{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "plt.style.use('seaborn-notebook')\n", "from sklearn import preprocessing\n", "from sklearn.model_selection import LeavePOut\n", "from sklearn.model_selection import cross_validate\n", "from sklearn.svm import SVR\n", "from sklearn.ensemble import RandomForestRegressor\n", "from sklearn.multioutput import MultiOutputRegressor\n", "from sklearn.inspection import permutation_importance\n", "from sklearn.metrics import r2_score\n", "from sklearn.metrics import mean_absolute_error\n", "from matplotlib import rcParams" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "plt.rc('font', family='Trebuchet MS', size=36)\n", "plt.rc('axes', linewidth=2)\n", "plt.rc('lines', markersize=15)\n", "plt.rc('xtick.major', size=16, width = 3)\n", "plt.rc('ytick.major', size=16, width = 3)\n", "plt.rc('ytick.minor', size=16)\n", "plt.rc('xtick', labelsize=32)\n", "plt.rc('ytick', labelsize=32)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "unProcessed = pd.read_csv(\"Freeze Vectors Divided.csv\", index_col=0, low_memory=False)\n", "unProcessed.convert_dtypes()\n", "\n", "scale = preprocessing.RobustScaler()\n", "dat = pd.DataFrame(scale.fit_transform(unProcessed), index=unProcessed.index, columns=unProcessed.columns)\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", " | Si umol/m^2 | \n", "Al/Si s/b | \n", "Fe/Si s/b | \n", "Mg/Si s/b | \n", "Ca/Si s/b | \n", "Na/Si s/b | \n", "K/Si s/b | \n", "0 | \n", "1 | \n", "2 | \n", "3 | \n", "4 | \n", "5 | \n", "6 | \n", "7 | \n", "8 | \n", "9 | \n", "10 | \n", "
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
AST_A_10min | \n", "-0.053059 | \n", "2.682076 | \n", "2.199717 | \n", "0.803027 | \n", "1.862123 | \n", "0.503870 | \n", "1.619244 | \n", "-0.146078 | \n", "-0.165181 | \n", "-0.183569 | \n", "-0.202997 | \n", "-0.225524 | \n", "-0.223333 | \n", "-0.277003 | \n", "-0.348180 | \n", "-0.369172 | \n", "-0.366859 | \n", "-0.347859 | \n", "
AST_A_120hr | \n", "-0.054771 | \n", "3.084502 | \n", "1.897744 | \n", "0.513241 | \n", "1.332531 | \n", "0.483612 | \n", "1.185455 | \n", "0.002463 | \n", "0.005141 | \n", "0.011443 | \n", "0.017425 | \n", "0.021756 | \n", "0.024785 | \n", "-0.026867 | \n", "-0.038483 | \n", "-0.045739 | \n", "-0.050197 | \n", "-0.051863 | \n", "
AST_A_1hr | \n", "0.003423 | \n", "3.110240 | \n", "1.784189 | \n", "0.428372 | \n", "0.983944 | \n", "0.350353 | \n", "0.955380 | \n", "0.011782 | \n", "0.018574 | \n", "0.030898 | \n", "0.044833 | \n", "0.059362 | \n", "0.070052 | \n", "0.026867 | \n", "0.038483 | \n", "0.045739 | \n", "0.050197 | \n", "0.051863 | \n", "
AST_A_24hr | \n", "0.126658 | \n", "3.015029 | \n", "1.419411 | \n", "0.230875 | \n", "0.518724 | \n", "0.086836 | \n", "0.681605 | \n", "1.925954 | \n", "1.837405 | \n", "1.710577 | \n", "1.578941 | \n", "1.460676 | \n", "1.224780 | \n", "0.990617 | \n", "1.033223 | \n", "0.916700 | \n", "0.768214 | \n", "0.618722 | \n", "
AST_A_4hr | \n", "0.183141 | \n", "2.466868 | \n", "1.055938 | \n", "-0.122206 | \n", "0.037797 | \n", "-0.294030 | \n", "0.310547 | \n", "-0.076926 | \n", "-0.096987 | \n", "-0.119798 | \n", "-0.147158 | \n", "-0.180390 | \n", "-0.194411 | \n", "-0.262808 | \n", "-0.346983 | \n", "-0.382641 | \n", "-0.392068 | \n", "-0.380436 | \n", "