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{
"cells": [
{
"cell_type": "markdown",
"id": "d2efdfdb",
"metadata": {},
"source": [
"# CANDOGLIA RIVER BASIN\n",
"\n",
"13/10/2021\n",
"\n",
"In this notebook results of different feature selections are compared for the Candoglia basin (of which we have around 18 years of data)\n",
"\n",
"Input data is clipped from ERA5 metereological reanalysis quantile mapped and downscaled.\n",
"\n",
"Monthly averages (for the previous year) of pecipitation, temperature and potential evapotranspiration are selected as input.\n",
"\n",
"\n",
"The settings are the following:\n",
"\n",
" A) 180 features are selected with PCA, the same numeriosity as setting C) ;\n",
"\n",
" B) 36 features are selectedwith PCA, the same numeriosity as setting D) ;\n",
" \n",
" C) metereological inputs spatial statistics are used as input: mean, the 5th, 25th, 75th and 95th quantiles are selected.\n",
" \n",
" D) metereological inputs are spatially averaged.\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "19d31cfb",
"metadata": {},
"source": [
"import sys\n",
"sys.path.append('/time_unit')"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "efcc49ff",
"metadata": {},
"outputs": [],
"source": [
"from sf_runoff import daily_climatology, spatial_avg_daily_input, spatial_stats_daily_input, compute_anomalies\n",
"from nested_CV import SVR_nested_CV_gridsearch, SVR_PCA_nested_CV_gridsearch\n",
"from test import evaluate_prediction, plot_prediction, plot_anomalies\n",
"from test import nested_CV_PCA_SVR_predict, nested_CV_SVR_predict\n",
"from classic_CV_predict import classic_CV_PCA_SVR_predict, classic_CV_SVR_predict\n",
"\n",
"\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"import numpy as np\n",
"from scipy.stats import gaussian_kde\n",
"\n",
"from sklearn.svm import SVR\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.pipeline import make_pipeline\n",
"from sklearn.compose import TransformedTargetRegressor\n",
"from sklearn.model_selection import GridSearchCV,TimeSeriesSplit\n",
"from sklearn.metrics import mean_squared_error\n",
"from sklearn.decomposition import PCA\n",
"\n",
"\n",
"import matplotlib.pyplot as plt\n",
"\n",
"import os\n",
"\n",
"import pdb\n",
"import seaborn as sns"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "cd514cbe",
"metadata": {},
"outputs": [],
"source": [
"path=r'C:\\Users\\mmazzolini\\OneDrive - Scientific Network South Tyrol\\Documents\\conda\\daily_input\\\\'\n",
"\n",
"daily_input = pd.read_csv(path+'CANDOGLIA_TOCE_2000_2019.csv', index_col=0, parse_dates=True)\n",
"\n",
"daily_input_TPE = spatial_avg_daily_input(daily_input)\n",
"\n",
"daily_input_stat = spatial_stats_daily_input(daily_input)"
]
},
{
"cell_type": "markdown",
"id": "6799ce57",
"metadata": {},
"source": [
"import sys, importlib\n",
"importlib.reload(sys.modules['test'])\n"
]
},
{
"cell_type": "code",
"id": "a024e5fe",
"metadata": {},
"outputs": [],
"source": [
"\n",
"\n",
"#define the possible parameters value (where Gridsearch is applied)\n",
"\n",
"C_range=np.logspace(-2, 2, 10)\n",
"epsilon_range=np.logspace(-6, -2, 10)\n",
"#n_range = [17, 50, 200]\n",
"components_range = [5*3*12]\n",
"#do not enlarge t_range for now\n",
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"n_splits=5\n",
"test_size=365"
]
},
{
"cell_type": "markdown",
"id": "e7d5c48a",
"metadata": {},
"source": [
"# A) PCA+SVR"
]
},
{
"cell_type": "markdown",
"id": "18861993",
"metadata": {},
"source": [
"### TRAIN A PCA+SVR MODEL "
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "aacb3a01",
"metadata": {
"scrolled": true
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 't_unit' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m-----------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32mC:\\Users\\MMAZZO~1\\AppData\\Local\\Temp/ipykernel_11492/1336338538.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mt_unit\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mC\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0meps\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mSVR_PCA_nested_CV_gridsearch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdaily_input\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mC_range\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepsilon_range\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcomponents_range\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mt_range\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mn_splits\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtest_size\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf'C={C}'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf'eps={eps}'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\OneDrive - Scientific Network South Tyrol\\Documents\\conda\\Runoff_prediction\\nested_CV.py\u001b[0m in \u001b[0;36mSVR_PCA_nested_CV_gridsearch\u001b[1;34m(daily_input, C_range, epsilon_range, components_range, t_range, n_splits, test_size)\u001b[0m\n\u001b[0;32m 104\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 105\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mt_length\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mt_range\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 106\u001b[1;33m \u001b[0mit_matrix\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcreate_it_matrix\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdaily_input\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mt_length\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 107\u001b[0m \u001b[0mtscv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTimeSeriesSplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgap\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mt_unit\u001b[0m \u001b[1;33m,\u001b[0m\u001b[0mn_splits\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mn_splits\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtest_size\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 108\u001b[0m \u001b[0msets\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtscv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mit_matrix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\OneDrive - Scientific Network South Tyrol\\Documents\\conda\\Runoff_prediction\\sf_runoff.py\u001b[0m in \u001b[0;36mcreate_it_matrix\u001b[1;34m(daily_input, t_length)\u001b[0m\n\u001b[0;32m 37\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 38\u001b[0m \u001b[1;31m# Compute the t_unit days average runoff\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 39\u001b[1;33m \u001b[0mrunoff_t_unit\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrunoff\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrolling\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mt_unit\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmin_periods\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mt_unit\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 40\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 41\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mNameError\u001b[0m: name 't_unit' is not defined"
]
}
],
"source": [
"C,eps,n=SVR_PCA_nested_CV_gridsearch(daily_input, C_range, epsilon_range, components_range, t_range,n_splits,test_size)\n",
"print(f'C={C}')\n",
"print(f'eps={eps}')\n",
"print(f'n={n}')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "945f9297",
"metadata": {},
"outputs": [],
"source": [
"C=0.5994842503189409\n",
"eps=5.994842503189409e-05\n",
"n=180"
]
},
{
"cell_type": "markdown",
"id": "e9644d4d",
"metadata": {},
"source": [
"C=0.21544346900318834\n",
"epsilon=0.003593813663804626\n",
"n=180\n"
]
},
{
"cell_type": "markdown",
"id": "c4d61ea5",
"metadata": {},
"source": [
"### PREDICT RUNOFF ON TEST SET AND QUANTIFY THE PERFORMANCE"
]
},
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