106 lines
2.9 KiB
Plaintext
106 lines
2.9 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Decison Tree"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Import Data from Database"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# connect to the database\n",
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"conn = sqlite3.connect('../features.db')\n",
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"c = conn.cursor()\n",
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"# get training, validation and test data\n",
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"train = pd.read_sql_query(\"SELECT * FROM train\", conn)\n",
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"valid = pd.read_sql_query(\"SELECT * FROM validation\", conn)\n",
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"test = pd.read_sql_query(\"SELECT * FROM test\", conn)\n",
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"# close the connection\n",
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"conn.close()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Format Data for Machine Learning"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# get the target and features\n",
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"train_y = train['y']\n",
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"train_y = train_y.map({'GSVT': 0, 'AFIB': 1, 'SR': 2, 'SB': 3})\n",
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"train_x = train.drop(columns=['y'])\n",
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"\n",
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"valid_y = valid['y']\n",
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"valid_y = valid_y.map({'GSVT': 0, 'AFIB': 1, 'SR': 2, 'SB': 3})\n",
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"valid_x = valid.drop(columns=['y'])\n",
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"\n",
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"test_y = test['y']\n",
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"test_y = test_y.map({'GSVT': 0, 'AFIB': 1, 'SR': 2, 'SB': 3})\n",
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"test_x = test.drop(columns=['y'])\n",
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"\n",
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"# drop id column\n",
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"train_x = train_x.drop(columns=['id'])\n",
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"valid_x = valid_x.drop(columns=['id'])\n",
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"test_x = test_x.drop(columns=['id'])\n",
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"\n",
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"print('train_x shape:', train_x.shape)\n",
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"print('test_x shape:', test_x.shape)\n",
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"print('valid_x shape:', valid_x.shape)\n",
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"# print column names\n",
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"print('features:', train_x.columns.to_list())\n",
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"feature_names = train_x.columns.to_list()\n",
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"\n",
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"# Create an imputer object with a mean filling strategy\n",
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"imputer = SimpleImputer(strategy='mean')\n",
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"\n",
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"train_x = imputer.fit_transform(train_x)\n",
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"valid_x = imputer.transform(valid_x)\n",
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"test_x = imputer.transform(test_x)\n",
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"\n",
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"# Scale Data between 0 and 1\n",
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"scaler = MinMaxScaler()\n",
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"# Fit the scaler to your data and then transform it\n",
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"train_x = scaler.fit_transform(train_x)\n",
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"valid_x = scaler.transform(valid_x)\n",
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"test_x = scaler.transform(test_x)\n",
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"\n",
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"\n",
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"\n",
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"# use xgboost\n",
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"dtrain = xgb.DMatrix(train_x, label=train_y)\n",
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"dvalid = xgb.DMatrix(valid_x, label=valid_y)\n",
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"dtest = xgb.DMatrix(test_x, label=test_y)\n",
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"\n",
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"num_classes= len(set(valid_y.to_list()))\n",
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"print('number of classes:', num_classes)"
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]
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}
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],
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"metadata": {
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"language_info": {
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"name": "python"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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