3A7dFxNx2NirHEkuUJ//L1FaAOXPavjSqo55//vleNyHLyy+/3OsmZLvxxht73YQsq622Wq+b0LT58+e3/N1OhGfOyGOtshFxK7BHG5vUqsOBGyUdCXw/Il5rR6VN/WsuaRxwMrBG3yEW3AudJumYiJjYjgaZmVmeXt/zHORuApYBTgW+JelJFl2qEhGxQU6lDcNT0snAUaTtlS4B/gzMBJYnTf3dAzhX0kYRcUy/FZmZWdt16p7nYuRROjDxtdEmCTsCRwO/BT4ZEU/XKDMa+ClwlKTfRMQt7W6kmZn1zz3P/nVq4mujX1e+QLrBu0et4AQoHma6F2k21Rfa2TgzM2tskG8Mv1hqFJ7bAxdExOx6hYrzF9K9HSPMzKzg8Oy+Rvc8RwOPNFnXw9R/2KiZmXWAA7H7GoXn8yyYYdvIGqTlK2Zm1iWeMNQbjX7iU0iPcqlbrjj/cWBqvXJmZtZ+HrbtvkbheQ6wOfBjSSNrFZA0oqJcJx6mamZmdTg8u6/R3rZXS/opMA4YK+lC4A7S8OwKwFuBfUj7H14cEVd3trlmZlbNgdh9zewwNI40aeiLwLEsvNhUwKvAt4Dj2t04MzOrz/c8G5O0HHAEsBuwGrBfRNxW7FPwWeDnEXFvTp0NwzMi5gPHSjqT9OTvN5J2F5oJ/A24NiKmZ/1NzMysbdzz7J+kVYDfA68H/lW8j4K0T4Gk/YHXAUfm1Nv0TuXFJgnn51RuZmbWYycDq5OerPIoUN3ZuxrYJbfSlvv6kpaQ9DZJH5G0eav1mJnZwHjCUF3vA34YEX+h9h63DwJr51baaAnKWElnSlq16vj6pA3ibwF+BvxV0rm5Fzczs4FzeNY1mjRc25/5wFK5lTbqeY4D3lXjnub5pOd6/gE4Hfg7sH8xdmxmZl2SG5xDMDz/DdR73NhbSMO5WRqF5zbA/1UekLQJsANwc0TsEBH/U5S7H9gvtwFmZmYddC1wkKRFdsuTtC0pt7KXWTYKz9VJoVhpLGnc+Md9ByLiFeBi4L9yG2BmZgPjnmddJwJzSXsUnELKr/0lXQLcDEwDvp1baaPZtksCr1Qd27p4n1x1/DHSxglmZtZFQzAQmxYR/5a0HfB94EDS/gT7kkL0WuAzEfFsbr2NwvNR0rZ7ld4OTI+Ix6qOL03aSN7MzLrI4VlfkVd7SloeeAMpQP/VSmj2aTRsewuwn6Q3Akj6ALARcF2Nsm8Cnmi1IWZm1hoP2zYnImZGxJSIuH0gwQmNw/MU0tDtXZKmA5cDrwHjKwtJGg7sQdrFwczMbFCQtLekC+qcnyjpw7n11g3PiHgI2Ik0LvwMqcc5NiLuqSq6c3HeG8ObmXWRl6o0dChpLWd/5gGfz620mb1tpwLvb1Dmt6RhWzMz67IhGIg5NiWNmvbnDhpkXC1N721rZmaDk8OzrmVIvcv+BLBcbqV+jo2ZWcl52Lauh0irRPrzdjqww5CZmQ1yDs+6rgQ+Iumg6hOSDgQ+AlyRW6mHbc3MSmyIBmKObwF7AhMkHQHcWRx/M7AZcB/wzdxKHZ5mZiXn8OxfRLwo6b9JSy/3JgUmwHPA2cAxETEzt16Hp5mZLdYi4gXgs5I+R3pEGcCMiKj1fM+mODzNzErOPc/mFGH5dDvqcniamZWcw7MxSRuRtpddmbS37UIiot9diGpxeJqZlVwnwlPSUcBbgS2B9YFHImK9FurZDzgC2ASYCVwDHBURbekBNnH91YCJwDv7DtUoFoDD08xsqOjgbNtvAs8CfwFe10oFxezW00iPsDwMWAs4Ethe0jYR8XJ7mlrX90nBeTZwI2kr2QFzeJqZlVyHwnODiHiwqP9vwLKZbRoNnAxMAXaJiHnF8SnAL0lhmr1EpAXvBH4UEYe2s1JvkmBmVnKd2CShLzgHYC/Sc57P6gvOot5rgAeBfQZYf7OGAXd1olIzMyuxQbrD0NbF+201zv0R2ERSVm+2RbeQNkRoK4enmVnJtRCeoyVNrXgd0oFmrVm8P1Hj3BOkiTtr1jjXbkcCH5D0oXZW6nueZmZDz4yI2KrD11i6eH+1xrnZVWU66WzgJeDnkqaRhoyrn7ISEbFLTqUOTzMz64RZxfuSwCtV55aqKtNJryctRel7cso67ajU4WlmVmKDeGP4acX7GOBfVefGkAJtGh3WytrUZviep5lZyQ3SCUNTivfta5zbDrgvIl7qVmPazeFpZlZyvQ5PSetI2kTSiIrDV5OGaw+VNLyi7PtJQ6kXtb0hXeRhWzOzkutQIO4LrFt8XAUYKemY4vMjEXFhRfELgJ1I2/g9DBART0s6Fvgu8FtJl5CGa78I3Auc0fZG90PSBqQtArcFVmTRjmNExAY5dZY6PAfxWH9N8+fP73UTsowaNarXTciyzDLL9LoJWVZZZZVeNyHb6NGjGxcaRFZYYYVeN6Fpw4cPb1yoHx36d/AgUiBWOql4nwxcSAMRMV7SM6TgOpO0t+3Pga92a8hW0puA35MmLt1H6vXeQ9ogfnXgAeDx3HpLHZ5mZkNdpzoRETG2HWUj4nzg/AE3qHVfB14DtiHtazsdOCwibpT0KdIWgXvmVup7nmZmtjh7OzAhIu4jzfCF4skqEXEOcB3wrdxKHZ5mZiXX6wlDg9xypKFZSD1QgMp7PLeSAjaLh23NzEpuCAZijqdI9zaJiBclvQxsXHF+RSD7hrPD08zMFmd3ApVbEU4GDpN0O2n09VBaeOqKh23NzErOw7Z1XUzaCL9v+cCxwArATcDvSA/6Pjq3Uvc8zcxKbggGYtMi4lLg0orPd0jaHPgAaYP461p5dqnD08ysxIZob3JAIuIx0rrTlnnY1szMFluS5kn6RJ3ze0uqfkRZQ+55mpmVnHuedTX64bT0w3N4mpmVnMNzQNYBXsz9ksPTzMwWK5L2ZOEt9w6RtGuNoisBu5L2vs3i8DQzKzn3PBexBTCu+HMAOxavai8BfyCt9cziCUNmZiXndZ4Li4gTI2JYRAwj3dPcp+9z1Wv5iNgtIv6Vew33PM3MSmyoBOIArA883e5KHZ5mZrbYiohHqo9JWoJ0T3Ql4JqI+HduvR62NTMrOQ/b9k/SdyRNqfgs4Lekh3L/L3C3pA1y63V4mpnZ4uzdwC0Vn99Pmjx0KtC3ecJXcyv1sK2ZWckNtd5kprWB+ys+vx94KCK+ClDsc/vJ3EodnmZmJefwrGskMLfi886kYds+DwJr5FbqYVszM1ucPQZsD//pZb6e9EzPPquS1ntmcc/TzKzk3POs62fAsZJWBTYHZgLXVpx/C/BAbqUOTzOzEhuKM2gznUK677kX8AKwX0Q8DyBpBWAP4PTcSh2eZma22IqIV4GDile1F0n3O2fl1uvwNDMrOfc8WxMR80m90WwOTzOzknN4LiBpR4CIuLnycyN95Zvl8DQzKzmH50ImASFpVES81ve5TnkV54fnXMThaWZmi5MDSWE4p/h8QCcu4vA0Mys59zwXiIjzqz5P7MR1HJ5mZiXmpSq94fA0M7PFlqS3Ae8FNgaWJ22ScB/w64i4rdV6HZ5mZiXnnueiJC0PXEJ6qkqtH9BRkn4NfDIiXsyt3+FpZlZyDs+aLgd2BX4P/AT4K6nXuTzwX8DBwPuAS4Hdcyv3xvBmZrYIScMkHSHpXkmzJT0mabykZZr8fvTzyt6EvYW2v4sUnOMjYseImBgRd0TEA8X7xIjYATgNeJekd+Zewz1PM7OS61DP83TgC8CVwHhg0+LzWyTtWuzO08gtwISqY3NqFWyzjwOPAF9uUO7LwIdID8W+IecCDk8zM1tI8eiuzwNXRMSHKo4/BJwJfAy4uImqHoyIn3amlXVtCVwVEfU2RyAi5ku6itRLzeJhWzOzEutbqpLzasLHSZNszqg6fg5pE/V9Mto3UtKyzZZvkzGkGbXNuA9YK/cCDk8zM6u2NTAfuL3yYETMBu4szjfjw6SwfVHSdElnFY8B67TlSU9MacaLQHa4e9jWzKzkWrjnOVrS1IrPEyKi8t7kmsCM4nFe1Z4A3iZpZLF3bH9uBy4D/kUKs92BQ4GdJL0tIjo5cWgY9fezrVU+i8PTzGzomRERW9U5vzRQKzgBZleU6Tc8I2LbqkMXSPor8A3gsOK9k3aXtHoT5bZspXKHp5mZVZsFrNrPuaUqyuQ6FTietONPp8PzE8WrGTm9VMDhaWZWeh1YqjIN2EzSkjWGbseQeq71hmxriog5kqYBo9vRyDp27nD9Dk8zM1vEFGA3YBvSWk0AJC0FbAFkPTi66vtrAX8ceBP7FxGTO1k/LAbh6W2pOuf555/vdROy3HPPPb1uQpb777+/103IduKJJ/a6CVn22GOPXjehaTNnzmz5ux34d/BS4GjgcCrCE/gU6V7nRRXX3gAYERH3VhxbOSKeqVHvSaTcuabdDe620oenmZm1V0TcLekHwKGSrgCuZcEOQ5NZeIOE3wHrsvDm68dI2g64CXiUtBRkd9Jw6p+Aszr+l+gwh6eZWcl1aATucOBh4BDSBJ8ZpNA7romt+SYBmwH7AysD84D7ga8BpxXrRUvN4WlmZouIiHmkPW3HNyi3Xo1jVwNXd6Zlg4PD08ysxDK23LM28vZ8ZmZmmRyeZmZmmTxsa2ZWch627T73PM3MzDK552lmVnLueXafe55mZmaZ3PM0Mys59zy7zz1PMzOzTO55mpmVnHue3eeep5mZWSb3PM3MSszb8/WGe55mZmaZ3PM0Mys59zy7zz1PMzOzTA5PMzOzTB62NTMrOQ/bdp97nmZmZpnc8zQzKzn3PLvPPU8zM7NM7nmamZXYn//8599IGp35tRkdacwQ4vA0MyuxiHh3r9swFHnY1szMLJPD08zMLJPD08zMLJPD08zMLJPD08zMLJPD08zMLJPD08zMLJPD08zMLJPD08zMLJPD08zMLJPD08zMLJPD08zMLJPD08zMLJPD08zMLJPD08zMLJPD08zMLJPD08zMLJPD08zMLJMiotdtaJmkp4FHOlD1aGBGB+o1M+vPuhGxSq8bYc0pdXh2iqSpEbFVr9thZmaDk4dtzczMMjk8zczMMjk8a5vQ6waYmdng5XueZmZmmdzzNDMzy+TwNDMzy+TwLEgaJukISfdKmi3pMUnjJS3T67aZmdng4vBc4HTgNODvwOeBy4AvANdI8s/JzMz+Y4leN2AwkLQ5KTCviIgPVRx/CDgT+BhwcY+aZ2Zmg4x7VMnHAQFnVB0/B5gF7NPtBpmZ2eDl8Ey2BuYDt1cejIjZwJ3FeTMzM8Dh2WdNYEZEvFrj3BPAaEkju9wmMzMbpByeydJAreAEmF1RxszMzOFZmAUs2c+5pSrKmJmZOTwL00hDs7UCdAxpSPe1LrfJzMwGKYdnMoX0s9im8qCkpYAtgKk9aJOZmQ1SDs/kUiCAw6uOf4p0r/OibjfIzMwGLz9VpSDpLOBQ4ErgWmBT0g5DtwLviIj5PWyemZkNIg7PgqThpJ7nIcB6wAxSj/S4iHipdy0zM7PBxuFpZmaWyfc8zczMMjk8zczMMjk8zczMMjk8zczMMjk8zczMMjk8zczMMjk8zczMMjk8zQZI0vmSvGDabAhxeFpLJL1e0gRJ90qaJek5Sf+QNFHSzv18Z09Jv5Y0XdJrkqZJ+oWknfopP0lSSJojafV+ynyvKBOSxrbvb2hm1r8let0AKx9JWwGTgTnABcA9wChgI2A34EXgporyw4HzgH2BvwPfA54E1gX2AyZJOiUijq5xubnF+77AqVXtGAl8kvTA8qUwM+sSh6e14njS02a2iIi7qk/W6CWeQAq/C4CDImJuRdlvA1cDR0n6V0ScW/XdV4EbgQOoCk9gT2Bl4GLgEy3/bczMMnnY1lqxEfBMreAEiIh/9/1Z0qrA/wCPAv+vMjiLsrNIvceXgZOL3mS184BNJW1bdfwA4C7gjpzGS1pd0pmSHpT0ajGMfIOkd1aV27E4/oKkVyT9RdJBTV5jkqSHaxxfrxhiPqHi2Nji2DhJn5V0n6TZku6W9L6izJskXS9ppqRnivaPqHVNSWtKuqQYSp8l6TeSNq4qu5SkE4przZL0fHG96l9QzKwGh6e14gFgZUkfbKLse0lDqhdGxOxaBSJiOqn3uQawfY0ivwKmAwf2HZA0hjREXN1TrUvSesCfgc8Ck4AjSD3amcCuFeXeT+rxbgqMB44mDVP/WNI3cq6Z4XPAkaRfFr4KLANcKWmvoi3/BL5MGjL/PPCVGnUsA9wMzCva/H1gLHB1MXze5wekEYQ/kn4GXwN+B7yjzX8ns8VTRPjlV9aLFHCvkR4g/k9SgH0G2LRG2fFFuQ82qPPIotyhFccmAS9V1PM8MKr4fDRpSHdlUs82gLFNtP3aouy7apwbVrwPBx4prrdmxfmRpOe7zgM2qjh+fvpPaaG6JgEP17jGesX1T6g4NrY49gSwQsXx/yqOz6/++ZF+AXiyxjUD+HLV8S9V/52BZ4Fre/3/Jb/8KuvLPU/LFhG3AVsCE4EVSMOnPwT+LulmSa+vKL588f5Cg2pnFu8r9HP+3OJcX293HHB1RDzTbLslrQS8G7g+In5TfT4WPPB8S2Ad4NyImFZx/jXgO6QRmz2bvW6G8yPiPz+niPgr6ecyLSKuqCr7e2B1SctWHZ8PnFl17MbifaOKYy8Am0t648CbbTb0ODytJRFxd0SMi4jVSL2p/YFbgB1IQ4R99y4bhWKfuiEbEfcAU4ADJO1ACoLzMpu9ISAa3yNdv3i/p8a5vmOvr3FuoB6scew54KF+jkPqeVeaFosOjz9To+zhwIrA3ZIekPTjYimR/00wa4L/Q7EBi4hHIuICYCfSsOYbgW2K038r3t/aoJq+83fXKXMu6Z7c8aQhzkV6j4NIf5sm1JvhPi/zOKRfBrLLRsTVpF969iX1THcBriItG6o1acvMKjg8rW0iIoA/FR/HFO/Xku5N7iNpyVrfk7QKaRj0SeC2Ope4hLSmcxdgYsUwa7P+RQq1LRqU6+sBbl7j3GZVZfrzLLBSjeOd6LG2JCKejYifRsSnSO36DmnkoBND0maLFYenZZP0TkmL9KAkjSLNgIW0GQIR8RRwGmlDhLOrZnz2fedCYFngmOK+Yk3F/cBPAycC/5vb7oh4FrgOeI+kXavPS+rrmf2FtLTmgMo1q8XSkL7JN1c3uNw/geUk9fXAKYZEj8htd7tJGi7pdZXHil98+oaza4W+mVXwJgnWitNJS1V+SRpmnQWsTdqoYGPggoioHH49jhSeBwBbS7qEhXcYWh/4Viy6QcIiiuHhgTgU+ANwnaSJpFmro4BtgYeBr0TEPEmHAlcCUyRNIO2atDewHfDNiLi/wXUmAF8kLTX5Hml28ocZHP/NLQc8WfzvdwdpGdD6pBnTzwHX9LBtZqUwGP5DtvI5kjS093bgQ8DrSBN9/gp8m7R04z8ibYzwSUmXA4eQel8rkIY2/wAcGBGTutHwiHio2F7wWGB3Ung/R9psYUJFuWsk7QIcQ+ptjgT+ARwcET9p8jp7Ad8ETiJN2rmQdN/23nb+nVowCziDNPy9K6nX/yTwS+CUyhnGZlab0miNmZmZNcv3PM3MzDI5PM3MzDI5PM3MzDI5PM3MzDI5PM3MzDI5PM3MzDI5PM3MzDI5PM3MzDI5PM3MzDI5PM3MzDL9f+nzLUzgugjpAAAAAElFTkSuQmCC\n"
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"som4_4 = susi.SOMClassifier(n_rows=4, n_columns=4, n_iter_supervised=number_of_epochs)\n",
"train(som4_4, \"4 x 4\")\n",
"plot_probability(som4_4, \"4 x 4\")\n",
"plot_u_matrix(som4_4, \"4 x 4\")"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 49,
"outputs": [
{
"data": {
"text/plain": "<Figure size 504x360 with 2 Axes>",
"image/png": 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\n"
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plot_estim_map(som4_4, \"4 x 4\")"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 50,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy: 74.0 %\n",
"Area under ROC of Model On Test Set - 66.50%\n"
]
},
{
"data": {
"text/plain": "<Figure size 720x504 with 2 Axes>",
"image/png": 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\n"
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": "<Figure size 432x432 with 2 Axes>",
"image/png": 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\n"
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": "<Figure size 504x360 with 2 Axes>",
"image/png": 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\n"
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"som7_7 = susi.SOMClassifier(n_rows=7, n_columns=7, n_iter_supervised=number_of_epochs)\n",
"train(som7_7, \"7 x 7\")\n",
"plot_probability(som7_7, \"7 x 7\")\n",
"plot_u_matrix(som7_7, \"7 x 7\")\n",
"plot_estim_map(som7_7, \"7 x 7\")"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 52,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy: 72.7 %\n",
"Area under ROC of Model On Test Set - 75.90%\n"
]
},
{
"data": {
"text/plain": "<Figure size 720x504 with 2 Axes>",
"image/png": 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\n"
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": "<Figure size 432x432 with 2 Axes>",
"image/png": 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\n"
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": "<Figure size 504x360 with 2 Axes>",
"image/png": 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\n"
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"som10_10 = susi.SOMClassifier(n_rows=10, n_columns=10, n_iter_supervised=number_of_epochs)\n",
"train(som10_10, \"10 x 10\")\n",
"plot_probability(som10_10, \"10 x 10\")\n",
"plot_u_matrix(som10_10, \"10 x 10\")\n",
"plot_estim_map(som10_10, \"10 x 10\")\n",
"#"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 54,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy: 77.3 %\n",
"Area under ROC of Model On Test Set - 81.02%\n"
]
},
{
"data": {
"text/plain": "<Figure size 720x504 with 2 Axes>",
"image/png": 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\n"
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": "<Figure size 432x432 with 2 Axes>",
"image/png": 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\n"
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": "<Figure size 504x360 with 2 Axes>",
"image/png": 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\n"
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"som19_2 = susi.SOMClassifier(n_rows=19, n_columns=2, n_iter_supervised=number_of_epochs)\n",
"train(som19_2, \"19 x 2\")\n",
"plot_probability(som19_2, \"19 x 2\")\n",
"plot_u_matrix(som19_2, \"19 x 2\")\n",
"plot_estim_map(som19_2, \"19 x 2\")\n",
"#\n"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
}
],
"metadata": {
"kernelspec": {
"name": "pycharm-14024ecd",
"language": "python",
"display_name": "PyCharm (ethereum-fraud-detection)"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}