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@Wann-Jiun
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Xgboost wind power prediction
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Load libraries needed\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pylab as plt\n",
"%matplotlib inline\n",
"from matplotlib.pylab import rcParams\n",
"rcParams['figure.figsize'] = 15, 6\n",
"\n",
"import xgboost as xgb\n",
"from sklearn.cross_validation import KFold\n",
"from sklearn.cross_validation import train_test_split\n",
"from sklearn.grid_search import GridSearchCV \n",
"from sklearn.metrics import mean_squared_error \n",
"import pickle\n",
"\n",
"def rmse(y_true, y_pred):\n",
" return np.sqrt(mean_squared_error(y_true, y_pred)) "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/wannjiun/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:16: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
"/Users/wannjiun/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:17: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
"/Users/wannjiun/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:18: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
"/Users/wannjiun/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:19: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
"/Users/wannjiun/anaconda/lib/python2.7/site-packages/pandas/core/indexing.py:465: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" self.obj[item] = s\n"
]
}
],
"source": [
"# Import wind power generation dataset\n",
"path = './data/'\n",
"\n",
"data = pd.read_csv(path + 'train.csv').drop(['wp2','wp3','wp4','wp5','wp6','wp7'],1)\n",
"data.columns = ['time', 'wp1']\n",
"\n",
"time_train1 = 1000\n",
"data_train = data.iloc[0:time_train1+1,:]\n",
"\n",
"time_train2 = 1000\n",
"data_w = pd.read_csv(path + 'windforecasts_wf1.csv') \n",
"data_w.columns = ['date', 'hour', 'u', 'v', 'ws', 'wd']\n",
"data_w_train = data_w.iloc[0:time_train2,:]\n",
"\n",
"time_train = 24*2 \n",
"data_train['u'] = [0]*len(data_train)\n",
"data_train['v'] = [0]*len(data_train)\n",
"data_train['ws'] = [0]*len(data_train)\n",
"data_train['wd'] = [0]*len(data_train)\n",
" \n",
"data_train \n",
"for j in range(10):\n",
" for i in range(time_train):\n",
" data_train.iloc[12*j+i+1,2] = data_w_train['u'][48*j+i]\n",
" data_train.iloc[12*j+i+1,3] = data_w_train['v'][48*j+i]\n",
" data_train.iloc[12*j+i+1,4] = data_w_train['ws'][48*j+i]\n",
" data_train.iloc[12*j+i+1,5] = data_w_train['wd'][48*j+i] \n",
" \n",
"df = data_train.iloc[0:157,:] "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/wannjiun/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:2: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" from ipykernel import kernelapp as app\n"
]
}
],
"source": [
"label_df = df['wp1']\n",
"df.drop(['time','wp1'], axis=1, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Training and test set splitting \n",
"# X_train, X_test, y_train, y_test = train_test_split(df, label_df, test_size=0.0)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"X_train = df\n",
"y_train = label_df"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"XGBoost - RMSE on training set: 0.032463\n",
"XGBoost - R^2 on training set: 0.953729\n",
"----------------------------------------\n"
]
}
],
"source": [
"## Xgboost\n",
"xgb_regr = xgb.XGBRegressor()\n",
"xgb_regr.fit(X_train, y_train)\n",
"\n",
"# Run prediction on training set to get a rough idea of how well it does.\n",
"y_train_pred_xgb = xgb_regr.predict(X_train)\n",
"print \"XGBoost - RMSE on training set: %f\" % rmse(y_train, y_train_pred_xgb) \n",
"print \"XGBoost - R^2 on training set: %f\" % xgb_regr.score(X_train, y_train)\n",
"print '-'*40\n",
"\n",
"# Run prediction on test set to get a rough idea of how well it does.\n",
"# y_test_pred_xgb = xgb_regr.predict(X_test)\n",
"# print \"XGBoost - RMSE on test set: %f\" % rmse(y_test, y_test_pred_xgb) \n",
"# print \"XGBoost - R^2 on test set: %f\" % xgb_regr.score(X_test, y_test) "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x1040d1210>]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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Y7caYu4I8f5ExpsEY83rnx5e8uK+IiMS+J5+E666DG25wn3d0DHx+0x4/x3NH0KoJfTZE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\naMZYmg6Ef4XNbdsg42ScVPKWLnUJKjO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0NJdjnjQJ2VPfxfTpwKefcsdJz55c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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1040d14d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(y_train) \n",
"plt.plot(y_train_pred_xgb, color='red')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Linear Regression - RMSE on training set: 0.112470\n",
"Linear Regression - R^2 on training set: 0.444582\n",
"----------------------------------------\n"
]
}
],
"source": [
"# Simple linear regression \n",
"from sklearn import linear_model\n",
"\n",
"regr = linear_model.LinearRegression()\n",
"\n",
"# Train the model using the training sets\n",
"regr.fit(X_train, y_train)\n",
"\n",
"# Run prediction on training set to get a rough idea of how well it does.\n",
"y_train_pred_regr = regr.predict(X_train)\n",
"print \"Linear Regression - RMSE on training set: %f\" % rmse(y_train, y_train_pred_regr) \n",
"print \"Linear Regression - R^2 on training set: %f\" % regr.score(X_train, y_train)\n",
"print '-'*40\n",
"\n",
"# Run prediction on test set to get a rough idea of how well it does.\n",
"# y_test_pred_xgb = xgb_regr.predict(X_test)\n",
"# print \"Linear Regression - RMSE on test set: %f\" % rmse(y_test, y_test_pred_xgb) \n",
"# print \"Linear Regression - R^2 on test set: %f\" % xgb_regr.score(X_test, y_test) "
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x11a334610>]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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FU3q6VHPr1DR5f3j3XXlTcKZ0aQZ6PrDMZ6gLDPKIiEJJSgowbZosneCl8+eB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\nIr16iZQpo7/Hn36qv1sZbd4sUqiQXkeFkt2ux/b11PTkkyIDBoS2DK4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xY8/B3vTpGphy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\nIiIiIiKiGPL/5oE/bLH3+3cAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11a334150>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(y_train) \n",
"plt.plot(y_train_pred_regr, color='red')"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [Root]",
"language": "python",
"name": "Python [Root]"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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