diff --git a/model-py/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/model-py/.ipynb_checkpoints/Untitled-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..2fd64429bf421126b7000c94ce0f6fd186fbd01f
--- /dev/null
+++ b/model-py/.ipynb_checkpoints/Untitled-checkpoint.ipynb
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/model-py/.ipynb_checkpoints/model-xgboost-checkpoint.ipynb b/model-py/.ipynb_checkpoints/model-xgboost-checkpoint.ipynb
index 3ad459b5387291eef1ebcab28488910c56c75c5a..05f39b960a15632b7668e6a1ba5313c2ae0139ba 100644
--- a/model-py/.ipynb_checkpoints/model-xgboost-checkpoint.ipynb
+++ b/model-py/.ipynb_checkpoints/model-xgboost-checkpoint.ipynb
@@ -9,6 +9,7 @@
"# math and models\n",
"import numpy as np\n",
"import pandas as pd\n",
+ "import pandas_profiling\n",
"import xgboost as xgb\n",
"from matplotlib import pyplot\n",
"from sklearn.feature_selection import SelectFromModel"
@@ -16,7 +17,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
@@ -26,7 +27,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
@@ -37,55 +38,44 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# df_x are your input features\n",
- "x_train = pd.concat([df_train[['Age','Ecc','Pol','Sulc0']],df_train.filter(regex='[^Thick1]5')], axis = 1)\n",
- "x_test = pd.concat([df_test[['Age','Ecc','Pol','Sulc0']],df_test.filter(regex='[^Thick1]5')], axis = 1)\n",
+ "x_train = pd.concat([df_train[['Age','Ecc','Pol','Sulc2']],df_train.filter(regex='[^Thick1]2')], axis = 1)\n",
+ "x_test = pd.concat([df_test[['Age','Ecc','Pol','Sulc2']],df_test.filter(regex='[^Thick1]2')], axis = 1)\n",
"\n",
"# df_y is your output feature (the one you want to predict)\n",
- "y_train = df_train[['Thick5']]\n",
- "y_test = df_test[['Thick5']]"
+ "y_train = df_train[['Thick2']]\n",
+ "y_test = df_test[['Thick2']]"
]
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Index(['Age', 'Ecc', 'Pol', 'Sulc0', 'Curv5', 'PialCurv5', 'Area5', 'MidArea5',\n",
- " 'PialArea5', 'LGI5'],\n",
- " dtype='object')"
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
- "x_test.columns"
+ "x_train = x_train.drop('Age',axis = 1)\n",
+ "x_test = x_test.drop('Age',axis = 1)"
]
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# Convert input data from numpy to XGBoost format\n",
"dtrain = xgb.DMatrix(x_train, y_train)\n",
- "dtest = xgb.DMatrix(x_test, y_test)"
+ "dtest = xgb.DMatrix(x_test, y_test)\n",
+ "dfull = xgb.DMatrix(pd.concat([x_train,x_test]),pd.concat([y_train,y_test]))"
]
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
@@ -94,3265 +84,1234 @@
"\n",
"param = {'tree_method': 'gpu_hist',\n",
" 'max_depth': 6,\n",
- " 'eta': 0.01,\n",
+ " 'eta': 0.05,\n",
" 'verbosity': 2,\n",
- " 'eval_metric': ['mae','rmse']}\n",
+ " 'eval_metric': ['mae','rmse'],\n",
+ " 'early_stopping_rounds': 10}\n",
"\n",
- "watchlist = [(dtest,'eval'),(dtrain,'train')]"
+ "watchlist = [(dtrain,'train'),(dtest,'eval')]"
]
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "[0]\teval-mae:1.33779\teval-rmse:1.44401\ttrain-mae:1.33084\ttrain-rmse:1.43634\n",
- "[1]\teval-mae:1.32446\teval-rmse:1.43111\ttrain-mae:1.31753\ttrain-rmse:1.42347\n",
- "[2]\teval-mae:1.31127\teval-rmse:1.41836\ttrain-mae:1.30435\ttrain-rmse:1.41075\n",
- "[3]\teval-mae:1.29821\teval-rmse:1.40575\ttrain-mae:1.29131\ttrain-rmse:1.39816\n",
- "[4]\teval-mae:1.28528\teval-rmse:1.39328\ttrain-mae:1.2784\ttrain-rmse:1.38573\n",
- "[5]\teval-mae:1.27248\teval-rmse:1.38094\ttrain-mae:1.26561\ttrain-rmse:1.37342\n",
- "[6]\teval-mae:1.25981\teval-rmse:1.36875\ttrain-mae:1.25296\ttrain-rmse:1.36126\n",
- "[7]\teval-mae:1.24726\teval-rmse:1.35669\ttrain-mae:1.24043\ttrain-rmse:1.34922\n",
- "[8]\teval-mae:1.23484\teval-rmse:1.34475\ttrain-mae:1.22803\ttrain-rmse:1.33731\n",
- "[9]\teval-mae:1.22255\teval-rmse:1.33298\ttrain-mae:1.21575\ttrain-rmse:1.32556\n",
- "[10]\teval-mae:1.21038\teval-rmse:1.32132\ttrain-mae:1.20359\ttrain-rmse:1.31393\n",
- "[11]\teval-mae:1.19832\teval-rmse:1.30978\ttrain-mae:1.19155\ttrain-rmse:1.30241\n",
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- "[14]\teval-mae:1.16289\teval-rmse:1.27593\ttrain-mae:1.15616\ttrain-rmse:1.26864\n",
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- "[24]\teval-mae:1.05222\teval-rmse:1.17104\ttrain-mae:1.04564\ttrain-rmse:1.164\n",
- "[25]\teval-mae:1.04176\teval-rmse:1.16122\ttrain-mae:1.0352\ttrain-rmse:1.1542\n",
- "[26]\teval-mae:1.03141\teval-rmse:1.15147\ttrain-mae:1.02487\ttrain-rmse:1.14448\n",
- "[27]\teval-mae:1.02117\teval-rmse:1.14184\ttrain-mae:1.01464\ttrain-rmse:1.13487\n",
- "[28]\teval-mae:1.01104\teval-rmse:1.13229\ttrain-mae:1.00453\ttrain-rmse:1.12535\n",
- "[29]\teval-mae:1.00102\teval-rmse:1.12291\ttrain-mae:0.994518\ttrain-rmse:1.11599\n",
- "[30]\teval-mae:0.991099\teval-rmse:1.11359\ttrain-mae:0.984617\ttrain-rmse:1.10669\n",
- "[31]\teval-mae:0.981289\teval-rmse:1.1044\ttrain-mae:0.974824\ttrain-rmse:1.09753\n",
- "[32]\teval-mae:0.97158\teval-rmse:1.09529\ttrain-mae:0.965136\ttrain-rmse:1.08845\n",
- "[33]\teval-mae:0.96198\teval-rmse:1.08629\ttrain-mae:0.955555\ttrain-rmse:1.07947\n",
- "[34]\teval-mae:0.95249\teval-rmse:1.07743\ttrain-mae:0.94608\ttrain-rmse:1.07063\n",
- "[35]\teval-mae:0.943102\teval-rmse:1.06863\ttrain-mae:0.936712\ttrain-rmse:1.06186\n",
- "[36]\teval-mae:0.93382\teval-rmse:1.05995\ttrain-mae:0.92745\ttrain-rmse:1.0532\n",
- "[37]\teval-mae:0.924641\teval-rmse:1.05136\ttrain-mae:0.918291\ttrain-rmse:1.04463\n",
- "[38]\teval-mae:0.91557\teval-rmse:1.04291\ttrain-mae:0.909238\ttrain-rmse:1.03621\n",
- "[39]\teval-mae:0.906595\teval-rmse:1.03451\ttrain-mae:0.900288\ttrain-rmse:1.02784\n",
- "[40]\teval-mae:0.897729\teval-rmse:1.02622\ttrain-mae:0.891446\ttrain-rmse:1.01957\n",
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- "[44]\teval-mae:0.863298\teval-rmse:0.994037\ttrain-mae:0.857111\ttrain-rmse:0.987493\n",
- "[45]\teval-mae:0.854951\teval-rmse:0.986254\ttrain-mae:0.848786\ttrain-rmse:0.979732\n",
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- "[48]\teval-mae:0.830512\teval-rmse:0.963387\ttrain-mae:0.824423\ttrain-rmse:0.956937\n",
- "[49]\teval-mae:0.822567\teval-rmse:0.955929\ttrain-mae:0.816505\ttrain-rmse:0.949503\n",
- "[50]\teval-mae:0.814725\teval-rmse:0.948562\ttrain-mae:0.808695\ttrain-rmse:0.942164\n",
- "[51]\teval-mae:0.806985\teval-rmse:0.941281\ttrain-mae:0.800982\ttrain-rmse:0.934907\n",
- "[52]\teval-mae:0.799351\teval-rmse:0.934131\ttrain-mae:0.793372\ttrain-rmse:0.927777\n",
- "[53]\teval-mae:0.791809\teval-rmse:0.927036\ttrain-mae:0.785859\ttrain-rmse:0.920705\n",
- "[54]\teval-mae:0.784367\teval-rmse:0.920012\ttrain-mae:0.77845\ttrain-rmse:0.913709\n",
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- "[58]\teval-mae:0.755577\teval-rmse:0.892783\ttrain-mae:0.749785\ttrain-rmse:0.886587\n",
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- "[96]\teval-mae:0.552744\teval-rmse:0.692701\ttrain-mae:0.548176\ttrain-rmse:0.687471\n",
- "[97]\teval-mae:0.548907\teval-rmse:0.688688\ttrain-mae:0.544373\ttrain-rmse:0.683483\n",
- "[98]\teval-mae:0.545115\teval-rmse:0.684713\ttrain-mae:0.540615\ttrain-rmse:0.679535\n",
- "[99]\teval-mae:0.541395\teval-rmse:0.680788\ttrain-mae:0.536926\ttrain-rmse:0.675631\n",
- "[100]\teval-mae:0.537739\teval-rmse:0.676918\ttrain-mae:0.533301\ttrain-rmse:0.671785\n",
- "[101]\teval-mae:0.534144\teval-rmse:0.673102\ttrain-mae:0.52974\ttrain-rmse:0.667997\n",
- "[102]\teval-mae:0.530608\teval-rmse:0.669336\ttrain-mae:0.526234\ttrain-rmse:0.664255\n",
- "[103]\teval-mae:0.527132\teval-rmse:0.665635\ttrain-mae:0.522788\ttrain-rmse:0.660577\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[104]\teval-mae:0.523706\teval-rmse:0.661967\ttrain-mae:0.519392\ttrain-rmse:0.656931\n",
- "[105]\teval-mae:0.520335\teval-rmse:0.658347\ttrain-mae:0.51605\ttrain-rmse:0.653335\n",
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- "[107]\teval-mae:0.5138\teval-rmse:0.651296\ttrain-mae:0.509572\ttrain-rmse:0.646331\n",
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- "[109]\teval-mae:0.507473\teval-rmse:0.644426\ttrain-mae:0.503313\ttrain-rmse:0.63952\n",
- "[110]\teval-mae:0.504401\teval-rmse:0.641077\ttrain-mae:0.500273\ttrain-rmse:0.6362\n",
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- ]
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- ]
- },
- {
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- "output_type": "stream",
- "text": [
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- "[711]\teval-mae:0.322532\teval-rmse:0.410595\ttrain-mae:0.320351\ttrain-rmse:0.407455\n",
- "[712]\teval-mae:0.32252\teval-rmse:0.410579\ttrain-mae:0.320336\ttrain-rmse:0.407436\n",
- "[713]\teval-mae:0.322501\teval-rmse:0.410561\ttrain-mae:0.320318\ttrain-rmse:0.407417\n",
- "[714]\teval-mae:0.322482\teval-rmse:0.410537\ttrain-mae:0.320296\ttrain-rmse:0.407391\n",
- "[715]\teval-mae:0.322472\teval-rmse:0.410525\ttrain-mae:0.320284\ttrain-rmse:0.407376\n",
- "[716]\teval-mae:0.322452\teval-rmse:0.410506\ttrain-mae:0.320264\ttrain-rmse:0.407357\n",
- "[717]\teval-mae:0.322453\teval-rmse:0.410507\ttrain-mae:0.320254\ttrain-rmse:0.407344\n",
- "[718]\teval-mae:0.322441\teval-rmse:0.410493\ttrain-mae:0.320239\ttrain-rmse:0.407328\n",
- "[719]\teval-mae:0.322425\teval-rmse:0.410477\ttrain-mae:0.320223\ttrain-rmse:0.407312\n",
- "[720]\teval-mae:0.322422\teval-rmse:0.410472\ttrain-mae:0.32021\ttrain-rmse:0.407295\n",
- "[721]\teval-mae:0.322412\teval-rmse:0.410459\ttrain-mae:0.320198\ttrain-rmse:0.40728\n",
- "[722]\teval-mae:0.322394\teval-rmse:0.410443\ttrain-mae:0.320179\ttrain-rmse:0.407262\n",
- "[723]\teval-mae:0.322374\teval-rmse:0.41042\ttrain-mae:0.320157\ttrain-rmse:0.407237\n",
- "[724]\teval-mae:0.322363\teval-rmse:0.410407\ttrain-mae:0.320145\ttrain-rmse:0.407222\n",
- "[725]\teval-mae:0.322364\teval-rmse:0.410407\ttrain-mae:0.320135\ttrain-rmse:0.407209\n",
- "[726]\teval-mae:0.322346\teval-rmse:0.410391\ttrain-mae:0.320116\ttrain-rmse:0.407191\n",
- "[727]\teval-mae:0.32233\teval-rmse:0.410373\ttrain-mae:0.320097\ttrain-rmse:0.407171\n",
- "[728]\teval-mae:0.322318\teval-rmse:0.410359\ttrain-mae:0.320084\ttrain-rmse:0.407154\n",
- "[729]\teval-mae:0.322306\teval-rmse:0.410347\ttrain-mae:0.320071\ttrain-rmse:0.407141\n",
- "[730]\teval-mae:0.322293\teval-rmse:0.410333\ttrain-mae:0.320056\ttrain-rmse:0.407124\n",
- "[731]\teval-mae:0.32228\teval-rmse:0.410317\ttrain-mae:0.320039\ttrain-rmse:0.407104\n",
- "[732]\teval-mae:0.32228\teval-rmse:0.410317\ttrain-mae:0.32003\ttrain-rmse:0.407092\n",
- "[733]\teval-mae:0.322268\teval-rmse:0.410305\ttrain-mae:0.320016\ttrain-rmse:0.407077\n",
- "[734]\teval-mae:0.322255\teval-rmse:0.410291\ttrain-mae:0.320001\ttrain-rmse:0.407061\n",
- "[735]\teval-mae:0.32224\teval-rmse:0.410274\ttrain-mae:0.319984\ttrain-rmse:0.407041\n",
- "[736]\teval-mae:0.322229\teval-rmse:0.410262\ttrain-mae:0.319971\ttrain-rmse:0.407025\n",
- "[737]\teval-mae:0.322215\teval-rmse:0.410246\ttrain-mae:0.319954\ttrain-rmse:0.407006\n",
- "[738]\teval-mae:0.322201\teval-rmse:0.410229\ttrain-mae:0.319938\ttrain-rmse:0.406986\n",
- "[739]\teval-mae:0.322187\teval-rmse:0.410209\ttrain-mae:0.319923\ttrain-rmse:0.406966\n",
- "[740]\teval-mae:0.322189\teval-rmse:0.410211\ttrain-mae:0.319913\ttrain-rmse:0.406954\n",
- "[741]\teval-mae:0.322167\teval-rmse:0.410182\ttrain-mae:0.319887\ttrain-rmse:0.406921\n",
- "[742]\teval-mae:0.322154\teval-rmse:0.410169\ttrain-mae:0.319874\ttrain-rmse:0.406905\n",
- "[743]\teval-mae:0.322135\teval-rmse:0.410144\ttrain-mae:0.319853\ttrain-rmse:0.406878\n",
- "[744]\teval-mae:0.322121\teval-rmse:0.41013\ttrain-mae:0.319839\ttrain-rmse:0.406864\n",
- "[745]\teval-mae:0.322107\teval-rmse:0.410111\ttrain-mae:0.319824\ttrain-rmse:0.406843\n",
- "[746]\teval-mae:0.322072\teval-rmse:0.410069\ttrain-mae:0.319785\ttrain-rmse:0.406799\n",
- "[747]\teval-mae:0.32205\teval-rmse:0.410046\ttrain-mae:0.319762\ttrain-rmse:0.406776\n",
- "[748]\teval-mae:0.322026\teval-rmse:0.410016\ttrain-mae:0.319735\ttrain-rmse:0.406742\n",
- "[749]\teval-mae:0.322008\teval-rmse:0.409998\ttrain-mae:0.319717\ttrain-rmse:0.406725\n",
- "[750]\teval-mae:0.321991\teval-rmse:0.409978\ttrain-mae:0.319697\ttrain-rmse:0.406701\n",
- "[751]\teval-mae:0.321991\teval-rmse:0.409977\ttrain-mae:0.319686\ttrain-rmse:0.406687\n",
- "[752]\teval-mae:0.32198\teval-rmse:0.409964\ttrain-mae:0.319673\ttrain-rmse:0.406671\n",
- "[753]\teval-mae:0.321966\teval-rmse:0.409947\ttrain-mae:0.319656\ttrain-rmse:0.406651\n",
- "[754]\teval-mae:0.321951\teval-rmse:0.409932\ttrain-mae:0.31964\ttrain-rmse:0.406634\n",
- "[755]\teval-mae:0.321936\teval-rmse:0.409915\ttrain-mae:0.319623\ttrain-rmse:0.406615\n",
- "[756]\teval-mae:0.321922\teval-rmse:0.409896\ttrain-mae:0.319609\ttrain-rmse:0.406596\n",
- "[757]\teval-mae:0.321908\teval-rmse:0.409883\ttrain-mae:0.319593\ttrain-rmse:0.40658\n",
- "[758]\teval-mae:0.321911\teval-rmse:0.409885\ttrain-mae:0.319584\ttrain-rmse:0.406568\n",
- "[759]\teval-mae:0.3219\teval-rmse:0.409873\ttrain-mae:0.319571\ttrain-rmse:0.406554\n",
- "[760]\teval-mae:0.321884\teval-rmse:0.409857\ttrain-mae:0.319554\ttrain-rmse:0.406535\n",
- "[761]\teval-mae:0.321875\teval-rmse:0.409845\ttrain-mae:0.319542\ttrain-rmse:0.406521\n",
- "[762]\teval-mae:0.321876\teval-rmse:0.409846\ttrain-mae:0.319533\ttrain-rmse:0.406509\n",
- "[763]\teval-mae:0.321859\teval-rmse:0.409826\ttrain-mae:0.319514\ttrain-rmse:0.406485\n",
- "[764]\teval-mae:0.321847\teval-rmse:0.409815\ttrain-mae:0.319501\ttrain-rmse:0.406473\n",
- "[765]\teval-mae:0.321838\teval-rmse:0.409804\ttrain-mae:0.319489\ttrain-rmse:0.406458\n",
- "[766]\teval-mae:0.321826\teval-rmse:0.409788\ttrain-mae:0.319475\ttrain-rmse:0.406441\n",
- "[767]\teval-mae:0.321806\teval-rmse:0.409764\ttrain-mae:0.319453\ttrain-rmse:0.406415\n",
- "[768]\teval-mae:0.321806\teval-rmse:0.409763\ttrain-mae:0.319443\ttrain-rmse:0.406402\n",
- "[769]\teval-mae:0.321797\teval-rmse:0.409753\ttrain-mae:0.319432\ttrain-rmse:0.406389\n",
- "[770]\teval-mae:0.321767\teval-rmse:0.409718\ttrain-mae:0.3194\ttrain-rmse:0.40635\n",
- "[771]\teval-mae:0.321758\teval-rmse:0.409707\ttrain-mae:0.319389\ttrain-rmse:0.406337\n",
- "[772]\teval-mae:0.321736\teval-rmse:0.409682\ttrain-mae:0.319366\ttrain-rmse:0.40631\n",
- "[773]\teval-mae:0.321721\teval-rmse:0.409665\ttrain-mae:0.31935\ttrain-rmse:0.406291\n",
- "[774]\teval-mae:0.321706\teval-rmse:0.409649\ttrain-mae:0.319334\ttrain-rmse:0.406273\n",
- "[775]\teval-mae:0.321695\teval-rmse:0.409638\ttrain-mae:0.319321\ttrain-rmse:0.406259\n",
- "[776]\teval-mae:0.32168\teval-rmse:0.409618\ttrain-mae:0.319306\ttrain-rmse:0.40624\n",
- "[777]\teval-mae:0.321671\teval-rmse:0.409607\ttrain-mae:0.319295\ttrain-rmse:0.406226\n",
- "[778]\teval-mae:0.321644\teval-rmse:0.409577\ttrain-mae:0.319266\ttrain-rmse:0.406192\n",
- "[779]\teval-mae:0.32163\teval-rmse:0.409563\ttrain-mae:0.319251\ttrain-rmse:0.406178\n",
- "[780]\teval-mae:0.321631\teval-rmse:0.409563\ttrain-mae:0.319241\ttrain-rmse:0.406165\n",
- "[781]\teval-mae:0.321622\teval-rmse:0.409552\ttrain-mae:0.31923\ttrain-rmse:0.406152\n",
- "[782]\teval-mae:0.321605\teval-rmse:0.409534\ttrain-mae:0.319212\ttrain-rmse:0.406131\n",
- "[783]\teval-mae:0.321606\teval-rmse:0.409535\ttrain-mae:0.319203\ttrain-rmse:0.40612\n",
- "[784]\teval-mae:0.321596\teval-rmse:0.409525\ttrain-mae:0.319191\ttrain-rmse:0.406109\n",
- "[785]\teval-mae:0.321581\teval-rmse:0.409507\ttrain-mae:0.319173\ttrain-rmse:0.406087\n",
- "[786]\teval-mae:0.321571\teval-rmse:0.409496\ttrain-mae:0.319161\ttrain-rmse:0.406072\n",
- "[787]\teval-mae:0.32154\teval-rmse:0.409461\ttrain-mae:0.319127\ttrain-rmse:0.406033\n",
- "[788]\teval-mae:0.321526\teval-rmse:0.409447\ttrain-mae:0.319111\ttrain-rmse:0.406018\n",
- "[789]\teval-mae:0.32151\teval-rmse:0.409428\ttrain-mae:0.319092\ttrain-rmse:0.405996\n",
- "[790]\teval-mae:0.321502\teval-rmse:0.409418\ttrain-mae:0.319081\ttrain-rmse:0.405983\n",
- "[791]\teval-mae:0.321502\teval-rmse:0.409419\ttrain-mae:0.319071\ttrain-rmse:0.405971\n",
- "[792]\teval-mae:0.321473\teval-rmse:0.409384\ttrain-mae:0.319039\ttrain-rmse:0.405932\n",
- "[793]\teval-mae:0.321465\teval-rmse:0.409375\ttrain-mae:0.31903\ttrain-rmse:0.405923\n",
- "[794]\teval-mae:0.321451\teval-rmse:0.409361\ttrain-mae:0.319015\ttrain-rmse:0.405908\n",
- "[795]\teval-mae:0.321452\teval-rmse:0.409361\ttrain-mae:0.319005\ttrain-rmse:0.405896\n",
- "[796]\teval-mae:0.321441\teval-rmse:0.409349\ttrain-mae:0.318994\ttrain-rmse:0.405882\n",
- "[797]\teval-mae:0.321421\teval-rmse:0.409325\ttrain-mae:0.318973\ttrain-rmse:0.405856\n",
- "[798]\teval-mae:0.321405\teval-rmse:0.409306\ttrain-mae:0.318954\ttrain-rmse:0.405834\n",
- "[799]\teval-mae:0.321396\teval-rmse:0.409295\ttrain-mae:0.318943\ttrain-rmse:0.40582\n",
- "[800]\teval-mae:0.321366\teval-rmse:0.409261\ttrain-mae:0.31891\ttrain-rmse:0.405782\n",
- "[801]\teval-mae:0.321353\teval-rmse:0.40925\ttrain-mae:0.318897\ttrain-rmse:0.405771\n",
- "[802]\teval-mae:0.32134\teval-rmse:0.409238\ttrain-mae:0.318883\ttrain-rmse:0.405757\n",
- "[803]\teval-mae:0.321332\teval-rmse:0.409229\ttrain-mae:0.318872\ttrain-rmse:0.405745\n",
- "[804]\teval-mae:0.321316\teval-rmse:0.409208\ttrain-mae:0.318854\ttrain-rmse:0.405722\n",
- "[805]\teval-mae:0.321306\teval-rmse:0.409195\ttrain-mae:0.318843\ttrain-rmse:0.405707\n",
- "[806]\teval-mae:0.321273\teval-rmse:0.409158\ttrain-mae:0.318807\ttrain-rmse:0.405666\n",
- "[807]\teval-mae:0.321273\teval-rmse:0.409158\ttrain-mae:0.318797\ttrain-rmse:0.405654\n",
- "[808]\teval-mae:0.321263\teval-rmse:0.409148\ttrain-mae:0.318786\ttrain-rmse:0.405643\n",
- "[809]\teval-mae:0.321243\teval-rmse:0.409124\ttrain-mae:0.318764\ttrain-rmse:0.405616\n",
- "[810]\teval-mae:0.321232\teval-rmse:0.409112\ttrain-mae:0.31875\ttrain-rmse:0.4056\n"
+ "[0]\ttrain-mae:1.27672\ttrain-rmse:1.36743\teval-mae:1.28363\teval-rmse:1.37484\n",
+ "Multiple eval metrics have been passed: 'eval-rmse' will be used for early stopping.\n",
+ "\n",
+ "Will train until eval-rmse hasn't improved in 10 rounds.\n",
+ "[1]\ttrain-mae:1.21288\ttrain-rmse:1.30587\teval-mae:1.2197\teval-rmse:1.31314\n",
+ "[2]\ttrain-mae:1.15224\ttrain-rmse:1.24751\teval-mae:1.15898\teval-rmse:1.25464\n",
+ "[3]\ttrain-mae:1.09463\ttrain-rmse:1.19253\teval-mae:1.10129\teval-rmse:1.19952\n",
+ "[4]\ttrain-mae:1.0399\ttrain-rmse:1.14059\teval-mae:1.04647\teval-rmse:1.14744\n",
+ "[5]\ttrain-mae:0.987932\ttrain-rmse:1.09151\teval-mae:0.994414\teval-rmse:1.09821\n",
+ "[6]\ttrain-mae:0.938609\ttrain-rmse:1.04489\teval-mae:0.945005\teval-rmse:1.05144\n",
+ "[7]\ttrain-mae:0.891861\ttrain-rmse:1.00127\teval-mae:0.898157\teval-rmse:1.00769\n",
+ "[8]\ttrain-mae:0.847623\ttrain-rmse:0.960066\teval-mae:0.853824\teval-rmse:0.966359\n",
+ "[9]\ttrain-mae:0.805869\ttrain-rmse:0.921317\teval-mae:0.811964\teval-rmse:0.927475\n",
+ "[10]\ttrain-mae:0.766524\ttrain-rmse:0.884767\teval-mae:0.772506\teval-rmse:0.8908\n",
+ "[11]\ttrain-mae:0.729634\ttrain-rmse:0.850456\teval-mae:0.735465\teval-rmse:0.856336\n",
+ "[12]\ttrain-mae:0.695057\ttrain-rmse:0.817833\teval-mae:0.700745\teval-rmse:0.823589\n",
+ "[13]\ttrain-mae:0.662784\ttrain-rmse:0.787434\teval-mae:0.668343\teval-rmse:0.793095\n",
+ "[14]\ttrain-mae:0.632847\ttrain-rmse:0.758978\teval-mae:0.638253\teval-rmse:0.764526\n",
+ "[15]\ttrain-mae:0.605081\ttrain-rmse:0.732242\teval-mae:0.610288\teval-rmse:0.737637\n",
+ "[16]\ttrain-mae:0.579316\ttrain-rmse:0.707245\teval-mae:0.584357\teval-rmse:0.712522\n",
+ "[17]\ttrain-mae:0.55561\ttrain-rmse:0.683889\teval-mae:0.560452\teval-rmse:0.689017\n",
+ "[18]\ttrain-mae:0.53363\ttrain-rmse:0.661892\teval-mae:0.538297\teval-rmse:0.666906\n",
+ "[19]\ttrain-mae:0.5133\ttrain-rmse:0.641035\teval-mae:0.517787\teval-rmse:0.645913\n",
+ "[20]\ttrain-mae:0.494727\ttrain-rmse:0.621834\teval-mae:0.499044\teval-rmse:0.626593\n",
+ "[21]\ttrain-mae:0.477541\ttrain-rmse:0.603761\teval-mae:0.481658\teval-rmse:0.60837\n",
+ "[22]\ttrain-mae:0.461808\ttrain-rmse:0.586907\teval-mae:0.465734\teval-rmse:0.591381\n",
+ "[23]\ttrain-mae:0.447517\ttrain-rmse:0.571426\teval-mae:0.451258\teval-rmse:0.575764\n",
+ "[24]\ttrain-mae:0.434268\ttrain-rmse:0.556834\teval-mae:0.437822\teval-rmse:0.561038\n",
+ "[25]\ttrain-mae:0.422359\ttrain-rmse:0.543547\teval-mae:0.425731\teval-rmse:0.547612\n",
+ "[26]\ttrain-mae:0.411346\ttrain-rmse:0.531001\teval-mae:0.414565\teval-rmse:0.534953\n",
+ "[27]\ttrain-mae:0.401192\ttrain-rmse:0.519221\teval-mae:0.404236\teval-rmse:0.523031\n",
+ "[28]\ttrain-mae:0.392167\ttrain-rmse:0.508667\teval-mae:0.395063\teval-rmse:0.512361\n",
+ "[29]\ttrain-mae:0.383868\ttrain-rmse:0.498735\teval-mae:0.386633\teval-rmse:0.502313\n",
+ "[30]\ttrain-mae:0.376061\ttrain-rmse:0.489258\teval-mae:0.378671\teval-rmse:0.492688\n",
+ "[31]\ttrain-mae:0.3692\ttrain-rmse:0.480907\teval-mae:0.371707\teval-rmse:0.484242\n",
+ "[32]\ttrain-mae:0.362716\ttrain-rmse:0.472794\teval-mae:0.365103\teval-rmse:0.476015\n",
+ "[33]\ttrain-mae:0.357052\ttrain-rmse:0.46567\teval-mae:0.35932\teval-rmse:0.468779\n",
+ "[34]\ttrain-mae:0.351599\ttrain-rmse:0.458708\teval-mae:0.353747\teval-rmse:0.461694\n",
+ "[35]\ttrain-mae:0.346819\ttrain-rmse:0.452491\teval-mae:0.348871\teval-rmse:0.455371\n",
+ "[36]\ttrain-mae:0.342166\ttrain-rmse:0.446283\teval-mae:0.344092\teval-rmse:0.449038\n",
+ "[37]\ttrain-mae:0.338038\ttrain-rmse:0.440782\teval-mae:0.339862\teval-rmse:0.443436\n",
+ "[38]\ttrain-mae:0.334113\ttrain-rmse:0.435407\teval-mae:0.335824\teval-rmse:0.437944\n",
+ "[39]\ttrain-mae:0.330567\ttrain-rmse:0.430624\teval-mae:0.332197\teval-rmse:0.433067\n",
+ "[40]\ttrain-mae:0.32731\ttrain-rmse:0.426093\teval-mae:0.328848\teval-rmse:0.428433\n",
+ "[41]\ttrain-mae:0.324226\ttrain-rmse:0.421782\teval-mae:0.325676\teval-rmse:0.42402\n",
+ "[42]\ttrain-mae:0.321641\ttrain-rmse:0.418196\teval-mae:0.323019\teval-rmse:0.420357\n",
+ "[43]\ttrain-mae:0.319259\ttrain-rmse:0.414871\teval-mae:0.320572\teval-rmse:0.416958\n",
+ "[44]\ttrain-mae:0.317176\ttrain-rmse:0.411831\teval-mae:0.318401\teval-rmse:0.413845\n",
+ "[45]\ttrain-mae:0.315156\ttrain-rmse:0.40898\teval-mae:0.316318\teval-rmse:0.410915\n",
+ "[46]\ttrain-mae:0.31318\ttrain-rmse:0.406111\teval-mae:0.314286\teval-rmse:0.407977\n",
+ "[47]\ttrain-mae:0.311411\ttrain-rmse:0.403571\teval-mae:0.312446\teval-rmse:0.405354\n",
+ "[48]\ttrain-mae:0.3097\ttrain-rmse:0.401051\teval-mae:0.310679\teval-rmse:0.402766\n",
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]
},
{
"name": "stdout",
"output_type": "stream",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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+ "[300]\ttrain-mae:0.269963\ttrain-rmse:0.345995\teval-mae:0.27287\teval-rmse:0.350009\n",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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+ "[403]\ttrain-mae:0.267655\ttrain-rmse:0.343391\teval-mae:0.271316\teval-rmse:0.34836\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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+ "[605]\ttrain-mae:0.264717\ttrain-rmse:0.340008\teval-mae:0.269572\teval-rmse:0.346528\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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- "[1481]\teval-mae:0.316203\teval-rmse:0.403567\ttrain-mae:0.312171\ttrain-rmse:0.398059\n",
- "[1482]\teval-mae:0.316196\teval-rmse:0.40356\ttrain-mae:0.312163\ttrain-rmse:0.398049\n",
- "[1483]\teval-mae:0.316195\teval-rmse:0.403558\ttrain-mae:0.312161\ttrain-rmse:0.398045\n",
- "[1484]\teval-mae:0.316197\teval-rmse:0.403561\ttrain-mae:0.312156\ttrain-rmse:0.39804\n",
- "[1485]\teval-mae:0.316193\teval-rmse:0.403557\ttrain-mae:0.312151\ttrain-rmse:0.398034\n",
- "[1486]\teval-mae:0.316189\teval-rmse:0.403552\ttrain-mae:0.312145\ttrain-rmse:0.398029\n",
- "[1487]\teval-mae:0.316183\teval-rmse:0.403547\ttrain-mae:0.312139\ttrain-rmse:0.398021\n",
- "[1488]\teval-mae:0.316176\teval-rmse:0.403538\ttrain-mae:0.31213\ttrain-rmse:0.39801\n",
- "[1489]\teval-mae:0.316168\teval-rmse:0.40353\ttrain-mae:0.312121\ttrain-rmse:0.398001\n",
- "[1490]\teval-mae:0.316161\teval-rmse:0.403522\ttrain-mae:0.312113\ttrain-rmse:0.397992\n",
- "[1491]\teval-mae:0.316159\teval-rmse:0.40352\ttrain-mae:0.31211\ttrain-rmse:0.397988\n",
- "[1492]\teval-mae:0.316151\teval-rmse:0.403511\ttrain-mae:0.312101\ttrain-rmse:0.397978\n",
- "[1493]\teval-mae:0.316145\teval-rmse:0.403506\ttrain-mae:0.312095\ttrain-rmse:0.397972\n",
- "[1494]\teval-mae:0.316148\teval-rmse:0.40351\ttrain-mae:0.31209\ttrain-rmse:0.397966\n",
- "[1495]\teval-mae:0.316144\teval-rmse:0.403506\ttrain-mae:0.312085\ttrain-rmse:0.397961\n",
- "[1496]\teval-mae:0.316144\teval-rmse:0.403506\ttrain-mae:0.312079\ttrain-rmse:0.397954\n",
- "[1497]\teval-mae:0.316142\teval-rmse:0.403504\ttrain-mae:0.312077\ttrain-rmse:0.397951\n",
- "[1498]\teval-mae:0.316142\teval-rmse:0.403503\ttrain-mae:0.312075\ttrain-rmse:0.397949\n",
- "[1499]\teval-mae:0.31613\teval-rmse:0.40349\ttrain-mae:0.312062\ttrain-rmse:0.397934\n",
- "[1500]\teval-mae:0.316121\teval-rmse:0.403479\ttrain-mae:0.312051\ttrain-rmse:0.397921\n",
- "[1501]\teval-mae:0.316118\teval-rmse:0.403476\ttrain-mae:0.312048\ttrain-rmse:0.397917\n",
- "[1502]\teval-mae:0.316114\teval-rmse:0.403472\ttrain-mae:0.312042\ttrain-rmse:0.397911\n",
- "[1503]\teval-mae:0.316105\teval-rmse:0.403462\ttrain-mae:0.312033\ttrain-rmse:0.3979\n",
- "[1504]\teval-mae:0.316096\teval-rmse:0.40345\ttrain-mae:0.312022\ttrain-rmse:0.397885\n",
- "[1505]\teval-mae:0.316092\teval-rmse:0.403446\ttrain-mae:0.312016\ttrain-rmse:0.397879\n",
- "[1506]\teval-mae:0.31609\teval-rmse:0.403443\ttrain-mae:0.312013\ttrain-rmse:0.397874\n",
- "[1507]\teval-mae:0.316081\teval-rmse:0.403434\ttrain-mae:0.312001\ttrain-rmse:0.397861\n",
- "[1508]\teval-mae:0.316075\teval-rmse:0.403426\ttrain-mae:0.311993\ttrain-rmse:0.39785\n",
- "[1509]\teval-mae:0.31607\teval-rmse:0.403422\ttrain-mae:0.311988\ttrain-rmse:0.397845\n",
- "[1510]\teval-mae:0.316066\teval-rmse:0.403417\ttrain-mae:0.311982\ttrain-rmse:0.397838\n",
- "[1511]\teval-mae:0.316061\teval-rmse:0.403411\ttrain-mae:0.311976\ttrain-rmse:0.397831\n",
- "[1512]\teval-mae:0.316055\teval-rmse:0.403405\ttrain-mae:0.311969\ttrain-rmse:0.397823\n"
+ "[707]\ttrain-mae:0.263662\ttrain-rmse:0.338778\teval-mae:0.269049\teval-rmse:0.345964\n",
+ "[708]\ttrain-mae:0.263642\ttrain-rmse:0.338754\teval-mae:0.269033\teval-rmse:0.345946\n",
+ "[709]\ttrain-mae:0.263627\ttrain-rmse:0.338736\teval-mae:0.269022\teval-rmse:0.345933\n",
+ "[710]\ttrain-mae:0.26362\ttrain-rmse:0.338728\teval-mae:0.269019\teval-rmse:0.34593\n",
+ "[711]\ttrain-mae:0.263592\ttrain-rmse:0.338694\teval-mae:0.268994\teval-rmse:0.345903\n",
+ "[712]\ttrain-mae:0.263584\ttrain-rmse:0.338686\teval-mae:0.268989\teval-rmse:0.345897\n",
+ "[713]\ttrain-mae:0.263575\ttrain-rmse:0.338675\teval-mae:0.268983\teval-rmse:0.345891\n",
+ "[714]\ttrain-mae:0.263562\ttrain-rmse:0.338658\teval-mae:0.268974\teval-rmse:0.345879\n",
+ "[715]\ttrain-mae:0.263552\ttrain-rmse:0.338646\teval-mae:0.268968\teval-rmse:0.345872\n",
+ "[716]\ttrain-mae:0.263528\ttrain-rmse:0.338619\teval-mae:0.268947\teval-rmse:0.345847\n",
+ "[717]\ttrain-mae:0.263504\ttrain-rmse:0.338588\teval-mae:0.268927\teval-rmse:0.345822\n",
+ "[718]\ttrain-mae:0.263494\ttrain-rmse:0.338576\teval-mae:0.268922\teval-rmse:0.345817\n",
+ "[719]\ttrain-mae:0.263486\ttrain-rmse:0.338567\teval-mae:0.268918\teval-rmse:0.345812\n",
+ "[720]\ttrain-mae:0.263469\ttrain-rmse:0.338549\teval-mae:0.268915\teval-rmse:0.345809\n",
+ "[721]\ttrain-mae:0.263449\ttrain-rmse:0.338524\teval-mae:0.268897\teval-rmse:0.345789\n",
+ "[722]\ttrain-mae:0.263442\ttrain-rmse:0.338517\teval-mae:0.268895\teval-rmse:0.345787\n",
+ "[723]\ttrain-mae:0.263436\ttrain-rmse:0.338509\teval-mae:0.268895\teval-rmse:0.345788\n",
+ "[724]\ttrain-mae:0.263417\ttrain-rmse:0.338485\teval-mae:0.268883\teval-rmse:0.345773\n",
+ "[725]\ttrain-mae:0.263412\ttrain-rmse:0.338479\teval-mae:0.26888\teval-rmse:0.34577\n",
+ "[726]\ttrain-mae:0.263392\ttrain-rmse:0.338456\teval-mae:0.268863\teval-rmse:0.345751\n",
+ "[727]\ttrain-mae:0.263378\ttrain-rmse:0.338438\teval-mae:0.268853\teval-rmse:0.34574\n",
+ "[728]\ttrain-mae:0.263366\ttrain-rmse:0.338425\teval-mae:0.268847\teval-rmse:0.345732\n",
+ "[729]\ttrain-mae:0.263362\ttrain-rmse:0.338421\teval-mae:0.268843\teval-rmse:0.345729\n",
+ "[730]\ttrain-mae:0.263356\ttrain-rmse:0.338414\teval-mae:0.268839\teval-rmse:0.345725\n",
+ "[731]\ttrain-mae:0.263345\ttrain-rmse:0.338401\teval-mae:0.268831\teval-rmse:0.345716\n",
+ "[732]\ttrain-mae:0.263331\ttrain-rmse:0.338385\teval-mae:0.268819\teval-rmse:0.345703\n",
+ "[733]\ttrain-mae:0.263313\ttrain-rmse:0.338363\teval-mae:0.268815\teval-rmse:0.345699\n",
+ "[734]\ttrain-mae:0.2633\ttrain-rmse:0.338346\teval-mae:0.26881\teval-rmse:0.345692\n",
+ "[735]\ttrain-mae:0.263295\ttrain-rmse:0.338342\teval-mae:0.268809\teval-rmse:0.345691\n",
+ "[736]\ttrain-mae:0.26329\ttrain-rmse:0.338336\teval-mae:0.268808\teval-rmse:0.34569\n",
+ "[737]\ttrain-mae:0.263285\ttrain-rmse:0.33833\teval-mae:0.268805\teval-rmse:0.345688\n",
+ "[738]\ttrain-mae:0.263278\ttrain-rmse:0.338322\teval-mae:0.268808\teval-rmse:0.34569\n",
+ "[739]\ttrain-mae:0.263264\ttrain-rmse:0.338306\teval-mae:0.26881\teval-rmse:0.345695\n",
+ "[740]\ttrain-mae:0.263243\ttrain-rmse:0.338282\teval-mae:0.2688\teval-rmse:0.345683\n",
+ "[741]\ttrain-mae:0.263228\ttrain-rmse:0.338266\teval-mae:0.268793\teval-rmse:0.345677\n",
+ "[742]\ttrain-mae:0.263222\ttrain-rmse:0.338258\teval-mae:0.268789\teval-rmse:0.345673\n",
+ "[743]\ttrain-mae:0.263208\ttrain-rmse:0.338243\teval-mae:0.268783\teval-rmse:0.345668\n",
+ "[744]\ttrain-mae:0.263191\ttrain-rmse:0.338226\teval-mae:0.268777\teval-rmse:0.345664\n",
+ "[745]\ttrain-mae:0.263182\ttrain-rmse:0.338216\teval-mae:0.268773\teval-rmse:0.345659\n",
+ "[746]\ttrain-mae:0.263175\ttrain-rmse:0.338207\teval-mae:0.268772\teval-rmse:0.345659\n",
+ "[747]\ttrain-mae:0.263162\ttrain-rmse:0.33819\teval-mae:0.268762\teval-rmse:0.345648\n",
+ "[748]\ttrain-mae:0.263149\ttrain-rmse:0.338174\teval-mae:0.268757\teval-rmse:0.345641\n",
+ "[749]\ttrain-mae:0.263142\ttrain-rmse:0.338166\teval-mae:0.268752\teval-rmse:0.345635\n",
+ "[750]\ttrain-mae:0.263125\ttrain-rmse:0.338147\teval-mae:0.268741\teval-rmse:0.345623\n",
+ "[751]\ttrain-mae:0.263115\ttrain-rmse:0.338136\teval-mae:0.268735\teval-rmse:0.345617\n",
+ "[752]\ttrain-mae:0.263107\ttrain-rmse:0.338125\teval-mae:0.268733\teval-rmse:0.345614\n",
+ "[753]\ttrain-mae:0.263101\ttrain-rmse:0.338119\teval-mae:0.26873\teval-rmse:0.34561\n",
+ "[754]\ttrain-mae:0.263092\ttrain-rmse:0.338107\teval-mae:0.268729\teval-rmse:0.345609\n",
+ "[755]\ttrain-mae:0.263079\ttrain-rmse:0.338093\teval-mae:0.26873\teval-rmse:0.34561\n",
+ "[756]\ttrain-mae:0.263074\ttrain-rmse:0.338088\teval-mae:0.268728\teval-rmse:0.345608\n",
+ "[757]\ttrain-mae:0.26307\ttrain-rmse:0.338084\teval-mae:0.268727\teval-rmse:0.345607\n",
+ "[758]\ttrain-mae:0.263055\ttrain-rmse:0.338069\teval-mae:0.268723\teval-rmse:0.345606\n",
+ "[759]\ttrain-mae:0.263046\ttrain-rmse:0.338058\teval-mae:0.268718\teval-rmse:0.345601\n",
+ "[760]\ttrain-mae:0.263039\ttrain-rmse:0.338051\teval-mae:0.268715\teval-rmse:0.345598\n",
+ "[761]\ttrain-mae:0.263035\ttrain-rmse:0.338046\teval-mae:0.268713\teval-rmse:0.345596\n",
+ "[762]\ttrain-mae:0.26303\ttrain-rmse:0.33804\teval-mae:0.26871\teval-rmse:0.345591\n",
+ "[763]\ttrain-mae:0.263021\ttrain-rmse:0.338027\teval-mae:0.268708\teval-rmse:0.345589\n",
+ "[764]\ttrain-mae:0.263012\ttrain-rmse:0.338015\teval-mae:0.268709\teval-rmse:0.34559\n",
+ "[765]\ttrain-mae:0.263006\ttrain-rmse:0.338009\teval-mae:0.268704\teval-rmse:0.345585\n",
+ "[766]\ttrain-mae:0.262998\ttrain-rmse:0.338\teval-mae:0.268703\teval-rmse:0.345584\n",
+ "[767]\ttrain-mae:0.262992\ttrain-rmse:0.337993\teval-mae:0.268703\teval-rmse:0.345585\n",
+ "[768]\ttrain-mae:0.262986\ttrain-rmse:0.337986\teval-mae:0.268704\teval-rmse:0.345584\n",
+ "[769]\ttrain-mae:0.262984\ttrain-rmse:0.337983\teval-mae:0.268704\teval-rmse:0.345584\n",
+ "[770]\ttrain-mae:0.262979\ttrain-rmse:0.337976\teval-mae:0.268705\teval-rmse:0.345585\n",
+ "[771]\ttrain-mae:0.262976\ttrain-rmse:0.337972\teval-mae:0.268705\teval-rmse:0.345585\n",
+ "[772]\ttrain-mae:0.262966\ttrain-rmse:0.337961\teval-mae:0.26871\teval-rmse:0.345591\n",
+ "[773]\ttrain-mae:0.262961\ttrain-rmse:0.337955\teval-mae:0.268708\teval-rmse:0.345587\n",
+ "[774]\ttrain-mae:0.262954\ttrain-rmse:0.337946\teval-mae:0.268706\teval-rmse:0.345586\n",
+ "[775]\ttrain-mae:0.262944\ttrain-rmse:0.337934\teval-mae:0.268707\teval-rmse:0.345588\n",
+ "[776]\ttrain-mae:0.262938\ttrain-rmse:0.337925\teval-mae:0.268708\teval-rmse:0.34559\n",
+ "Stopping. Best iteration:\n",
+ "[766]\ttrain-mae:0.262998\ttrain-rmse:0.338\teval-mae:0.268703\teval-rmse:0.345584\n",
+ "\n"
]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[1513]\teval-mae:0.316045\teval-rmse:0.403395\ttrain-mae:0.311957\ttrain-rmse:0.397809\n",
- "[1514]\teval-mae:0.316043\teval-rmse:0.403393\ttrain-mae:0.311953\ttrain-rmse:0.397806\n",
- "[1515]\teval-mae:0.316041\teval-rmse:0.40339\ttrain-mae:0.311951\ttrain-rmse:0.397804\n",
- "[1516]\teval-mae:0.316039\teval-rmse:0.403388\ttrain-mae:0.311947\ttrain-rmse:0.397799\n",
- "[1517]\teval-mae:0.316031\teval-rmse:0.40338\ttrain-mae:0.311938\ttrain-rmse:0.397788\n",
- "[1518]\teval-mae:0.316022\teval-rmse:0.403369\ttrain-mae:0.311928\ttrain-rmse:0.397776\n",
- "[1519]\teval-mae:0.316013\teval-rmse:0.403358\ttrain-mae:0.311918\ttrain-rmse:0.397762\n",
- "[1520]\teval-mae:0.316012\teval-rmse:0.403357\ttrain-mae:0.311915\ttrain-rmse:0.397759\n",
- "[1521]\teval-mae:0.316004\teval-rmse:0.403347\ttrain-mae:0.311905\ttrain-rmse:0.397747\n",
- "[1522]\teval-mae:0.316\teval-rmse:0.403343\ttrain-mae:0.3119\ttrain-rmse:0.39774\n",
- "[1523]\teval-mae:0.315998\teval-rmse:0.40334\ttrain-mae:0.311896\ttrain-rmse:0.397735\n",
- "[1524]\teval-mae:0.315988\teval-rmse:0.40333\ttrain-mae:0.311885\ttrain-rmse:0.397723\n",
- "[1525]\teval-mae:0.315984\teval-rmse:0.403326\ttrain-mae:0.311881\ttrain-rmse:0.397719\n",
- "[1526]\teval-mae:0.315986\teval-rmse:0.403328\ttrain-mae:0.311876\ttrain-rmse:0.397713\n",
- "[1527]\teval-mae:0.315983\teval-rmse:0.403325\ttrain-mae:0.311871\ttrain-rmse:0.397707\n",
- "[1528]\teval-mae:0.31598\teval-rmse:0.403321\ttrain-mae:0.311866\ttrain-rmse:0.397702\n",
- "[1529]\teval-mae:0.315975\teval-rmse:0.403315\ttrain-mae:0.311861\ttrain-rmse:0.397695\n",
- "[1530]\teval-mae:0.31597\teval-rmse:0.40331\ttrain-mae:0.311855\ttrain-rmse:0.397689\n",
- "[1531]\teval-mae:0.315967\teval-rmse:0.403308\ttrain-mae:0.311851\ttrain-rmse:0.397685\n",
- "[1532]\teval-mae:0.315966\teval-rmse:0.403305\ttrain-mae:0.311848\ttrain-rmse:0.397681\n",
- "[1533]\teval-mae:0.315959\teval-rmse:0.403299\ttrain-mae:0.31184\ttrain-rmse:0.397672\n",
- "[1534]\teval-mae:0.315951\teval-rmse:0.40329\ttrain-mae:0.311831\ttrain-rmse:0.397661\n",
- "[1535]\teval-mae:0.315953\teval-rmse:0.403292\ttrain-mae:0.311827\ttrain-rmse:0.397656\n",
- "[1536]\teval-mae:0.315944\teval-rmse:0.403281\ttrain-mae:0.311817\ttrain-rmse:0.397644\n",
- "[1537]\teval-mae:0.315939\teval-rmse:0.403275\ttrain-mae:0.311811\ttrain-rmse:0.397637\n",
- "[1538]\teval-mae:0.315929\teval-rmse:0.403265\ttrain-mae:0.3118\ttrain-rmse:0.397624\n",
- "[1539]\teval-mae:0.315924\teval-rmse:0.40326\ttrain-mae:0.311794\ttrain-rmse:0.397619\n",
- "[1540]\teval-mae:0.315922\teval-rmse:0.403257\ttrain-mae:0.31179\ttrain-rmse:0.397615\n",
- "[1541]\teval-mae:0.315914\teval-rmse:0.403247\ttrain-mae:0.311781\ttrain-rmse:0.397601\n",
- "[1542]\teval-mae:0.31591\teval-rmse:0.403242\ttrain-mae:0.311776\ttrain-rmse:0.397597\n",
- "[1543]\teval-mae:0.315912\teval-rmse:0.403244\ttrain-mae:0.311772\ttrain-rmse:0.397591\n",
- "[1544]\teval-mae:0.315908\teval-rmse:0.403241\ttrain-mae:0.311768\ttrain-rmse:0.397587\n",
- "[1545]\teval-mae:0.315906\teval-rmse:0.403238\ttrain-mae:0.311764\ttrain-rmse:0.397582\n",
- "[1546]\teval-mae:0.315904\teval-rmse:0.403236\ttrain-mae:0.311761\ttrain-rmse:0.39758\n",
- "[1547]\teval-mae:0.315895\teval-rmse:0.403227\ttrain-mae:0.31175\ttrain-rmse:0.397567\n",
- "[1548]\teval-mae:0.315895\teval-rmse:0.403226\ttrain-mae:0.311748\ttrain-rmse:0.397565\n",
- "[1549]\teval-mae:0.315891\teval-rmse:0.403222\ttrain-mae:0.311743\ttrain-rmse:0.397558\n",
- "[1550]\teval-mae:0.315893\teval-rmse:0.403224\ttrain-mae:0.311738\ttrain-rmse:0.397552\n",
- "[1551]\teval-mae:0.315891\teval-rmse:0.403222\ttrain-mae:0.311735\ttrain-rmse:0.39755\n",
- "[1552]\teval-mae:0.315884\teval-rmse:0.403214\ttrain-mae:0.311728\ttrain-rmse:0.397541\n",
- "[1553]\teval-mae:0.31588\teval-rmse:0.403209\ttrain-mae:0.311723\ttrain-rmse:0.397535\n",
- "[1554]\teval-mae:0.315878\teval-rmse:0.403208\ttrain-mae:0.31172\ttrain-rmse:0.397532\n",
- "[1555]\teval-mae:0.31587\teval-rmse:0.403199\ttrain-mae:0.311712\ttrain-rmse:0.397522\n",
- "[1556]\teval-mae:0.315862\teval-rmse:0.403188\ttrain-mae:0.311702\ttrain-rmse:0.39751\n",
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- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "[1846]\teval-mae:0.314907\teval-rmse:0.402159\ttrain-mae:0.310256\ttrain-rmse:0.395847\n",
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- "[1848]\teval-mae:0.314903\teval-rmse:0.402154\ttrain-mae:0.310249\ttrain-rmse:0.395838\n",
- "[1849]\teval-mae:0.314902\teval-rmse:0.402153\ttrain-mae:0.310247\ttrain-rmse:0.395835\n",
- "[1850]\teval-mae:0.314897\teval-rmse:0.402147\ttrain-mae:0.31024\ttrain-rmse:0.395827\n",
- "[1851]\teval-mae:0.314897\teval-rmse:0.402147\ttrain-mae:0.310238\ttrain-rmse:0.395824\n",
- "[1852]\teval-mae:0.314895\teval-rmse:0.402146\ttrain-mae:0.310235\ttrain-rmse:0.39582\n",
- "[1853]\teval-mae:0.314891\teval-rmse:0.402141\ttrain-mae:0.310229\ttrain-rmse:0.395814\n",
- "[1854]\teval-mae:0.314882\teval-rmse:0.40213\ttrain-mae:0.31022\ttrain-rmse:0.395802\n",
- "[1855]\teval-mae:0.314877\teval-rmse:0.402124\ttrain-mae:0.310214\ttrain-rmse:0.395795\n",
- "[1856]\teval-mae:0.314874\teval-rmse:0.402121\ttrain-mae:0.31021\ttrain-rmse:0.395791\n",
- "[1857]\teval-mae:0.314871\teval-rmse:0.402116\ttrain-mae:0.310206\ttrain-rmse:0.395785\n",
- "[1858]\teval-mae:0.314869\teval-rmse:0.402115\ttrain-mae:0.310202\ttrain-rmse:0.395782\n",
- "[1859]\teval-mae:0.314867\teval-rmse:0.402113\ttrain-mae:0.310199\ttrain-rmse:0.395779\n",
- "[1860]\teval-mae:0.314861\teval-rmse:0.402107\ttrain-mae:0.310193\ttrain-rmse:0.395772\n",
- "[1861]\teval-mae:0.31486\teval-rmse:0.402106\ttrain-mae:0.310189\ttrain-rmse:0.395767\n",
- "[1862]\teval-mae:0.314859\teval-rmse:0.402105\ttrain-mae:0.310186\ttrain-rmse:0.395764\n",
- "[1863]\teval-mae:0.314857\teval-rmse:0.402104\ttrain-mae:0.310183\ttrain-rmse:0.39576\n",
- "[1864]\teval-mae:0.31485\teval-rmse:0.402095\ttrain-mae:0.310174\ttrain-rmse:0.39575\n",
- "[1865]\teval-mae:0.314848\teval-rmse:0.402093\ttrain-mae:0.310171\ttrain-rmse:0.395746\n",
- "[1866]\teval-mae:0.314841\teval-rmse:0.402086\ttrain-mae:0.310163\ttrain-rmse:0.395738\n",
- "[1867]\teval-mae:0.314835\teval-rmse:0.402078\ttrain-mae:0.310156\ttrain-rmse:0.395729\n",
- "[1868]\teval-mae:0.314833\teval-rmse:0.402075\ttrain-mae:0.310153\ttrain-rmse:0.395725\n",
- "[1869]\teval-mae:0.314825\teval-rmse:0.402066\ttrain-mae:0.310144\ttrain-rmse:0.395714\n",
- "[1870]\teval-mae:0.314823\teval-rmse:0.402065\ttrain-mae:0.310141\ttrain-rmse:0.39571\n",
- "[1871]\teval-mae:0.314818\teval-rmse:0.40206\ttrain-mae:0.310134\ttrain-rmse:0.395704\n",
- "[1872]\teval-mae:0.314816\teval-rmse:0.402058\ttrain-mae:0.310131\ttrain-rmse:0.3957\n",
- "[1873]\teval-mae:0.314814\teval-rmse:0.402056\ttrain-mae:0.310128\ttrain-rmse:0.395696\n",
- "[1874]\teval-mae:0.31481\teval-rmse:0.402052\ttrain-mae:0.310122\ttrain-rmse:0.395689\n",
- "[1875]\teval-mae:0.314809\teval-rmse:0.40205\ttrain-mae:0.310119\ttrain-rmse:0.395686\n",
- "[1876]\teval-mae:0.314802\teval-rmse:0.402042\ttrain-mae:0.310111\ttrain-rmse:0.395675\n",
- "[1877]\teval-mae:0.314798\teval-rmse:0.402038\ttrain-mae:0.310105\ttrain-rmse:0.395667\n",
- "[1878]\teval-mae:0.314794\teval-rmse:0.402034\ttrain-mae:0.310099\ttrain-rmse:0.39566\n",
- "[1879]\teval-mae:0.314787\teval-rmse:0.402027\ttrain-mae:0.310091\ttrain-rmse:0.395651\n",
- "[1880]\teval-mae:0.314785\teval-rmse:0.402025\ttrain-mae:0.310088\ttrain-rmse:0.395647\n",
- "[1881]\teval-mae:0.314784\teval-rmse:0.402025\ttrain-mae:0.310085\ttrain-rmse:0.395644\n",
- "[1882]\teval-mae:0.314777\teval-rmse:0.402017\ttrain-mae:0.310077\ttrain-rmse:0.395634\n",
- "[1883]\teval-mae:0.314773\teval-rmse:0.402013\ttrain-mae:0.310072\ttrain-rmse:0.395629\n",
- "[1884]\teval-mae:0.31477\teval-rmse:0.40201\ttrain-mae:0.310068\ttrain-rmse:0.395625\n",
- "[1885]\teval-mae:0.314766\teval-rmse:0.402006\ttrain-mae:0.310061\ttrain-rmse:0.395617\n",
- "[1886]\teval-mae:0.31476\teval-rmse:0.402\ttrain-mae:0.310054\ttrain-rmse:0.395608\n",
- "[1887]\teval-mae:0.314754\teval-rmse:0.401992\ttrain-mae:0.310048\ttrain-rmse:0.3956\n",
- "[1888]\teval-mae:0.314751\teval-rmse:0.401988\ttrain-mae:0.310043\ttrain-rmse:0.395595\n",
- "[1889]\teval-mae:0.314752\teval-rmse:0.40199\ttrain-mae:0.31004\ttrain-rmse:0.395591\n",
- "[1890]\teval-mae:0.314745\teval-rmse:0.401982\ttrain-mae:0.310032\ttrain-rmse:0.395582\n",
- "[1891]\teval-mae:0.31474\teval-rmse:0.401977\ttrain-mae:0.310025\ttrain-rmse:0.395575\n",
- "[1892]\teval-mae:0.314733\teval-rmse:0.401971\ttrain-mae:0.310017\ttrain-rmse:0.395565\n",
- "[1893]\teval-mae:0.314727\teval-rmse:0.401964\ttrain-mae:0.310008\ttrain-rmse:0.395555\n",
- "[1894]\teval-mae:0.314724\teval-rmse:0.40196\ttrain-mae:0.310001\ttrain-rmse:0.395546\n",
- "[1895]\teval-mae:0.314724\teval-rmse:0.40196\ttrain-mae:0.309999\ttrain-rmse:0.395544\n",
- "[1896]\teval-mae:0.314724\teval-rmse:0.40196\ttrain-mae:0.309998\ttrain-rmse:0.395542\n",
- "[1897]\teval-mae:0.31472\teval-rmse:0.401957\ttrain-mae:0.309992\ttrain-rmse:0.395537\n",
- "[1898]\teval-mae:0.314715\teval-rmse:0.401952\ttrain-mae:0.309986\ttrain-rmse:0.39553\n",
- "[1899]\teval-mae:0.314716\teval-rmse:0.401953\ttrain-mae:0.309983\ttrain-rmse:0.395527\n",
- "[1900]\teval-mae:0.314708\teval-rmse:0.401943\ttrain-mae:0.309973\ttrain-rmse:0.395515\n",
- "[1901]\teval-mae:0.314706\teval-rmse:0.401942\ttrain-mae:0.30997\ttrain-rmse:0.395513\n",
- "[1902]\teval-mae:0.314702\teval-rmse:0.401937\ttrain-mae:0.309964\ttrain-rmse:0.395505\n",
- "[1903]\teval-mae:0.3147\teval-rmse:0.401935\ttrain-mae:0.309961\ttrain-rmse:0.395502\n",
- "[1904]\teval-mae:0.314698\teval-rmse:0.401934\ttrain-mae:0.309958\ttrain-rmse:0.395498\n",
- "[1905]\teval-mae:0.314696\teval-rmse:0.401932\ttrain-mae:0.309954\ttrain-rmse:0.395495\n",
- "[1906]\teval-mae:0.314693\teval-rmse:0.401929\ttrain-mae:0.30995\ttrain-rmse:0.395491\n",
- "[1907]\teval-mae:0.314687\teval-rmse:0.401922\ttrain-mae:0.309943\ttrain-rmse:0.395483\n",
- "[1908]\teval-mae:0.314686\teval-rmse:0.401921\ttrain-mae:0.309941\ttrain-rmse:0.395481\n",
- "[1909]\teval-mae:0.314686\teval-rmse:0.401922\ttrain-mae:0.309938\ttrain-rmse:0.395477\n",
- "[1910]\teval-mae:0.314685\teval-rmse:0.401922\ttrain-mae:0.309934\ttrain-rmse:0.395473\n",
- "[1911]\teval-mae:0.314684\teval-rmse:0.401921\ttrain-mae:0.309931\ttrain-rmse:0.39547\n",
- "[1912]\teval-mae:0.314678\teval-rmse:0.401913\ttrain-mae:0.309923\ttrain-rmse:0.395459\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[1913]\teval-mae:0.314673\teval-rmse:0.401906\ttrain-mae:0.309917\ttrain-rmse:0.395452\n",
- "[1914]\teval-mae:0.314672\teval-rmse:0.401905\ttrain-mae:0.309915\ttrain-rmse:0.395449\n",
- "[1915]\teval-mae:0.314664\teval-rmse:0.401896\ttrain-mae:0.309906\ttrain-rmse:0.395438\n",
- "[1916]\teval-mae:0.314663\teval-rmse:0.401895\ttrain-mae:0.309904\ttrain-rmse:0.395436\n",
- "[1917]\teval-mae:0.31466\teval-rmse:0.401892\ttrain-mae:0.3099\ttrain-rmse:0.395432\n",
- "[1918]\teval-mae:0.314652\teval-rmse:0.401883\ttrain-mae:0.309892\ttrain-rmse:0.395421\n",
- "[1919]\teval-mae:0.314652\teval-rmse:0.401882\ttrain-mae:0.309888\ttrain-rmse:0.395417\n",
- "[1920]\teval-mae:0.314649\teval-rmse:0.401879\ttrain-mae:0.309883\ttrain-rmse:0.395411\n",
- "[1921]\teval-mae:0.314649\teval-rmse:0.401879\ttrain-mae:0.309881\ttrain-rmse:0.395409\n",
- "[1922]\teval-mae:0.31465\teval-rmse:0.40188\ttrain-mae:0.309878\ttrain-rmse:0.395405\n",
- "[1923]\teval-mae:0.314649\teval-rmse:0.40188\ttrain-mae:0.309875\ttrain-rmse:0.395401\n",
- "[1924]\teval-mae:0.314649\teval-rmse:0.40188\ttrain-mae:0.309872\ttrain-rmse:0.395398\n",
- "[1925]\teval-mae:0.314645\teval-rmse:0.401876\ttrain-mae:0.309868\ttrain-rmse:0.395393\n",
- "[1926]\teval-mae:0.314644\teval-rmse:0.401875\ttrain-mae:0.309864\ttrain-rmse:0.395389\n",
- "[1927]\teval-mae:0.314635\teval-rmse:0.401865\ttrain-mae:0.309854\ttrain-rmse:0.395377\n",
- "[1928]\teval-mae:0.314634\teval-rmse:0.401864\ttrain-mae:0.309852\ttrain-rmse:0.395375\n",
- "[1929]\teval-mae:0.314628\teval-rmse:0.401857\ttrain-mae:0.309845\ttrain-rmse:0.395365\n",
- "[1930]\teval-mae:0.314626\teval-rmse:0.401855\ttrain-mae:0.309841\ttrain-rmse:0.395362\n",
- "[1931]\teval-mae:0.314624\teval-rmse:0.401854\ttrain-mae:0.309838\ttrain-rmse:0.395359\n",
- "[1932]\teval-mae:0.314619\teval-rmse:0.401848\ttrain-mae:0.309832\ttrain-rmse:0.395352\n",
- "[1933]\teval-mae:0.314615\teval-rmse:0.401845\ttrain-mae:0.309826\ttrain-rmse:0.395345\n",
- "[1934]\teval-mae:0.314612\teval-rmse:0.401841\ttrain-mae:0.309821\ttrain-rmse:0.39534\n",
- "[1935]\teval-mae:0.314604\teval-rmse:0.401832\ttrain-mae:0.309813\ttrain-rmse:0.39533\n",
- "[1936]\teval-mae:0.314602\teval-rmse:0.401831\ttrain-mae:0.30981\ttrain-rmse:0.395327\n",
- "[1937]\teval-mae:0.314594\teval-rmse:0.401821\ttrain-mae:0.309801\ttrain-rmse:0.395315\n",
- "[1938]\teval-mae:0.314593\teval-rmse:0.401819\ttrain-mae:0.309798\ttrain-rmse:0.395312\n",
- "[1939]\teval-mae:0.314591\teval-rmse:0.401818\ttrain-mae:0.309796\ttrain-rmse:0.395309\n",
- "[1940]\teval-mae:0.314586\teval-rmse:0.401811\ttrain-mae:0.30979\ttrain-rmse:0.395302\n",
- "[1941]\teval-mae:0.314585\teval-rmse:0.40181\ttrain-mae:0.309787\ttrain-rmse:0.395299\n",
- "[1942]\teval-mae:0.31458\teval-rmse:0.401805\ttrain-mae:0.309781\ttrain-rmse:0.395292\n",
- "[1943]\teval-mae:0.314578\teval-rmse:0.401803\ttrain-mae:0.309777\ttrain-rmse:0.395288\n",
- "[1944]\teval-mae:0.314572\teval-rmse:0.401797\ttrain-mae:0.30977\ttrain-rmse:0.395281\n",
- "[1945]\teval-mae:0.314568\teval-rmse:0.401793\ttrain-mae:0.309766\ttrain-rmse:0.395276\n",
- "[1946]\teval-mae:0.314566\teval-rmse:0.401791\ttrain-mae:0.309763\ttrain-rmse:0.395273\n",
- "[1947]\teval-mae:0.314564\teval-rmse:0.401787\ttrain-mae:0.309756\ttrain-rmse:0.395264\n",
- "[1948]\teval-mae:0.314563\teval-rmse:0.401787\ttrain-mae:0.309754\ttrain-rmse:0.395262\n",
- "[1949]\teval-mae:0.314561\teval-rmse:0.401785\ttrain-mae:0.309751\ttrain-rmse:0.395258\n",
- "[1950]\teval-mae:0.314563\teval-rmse:0.401787\ttrain-mae:0.309748\ttrain-rmse:0.395255\n",
- "[1951]\teval-mae:0.314557\teval-rmse:0.401781\ttrain-mae:0.30974\ttrain-rmse:0.395246\n",
- "[1952]\teval-mae:0.314555\teval-rmse:0.401779\ttrain-mae:0.309738\ttrain-rmse:0.395243\n",
- "[1953]\teval-mae:0.314549\teval-rmse:0.401773\ttrain-mae:0.30973\ttrain-rmse:0.395235\n",
- "[1954]\teval-mae:0.314544\teval-rmse:0.401768\ttrain-mae:0.309724\ttrain-rmse:0.395229\n",
- "[1955]\teval-mae:0.314545\teval-rmse:0.40177\ttrain-mae:0.309721\ttrain-rmse:0.395225\n",
- "[1956]\teval-mae:0.314536\teval-rmse:0.401759\ttrain-mae:0.309711\ttrain-rmse:0.395213\n",
- "[1957]\teval-mae:0.314535\teval-rmse:0.401759\ttrain-mae:0.309709\ttrain-rmse:0.395211\n",
- "[1958]\teval-mae:0.314533\teval-rmse:0.401755\ttrain-mae:0.309702\ttrain-rmse:0.395202\n",
- "[1959]\teval-mae:0.314532\teval-rmse:0.401754\ttrain-mae:0.309699\ttrain-rmse:0.395199\n",
- "[1960]\teval-mae:0.31453\teval-rmse:0.401752\ttrain-mae:0.309697\ttrain-rmse:0.395195\n",
- "[1961]\teval-mae:0.314527\teval-rmse:0.401747\ttrain-mae:0.309692\ttrain-rmse:0.395189\n",
- "[1962]\teval-mae:0.314526\teval-rmse:0.401747\ttrain-mae:0.309689\ttrain-rmse:0.395186\n",
- "[1963]\teval-mae:0.314525\teval-rmse:0.401746\ttrain-mae:0.309686\ttrain-rmse:0.395183\n",
- "[1964]\teval-mae:0.314524\teval-rmse:0.401746\ttrain-mae:0.309683\ttrain-rmse:0.395179\n",
- "[1965]\teval-mae:0.314523\teval-rmse:0.401745\ttrain-mae:0.309682\ttrain-rmse:0.395177\n",
- "[1966]\teval-mae:0.314523\teval-rmse:0.401743\ttrain-mae:0.30968\ttrain-rmse:0.395174\n",
- "[1967]\teval-mae:0.314517\teval-rmse:0.401737\ttrain-mae:0.309673\ttrain-rmse:0.395166\n",
- "[1968]\teval-mae:0.314514\teval-rmse:0.401734\ttrain-mae:0.30967\ttrain-rmse:0.395163\n",
- "[1969]\teval-mae:0.31451\teval-rmse:0.40173\ttrain-mae:0.309664\ttrain-rmse:0.395157\n",
- "[1970]\teval-mae:0.314504\teval-rmse:0.401724\ttrain-mae:0.309657\ttrain-rmse:0.395149\n",
- "[1971]\teval-mae:0.314503\teval-rmse:0.401723\ttrain-mae:0.309654\ttrain-rmse:0.395146\n",
- "[1972]\teval-mae:0.314496\teval-rmse:0.401716\ttrain-mae:0.309647\ttrain-rmse:0.395137\n",
- "[1973]\teval-mae:0.314492\teval-rmse:0.401712\ttrain-mae:0.309641\ttrain-rmse:0.395131\n",
- "[1974]\teval-mae:0.314487\teval-rmse:0.401706\ttrain-mae:0.309635\ttrain-rmse:0.395124\n",
- "[1975]\teval-mae:0.314486\teval-rmse:0.401705\ttrain-mae:0.309632\ttrain-rmse:0.395119\n",
- "[1976]\teval-mae:0.314487\teval-rmse:0.401707\ttrain-mae:0.309629\ttrain-rmse:0.395116\n",
- "[1977]\teval-mae:0.314481\teval-rmse:0.4017\ttrain-mae:0.309622\ttrain-rmse:0.395107\n",
- "[1978]\teval-mae:0.314475\teval-rmse:0.401693\ttrain-mae:0.309614\ttrain-rmse:0.395098\n",
- "[1979]\teval-mae:0.314474\teval-rmse:0.401692\ttrain-mae:0.309612\ttrain-rmse:0.395096\n",
- "[1980]\teval-mae:0.314472\teval-rmse:0.401691\ttrain-mae:0.309609\ttrain-rmse:0.395093\n",
- "[1981]\teval-mae:0.31447\teval-rmse:0.40169\ttrain-mae:0.309606\ttrain-rmse:0.395089\n",
- "[1982]\teval-mae:0.314468\teval-rmse:0.401687\ttrain-mae:0.309602\ttrain-rmse:0.395086\n",
- "[1983]\teval-mae:0.314459\teval-rmse:0.401677\ttrain-mae:0.309592\ttrain-rmse:0.395074\n",
- "[1984]\teval-mae:0.314455\teval-rmse:0.401673\ttrain-mae:0.309586\ttrain-rmse:0.395067\n",
- "[1985]\teval-mae:0.314448\teval-rmse:0.401664\ttrain-mae:0.309578\ttrain-rmse:0.395057\n",
- "[1986]\teval-mae:0.314448\teval-rmse:0.401664\ttrain-mae:0.309575\ttrain-rmse:0.395054\n",
- "[1987]\teval-mae:0.314446\teval-rmse:0.401662\ttrain-mae:0.309572\ttrain-rmse:0.395051\n",
- "[1988]\teval-mae:0.314444\teval-rmse:0.40166\ttrain-mae:0.309569\ttrain-rmse:0.395047\n",
- "[1989]\teval-mae:0.314444\teval-rmse:0.40166\ttrain-mae:0.309567\ttrain-rmse:0.395044\n",
- "[1990]\teval-mae:0.31444\teval-rmse:0.401655\ttrain-mae:0.30956\ttrain-rmse:0.395036\n",
- "[1991]\teval-mae:0.314434\teval-rmse:0.401649\ttrain-mae:0.309554\ttrain-rmse:0.395029\n",
- "[1992]\teval-mae:0.314429\teval-rmse:0.401645\ttrain-mae:0.309547\ttrain-rmse:0.395022\n",
- "[1993]\teval-mae:0.314428\teval-rmse:0.401644\ttrain-mae:0.309545\ttrain-rmse:0.39502\n",
- "[1994]\teval-mae:0.314426\teval-rmse:0.401641\ttrain-mae:0.309539\ttrain-rmse:0.395011\n",
- "[1995]\teval-mae:0.314424\teval-rmse:0.401639\ttrain-mae:0.309535\ttrain-rmse:0.395008\n",
- "[1996]\teval-mae:0.314423\teval-rmse:0.401638\ttrain-mae:0.309534\ttrain-rmse:0.395006\n",
- "[1997]\teval-mae:0.314422\teval-rmse:0.401637\ttrain-mae:0.309531\ttrain-rmse:0.395003\n",
- "[1998]\teval-mae:0.31442\teval-rmse:0.401636\ttrain-mae:0.309528\ttrain-rmse:0.395\n",
- "[1999]\teval-mae:0.314416\teval-rmse:0.401631\ttrain-mae:0.309522\ttrain-rmse:0.394994\n",
- "[2000]\teval-mae:0.314412\teval-rmse:0.401627\ttrain-mae:0.309519\ttrain-rmse:0.394989\n",
- "[2001]\teval-mae:0.31441\teval-rmse:0.401624\ttrain-mae:0.309514\ttrain-rmse:0.394984\n",
- "[2002]\teval-mae:0.314407\teval-rmse:0.401621\ttrain-mae:0.309508\ttrain-rmse:0.394977\n",
- "[2003]\teval-mae:0.314406\teval-rmse:0.40162\ttrain-mae:0.309506\ttrain-rmse:0.394974\n",
- "[2004]\teval-mae:0.314406\teval-rmse:0.40162\ttrain-mae:0.309504\ttrain-rmse:0.394971\n",
- "[2005]\teval-mae:0.314402\teval-rmse:0.401617\ttrain-mae:0.309499\ttrain-rmse:0.394966\n",
- "[2006]\teval-mae:0.314401\teval-rmse:0.401615\ttrain-mae:0.309497\ttrain-rmse:0.394964\n",
- "[2007]\teval-mae:0.314401\teval-rmse:0.401615\ttrain-mae:0.309495\ttrain-rmse:0.394961\n",
- "[2008]\teval-mae:0.314398\teval-rmse:0.401613\ttrain-mae:0.309491\ttrain-rmse:0.394957\n",
- "[2009]\teval-mae:0.314397\teval-rmse:0.401612\ttrain-mae:0.309489\ttrain-rmse:0.394956\n",
- "[2010]\teval-mae:0.314395\teval-rmse:0.40161\ttrain-mae:0.309486\ttrain-rmse:0.394952\n",
- "[2011]\teval-mae:0.314395\teval-rmse:0.40161\ttrain-mae:0.309484\ttrain-rmse:0.394949\n",
- "[2012]\teval-mae:0.314396\teval-rmse:0.401611\ttrain-mae:0.309481\ttrain-rmse:0.394946\n"
- ]
- },
- {
- "name": "stdout",
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- "text": [
- "[2013]\teval-mae:0.314389\teval-rmse:0.401602\ttrain-mae:0.309473\ttrain-rmse:0.394937\n",
- "[2014]\teval-mae:0.314387\teval-rmse:0.4016\ttrain-mae:0.30947\ttrain-rmse:0.394934\n",
- "[2015]\teval-mae:0.314386\teval-rmse:0.4016\ttrain-mae:0.309469\ttrain-rmse:0.394932\n",
- "[2016]\teval-mae:0.314386\teval-rmse:0.401599\ttrain-mae:0.309465\ttrain-rmse:0.394928\n",
- "[2017]\teval-mae:0.314378\teval-rmse:0.401589\ttrain-mae:0.309456\ttrain-rmse:0.394917\n",
- "[2018]\teval-mae:0.314374\teval-rmse:0.401586\ttrain-mae:0.309451\ttrain-rmse:0.394911\n",
- "[2019]\teval-mae:0.314367\teval-rmse:0.401578\ttrain-mae:0.309443\ttrain-rmse:0.394901\n",
- "[2020]\teval-mae:0.314365\teval-rmse:0.401576\ttrain-mae:0.30944\ttrain-rmse:0.394898\n",
- "[2021]\teval-mae:0.314361\teval-rmse:0.401572\ttrain-mae:0.309436\ttrain-rmse:0.394893\n",
- "[2022]\teval-mae:0.314361\teval-rmse:0.401572\ttrain-mae:0.309433\ttrain-rmse:0.39489\n",
- "[2023]\teval-mae:0.31436\teval-rmse:0.401572\ttrain-mae:0.309431\ttrain-rmse:0.394887\n",
- "[2024]\teval-mae:0.314359\teval-rmse:0.40157\ttrain-mae:0.309428\ttrain-rmse:0.394884\n",
- "[2025]\teval-mae:0.314355\teval-rmse:0.401565\ttrain-mae:0.309423\ttrain-rmse:0.394878\n",
- "[2026]\teval-mae:0.314354\teval-rmse:0.401564\ttrain-mae:0.309418\ttrain-rmse:0.394872\n",
- "[2027]\teval-mae:0.314353\teval-rmse:0.401563\ttrain-mae:0.309416\ttrain-rmse:0.39487\n",
- "[2028]\teval-mae:0.314349\teval-rmse:0.401559\ttrain-mae:0.309411\ttrain-rmse:0.394864\n",
- "[2029]\teval-mae:0.314341\teval-rmse:0.401549\ttrain-mae:0.309401\ttrain-rmse:0.394853\n",
- "[2030]\teval-mae:0.314337\teval-rmse:0.401545\ttrain-mae:0.309397\ttrain-rmse:0.394847\n",
- "[2031]\teval-mae:0.314336\teval-rmse:0.401543\ttrain-mae:0.309395\ttrain-rmse:0.394845\n",
- "[2032]\teval-mae:0.314334\teval-rmse:0.401541\ttrain-mae:0.309392\ttrain-rmse:0.394842\n",
- "[2033]\teval-mae:0.314329\teval-rmse:0.401537\ttrain-mae:0.309387\ttrain-rmse:0.394836\n",
- "[2034]\teval-mae:0.314328\teval-rmse:0.401535\ttrain-mae:0.309383\ttrain-rmse:0.394832\n",
- "[2035]\teval-mae:0.314322\teval-rmse:0.401528\ttrain-mae:0.309377\ttrain-rmse:0.394824\n",
- "[2036]\teval-mae:0.31432\teval-rmse:0.401526\ttrain-mae:0.309374\ttrain-rmse:0.394821\n",
- "[2037]\teval-mae:0.31432\teval-rmse:0.401526\ttrain-mae:0.309372\ttrain-rmse:0.394819\n",
- "[2038]\teval-mae:0.314315\teval-rmse:0.401521\ttrain-mae:0.309366\ttrain-rmse:0.394812\n",
- "[2039]\teval-mae:0.314306\teval-rmse:0.401511\ttrain-mae:0.309357\ttrain-rmse:0.3948\n",
- "[2040]\teval-mae:0.3143\teval-rmse:0.401504\ttrain-mae:0.30935\ttrain-rmse:0.394792\n",
- "[2041]\teval-mae:0.314299\teval-rmse:0.401503\ttrain-mae:0.309347\ttrain-rmse:0.39479\n",
- "[2042]\teval-mae:0.314292\teval-rmse:0.401495\ttrain-mae:0.30934\ttrain-rmse:0.39478\n",
- "[2043]\teval-mae:0.314287\teval-rmse:0.401489\ttrain-mae:0.309333\ttrain-rmse:0.394773\n",
- "[2044]\teval-mae:0.314285\teval-rmse:0.401487\ttrain-mae:0.309331\ttrain-rmse:0.39477\n",
- "[2045]\teval-mae:0.314284\teval-rmse:0.401486\ttrain-mae:0.309328\ttrain-rmse:0.394767\n",
- "[2046]\teval-mae:0.314284\teval-rmse:0.401486\ttrain-mae:0.309326\ttrain-rmse:0.394765\n",
- "[2047]\teval-mae:0.314282\teval-rmse:0.401485\ttrain-mae:0.309323\ttrain-rmse:0.394762\n",
- "[2048]\teval-mae:0.314281\teval-rmse:0.401484\ttrain-mae:0.30932\ttrain-rmse:0.394758\n",
- "[2049]\teval-mae:0.314276\teval-rmse:0.40148\ttrain-mae:0.309315\ttrain-rmse:0.394753\n",
- "[2050]\teval-mae:0.314268\teval-rmse:0.401471\ttrain-mae:0.309305\ttrain-rmse:0.394742\n",
- "[2051]\teval-mae:0.314268\teval-rmse:0.401471\ttrain-mae:0.309303\ttrain-rmse:0.394739\n",
- "[2052]\teval-mae:0.314266\teval-rmse:0.401469\ttrain-mae:0.3093\ttrain-rmse:0.394737\n",
- "[2053]\teval-mae:0.314264\teval-rmse:0.401467\ttrain-mae:0.309297\ttrain-rmse:0.394732\n",
- "[2054]\teval-mae:0.314263\teval-rmse:0.401466\ttrain-mae:0.309292\ttrain-rmse:0.394726\n",
- "[2055]\teval-mae:0.314263\teval-rmse:0.401466\ttrain-mae:0.309291\ttrain-rmse:0.394725\n",
- "[2056]\teval-mae:0.314258\teval-rmse:0.401461\ttrain-mae:0.309285\ttrain-rmse:0.394718\n",
- "[2057]\teval-mae:0.314253\teval-rmse:0.401455\ttrain-mae:0.309278\ttrain-rmse:0.39471\n",
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- "[2059]\teval-mae:0.314251\teval-rmse:0.401454\ttrain-mae:0.309273\ttrain-rmse:0.394705\n",
- "[2060]\teval-mae:0.314245\teval-rmse:0.401448\ttrain-mae:0.309266\ttrain-rmse:0.394697\n",
- "[2061]\teval-mae:0.314239\teval-rmse:0.401442\ttrain-mae:0.309259\ttrain-rmse:0.394688\n",
- "[2062]\teval-mae:0.314235\teval-rmse:0.401437\ttrain-mae:0.309253\ttrain-rmse:0.394682\n",
- "[2063]\teval-mae:0.314231\teval-rmse:0.401434\ttrain-mae:0.309248\ttrain-rmse:0.394676\n",
- "[2064]\teval-mae:0.314229\teval-rmse:0.401432\ttrain-mae:0.309245\ttrain-rmse:0.394673\n",
- "[2065]\teval-mae:0.314228\teval-rmse:0.401431\ttrain-mae:0.309242\ttrain-rmse:0.39467\n",
- "[2066]\teval-mae:0.314227\teval-rmse:0.40143\ttrain-mae:0.309239\ttrain-rmse:0.394667\n",
- "[2067]\teval-mae:0.314222\teval-rmse:0.401425\ttrain-mae:0.309233\ttrain-rmse:0.39466\n",
- "[2068]\teval-mae:0.314219\teval-rmse:0.401421\ttrain-mae:0.309226\ttrain-rmse:0.394652\n",
- "[2069]\teval-mae:0.314212\teval-rmse:0.401414\ttrain-mae:0.309219\ttrain-rmse:0.394644\n",
- "[2070]\teval-mae:0.314208\teval-rmse:0.401409\ttrain-mae:0.309214\ttrain-rmse:0.394638\n",
- "[2071]\teval-mae:0.314207\teval-rmse:0.401408\ttrain-mae:0.309212\ttrain-rmse:0.394635\n",
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- "[2073]\teval-mae:0.314197\teval-rmse:0.401396\ttrain-mae:0.309199\ttrain-rmse:0.394621\n",
- "[2074]\teval-mae:0.314193\teval-rmse:0.401392\ttrain-mae:0.309194\ttrain-rmse:0.394615\n",
- "[2075]\teval-mae:0.314184\teval-rmse:0.401382\ttrain-mae:0.309186\ttrain-rmse:0.394604\n",
- "[2076]\teval-mae:0.314178\teval-rmse:0.401374\ttrain-mae:0.309178\ttrain-rmse:0.394595\n",
- "[2077]\teval-mae:0.314177\teval-rmse:0.401373\ttrain-mae:0.309177\ttrain-rmse:0.394593\n",
- "[2078]\teval-mae:0.314174\teval-rmse:0.401369\ttrain-mae:0.309173\ttrain-rmse:0.394588\n",
- "[2079]\teval-mae:0.314171\teval-rmse:0.401367\ttrain-mae:0.309169\ttrain-rmse:0.394584\n",
- "[2080]\teval-mae:0.314165\teval-rmse:0.40136\ttrain-mae:0.309161\ttrain-rmse:0.394575\n",
- "[2081]\teval-mae:0.314164\teval-rmse:0.401359\ttrain-mae:0.30916\ttrain-rmse:0.394573\n",
- "[2082]\teval-mae:0.314163\teval-rmse:0.401358\ttrain-mae:0.309157\ttrain-rmse:0.394571\n",
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- "[2084]\teval-mae:0.314162\teval-rmse:0.401358\ttrain-mae:0.30915\ttrain-rmse:0.394563\n",
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- "[2087]\teval-mae:0.314159\teval-rmse:0.401354\ttrain-mae:0.309143\ttrain-rmse:0.394554\n",
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- "[2089]\teval-mae:0.314149\teval-rmse:0.401344\ttrain-mae:0.30913\ttrain-rmse:0.394539\n",
- "[2090]\teval-mae:0.314143\teval-rmse:0.401336\ttrain-mae:0.309124\ttrain-rmse:0.394531\n",
- "[2091]\teval-mae:0.31414\teval-rmse:0.401331\ttrain-mae:0.309118\ttrain-rmse:0.394524\n",
- "[2092]\teval-mae:0.314138\teval-rmse:0.401329\ttrain-mae:0.309115\ttrain-rmse:0.39452\n",
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- "[2095]\teval-mae:0.314133\teval-rmse:0.401324\ttrain-mae:0.309105\ttrain-rmse:0.394508\n",
- "[2096]\teval-mae:0.314131\teval-rmse:0.401322\ttrain-mae:0.309102\ttrain-rmse:0.394504\n",
- "[2097]\teval-mae:0.314131\teval-rmse:0.401321\ttrain-mae:0.309097\ttrain-rmse:0.394498\n",
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- "[2099]\teval-mae:0.314121\teval-rmse:0.401313\ttrain-mae:0.309086\ttrain-rmse:0.394487\n",
- "[2100]\teval-mae:0.31412\teval-rmse:0.401311\ttrain-mae:0.309083\ttrain-rmse:0.394484\n",
- "[2101]\teval-mae:0.31412\teval-rmse:0.401312\ttrain-mae:0.30908\ttrain-rmse:0.394481\n",
- "[2102]\teval-mae:0.314118\teval-rmse:0.401311\ttrain-mae:0.309078\ttrain-rmse:0.394478\n",
- "[2103]\teval-mae:0.314114\teval-rmse:0.401306\ttrain-mae:0.309073\ttrain-rmse:0.394472\n",
- "[2104]\teval-mae:0.314113\teval-rmse:0.401305\ttrain-mae:0.309072\ttrain-rmse:0.39447\n",
- "[2105]\teval-mae:0.314111\teval-rmse:0.401303\ttrain-mae:0.309069\ttrain-rmse:0.394467\n",
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- "[2107]\teval-mae:0.314106\teval-rmse:0.401296\ttrain-mae:0.30906\ttrain-rmse:0.394455\n",
- "[2108]\teval-mae:0.314105\teval-rmse:0.401294\ttrain-mae:0.309057\ttrain-rmse:0.394452\n",
- "[2109]\teval-mae:0.314097\teval-rmse:0.401286\ttrain-mae:0.309049\ttrain-rmse:0.394443\n",
- "[2110]\teval-mae:0.314097\teval-rmse:0.401286\ttrain-mae:0.309046\ttrain-rmse:0.39444\n",
- "[2111]\teval-mae:0.314093\teval-rmse:0.401282\ttrain-mae:0.309042\ttrain-rmse:0.394434\n",
- "[2112]\teval-mae:0.314089\teval-rmse:0.401277\ttrain-mae:0.309037\ttrain-rmse:0.394428\n"
- ]
- },
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- "[2114]\teval-mae:0.314084\teval-rmse:0.401273\ttrain-mae:0.309029\ttrain-rmse:0.39442\n",
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- ]
- },
- {
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- "output_type": "stream",
- "text": [
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- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "[2577]\teval-mae:0.313197\teval-rmse:0.400335\ttrain-mae:0.307484\ttrain-rmse:0.392605\n",
- "[2578]\teval-mae:0.313197\teval-rmse:0.400335\ttrain-mae:0.307483\ttrain-rmse:0.392602\n",
- "[2579]\teval-mae:0.313191\teval-rmse:0.400328\ttrain-mae:0.307476\ttrain-rmse:0.392594\n",
- "[2580]\teval-mae:0.313187\teval-rmse:0.400323\ttrain-mae:0.307472\ttrain-rmse:0.392589\n",
- "[2581]\teval-mae:0.313186\teval-rmse:0.400322\ttrain-mae:0.30747\ttrain-rmse:0.392586\n",
- "[2582]\teval-mae:0.313184\teval-rmse:0.40032\ttrain-mae:0.307467\ttrain-rmse:0.392583\n",
- "[2583]\teval-mae:0.313184\teval-rmse:0.400321\ttrain-mae:0.307465\ttrain-rmse:0.39258\n",
- "[2584]\teval-mae:0.313183\teval-rmse:0.40032\ttrain-mae:0.307463\ttrain-rmse:0.392579\n",
- "[2585]\teval-mae:0.313183\teval-rmse:0.40032\ttrain-mae:0.307462\ttrain-rmse:0.392576\n",
- "[2586]\teval-mae:0.313181\teval-rmse:0.400318\ttrain-mae:0.307459\ttrain-rmse:0.392573\n",
- "[2587]\teval-mae:0.31318\teval-rmse:0.400316\ttrain-mae:0.307455\ttrain-rmse:0.392568\n",
- "[2588]\teval-mae:0.313178\teval-rmse:0.400314\ttrain-mae:0.307452\ttrain-rmse:0.392564\n",
- "[2589]\teval-mae:0.313177\teval-rmse:0.400313\ttrain-mae:0.307449\ttrain-rmse:0.392561\n",
- "[2590]\teval-mae:0.313176\teval-rmse:0.400313\ttrain-mae:0.307448\ttrain-rmse:0.392559\n",
- "[2591]\teval-mae:0.313176\teval-rmse:0.400312\ttrain-mae:0.307446\ttrain-rmse:0.392556\n",
- "[2592]\teval-mae:0.313174\teval-rmse:0.400311\ttrain-mae:0.307443\ttrain-rmse:0.392553\n",
- "[2593]\teval-mae:0.313172\teval-rmse:0.400309\ttrain-mae:0.30744\ttrain-rmse:0.39255\n",
- "[2594]\teval-mae:0.313171\teval-rmse:0.400308\ttrain-mae:0.307438\ttrain-rmse:0.392547\n",
- "[2595]\teval-mae:0.313171\teval-rmse:0.400308\ttrain-mae:0.307436\ttrain-rmse:0.392545\n",
- "[2596]\teval-mae:0.31317\teval-rmse:0.400307\ttrain-mae:0.307433\ttrain-rmse:0.392542\n",
- "[2597]\teval-mae:0.313167\teval-rmse:0.400304\ttrain-mae:0.30743\ttrain-rmse:0.392538\n",
- "[2598]\teval-mae:0.313164\teval-rmse:0.400301\ttrain-mae:0.307425\ttrain-rmse:0.392532\n",
- "[2599]\teval-mae:0.313157\teval-rmse:0.400293\ttrain-mae:0.307418\ttrain-rmse:0.392523\n",
- "[2600]\teval-mae:0.313153\teval-rmse:0.400288\ttrain-mae:0.307413\ttrain-rmse:0.392517\n",
- "[2601]\teval-mae:0.313152\teval-rmse:0.400288\ttrain-mae:0.307411\ttrain-rmse:0.392515\n",
- "[2602]\teval-mae:0.31315\teval-rmse:0.400286\ttrain-mae:0.307407\ttrain-rmse:0.392511\n",
- "[2603]\teval-mae:0.313148\teval-rmse:0.400284\ttrain-mae:0.307403\ttrain-rmse:0.392505\n",
- "[2604]\teval-mae:0.313146\teval-rmse:0.400282\ttrain-mae:0.3074\ttrain-rmse:0.392503\n",
- "[2605]\teval-mae:0.313143\teval-rmse:0.400279\ttrain-mae:0.307397\ttrain-rmse:0.392499\n",
- "[2606]\teval-mae:0.313142\teval-rmse:0.400277\ttrain-mae:0.307395\ttrain-rmse:0.392496\n",
- "[2607]\teval-mae:0.31314\teval-rmse:0.400276\ttrain-mae:0.307392\ttrain-rmse:0.392493\n",
- "[2608]\teval-mae:0.313139\teval-rmse:0.400275\ttrain-mae:0.307389\ttrain-rmse:0.39249\n",
- "[2609]\teval-mae:0.313137\teval-rmse:0.400272\ttrain-mae:0.307385\ttrain-rmse:0.392485\n",
- "[2610]\teval-mae:0.313134\teval-rmse:0.40027\ttrain-mae:0.307382\ttrain-rmse:0.392481\n",
- "[2611]\teval-mae:0.313132\teval-rmse:0.400268\ttrain-mae:0.307379\ttrain-rmse:0.392478\n",
- "[2612]\teval-mae:0.313126\teval-rmse:0.400261\ttrain-mae:0.307373\ttrain-rmse:0.39247\n"
- ]
- },
+ }
+ ],
+ "source": [
+ "# Train model\n",
+ "ml = xgb.train(param,dtrain,num_round,watchlist,early_stopping_rounds=10)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "[2613]\teval-mae:0.313125\teval-rmse:0.40026\ttrain-mae:0.30737\ttrain-rmse:0.392468\n",
- "[2614]\teval-mae:0.313123\teval-rmse:0.400258\ttrain-mae:0.307367\ttrain-rmse:0.392464\n",
- "[2615]\teval-mae:0.313121\teval-rmse:0.400256\ttrain-mae:0.307363\ttrain-rmse:0.392459\n",
- "[2616]\teval-mae:0.313116\teval-rmse:0.400251\ttrain-mae:0.307358\ttrain-rmse:0.392453\n",
- "[2617]\teval-mae:0.313111\teval-rmse:0.400244\ttrain-mae:0.307351\ttrain-rmse:0.392445\n",
- "[2618]\teval-mae:0.313109\teval-rmse:0.400242\ttrain-mae:0.307349\ttrain-rmse:0.392442\n",
- "[2619]\teval-mae:0.313108\teval-rmse:0.400242\ttrain-mae:0.307347\ttrain-rmse:0.392439\n",
- "[2620]\teval-mae:0.313106\teval-rmse:0.400239\ttrain-mae:0.307343\ttrain-rmse:0.392435\n",
- "[2621]\teval-mae:0.313101\teval-rmse:0.400234\ttrain-mae:0.307338\ttrain-rmse:0.392429\n",
- "[2622]\teval-mae:0.313101\teval-rmse:0.400234\ttrain-mae:0.307337\ttrain-rmse:0.392427\n",
- "[2623]\teval-mae:0.313099\teval-rmse:0.400231\ttrain-mae:0.307332\ttrain-rmse:0.392422\n",
- "[2624]\teval-mae:0.313098\teval-rmse:0.400231\ttrain-mae:0.30733\ttrain-rmse:0.39242\n",
- "[2625]\teval-mae:0.313094\teval-rmse:0.400226\ttrain-mae:0.307326\ttrain-rmse:0.392414\n",
- "[2626]\teval-mae:0.313094\teval-rmse:0.400226\ttrain-mae:0.307324\ttrain-rmse:0.392413\n",
- "[2627]\teval-mae:0.31309\teval-rmse:0.400222\ttrain-mae:0.30732\ttrain-rmse:0.392408\n",
- "[2628]\teval-mae:0.313089\teval-rmse:0.400222\ttrain-mae:0.307318\ttrain-rmse:0.392405\n",
- "[2629]\teval-mae:0.313087\teval-rmse:0.400219\ttrain-mae:0.307314\ttrain-rmse:0.392401\n",
- "[2630]\teval-mae:0.313087\teval-rmse:0.400219\ttrain-mae:0.307313\ttrain-rmse:0.392399\n",
- "[2631]\teval-mae:0.313084\teval-rmse:0.400216\ttrain-mae:0.307308\ttrain-rmse:0.392394\n",
- "[2632]\teval-mae:0.313082\teval-rmse:0.400214\ttrain-mae:0.307305\ttrain-rmse:0.392391\n",
- "[2633]\teval-mae:0.313082\teval-rmse:0.400214\ttrain-mae:0.307303\ttrain-rmse:0.392388\n",
- "[2634]\teval-mae:0.313081\teval-rmse:0.400213\ttrain-mae:0.307301\ttrain-rmse:0.392386\n",
- "[2635]\teval-mae:0.313079\teval-rmse:0.400211\ttrain-mae:0.307298\ttrain-rmse:0.392382\n",
- "[2636]\teval-mae:0.313079\teval-rmse:0.400211\ttrain-mae:0.307296\ttrain-rmse:0.392379\n",
- "[2637]\teval-mae:0.313077\teval-rmse:0.400209\ttrain-mae:0.307292\ttrain-rmse:0.392375\n",
- "[2638]\teval-mae:0.313076\teval-rmse:0.400208\ttrain-mae:0.30729\ttrain-rmse:0.392373\n",
- "[2639]\teval-mae:0.313073\teval-rmse:0.400205\ttrain-mae:0.307286\ttrain-rmse:0.392368\n",
- "[2640]\teval-mae:0.313072\teval-rmse:0.400205\ttrain-mae:0.307284\ttrain-rmse:0.392366\n",
- "[2641]\teval-mae:0.313072\teval-rmse:0.400205\ttrain-mae:0.307282\ttrain-rmse:0.392364\n",
- "[2642]\teval-mae:0.313069\teval-rmse:0.400202\ttrain-mae:0.30728\ttrain-rmse:0.392361\n",
- "[2643]\teval-mae:0.313065\teval-rmse:0.400197\ttrain-mae:0.307275\ttrain-rmse:0.392356\n",
- "[2644]\teval-mae:0.313063\teval-rmse:0.400195\ttrain-mae:0.307272\ttrain-rmse:0.392351\n",
- "[2645]\teval-mae:0.313062\teval-rmse:0.400194\ttrain-mae:0.307269\ttrain-rmse:0.392348\n",
- "[2646]\teval-mae:0.313061\teval-rmse:0.400194\ttrain-mae:0.307267\ttrain-rmse:0.392346\n",
- "[2647]\teval-mae:0.31306\teval-rmse:0.400193\ttrain-mae:0.307265\ttrain-rmse:0.392343\n",
- "[2648]\teval-mae:0.31306\teval-rmse:0.400192\ttrain-mae:0.307263\ttrain-rmse:0.392342\n",
- "[2649]\teval-mae:0.313059\teval-rmse:0.400192\ttrain-mae:0.307261\ttrain-rmse:0.392339\n",
- "[2650]\teval-mae:0.313058\teval-rmse:0.400191\ttrain-mae:0.30726\ttrain-rmse:0.392338\n",
- "[2651]\teval-mae:0.313058\teval-rmse:0.400191\ttrain-mae:0.307258\ttrain-rmse:0.392336\n",
- "[2652]\teval-mae:0.313058\teval-rmse:0.400191\ttrain-mae:0.307256\ttrain-rmse:0.392333\n",
- "[2653]\teval-mae:0.313058\teval-rmse:0.400191\ttrain-mae:0.307255\ttrain-rmse:0.392332\n",
- "[2654]\teval-mae:0.313053\teval-rmse:0.400185\ttrain-mae:0.307249\ttrain-rmse:0.392325\n",
- "[2655]\teval-mae:0.313051\teval-rmse:0.400183\ttrain-mae:0.307246\ttrain-rmse:0.392321\n",
- "[2656]\teval-mae:0.313049\teval-rmse:0.400181\ttrain-mae:0.307243\ttrain-rmse:0.392317\n",
- "[2657]\teval-mae:0.313048\teval-rmse:0.400179\ttrain-mae:0.30724\ttrain-rmse:0.392313\n",
- "[2658]\teval-mae:0.313047\teval-rmse:0.400179\ttrain-mae:0.307238\ttrain-rmse:0.39231\n",
- "[2659]\teval-mae:0.313046\teval-rmse:0.400178\ttrain-mae:0.307236\ttrain-rmse:0.392308\n",
- "[2660]\teval-mae:0.313045\teval-rmse:0.400177\ttrain-mae:0.307234\ttrain-rmse:0.392306\n",
- "[2661]\teval-mae:0.313045\teval-rmse:0.400177\ttrain-mae:0.307233\ttrain-rmse:0.392304\n",
- "[2662]\teval-mae:0.313041\teval-rmse:0.400172\ttrain-mae:0.307227\ttrain-rmse:0.392297\n",
- "[2663]\teval-mae:0.31304\teval-rmse:0.400171\ttrain-mae:0.307225\ttrain-rmse:0.392296\n",
- "[2664]\teval-mae:0.313038\teval-rmse:0.400168\ttrain-mae:0.307223\ttrain-rmse:0.392293\n",
- "[2665]\teval-mae:0.313037\teval-rmse:0.400168\ttrain-mae:0.307221\ttrain-rmse:0.392291\n",
- "[2666]\teval-mae:0.313037\teval-rmse:0.400168\ttrain-mae:0.30722\ttrain-rmse:0.392289\n",
- "[2667]\teval-mae:0.313036\teval-rmse:0.400167\ttrain-mae:0.307217\ttrain-rmse:0.392287\n",
- "[2668]\teval-mae:0.313031\teval-rmse:0.400161\ttrain-mae:0.307211\ttrain-rmse:0.392279\n",
- "[2669]\teval-mae:0.313031\teval-rmse:0.40016\ttrain-mae:0.30721\ttrain-rmse:0.392277\n",
- "[2670]\teval-mae:0.313029\teval-rmse:0.400159\ttrain-mae:0.307207\ttrain-rmse:0.392274\n",
- "[2671]\teval-mae:0.313029\teval-rmse:0.400159\ttrain-mae:0.307206\ttrain-rmse:0.392272\n",
- "[2672]\teval-mae:0.313028\teval-rmse:0.400158\ttrain-mae:0.307204\ttrain-rmse:0.392269\n",
- "[2673]\teval-mae:0.313028\teval-rmse:0.400158\ttrain-mae:0.307201\ttrain-rmse:0.392266\n",
- "[2674]\teval-mae:0.313026\teval-rmse:0.400156\ttrain-mae:0.307198\ttrain-rmse:0.392263\n",
- "[2675]\teval-mae:0.313026\teval-rmse:0.400156\ttrain-mae:0.307196\ttrain-rmse:0.39226\n",
- "[2676]\teval-mae:0.313025\teval-rmse:0.400154\ttrain-mae:0.307193\ttrain-rmse:0.392256\n",
- "[2677]\teval-mae:0.313024\teval-rmse:0.400154\ttrain-mae:0.307191\ttrain-rmse:0.392253\n",
- "[2678]\teval-mae:0.313023\teval-rmse:0.400153\ttrain-mae:0.307188\ttrain-rmse:0.392251\n",
- "[2679]\teval-mae:0.313022\teval-rmse:0.400152\ttrain-mae:0.307186\ttrain-rmse:0.392247\n",
- "[2680]\teval-mae:0.313021\teval-rmse:0.400151\ttrain-mae:0.307185\ttrain-rmse:0.392246\n",
- "[2681]\teval-mae:0.31302\teval-rmse:0.400151\ttrain-mae:0.307183\ttrain-rmse:0.392245\n",
- "[2682]\teval-mae:0.31302\teval-rmse:0.40015\ttrain-mae:0.307181\ttrain-rmse:0.392243\n",
- "[2683]\teval-mae:0.31302\teval-rmse:0.40015\ttrain-mae:0.30718\ttrain-rmse:0.39224\n",
- "[2684]\teval-mae:0.313015\teval-rmse:0.400145\ttrain-mae:0.307175\ttrain-rmse:0.392235\n",
- "[2685]\teval-mae:0.313015\teval-rmse:0.400145\ttrain-mae:0.307174\ttrain-rmse:0.392234\n",
- "[2686]\teval-mae:0.313012\teval-rmse:0.400143\ttrain-mae:0.307171\ttrain-rmse:0.392231\n",
- "[2687]\teval-mae:0.313012\teval-rmse:0.400142\ttrain-mae:0.307169\ttrain-rmse:0.392229\n",
- "[2688]\teval-mae:0.313007\teval-rmse:0.400137\ttrain-mae:0.307164\ttrain-rmse:0.392223\n",
- "[2689]\teval-mae:0.313006\teval-rmse:0.400136\ttrain-mae:0.307162\ttrain-rmse:0.392221\n",
- "[2690]\teval-mae:0.313005\teval-rmse:0.400135\ttrain-mae:0.307159\ttrain-rmse:0.392218\n",
- "[2691]\teval-mae:0.313005\teval-rmse:0.400135\ttrain-mae:0.307158\ttrain-rmse:0.392217\n",
- "[2692]\teval-mae:0.313003\teval-rmse:0.400133\ttrain-mae:0.307156\ttrain-rmse:0.392214\n",
- "[2693]\teval-mae:0.313003\teval-rmse:0.400132\ttrain-mae:0.307154\ttrain-rmse:0.392212\n",
- "[2694]\teval-mae:0.313003\teval-rmse:0.400132\ttrain-mae:0.307153\ttrain-rmse:0.392211\n",
- "[2695]\teval-mae:0.313002\teval-rmse:0.400132\ttrain-mae:0.307151\ttrain-rmse:0.392208\n",
- "[2696]\teval-mae:0.313002\teval-rmse:0.400132\ttrain-mae:0.307149\ttrain-rmse:0.392206\n",
- "[2697]\teval-mae:0.313001\teval-rmse:0.400131\ttrain-mae:0.307147\ttrain-rmse:0.392203\n",
- "[2698]\teval-mae:0.312998\teval-rmse:0.400129\ttrain-mae:0.307143\ttrain-rmse:0.3922\n",
- "[2699]\teval-mae:0.312998\teval-rmse:0.400129\ttrain-mae:0.307142\ttrain-rmse:0.392198\n",
- "[2700]\teval-mae:0.312997\teval-rmse:0.400128\ttrain-mae:0.307141\ttrain-rmse:0.392197\n",
- "[2701]\teval-mae:0.312997\teval-rmse:0.400128\ttrain-mae:0.307139\ttrain-rmse:0.392194\n",
- "[2702]\teval-mae:0.312997\teval-rmse:0.400128\ttrain-mae:0.307138\ttrain-rmse:0.392194\n",
- "[2703]\teval-mae:0.312997\teval-rmse:0.400128\ttrain-mae:0.307136\ttrain-rmse:0.392192\n",
- "[2704]\teval-mae:0.312996\teval-rmse:0.400127\ttrain-mae:0.307134\ttrain-rmse:0.392189\n",
- "[2705]\teval-mae:0.312993\teval-rmse:0.400124\ttrain-mae:0.30713\ttrain-rmse:0.392184\n",
- "[2706]\teval-mae:0.312988\teval-rmse:0.400119\ttrain-mae:0.307124\ttrain-rmse:0.392177\n",
- "[2707]\teval-mae:0.312988\teval-rmse:0.400118\ttrain-mae:0.307121\ttrain-rmse:0.392173\n",
- "[2708]\teval-mae:0.312986\teval-rmse:0.400116\ttrain-mae:0.307117\ttrain-rmse:0.392167\n",
- "[2709]\teval-mae:0.312985\teval-rmse:0.400114\ttrain-mae:0.307114\ttrain-rmse:0.392164\n",
- "[2710]\teval-mae:0.312984\teval-rmse:0.400113\ttrain-mae:0.307113\ttrain-rmse:0.392162\n",
- "[2711]\teval-mae:0.312983\teval-rmse:0.400111\ttrain-mae:0.30711\ttrain-rmse:0.392159\n",
- "[2712]\teval-mae:0.312981\teval-rmse:0.400109\ttrain-mae:0.307107\ttrain-rmse:0.392154\n"
+ "[0]\ttrain-mae:1.27835+4.70081e-05\ttrain-rmse:1.36919+6.37546e-05\ttest-mae:1.27834+0.000438496\ttest-rmse:1.36919+0.000567832\n",
+ "[1]\ttrain-mae:1.21443+4.47433e-05\ttrain-rmse:1.30747+6.57742e-05\ttest-mae:1.21443+0.000438651\ttest-rmse:1.30748+0.000621082\n",
+ "[2]\ttrain-mae:1.15371+4.24864e-05\ttrain-rmse:1.2491+6.07993e-05\ttest-mae:1.15371+0.000433114\ttest-rmse:1.2491+0.000562483\n",
+ "[3]\ttrain-mae:1.09602+4.02369e-05\ttrain-rmse:1.19403+5.61013e-05\ttest-mae:1.09602+0.000433435\ttest-rmse:1.19403+0.000569401\n",
+ "[4]\ttrain-mae:1.04123+3.83235e-05\ttrain-rmse:1.14199+7.12671e-05\ttest-mae:1.04123+0.000433383\ttest-rmse:1.142+0.000568698\n",
+ "[5]\ttrain-mae:0.989189+3.69119e-05\ttrain-rmse:1.09284+7.09933e-05\ttest-mae:0.989188+0.000429545\ttest-rmse:1.09286+0.000567858\n",
+ "[6]\ttrain-mae:0.939805+3.57028e-05\ttrain-rmse:1.04636+0.000117931\ttest-mae:0.939807+0.000429865\ttest-rmse:1.04638+0.000590861\n",
+ "[7]\ttrain-mae:0.892995+3.66176e-05\ttrain-rmse:1.00251+0.00012861\ttest-mae:0.892997+0.000432298\ttest-rmse:1.00254+0.000549273\n",
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+ "[59]\ttrain-mae:0.297935+0.000235788\ttrain-rmse:0.383413+0.000267603\ttest-mae:0.298119+0.000474292\ttest-rmse:0.383662+0.000632185\n",
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+ "[63]\ttrain-mae:0.295252+0.000290758\ttrain-rmse:0.379267+0.000342968\ttest-mae:0.29545+0.00045001\ttest-rmse:0.379533+0.000600967\n",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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- "[2714]\teval-mae:0.312976\teval-rmse:0.400104\ttrain-mae:0.307099\ttrain-rmse:0.392145\n",
- "[2715]\teval-mae:0.312976\teval-rmse:0.400104\ttrain-mae:0.307097\ttrain-rmse:0.392142\n",
- "[2716]\teval-mae:0.312973\teval-rmse:0.400101\ttrain-mae:0.307093\ttrain-rmse:0.392137\n",
- "[2717]\teval-mae:0.312971\teval-rmse:0.400099\ttrain-mae:0.307089\ttrain-rmse:0.392132\n",
- "[2718]\teval-mae:0.312969\teval-rmse:0.400097\ttrain-mae:0.307087\ttrain-rmse:0.392129\n",
- "[2719]\teval-mae:0.312965\teval-rmse:0.400093\ttrain-mae:0.307082\ttrain-rmse:0.392124\n",
- "[2720]\teval-mae:0.312961\teval-rmse:0.400087\ttrain-mae:0.307077\ttrain-rmse:0.392116\n",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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- "[2814]\teval-mae:0.312835\teval-rmse:0.39996\ttrain-mae:0.306842\ttrain-rmse:0.391846\n",
- "[2815]\teval-mae:0.312832\teval-rmse:0.399956\ttrain-mae:0.306839\ttrain-rmse:0.391841\n",
- "[2816]\teval-mae:0.31283\teval-rmse:0.399954\ttrain-mae:0.306835\ttrain-rmse:0.391836\n",
- "[2817]\teval-mae:0.312829\teval-rmse:0.399954\ttrain-mae:0.306833\ttrain-rmse:0.391834\n",
- "[2818]\teval-mae:0.312829\teval-rmse:0.399953\ttrain-mae:0.306831\ttrain-rmse:0.391832\n",
- "[2819]\teval-mae:0.312828\teval-rmse:0.399952\ttrain-mae:0.306828\ttrain-rmse:0.391829\n",
- "[2820]\teval-mae:0.312827\teval-rmse:0.399951\ttrain-mae:0.306826\ttrain-rmse:0.391827\n",
- "[2821]\teval-mae:0.312826\teval-rmse:0.39995\ttrain-mae:0.306824\ttrain-rmse:0.391824\n",
- "[2822]\teval-mae:0.312825\teval-rmse:0.399948\ttrain-mae:0.306822\ttrain-rmse:0.391821\n",
- "[2823]\teval-mae:0.312825\teval-rmse:0.399948\ttrain-mae:0.306821\ttrain-rmse:0.39182\n",
- "[2824]\teval-mae:0.312823\teval-rmse:0.399946\ttrain-mae:0.306818\ttrain-rmse:0.391817\n",
- "[2825]\teval-mae:0.31282\teval-rmse:0.399943\ttrain-mae:0.306813\ttrain-rmse:0.391812\n",
- "[2826]\teval-mae:0.312819\teval-rmse:0.399943\ttrain-mae:0.306812\ttrain-rmse:0.391811\n",
- "[2827]\teval-mae:0.312817\teval-rmse:0.399941\ttrain-mae:0.306809\ttrain-rmse:0.391808\n",
- "[2828]\teval-mae:0.312816\teval-rmse:0.39994\ttrain-mae:0.306808\ttrain-rmse:0.391806\n",
- "[2829]\teval-mae:0.312816\teval-rmse:0.39994\ttrain-mae:0.306806\ttrain-rmse:0.391804\n",
- "[2830]\teval-mae:0.312816\teval-rmse:0.39994\ttrain-mae:0.306805\ttrain-rmse:0.391803\n",
- "[2831]\teval-mae:0.312812\teval-rmse:0.399936\ttrain-mae:0.3068\ttrain-rmse:0.391797\n",
- "[2832]\teval-mae:0.312811\teval-rmse:0.399935\ttrain-mae:0.306798\ttrain-rmse:0.391796\n",
- "[2833]\teval-mae:0.312811\teval-rmse:0.399934\ttrain-mae:0.306797\ttrain-rmse:0.391794\n",
- "[2834]\teval-mae:0.312809\teval-rmse:0.399932\ttrain-mae:0.306794\ttrain-rmse:0.391791\n",
- "[2835]\teval-mae:0.312807\teval-rmse:0.399931\ttrain-mae:0.306792\ttrain-rmse:0.391788\n",
- "[2836]\teval-mae:0.312806\teval-rmse:0.39993\ttrain-mae:0.306789\ttrain-rmse:0.391785\n",
- "[2837]\teval-mae:0.312805\teval-rmse:0.399928\ttrain-mae:0.306787\ttrain-rmse:0.391781\n",
- "[2838]\teval-mae:0.312804\teval-rmse:0.399928\ttrain-mae:0.306784\ttrain-rmse:0.391778\n",
- "[2839]\teval-mae:0.312804\teval-rmse:0.399927\ttrain-mae:0.306783\ttrain-rmse:0.391777\n",
- "[2840]\teval-mae:0.312804\teval-rmse:0.399927\ttrain-mae:0.306781\ttrain-rmse:0.391774\n",
- "[2841]\teval-mae:0.312799\teval-rmse:0.399921\ttrain-mae:0.306776\ttrain-rmse:0.391767\n",
- "[2842]\teval-mae:0.312798\teval-rmse:0.399921\ttrain-mae:0.306774\ttrain-rmse:0.391766\n",
- "[2843]\teval-mae:0.312797\teval-rmse:0.399919\ttrain-mae:0.306772\ttrain-rmse:0.391763\n",
- "[2844]\teval-mae:0.312797\teval-rmse:0.399919\ttrain-mae:0.306769\ttrain-rmse:0.391759\n",
- "[2845]\teval-mae:0.312796\teval-rmse:0.399917\ttrain-mae:0.306767\ttrain-rmse:0.391756\n",
- "[2846]\teval-mae:0.312793\teval-rmse:0.399915\ttrain-mae:0.306762\ttrain-rmse:0.391751\n",
- "[2847]\teval-mae:0.312792\teval-rmse:0.399914\ttrain-mae:0.30676\ttrain-rmse:0.391749\n",
- "[2848]\teval-mae:0.312791\teval-rmse:0.399913\ttrain-mae:0.306759\ttrain-rmse:0.391747\n",
- "[2849]\teval-mae:0.31279\teval-rmse:0.399913\ttrain-mae:0.306756\ttrain-rmse:0.391744\n",
- "[2850]\teval-mae:0.312789\teval-rmse:0.399912\ttrain-mae:0.306753\ttrain-rmse:0.391741\n",
- "[2851]\teval-mae:0.312787\teval-rmse:0.39991\ttrain-mae:0.30675\ttrain-rmse:0.391736\n",
- "[2852]\teval-mae:0.312787\teval-rmse:0.399909\ttrain-mae:0.306747\ttrain-rmse:0.391733\n",
- "[2853]\teval-mae:0.312786\teval-rmse:0.399908\ttrain-mae:0.306745\ttrain-rmse:0.391731\n",
- "[2854]\teval-mae:0.312783\teval-rmse:0.399905\ttrain-mae:0.306742\ttrain-rmse:0.391726\n",
- "[2855]\teval-mae:0.312782\teval-rmse:0.399905\ttrain-mae:0.306739\ttrain-rmse:0.391724\n",
- "[2856]\teval-mae:0.312781\teval-rmse:0.399903\ttrain-mae:0.306737\ttrain-rmse:0.391721\n",
- "[2857]\teval-mae:0.312767\teval-rmse:0.399888\ttrain-mae:0.306724\ttrain-rmse:0.391706\n",
- "[2858]\teval-mae:0.312767\teval-rmse:0.399887\ttrain-mae:0.306723\ttrain-rmse:0.391704\n",
- "[2859]\teval-mae:0.312766\teval-rmse:0.399887\ttrain-mae:0.306721\ttrain-rmse:0.391702\n",
- "[2860]\teval-mae:0.312764\teval-rmse:0.399885\ttrain-mae:0.306718\ttrain-rmse:0.391699\n",
- "[2861]\teval-mae:0.312764\teval-rmse:0.399885\ttrain-mae:0.306716\ttrain-rmse:0.391697\n",
- "[2862]\teval-mae:0.312762\teval-rmse:0.399883\ttrain-mae:0.306714\ttrain-rmse:0.391694\n",
- "[2863]\teval-mae:0.31276\teval-rmse:0.399881\ttrain-mae:0.306711\ttrain-rmse:0.391692\n",
- "[2864]\teval-mae:0.312759\teval-rmse:0.399881\ttrain-mae:0.30671\ttrain-rmse:0.39169\n",
- "[2865]\teval-mae:0.312758\teval-rmse:0.399879\ttrain-mae:0.306708\ttrain-rmse:0.391688\n",
- "[2866]\teval-mae:0.312759\teval-rmse:0.39988\ttrain-mae:0.306706\ttrain-rmse:0.391686\n",
- "[2867]\teval-mae:0.312758\teval-rmse:0.399879\ttrain-mae:0.306704\ttrain-rmse:0.391684\n",
- "[2868]\teval-mae:0.312757\teval-rmse:0.399878\ttrain-mae:0.306702\ttrain-rmse:0.39168\n",
- "[2869]\teval-mae:0.312756\teval-rmse:0.399877\ttrain-mae:0.3067\ttrain-rmse:0.391678\n",
- "[2870]\teval-mae:0.312755\teval-rmse:0.399877\ttrain-mae:0.306697\ttrain-rmse:0.391675\n",
- "[2871]\teval-mae:0.312755\teval-rmse:0.399876\ttrain-mae:0.306697\ttrain-rmse:0.391674\n",
- "[2872]\teval-mae:0.312754\teval-rmse:0.399876\ttrain-mae:0.306694\ttrain-rmse:0.391671\n",
- "[2873]\teval-mae:0.312741\teval-rmse:0.399862\ttrain-mae:0.306682\ttrain-rmse:0.391657\n",
- "[2874]\teval-mae:0.312741\teval-rmse:0.399861\ttrain-mae:0.30668\ttrain-rmse:0.391654\n",
- "[2875]\teval-mae:0.31274\teval-rmse:0.39986\ttrain-mae:0.306678\ttrain-rmse:0.391652\n",
- "[2876]\teval-mae:0.312727\teval-rmse:0.399846\ttrain-mae:0.306665\ttrain-rmse:0.391637\n",
- "[2877]\teval-mae:0.312727\teval-rmse:0.399845\ttrain-mae:0.306663\ttrain-rmse:0.391635\n",
- "[2878]\teval-mae:0.312726\teval-rmse:0.399844\ttrain-mae:0.30666\ttrain-rmse:0.391632\n",
- "[2879]\teval-mae:0.312722\teval-rmse:0.39984\ttrain-mae:0.306656\ttrain-rmse:0.391626\n",
- "[2880]\teval-mae:0.312718\teval-rmse:0.399836\ttrain-mae:0.306651\ttrain-rmse:0.391621\n",
- "[2881]\teval-mae:0.312704\teval-rmse:0.39982\ttrain-mae:0.306638\ttrain-rmse:0.391606\n",
- "[2882]\teval-mae:0.312699\teval-rmse:0.399814\ttrain-mae:0.306632\ttrain-rmse:0.391599\n",
- "[2883]\teval-mae:0.312695\teval-rmse:0.39981\ttrain-mae:0.306628\ttrain-rmse:0.391594\n",
- "[2884]\teval-mae:0.312694\teval-rmse:0.399809\ttrain-mae:0.306626\ttrain-rmse:0.391592\n",
- "[2885]\teval-mae:0.312693\teval-rmse:0.399808\ttrain-mae:0.306625\ttrain-rmse:0.391591\n",
- "[2886]\teval-mae:0.312692\teval-rmse:0.399807\ttrain-mae:0.306623\ttrain-rmse:0.391589\n",
- "[2887]\teval-mae:0.312692\teval-rmse:0.399807\ttrain-mae:0.306621\ttrain-rmse:0.391587\n",
- "[2888]\teval-mae:0.312692\teval-rmse:0.399806\ttrain-mae:0.306619\ttrain-rmse:0.391583\n",
- "[2889]\teval-mae:0.312692\teval-rmse:0.399806\ttrain-mae:0.306617\ttrain-rmse:0.391581\n",
- "[2890]\teval-mae:0.312692\teval-rmse:0.399806\ttrain-mae:0.306616\ttrain-rmse:0.39158\n",
- "[2891]\teval-mae:0.312691\teval-rmse:0.399806\ttrain-mae:0.306614\ttrain-rmse:0.391578\n",
- "[2892]\teval-mae:0.312691\teval-rmse:0.399806\ttrain-mae:0.306613\ttrain-rmse:0.391576\n",
- "[2893]\teval-mae:0.312691\teval-rmse:0.399805\ttrain-mae:0.306612\ttrain-rmse:0.391575\n",
- "[2894]\teval-mae:0.31269\teval-rmse:0.399804\ttrain-mae:0.30661\ttrain-rmse:0.391573\n",
- "[2895]\teval-mae:0.312689\teval-rmse:0.399804\ttrain-mae:0.306608\ttrain-rmse:0.391571\n",
- "[2896]\teval-mae:0.312688\teval-rmse:0.399803\ttrain-mae:0.306605\ttrain-rmse:0.391567\n",
- "[2897]\teval-mae:0.312685\teval-rmse:0.3998\ttrain-mae:0.306601\ttrain-rmse:0.391563\n",
- "[2898]\teval-mae:0.312681\teval-rmse:0.399795\ttrain-mae:0.306596\ttrain-rmse:0.391556\n",
- "[2899]\teval-mae:0.31268\teval-rmse:0.399794\ttrain-mae:0.306594\ttrain-rmse:0.391554\n",
- "[2900]\teval-mae:0.312678\teval-rmse:0.399792\ttrain-mae:0.306591\ttrain-rmse:0.391549\n",
- "[2901]\teval-mae:0.312678\teval-rmse:0.399792\ttrain-mae:0.30659\ttrain-rmse:0.391548\n",
- "[2902]\teval-mae:0.312675\teval-rmse:0.399788\ttrain-mae:0.306586\ttrain-rmse:0.391544\n",
- "[2903]\teval-mae:0.312671\teval-rmse:0.399783\ttrain-mae:0.306581\ttrain-rmse:0.391537\n",
- "[2904]\teval-mae:0.31267\teval-rmse:0.399783\ttrain-mae:0.306579\ttrain-rmse:0.391535\n",
- "[2905]\teval-mae:0.312669\teval-rmse:0.399782\ttrain-mae:0.306577\ttrain-rmse:0.391533\n",
- "[2906]\teval-mae:0.312668\teval-rmse:0.399781\ttrain-mae:0.306576\ttrain-rmse:0.391531\n",
- "[2907]\teval-mae:0.312667\teval-rmse:0.39978\ttrain-mae:0.306574\ttrain-rmse:0.391529\n",
- "[2908]\teval-mae:0.312665\teval-rmse:0.399778\ttrain-mae:0.306571\ttrain-rmse:0.391525\n",
- "[2909]\teval-mae:0.312665\teval-rmse:0.399779\ttrain-mae:0.30657\ttrain-rmse:0.391524\n",
- "[2910]\teval-mae:0.312664\teval-rmse:0.399778\ttrain-mae:0.306568\ttrain-rmse:0.391522\n",
- "[2911]\teval-mae:0.312663\teval-rmse:0.399777\ttrain-mae:0.306565\ttrain-rmse:0.39152\n",
- "[2912]\teval-mae:0.312662\teval-rmse:0.399776\ttrain-mae:0.306564\ttrain-rmse:0.391518\n"
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+ "[168]\ttrain-mae:0.276397+0.000190639\ttrain-rmse:0.353318+0.000231873\ttest-mae:0.276764+0.000348503\ttest-rmse:0.353821+0.000474686\n",
+ "[169]\ttrain-mae:0.276322+0.000196475\ttrain-rmse:0.353229+0.000241044\ttest-mae:0.27669+0.000354513\ttest-rmse:0.353733+0.000483873\n",
+ "[170]\ttrain-mae:0.276273+0.000189614\ttrain-rmse:0.353172+0.000232793\ttest-mae:0.276641+0.000349419\ttest-rmse:0.353676+0.000475797\n",
+ "[171]\ttrain-mae:0.276183+0.000189487\ttrain-rmse:0.35307+0.000231127\ttest-mae:0.276554+0.000341385\ttest-rmse:0.353576+0.000463917\n",
+ "[172]\ttrain-mae:0.27612+0.000193012\ttrain-rmse:0.352999+0.000232898\ttest-mae:0.276491+0.000340648\ttest-rmse:0.353507+0.000465026\n",
+ "[173]\ttrain-mae:0.276057+0.000190603\ttrain-rmse:0.352928+0.000229468\ttest-mae:0.276429+0.000346612\ttest-rmse:0.353438+0.000470724\n",
+ "[174]\ttrain-mae:0.275984+0.00017355\ttrain-rmse:0.352845+0.000209924\ttest-mae:0.276356+0.000330867\ttest-rmse:0.353357+0.000447505\n",
+ "[175]\ttrain-mae:0.275908+0.000159416\ttrain-rmse:0.35276+0.000196216\ttest-mae:0.276282+0.000338768\ttest-rmse:0.353273+0.000454971\n",
+ "[176]\ttrain-mae:0.275827+0.00016149\ttrain-rmse:0.352666+0.000196607\ttest-mae:0.276202+0.000343201\ttest-rmse:0.353181+0.000462747\n",
+ "[177]\ttrain-mae:0.275743+0.000156989\ttrain-rmse:0.352573+0.000192866\ttest-mae:0.27612+0.000335285\ttest-rmse:0.35309+0.000453127\n",
+ "[178]\ttrain-mae:0.275668+0.000159987\ttrain-rmse:0.352486+0.00019583\ttest-mae:0.276046+0.000336481\ttest-rmse:0.353005+0.000449228\n",
+ "[179]\ttrain-mae:0.275597+0.000162643\ttrain-rmse:0.352404+0.000198767\ttest-mae:0.275976+0.000337952\ttest-rmse:0.352924+0.000449244\n",
+ "[180]\ttrain-mae:0.27553+0.000159684\ttrain-rmse:0.352328+0.00019408\ttest-mae:0.27591+0.000332396\ttest-rmse:0.35285+0.000442592\n",
+ "[181]\ttrain-mae:0.275469+0.000162485\ttrain-rmse:0.35226+0.000200197\ttest-mae:0.275851+0.000339503\ttest-rmse:0.352785+0.000452178\n",
+ "[182]\ttrain-mae:0.275396+0.000145761\ttrain-rmse:0.352176+0.00017957\ttest-mae:0.275779+0.000341098\ttest-rmse:0.352703+0.000455039\n",
+ "[183]\ttrain-mae:0.275329+0.000139595\ttrain-rmse:0.352101+0.000174397\ttest-mae:0.275715+0.000339229\ttest-rmse:0.35263+0.000454332\n",
+ "[184]\ttrain-mae:0.275263+0.000138882\ttrain-rmse:0.352025+0.000172322\ttest-mae:0.275649+0.000329967\ttest-rmse:0.352555+0.000444958\n",
+ "[185]\ttrain-mae:0.27518+0.00014706\ttrain-rmse:0.351934+0.000184583\ttest-mae:0.275569+0.000329136\ttest-rmse:0.352466+0.000445289\n",
+ "[186]\ttrain-mae:0.275116+0.000156061\ttrain-rmse:0.351862+0.000193049\ttest-mae:0.275506+0.000336923\ttest-rmse:0.352396+0.000448937\n",
+ "[187]\ttrain-mae:0.275054+0.000150728\ttrain-rmse:0.351793+0.000185399\ttest-mae:0.275445+0.000336321\ttest-rmse:0.352328+0.000450082\n",
+ "[188]\ttrain-mae:0.274995+0.00015882\ttrain-rmse:0.351729+0.000194245\ttest-mae:0.275388+0.000340124\ttest-rmse:0.352267+0.000454084\n",
+ "[189]\ttrain-mae:0.27494+0.00015319\ttrain-rmse:0.351667+0.000190593\ttest-mae:0.275334+0.000341734\ttest-rmse:0.352205+0.000454383\n",
+ "[190]\ttrain-mae:0.274881+0.000147461\ttrain-rmse:0.3516+0.000182641\ttest-mae:0.275276+0.000333965\ttest-rmse:0.352141+0.000443977\n",
+ "[191]\ttrain-mae:0.274824+0.000140032\ttrain-rmse:0.351538+0.000175261\ttest-mae:0.275221+0.000339328\ttest-rmse:0.35208+0.000450117\n",
+ "[192]\ttrain-mae:0.274758+0.000148305\ttrain-rmse:0.35146+0.000185012\ttest-mae:0.275156+0.000327837\ttest-rmse:0.352005+0.000435645\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "[2913]\teval-mae:0.312661\teval-rmse:0.399774\ttrain-mae:0.306561\ttrain-rmse:0.391514\n",
- "[2914]\teval-mae:0.31266\teval-rmse:0.399773\ttrain-mae:0.306559\ttrain-rmse:0.391512\n",
- "[2915]\teval-mae:0.312659\teval-rmse:0.399772\ttrain-mae:0.306556\ttrain-rmse:0.391508\n",
- "[2916]\teval-mae:0.312658\teval-rmse:0.399772\ttrain-mae:0.306555\ttrain-rmse:0.391507\n",
- "[2917]\teval-mae:0.312655\teval-rmse:0.399769\ttrain-mae:0.306551\ttrain-rmse:0.391502\n",
- "[2918]\teval-mae:0.312656\teval-rmse:0.399769\ttrain-mae:0.306549\ttrain-rmse:0.3915\n",
- "[2919]\teval-mae:0.312655\teval-rmse:0.399768\ttrain-mae:0.306547\ttrain-rmse:0.391498\n",
- "[2920]\teval-mae:0.312654\teval-rmse:0.399767\ttrain-mae:0.306546\ttrain-rmse:0.391496\n",
- "[2921]\teval-mae:0.312653\teval-rmse:0.399766\ttrain-mae:0.306545\ttrain-rmse:0.391495\n",
- "[2922]\teval-mae:0.312652\teval-rmse:0.399765\ttrain-mae:0.306543\ttrain-rmse:0.391493\n",
- "[2923]\teval-mae:0.312651\teval-rmse:0.399764\ttrain-mae:0.306541\ttrain-rmse:0.39149\n",
- "[2924]\teval-mae:0.312651\teval-rmse:0.399764\ttrain-mae:0.30654\ttrain-rmse:0.39149\n",
- "[2925]\teval-mae:0.31265\teval-rmse:0.399763\ttrain-mae:0.306538\ttrain-rmse:0.391488\n",
- "[2926]\teval-mae:0.312649\teval-rmse:0.399763\ttrain-mae:0.306536\ttrain-rmse:0.391485\n",
- "[2927]\teval-mae:0.312648\teval-rmse:0.399762\ttrain-mae:0.306533\ttrain-rmse:0.391483\n",
- "[2928]\teval-mae:0.312647\teval-rmse:0.399761\ttrain-mae:0.306532\ttrain-rmse:0.391481\n",
- "[2929]\teval-mae:0.312645\teval-rmse:0.39976\ttrain-mae:0.306528\ttrain-rmse:0.391477\n",
- "[2930]\teval-mae:0.312644\teval-rmse:0.399759\ttrain-mae:0.306526\ttrain-rmse:0.391474\n",
- "[2931]\teval-mae:0.312645\teval-rmse:0.39976\ttrain-mae:0.306524\ttrain-rmse:0.391472\n",
- "[2932]\teval-mae:0.312644\teval-rmse:0.399759\ttrain-mae:0.306523\ttrain-rmse:0.39147\n",
- "[2933]\teval-mae:0.312643\teval-rmse:0.399758\ttrain-mae:0.306522\ttrain-rmse:0.391469\n",
- "[2934]\teval-mae:0.312641\teval-rmse:0.399756\ttrain-mae:0.306519\ttrain-rmse:0.391465\n",
- "[2935]\teval-mae:0.312641\teval-rmse:0.399755\ttrain-mae:0.306517\ttrain-rmse:0.391464\n",
- "[2936]\teval-mae:0.31264\teval-rmse:0.399754\ttrain-mae:0.306515\ttrain-rmse:0.391461\n",
- "[2937]\teval-mae:0.312639\teval-rmse:0.399754\ttrain-mae:0.306514\ttrain-rmse:0.39146\n",
- "[2938]\teval-mae:0.312639\teval-rmse:0.399753\ttrain-mae:0.306512\ttrain-rmse:0.391456\n",
- "[2939]\teval-mae:0.312637\teval-rmse:0.399751\ttrain-mae:0.306509\ttrain-rmse:0.391453\n",
- "[2940]\teval-mae:0.312637\teval-rmse:0.399751\ttrain-mae:0.306506\ttrain-rmse:0.391449\n",
- "[2941]\teval-mae:0.312636\teval-rmse:0.399751\ttrain-mae:0.306503\ttrain-rmse:0.391445\n",
- "[2942]\teval-mae:0.312636\teval-rmse:0.399751\ttrain-mae:0.306502\ttrain-rmse:0.391444\n",
- "[2943]\teval-mae:0.312636\teval-rmse:0.39975\ttrain-mae:0.3065\ttrain-rmse:0.391443\n",
- "[2944]\teval-mae:0.312635\teval-rmse:0.39975\ttrain-mae:0.306499\ttrain-rmse:0.39144\n",
- "[2945]\teval-mae:0.312635\teval-rmse:0.399749\ttrain-mae:0.306498\ttrain-rmse:0.391439\n",
- "[2946]\teval-mae:0.312631\teval-rmse:0.399744\ttrain-mae:0.306493\ttrain-rmse:0.391433\n",
- "[2947]\teval-mae:0.31263\teval-rmse:0.399743\ttrain-mae:0.306491\ttrain-rmse:0.39143\n",
- "[2948]\teval-mae:0.31263\teval-rmse:0.399743\ttrain-mae:0.306489\ttrain-rmse:0.391428\n",
- "[2949]\teval-mae:0.31263\teval-rmse:0.399744\ttrain-mae:0.306487\ttrain-rmse:0.391426\n",
- "[2950]\teval-mae:0.312629\teval-rmse:0.399743\ttrain-mae:0.306486\ttrain-rmse:0.391424\n",
- "[2951]\teval-mae:0.312628\teval-rmse:0.399742\ttrain-mae:0.306484\ttrain-rmse:0.391422\n",
- "[2952]\teval-mae:0.312626\teval-rmse:0.39974\ttrain-mae:0.30648\ttrain-rmse:0.391419\n",
- "[2953]\teval-mae:0.312625\teval-rmse:0.399739\ttrain-mae:0.306479\ttrain-rmse:0.391418\n",
- "[2954]\teval-mae:0.312625\teval-rmse:0.399739\ttrain-mae:0.306478\ttrain-rmse:0.391417\n",
- "[2955]\teval-mae:0.312624\teval-rmse:0.399738\ttrain-mae:0.306476\ttrain-rmse:0.391415\n",
- "[2956]\teval-mae:0.312621\teval-rmse:0.399736\ttrain-mae:0.306473\ttrain-rmse:0.391411\n",
- "[2957]\teval-mae:0.312621\teval-rmse:0.399735\ttrain-mae:0.306471\ttrain-rmse:0.391409\n",
- "[2958]\teval-mae:0.312621\teval-rmse:0.399736\ttrain-mae:0.306468\ttrain-rmse:0.391405\n",
- "[2959]\teval-mae:0.312621\teval-rmse:0.399735\ttrain-mae:0.306466\ttrain-rmse:0.391403\n",
- "[2960]\teval-mae:0.31262\teval-rmse:0.399735\ttrain-mae:0.306465\ttrain-rmse:0.391401\n",
- "[2961]\teval-mae:0.31262\teval-rmse:0.399735\ttrain-mae:0.306462\ttrain-rmse:0.391399\n",
- "[2962]\teval-mae:0.312619\teval-rmse:0.399735\ttrain-mae:0.306461\ttrain-rmse:0.391397\n",
- "[2963]\teval-mae:0.31262\teval-rmse:0.399735\ttrain-mae:0.30646\ttrain-rmse:0.391395\n",
- "[2964]\teval-mae:0.312619\teval-rmse:0.399734\ttrain-mae:0.306458\ttrain-rmse:0.391394\n",
- "[2965]\teval-mae:0.312619\teval-rmse:0.399734\ttrain-mae:0.306457\ttrain-rmse:0.391392\n",
- "[2966]\teval-mae:0.312618\teval-rmse:0.399734\ttrain-mae:0.306455\ttrain-rmse:0.39139\n",
- "[2967]\teval-mae:0.312617\teval-rmse:0.399732\ttrain-mae:0.306453\ttrain-rmse:0.391387\n",
- "[2968]\teval-mae:0.312615\teval-rmse:0.399731\ttrain-mae:0.306451\ttrain-rmse:0.391385\n",
- "[2969]\teval-mae:0.312615\teval-rmse:0.39973\ttrain-mae:0.306449\ttrain-rmse:0.391383\n",
- "[2970]\teval-mae:0.312614\teval-rmse:0.39973\ttrain-mae:0.306448\ttrain-rmse:0.391381\n",
- "[2971]\teval-mae:0.312614\teval-rmse:0.39973\ttrain-mae:0.306447\ttrain-rmse:0.39138\n",
- "[2972]\teval-mae:0.312611\teval-rmse:0.399726\ttrain-mae:0.306443\ttrain-rmse:0.391375\n",
- "[2973]\teval-mae:0.312607\teval-rmse:0.399722\ttrain-mae:0.306438\ttrain-rmse:0.39137\n",
- "[2974]\teval-mae:0.312607\teval-rmse:0.399722\ttrain-mae:0.306437\ttrain-rmse:0.391369\n",
- "[2975]\teval-mae:0.312606\teval-rmse:0.399721\ttrain-mae:0.306435\ttrain-rmse:0.391367\n",
- "[2976]\teval-mae:0.312606\teval-rmse:0.399721\ttrain-mae:0.306434\ttrain-rmse:0.391365\n",
- "[2977]\teval-mae:0.312606\teval-rmse:0.39972\ttrain-mae:0.306432\ttrain-rmse:0.391363\n",
- "[2978]\teval-mae:0.312603\teval-rmse:0.399718\ttrain-mae:0.306429\ttrain-rmse:0.391359\n",
- "[2979]\teval-mae:0.312602\teval-rmse:0.399716\ttrain-mae:0.306427\ttrain-rmse:0.391357\n",
- "[2980]\teval-mae:0.312602\teval-rmse:0.399716\ttrain-mae:0.306426\ttrain-rmse:0.391355\n",
- "[2981]\teval-mae:0.312602\teval-rmse:0.399716\ttrain-mae:0.306424\ttrain-rmse:0.391353\n",
- "[2982]\teval-mae:0.312599\teval-rmse:0.399713\ttrain-mae:0.306421\ttrain-rmse:0.391349\n",
- "[2983]\teval-mae:0.312598\teval-rmse:0.399712\ttrain-mae:0.306418\ttrain-rmse:0.391345\n",
- "[2984]\teval-mae:0.312597\teval-rmse:0.399711\ttrain-mae:0.306416\ttrain-rmse:0.391343\n",
- "[2985]\teval-mae:0.312595\teval-rmse:0.399709\ttrain-mae:0.306413\ttrain-rmse:0.391339\n",
- "[2986]\teval-mae:0.312595\teval-rmse:0.399708\ttrain-mae:0.306412\ttrain-rmse:0.391338\n",
- "[2987]\teval-mae:0.312593\teval-rmse:0.399706\ttrain-mae:0.306409\ttrain-rmse:0.391334\n",
- "[2988]\teval-mae:0.312593\teval-rmse:0.399706\ttrain-mae:0.306408\ttrain-rmse:0.391333\n",
- "[2989]\teval-mae:0.312591\teval-rmse:0.399704\ttrain-mae:0.306405\ttrain-rmse:0.39133\n",
- "[2990]\teval-mae:0.31259\teval-rmse:0.399703\ttrain-mae:0.306403\ttrain-rmse:0.391327\n",
- "[2991]\teval-mae:0.312589\teval-rmse:0.399702\ttrain-mae:0.306401\ttrain-rmse:0.391325\n",
- "[2992]\teval-mae:0.312589\teval-rmse:0.399702\ttrain-mae:0.306399\ttrain-rmse:0.391322\n",
- "[2993]\teval-mae:0.312588\teval-rmse:0.399701\ttrain-mae:0.306398\ttrain-rmse:0.391321\n",
- "[2994]\teval-mae:0.312582\teval-rmse:0.399694\ttrain-mae:0.306391\ttrain-rmse:0.391313\n",
- "[2995]\teval-mae:0.312581\teval-rmse:0.399693\ttrain-mae:0.306389\ttrain-rmse:0.39131\n",
- "[2996]\teval-mae:0.31258\teval-rmse:0.399692\ttrain-mae:0.306387\ttrain-rmse:0.391307\n",
- "[2997]\teval-mae:0.312578\teval-rmse:0.39969\ttrain-mae:0.306384\ttrain-rmse:0.391304\n",
- "[2998]\teval-mae:0.312578\teval-rmse:0.39969\ttrain-mae:0.306383\ttrain-rmse:0.391303\n",
- "[2999]\teval-mae:0.312575\teval-rmse:0.399687\ttrain-mae:0.306379\ttrain-rmse:0.391299\n"
+ "[193]\ttrain-mae:0.2747+0.000154205\ttrain-rmse:0.351395+0.000193104\ttest-mae:0.275099+0.000328971\ttest-rmse:0.351941+0.000434047\n",
+ "[194]\ttrain-mae:0.274647+0.000156052\ttrain-rmse:0.351338+0.000193224\ttest-mae:0.275047+0.000323557\ttest-rmse:0.351886+0.000427798\n",
+ "[195]\ttrain-mae:0.274582+0.000157019\ttrain-rmse:0.351264+0.000194904\ttest-mae:0.274984+0.000327813\ttest-rmse:0.351814+0.000430109\n",
+ "[196]\ttrain-mae:0.274525+0.000149569\ttrain-rmse:0.3512+0.000184942\ttest-mae:0.274928+0.00031315\ttest-rmse:0.351751+0.000417871\n",
+ "[197]\ttrain-mae:0.274472+0.000151626\ttrain-rmse:0.351142+0.000189477\ttest-mae:0.274876+0.000321376\ttest-rmse:0.351694+0.00042863\n",
+ "[198]\ttrain-mae:0.274409+0.000155042\ttrain-rmse:0.351069+0.000191137\ttest-mae:0.274813+0.000315278\ttest-rmse:0.351623+0.000418018\n"
]
}
],
"source": [
- "# Train model\n",
- "model = xgb.train(param,dtrain,num_round,watchlist)"
+ "param = {'tree_method': 'gpu_hist',\n",
+ " 'max_depth': 6,\n",
+ " 'eta': 0.05,\n",
+ " 'verbosity': 2}\n",
+ "\n",
+ "cv_results = xgb.cv(params = param, dtrain = dfull, num_boost_round=3000, nfold = 10, metrics = ['mae','rmse'], \n",
+ " early_stopping_rounds=10,as_pandas=True, verbose_eval = True, seed = 123)"
]
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>train-mae-mean</th>\n",
+ " <th>train-mae-std</th>\n",
+ " <th>train-rmse-mean</th>\n",
+ " <th>train-rmse-std</th>\n",
+ " <th>test-mae-mean</th>\n",
+ " <th>test-mae-std</th>\n",
+ " <th>test-rmse-mean</th>\n",
+ " <th>test-rmse-std</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>1995</th>\n",
+ " <td>0.257738</td>\n",
+ " <td>0.000082</td>\n",
+ " <td>0.331716</td>\n",
+ " <td>0.000106</td>\n",
+ " <td>0.259442</td>\n",
+ " <td>0.000260</td>\n",
+ " <td>0.333988</td>\n",
+ " <td>0.000379</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1996</th>\n",
+ " <td>0.257736</td>\n",
+ " <td>0.000081</td>\n",
+ " <td>0.331713</td>\n",
+ " <td>0.000106</td>\n",
+ " <td>0.259441</td>\n",
+ " <td>0.000260</td>\n",
+ " <td>0.333986</td>\n",
+ " <td>0.000378</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1997</th>\n",
+ " <td>0.257732</td>\n",
+ " <td>0.000081</td>\n",
+ " <td>0.331708</td>\n",
+ " <td>0.000106</td>\n",
+ " <td>0.259437</td>\n",
+ " <td>0.000259</td>\n",
+ " <td>0.333982</td>\n",
+ " <td>0.000377</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1998</th>\n",
+ " <td>0.257729</td>\n",
+ " <td>0.000082</td>\n",
+ " <td>0.331705</td>\n",
+ " <td>0.000107</td>\n",
+ " <td>0.259434</td>\n",
+ " <td>0.000258</td>\n",
+ " <td>0.333979</td>\n",
+ " <td>0.000376</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1999</th>\n",
+ " <td>0.257726</td>\n",
+ " <td>0.000082</td>\n",
+ " <td>0.331701</td>\n",
+ " <td>0.000107</td>\n",
+ " <td>0.259432</td>\n",
+ " <td>0.000258</td>\n",
+ " <td>0.333977</td>\n",
+ " <td>0.000376</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
"text/plain": [
- "<Figure size 432x288 with 1 Axes>"
+ " train-mae-mean train-mae-std train-rmse-mean train-rmse-std \\\n",
+ "1995 0.257738 0.000082 0.331716 0.000106 \n",
+ "1996 0.257736 0.000081 0.331713 0.000106 \n",
+ "1997 0.257732 0.000081 0.331708 0.000106 \n",
+ "1998 0.257729 0.000082 0.331705 0.000107 \n",
+ "1999 0.257726 0.000082 0.331701 0.000107 \n",
+ "\n",
+ " test-mae-mean test-mae-std test-rmse-mean test-rmse-std \n",
+ "1995 0.259442 0.000260 0.333988 0.000379 \n",
+ "1996 0.259441 0.000260 0.333986 0.000378 \n",
+ "1997 0.259437 0.000259 0.333982 0.000377 \n",
+ "1998 0.259434 0.000258 0.333979 0.000376 \n",
+ "1999 0.259432 0.000258 0.333977 0.000376 "
]
},
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
}
],
+ "source": [
+ "cv_results.tail()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"# Plot feature importance values\n",
- "xgb.plot_importance(model)\n",
+ "xgb.plot_importance(ml)\n",
"pyplot.show()"
]
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "ename": "AttributeError",
- "evalue": "'Booster' object has no attribute 'feature_importances_'",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
- "\u001b[0;32m<ipython-input-11-0e44160bd346>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfeature_selection\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mSelectFromModel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mthresholds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msort\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfeature_importances_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mthresh\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mthresholds\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;31m# select features using threshold\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;31mAttributeError\u001b[0m: 'Booster' object has no attribute 'feature_importances_'"
- ]
- }
- ],
+ "outputs": [],
"source": [
- "# Try feature selection\n",
- "# adapted from https://machinelearningmastery.com/feature-importance-and-feature-selection-with-xgboost-in-python/\n",
- "from numpy import sort\n",
- "from sklearn.feature_selection import SelectFromModel\n",
- "\n",
- "thresholds = sort(model.feature_importances_)\n",
- "for thresh in thresholds:\n",
- " # select features using threshold\n",
- " selection = SelectFromModel(model, threshold=thresh, prefit=True)\n",
- " select_x_train = selection.transform(x_train)\n",
- " # train model\n",
- " selection_model = xgb.XGBRegressor()\n",
- " selection_model.fit(select_x_train, y_train)\n",
- " # eval model\n",
- " select_x_test = selection.transform(x_test)\n",
- " y_pred = selection_model.predict(select_x_test)\n",
- " predictions = [round(value) for value in y_pred]\n",
- " accuracy = accuracy_score(y_test, predictions)\n",
- " print(\"Thresh=%.3f, n=%d, Accuracy: %.2f%%\" % (thresh, select_x_train.shape[1], accuracy*100.0))"
+ "ml.dump_model(fout='/data/user/mdefende/Projects/ctmodel-ml/model-py/GBDT-out.txt',with_stats=True)"
]
}
],
diff --git a/model-py/create_cv_jobs.m b/model-py/create_cv_jobs.m
new file mode 100644
index 0000000000000000000000000000000000000000..b02271edaf159e22cd773a984cf3f3659d2fb4e7
--- /dev/null
+++ b/model-py/create_cv_jobs.m
@@ -0,0 +1,28 @@
+eta = [0.01,0.05,0.1,0.2];
+max_depth = 3:10;
+nboost = 1000:1000:5000;
+
+A = combvec(eta,max_depth,nboost)';
+jobdir = '/data/user/mdefende/Projects/ctmodel-ml/model-py/cv-jobs';
+for ii = 1:length(A)
+ jobname = ['eta' num2str(A(ii,1)) '_depth' num2str(A(ii,2)) '_nboost' num2str(A(ii,3)) '.sh'];
+
+ fid = fopen(fullfile(jobdir,jobname),'w');
+ fprintf(fid,'#!/bin/bash\n');
+ fprintf(fid,'#SBATCH --partition=pascalnodes\n');
+ fprintf(fid,'#SBATCH --time=6:00:00\n');
+ fprintf(fid,'#SBATCH --ntasks=4\n');
+ fprintf(fid,'#SBATCH --mem-per-cpu=25\n');
+ fprintf(fid,['#SBATCH --output=' jobname '.txt\n\n']);
+
+ fprintf(fid,'module load Anaconda3/5.3.0\n');
+ fprintf(fid,'source activate ctmodel-ml\n\n');
+
+ fprintf(fid,'cd /data/user/mdefende/Projects/ctmodel-ml/model-py\n\n');
+
+ fprintf(fid,['python model-indiv-cv.py ' num2str(A(ii,1)) ' ' num2str(A(ii,2)) ' ' num2str(A(ii,3)) '\n']);
+ fclose(fid);
+
+ system(['sbatch ' fullfile(jobdir,jobname)]);
+end
+
diff --git a/model-py/cv-jobs/eta0.01_depth10_nboost1000.sh b/model-py/cv-jobs/eta0.01_depth10_nboost1000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..e2182274f7dc5f8b45fe5773b058fc8ec73e0574
--- /dev/null
+++ b/model-py/cv-jobs/eta0.01_depth10_nboost1000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.01_depth10_nboost1000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.01 10 1000
diff --git a/model-py/cv-jobs/eta0.01_depth10_nboost2000.sh b/model-py/cv-jobs/eta0.01_depth10_nboost2000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..97158f606a7cae9e1392f3ea272c8a5c2a11f62c
--- /dev/null
+++ b/model-py/cv-jobs/eta0.01_depth10_nboost2000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.01_depth10_nboost2000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.01 10 2000
diff --git a/model-py/cv-jobs/eta0.01_depth10_nboost3000.sh b/model-py/cv-jobs/eta0.01_depth10_nboost3000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..f53f236dd7f1229b2852c3aa15c43d16c44cf568
--- /dev/null
+++ b/model-py/cv-jobs/eta0.01_depth10_nboost3000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.01_depth10_nboost3000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.01 10 3000
diff --git a/model-py/cv-jobs/eta0.01_depth10_nboost4000.sh b/model-py/cv-jobs/eta0.01_depth10_nboost4000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..be4eb841678e1ef3c34b2e8495d15ffa371bc811
--- /dev/null
+++ b/model-py/cv-jobs/eta0.01_depth10_nboost4000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.01_depth10_nboost4000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.01 10 4000
diff --git a/model-py/cv-jobs/eta0.01_depth10_nboost5000.sh b/model-py/cv-jobs/eta0.01_depth10_nboost5000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..2d68ee6a7bbfe78faf6d10028ff337b2076b0804
--- /dev/null
+++ b/model-py/cv-jobs/eta0.01_depth10_nboost5000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.01_depth10_nboost5000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.01 10 5000
diff --git a/model-py/cv-jobs/eta0.01_depth3_nboost1000.sh b/model-py/cv-jobs/eta0.01_depth3_nboost1000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..8a67cf2481589919d96320631a52cd9773d13a27
--- /dev/null
+++ b/model-py/cv-jobs/eta0.01_depth3_nboost1000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.01_depth3_nboost1000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.01 3 1000
diff --git a/model-py/cv-jobs/eta0.01_depth3_nboost2000.sh b/model-py/cv-jobs/eta0.01_depth3_nboost2000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..45c7d27d0f865e909c13e55e4dc0342f889940d7
--- /dev/null
+++ b/model-py/cv-jobs/eta0.01_depth3_nboost2000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.01_depth3_nboost2000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.01 3 2000
diff --git a/model-py/cv-jobs/eta0.01_depth3_nboost3000.sh b/model-py/cv-jobs/eta0.01_depth3_nboost3000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..e8028f3d7ed477c2c6d181fe019d1b7b02fa2ab5
--- /dev/null
+++ b/model-py/cv-jobs/eta0.01_depth3_nboost3000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.01_depth3_nboost3000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.01 3 3000
diff --git a/model-py/cv-jobs/eta0.01_depth3_nboost4000.sh b/model-py/cv-jobs/eta0.01_depth3_nboost4000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..8645993f465dfd03f34e0e45d553771a9bfe63c6
--- /dev/null
+++ b/model-py/cv-jobs/eta0.01_depth3_nboost4000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.01_depth3_nboost4000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.01 3 4000
diff --git a/model-py/cv-jobs/eta0.01_depth3_nboost5000.sh b/model-py/cv-jobs/eta0.01_depth3_nboost5000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..9f25f5f9d63f20be712042dd75b6760867cbef7f
--- /dev/null
+++ b/model-py/cv-jobs/eta0.01_depth3_nboost5000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.01_depth3_nboost5000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.01 3 5000
diff --git a/model-py/cv-jobs/eta0.01_depth4_nboost1000.sh b/model-py/cv-jobs/eta0.01_depth4_nboost1000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..b19004b3d4d28c9323b61cc3026357f96618f95b
--- /dev/null
+++ b/model-py/cv-jobs/eta0.01_depth4_nboost1000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.01_depth4_nboost1000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.01 4 1000
diff --git a/model-py/cv-jobs/eta0.01_depth4_nboost2000.sh b/model-py/cv-jobs/eta0.01_depth4_nboost2000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..83233dfc4d2448490f64a3fd739f646f2cff4e7e
--- /dev/null
+++ b/model-py/cv-jobs/eta0.01_depth4_nboost2000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.01_depth4_nboost2000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.01 4 2000
diff --git a/model-py/cv-jobs/eta0.01_depth4_nboost3000.sh b/model-py/cv-jobs/eta0.01_depth4_nboost3000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..b4d1b3bad2672013965d29e05690e7c0f806a541
--- /dev/null
+++ b/model-py/cv-jobs/eta0.01_depth4_nboost3000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.01_depth4_nboost3000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.01 4 3000
diff --git a/model-py/cv-jobs/eta0.01_depth4_nboost4000.sh b/model-py/cv-jobs/eta0.01_depth4_nboost4000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..12fe6c14fbfac2d900f1185d0b3be3a76a1312ce
--- /dev/null
+++ b/model-py/cv-jobs/eta0.01_depth4_nboost4000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.01_depth4_nboost4000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.01 4 4000
diff --git a/model-py/cv-jobs/eta0.01_depth4_nboost5000.sh b/model-py/cv-jobs/eta0.01_depth4_nboost5000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..97cdc416295a13c1f37e28427be73346b1150983
--- /dev/null
+++ b/model-py/cv-jobs/eta0.01_depth4_nboost5000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.01_depth4_nboost5000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.01 4 5000
diff --git a/model-py/cv-jobs/eta0.01_depth5_nboost1000.sh b/model-py/cv-jobs/eta0.01_depth5_nboost1000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..20a42012460fb5a007b2f3891751e0f1ebd25893
--- /dev/null
+++ b/model-py/cv-jobs/eta0.01_depth5_nboost1000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.01_depth5_nboost1000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.01 5 1000
diff --git a/model-py/cv-jobs/eta0.01_depth5_nboost2000.sh b/model-py/cv-jobs/eta0.01_depth5_nboost2000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..ed875b81d2ad329ec7e150ee65cb3a3b5243ceca
--- /dev/null
+++ b/model-py/cv-jobs/eta0.01_depth5_nboost2000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.01_depth5_nboost2000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.01 5 2000
diff --git a/model-py/cv-jobs/eta0.01_depth5_nboost3000.sh b/model-py/cv-jobs/eta0.01_depth5_nboost3000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..aa3e68be4850b290dd115a049a85fd32f8525f20
--- /dev/null
+++ b/model-py/cv-jobs/eta0.01_depth5_nboost3000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.01_depth5_nboost3000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.01 5 3000
diff --git a/model-py/cv-jobs/eta0.01_depth5_nboost4000.sh b/model-py/cv-jobs/eta0.01_depth5_nboost4000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..708461a27a98293f00e2bc76bb67afa8f96df0e3
--- /dev/null
+++ b/model-py/cv-jobs/eta0.01_depth5_nboost4000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.01_depth5_nboost4000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.01 5 4000
diff --git a/model-py/cv-jobs/eta0.01_depth5_nboost5000.sh b/model-py/cv-jobs/eta0.01_depth5_nboost5000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..d52333a7ff11c140ad193efbee0ca1df31bc0d53
--- /dev/null
+++ b/model-py/cv-jobs/eta0.01_depth5_nboost5000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.01_depth5_nboost5000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.01 5 5000
diff --git a/model-py/cv-jobs/eta0.01_depth6_nboost1000.sh b/model-py/cv-jobs/eta0.01_depth6_nboost1000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..344d1d66dc090380cee6f0b815fe3b0e84ae6f8f
--- /dev/null
+++ b/model-py/cv-jobs/eta0.01_depth6_nboost1000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.01_depth6_nboost1000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.01 6 1000
diff --git a/model-py/cv-jobs/eta0.01_depth6_nboost2000.sh b/model-py/cv-jobs/eta0.01_depth6_nboost2000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..4b287328ddf4b8896b263b628f02126ef8cae546
--- /dev/null
+++ b/model-py/cv-jobs/eta0.01_depth6_nboost2000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.01_depth6_nboost2000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.01 6 2000
diff --git a/model-py/cv-jobs/eta0.01_depth6_nboost3000.sh b/model-py/cv-jobs/eta0.01_depth6_nboost3000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..24d3bbf14542c87b04965ed17f68bfc462aa2f3b
--- /dev/null
+++ b/model-py/cv-jobs/eta0.01_depth6_nboost3000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.01_depth6_nboost3000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.01 6 3000
diff --git a/model-py/cv-jobs/eta0.01_depth6_nboost4000.sh b/model-py/cv-jobs/eta0.01_depth6_nboost4000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..1db7acf863120ac25ba4de5e3d77c8a569b29b99
--- /dev/null
+++ b/model-py/cv-jobs/eta0.01_depth6_nboost4000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.01_depth6_nboost4000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.01 6 4000
diff --git a/model-py/cv-jobs/eta0.01_depth6_nboost5000.sh b/model-py/cv-jobs/eta0.01_depth6_nboost5000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..fcf29e752ea3eeb2a35d49b325aa0678a34dee34
--- /dev/null
+++ b/model-py/cv-jobs/eta0.01_depth6_nboost5000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.01_depth6_nboost5000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.01 6 5000
diff --git a/model-py/cv-jobs/eta0.01_depth7_nboost1000.sh b/model-py/cv-jobs/eta0.01_depth7_nboost1000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..4d54facf6c4c7f81d65dcbf3cd4a719293a0f6e3
--- /dev/null
+++ b/model-py/cv-jobs/eta0.01_depth7_nboost1000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.01_depth7_nboost1000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.01 7 1000
diff --git a/model-py/cv-jobs/eta0.01_depth7_nboost2000.sh b/model-py/cv-jobs/eta0.01_depth7_nboost2000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..927727710c9ccf99e110db055f11b19c6b56201a
--- /dev/null
+++ b/model-py/cv-jobs/eta0.01_depth7_nboost2000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.01_depth7_nboost2000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.01 7 2000
diff --git a/model-py/cv-jobs/eta0.01_depth7_nboost3000.sh b/model-py/cv-jobs/eta0.01_depth7_nboost3000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..c1b81c006afcf73ad138823abd3eb450372a74c6
--- /dev/null
+++ b/model-py/cv-jobs/eta0.01_depth7_nboost3000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.01_depth7_nboost3000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.01 7 3000
diff --git a/model-py/cv-jobs/eta0.01_depth7_nboost4000.sh b/model-py/cv-jobs/eta0.01_depth7_nboost4000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..fad75421bbb30c9bc5522792cb97f127674f262d
--- /dev/null
+++ b/model-py/cv-jobs/eta0.01_depth7_nboost4000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.01_depth7_nboost4000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.01 7 4000
diff --git a/model-py/cv-jobs/eta0.01_depth7_nboost5000.sh b/model-py/cv-jobs/eta0.01_depth7_nboost5000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..3ef3410f5db6b0a3ad9775e9476c35f3d6c26f91
--- /dev/null
+++ b/model-py/cv-jobs/eta0.01_depth7_nboost5000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.01_depth7_nboost5000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.01 7 5000
diff --git a/model-py/cv-jobs/eta0.01_depth8_nboost1000.sh b/model-py/cv-jobs/eta0.01_depth8_nboost1000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..b179af771d76ad8d5f324133017fe81a3a5922c3
--- /dev/null
+++ b/model-py/cv-jobs/eta0.01_depth8_nboost1000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.01_depth8_nboost1000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.01 8 1000
diff --git a/model-py/cv-jobs/eta0.01_depth8_nboost2000.sh b/model-py/cv-jobs/eta0.01_depth8_nboost2000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..0cf005cc20d698e86a8c1e6de5e552ce7d2ca900
--- /dev/null
+++ b/model-py/cv-jobs/eta0.01_depth8_nboost2000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.01_depth8_nboost2000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.01 8 2000
diff --git a/model-py/cv-jobs/eta0.01_depth8_nboost3000.sh b/model-py/cv-jobs/eta0.01_depth8_nboost3000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..14617c84681575c70170096cd57566de44bdae2a
--- /dev/null
+++ b/model-py/cv-jobs/eta0.01_depth8_nboost3000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.01_depth8_nboost3000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.01 8 3000
diff --git a/model-py/cv-jobs/eta0.01_depth8_nboost4000.sh b/model-py/cv-jobs/eta0.01_depth8_nboost4000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..10afc2b2f9dd1dd0bc6d95b32d0f42454f67cfc9
--- /dev/null
+++ b/model-py/cv-jobs/eta0.01_depth8_nboost4000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.01_depth8_nboost4000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.01 8 4000
diff --git a/model-py/cv-jobs/eta0.01_depth8_nboost5000.sh b/model-py/cv-jobs/eta0.01_depth8_nboost5000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..d3473c29d41402ac8f77de96f24971f7e3a4bc05
--- /dev/null
+++ b/model-py/cv-jobs/eta0.01_depth8_nboost5000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.01_depth8_nboost5000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.01 8 5000
diff --git a/model-py/cv-jobs/eta0.01_depth9_nboost1000.sh b/model-py/cv-jobs/eta0.01_depth9_nboost1000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..4d0029ee035972da4d219706c5d81220a36869af
--- /dev/null
+++ b/model-py/cv-jobs/eta0.01_depth9_nboost1000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.01_depth9_nboost1000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.01 9 1000
diff --git a/model-py/cv-jobs/eta0.01_depth9_nboost2000.sh b/model-py/cv-jobs/eta0.01_depth9_nboost2000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..8fcdbbb9e8b9bda8e972de592c6d6c2a0c48985b
--- /dev/null
+++ b/model-py/cv-jobs/eta0.01_depth9_nboost2000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.01_depth9_nboost2000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.01 9 2000
diff --git a/model-py/cv-jobs/eta0.01_depth9_nboost3000.sh b/model-py/cv-jobs/eta0.01_depth9_nboost3000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..4fe81095dc21b8d7b442adb6de2ec993af57cdaf
--- /dev/null
+++ b/model-py/cv-jobs/eta0.01_depth9_nboost3000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.01_depth9_nboost3000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.01 9 3000
diff --git a/model-py/cv-jobs/eta0.01_depth9_nboost4000.sh b/model-py/cv-jobs/eta0.01_depth9_nboost4000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..423b9deb2ae1ac2eed21c6499452e59caad83b16
--- /dev/null
+++ b/model-py/cv-jobs/eta0.01_depth9_nboost4000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.01_depth9_nboost4000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.01 9 4000
diff --git a/model-py/cv-jobs/eta0.01_depth9_nboost5000.sh b/model-py/cv-jobs/eta0.01_depth9_nboost5000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..2707373c357a854256291984b9ba4a71a97cb757
--- /dev/null
+++ b/model-py/cv-jobs/eta0.01_depth9_nboost5000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.01_depth9_nboost5000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.01 9 5000
diff --git a/model-py/cv-jobs/eta0.05_depth10_nboost1000.sh b/model-py/cv-jobs/eta0.05_depth10_nboost1000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..3f9446d68880b946195c3be99d20bccb0a27c8fd
--- /dev/null
+++ b/model-py/cv-jobs/eta0.05_depth10_nboost1000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.05_depth10_nboost1000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.05 10 1000
diff --git a/model-py/cv-jobs/eta0.05_depth10_nboost2000.sh b/model-py/cv-jobs/eta0.05_depth10_nboost2000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..b2e7bf58220492614c7b6099af49ebb6c60a372f
--- /dev/null
+++ b/model-py/cv-jobs/eta0.05_depth10_nboost2000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.05_depth10_nboost2000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.05 10 2000
diff --git a/model-py/cv-jobs/eta0.05_depth10_nboost3000.sh b/model-py/cv-jobs/eta0.05_depth10_nboost3000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..9c4d0f4694f1da4f25d157fedecb6633a87eecc0
--- /dev/null
+++ b/model-py/cv-jobs/eta0.05_depth10_nboost3000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.05_depth10_nboost3000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.05 10 3000
diff --git a/model-py/cv-jobs/eta0.05_depth10_nboost4000.sh b/model-py/cv-jobs/eta0.05_depth10_nboost4000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..0a349c5b853bce164b6e238a9d8a60809e1563cb
--- /dev/null
+++ b/model-py/cv-jobs/eta0.05_depth10_nboost4000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.05_depth10_nboost4000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.05 10 4000
diff --git a/model-py/cv-jobs/eta0.05_depth10_nboost50.txt b/model-py/cv-jobs/eta0.05_depth10_nboost50.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ec45fb92dc9cf195e0052315305332e0ed27f020
--- /dev/null
+++ b/model-py/cv-jobs/eta0.05_depth10_nboost50.txt
@@ -0,0 +1 @@
+slurmstepd: error: *** JOB 2780986 ON c0113 CANCELLED AT 2019-09-05T16:11:01 ***
diff --git a/model-py/cv-jobs/eta0.05_depth10_nboost5000.sh b/model-py/cv-jobs/eta0.05_depth10_nboost5000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..ae8bd85ec510606ea6765feec7ba200b13029491
--- /dev/null
+++ b/model-py/cv-jobs/eta0.05_depth10_nboost5000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.05_depth10_nboost5000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.05 10 5000
diff --git a/model-py/cv-jobs/eta0.05_depth3_nboost1000.sh b/model-py/cv-jobs/eta0.05_depth3_nboost1000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..439d26f82cd99ccbc1e2070bd5cb41cecd638a21
--- /dev/null
+++ b/model-py/cv-jobs/eta0.05_depth3_nboost1000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.05_depth3_nboost1000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.05 3 1000
diff --git a/model-py/cv-jobs/eta0.05_depth3_nboost2000.sh b/model-py/cv-jobs/eta0.05_depth3_nboost2000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..182cae544e1b211c7978fee831dcbbc1eb4ff7cc
--- /dev/null
+++ b/model-py/cv-jobs/eta0.05_depth3_nboost2000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.05_depth3_nboost2000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.05 3 2000
diff --git a/model-py/cv-jobs/eta0.05_depth3_nboost3000.sh b/model-py/cv-jobs/eta0.05_depth3_nboost3000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..75b3b65a4887463f058ac5553c84297e0fb15c59
--- /dev/null
+++ b/model-py/cv-jobs/eta0.05_depth3_nboost3000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.05_depth3_nboost3000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.05 3 3000
diff --git a/model-py/cv-jobs/eta0.05_depth3_nboost4000.sh b/model-py/cv-jobs/eta0.05_depth3_nboost4000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..7d835ff7c4d40358bd0adb190e6c93ec9eb3b779
--- /dev/null
+++ b/model-py/cv-jobs/eta0.05_depth3_nboost4000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.05_depth3_nboost4000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.05 3 4000
diff --git a/model-py/cv-jobs/eta0.05_depth3_nboost5000.sh b/model-py/cv-jobs/eta0.05_depth3_nboost5000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..cb3f774590e54d568483fd784c125e4fbb36b072
--- /dev/null
+++ b/model-py/cv-jobs/eta0.05_depth3_nboost5000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.05_depth3_nboost5000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.05 3 5000
diff --git a/model-py/cv-jobs/eta0.05_depth4_nboost1000.sh b/model-py/cv-jobs/eta0.05_depth4_nboost1000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..ac17814d032b2ea2fdc52763cbb28528116ce170
--- /dev/null
+++ b/model-py/cv-jobs/eta0.05_depth4_nboost1000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.05_depth4_nboost1000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.05 4 1000
diff --git a/model-py/cv-jobs/eta0.05_depth4_nboost2000.sh b/model-py/cv-jobs/eta0.05_depth4_nboost2000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..70df54f682f286cc1c1e43be0fabc0a738553c1e
--- /dev/null
+++ b/model-py/cv-jobs/eta0.05_depth4_nboost2000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.05_depth4_nboost2000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.05 4 2000
diff --git a/model-py/cv-jobs/eta0.05_depth4_nboost3000.sh b/model-py/cv-jobs/eta0.05_depth4_nboost3000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..05fe2d7b07f1e908a2554b4e0e5adffd81e1854c
--- /dev/null
+++ b/model-py/cv-jobs/eta0.05_depth4_nboost3000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.05_depth4_nboost3000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.05 4 3000
diff --git a/model-py/cv-jobs/eta0.05_depth4_nboost4000.sh b/model-py/cv-jobs/eta0.05_depth4_nboost4000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..3a31fce1b932267dc400eb218e88d7454f905b4a
--- /dev/null
+++ b/model-py/cv-jobs/eta0.05_depth4_nboost4000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.05_depth4_nboost4000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.05 4 4000
diff --git a/model-py/cv-jobs/eta0.05_depth4_nboost5000.sh b/model-py/cv-jobs/eta0.05_depth4_nboost5000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..a795ad77bb24c3c8d17c448dc391419152c3dc2b
--- /dev/null
+++ b/model-py/cv-jobs/eta0.05_depth4_nboost5000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.05_depth4_nboost5000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.05 4 5000
diff --git a/model-py/cv-jobs/eta0.05_depth5_nboost1000.sh b/model-py/cv-jobs/eta0.05_depth5_nboost1000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..eccd1a58d807e6bc33257d774badd0f769c88240
--- /dev/null
+++ b/model-py/cv-jobs/eta0.05_depth5_nboost1000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.05_depth5_nboost1000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.05 5 1000
diff --git a/model-py/cv-jobs/eta0.05_depth5_nboost2000.sh b/model-py/cv-jobs/eta0.05_depth5_nboost2000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..41d62ec727663cd0ca87662bb6cbb69f24705d88
--- /dev/null
+++ b/model-py/cv-jobs/eta0.05_depth5_nboost2000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.05_depth5_nboost2000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.05 5 2000
diff --git a/model-py/cv-jobs/eta0.05_depth5_nboost3000.sh b/model-py/cv-jobs/eta0.05_depth5_nboost3000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..5c12d7272e23f771c6f87335d096311854a39801
--- /dev/null
+++ b/model-py/cv-jobs/eta0.05_depth5_nboost3000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.05_depth5_nboost3000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.05 5 3000
diff --git a/model-py/cv-jobs/eta0.05_depth5_nboost4000.sh b/model-py/cv-jobs/eta0.05_depth5_nboost4000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..28b013bfaf2924119dd8beb560f6e03a5c85e044
--- /dev/null
+++ b/model-py/cv-jobs/eta0.05_depth5_nboost4000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.05_depth5_nboost4000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.05 5 4000
diff --git a/model-py/cv-jobs/eta0.05_depth5_nboost5000.sh b/model-py/cv-jobs/eta0.05_depth5_nboost5000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..71ba0b7c79f16f85c9d17200027134021948037d
--- /dev/null
+++ b/model-py/cv-jobs/eta0.05_depth5_nboost5000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.05_depth5_nboost5000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.05 5 5000
diff --git a/model-py/cv-jobs/eta0.05_depth6_nboost1000.sh b/model-py/cv-jobs/eta0.05_depth6_nboost1000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..e8a79f89cfe996fd122e2cf2571db7d16369c0a9
--- /dev/null
+++ b/model-py/cv-jobs/eta0.05_depth6_nboost1000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.05_depth6_nboost1000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.05 6 1000
diff --git a/model-py/cv-jobs/eta0.05_depth6_nboost2000.sh b/model-py/cv-jobs/eta0.05_depth6_nboost2000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..2884677a559b3d69e0a4c8d1cfd88b04b2acaaf3
--- /dev/null
+++ b/model-py/cv-jobs/eta0.05_depth6_nboost2000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.05_depth6_nboost2000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.05 6 2000
diff --git a/model-py/cv-jobs/eta0.05_depth6_nboost3000.sh b/model-py/cv-jobs/eta0.05_depth6_nboost3000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..688442541fc115dc4155b291e701555c702e2d92
--- /dev/null
+++ b/model-py/cv-jobs/eta0.05_depth6_nboost3000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.05_depth6_nboost3000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.05 6 3000
diff --git a/model-py/cv-jobs/eta0.05_depth6_nboost4000.sh b/model-py/cv-jobs/eta0.05_depth6_nboost4000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..37d473281e4cf4d35674cc013707b251e4eaa17e
--- /dev/null
+++ b/model-py/cv-jobs/eta0.05_depth6_nboost4000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.05_depth6_nboost4000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.05 6 4000
diff --git a/model-py/cv-jobs/eta0.05_depth6_nboost5000.sh b/model-py/cv-jobs/eta0.05_depth6_nboost5000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..701474a4d4261e023a957891e30d6e3eac826d1f
--- /dev/null
+++ b/model-py/cv-jobs/eta0.05_depth6_nboost5000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.05_depth6_nboost5000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.05 6 5000
diff --git a/model-py/cv-jobs/eta0.05_depth7_nboost1000.sh b/model-py/cv-jobs/eta0.05_depth7_nboost1000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..a4bb50eb4e0ceb61b0070264b565806e8daa22d4
--- /dev/null
+++ b/model-py/cv-jobs/eta0.05_depth7_nboost1000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.05_depth7_nboost1000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.05 7 1000
diff --git a/model-py/cv-jobs/eta0.05_depth7_nboost2000.sh b/model-py/cv-jobs/eta0.05_depth7_nboost2000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..3584559f61ad0254854a064ccb5324634e0863b8
--- /dev/null
+++ b/model-py/cv-jobs/eta0.05_depth7_nboost2000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.05_depth7_nboost2000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.05 7 2000
diff --git a/model-py/cv-jobs/eta0.05_depth7_nboost3000.sh b/model-py/cv-jobs/eta0.05_depth7_nboost3000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..eab4ac36a4150fa982cc628c1ffcaa3c9e6f9c05
--- /dev/null
+++ b/model-py/cv-jobs/eta0.05_depth7_nboost3000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.05_depth7_nboost3000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.05 7 3000
diff --git a/model-py/cv-jobs/eta0.05_depth7_nboost4000.sh b/model-py/cv-jobs/eta0.05_depth7_nboost4000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..1aa377310fa4ef75bf7b56243175a04c3fdf1ae6
--- /dev/null
+++ b/model-py/cv-jobs/eta0.05_depth7_nboost4000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.05_depth7_nboost4000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.05 7 4000
diff --git a/model-py/cv-jobs/eta0.05_depth7_nboost5000.sh b/model-py/cv-jobs/eta0.05_depth7_nboost5000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..1f69ca37c388ad5e1da49dc54c976cd4e60f3fee
--- /dev/null
+++ b/model-py/cv-jobs/eta0.05_depth7_nboost5000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.05_depth7_nboost5000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.05 7 5000
diff --git a/model-py/cv-jobs/eta0.05_depth8_nboost1000.sh b/model-py/cv-jobs/eta0.05_depth8_nboost1000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..32bd4ee9582d799aa4298bd6d41c918ccdc49916
--- /dev/null
+++ b/model-py/cv-jobs/eta0.05_depth8_nboost1000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.05_depth8_nboost1000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.05 8 1000
diff --git a/model-py/cv-jobs/eta0.05_depth8_nboost2000.sh b/model-py/cv-jobs/eta0.05_depth8_nboost2000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..5f2633a5b0fa1f25d9c6ab609aa1c23e266656c6
--- /dev/null
+++ b/model-py/cv-jobs/eta0.05_depth8_nboost2000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.05_depth8_nboost2000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.05 8 2000
diff --git a/model-py/cv-jobs/eta0.05_depth8_nboost3000.sh b/model-py/cv-jobs/eta0.05_depth8_nboost3000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..04c04747295af9ccd68a85b90b7c028dd51350ce
--- /dev/null
+++ b/model-py/cv-jobs/eta0.05_depth8_nboost3000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.05_depth8_nboost3000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.05 8 3000
diff --git a/model-py/cv-jobs/eta0.05_depth8_nboost4000.sh b/model-py/cv-jobs/eta0.05_depth8_nboost4000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..24a9a058138d77a104a451078812e71896c55514
--- /dev/null
+++ b/model-py/cv-jobs/eta0.05_depth8_nboost4000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.05_depth8_nboost4000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.05 8 4000
diff --git a/model-py/cv-jobs/eta0.05_depth8_nboost5000.sh b/model-py/cv-jobs/eta0.05_depth8_nboost5000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..6a03fa420f191354ff90bc49b81b98020447a7da
--- /dev/null
+++ b/model-py/cv-jobs/eta0.05_depth8_nboost5000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.05_depth8_nboost5000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.05 8 5000
diff --git a/model-py/cv-jobs/eta0.05_depth9_nboost1000.sh b/model-py/cv-jobs/eta0.05_depth9_nboost1000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..0243565d5cb95c7dadb4b28707d9632f7851bbd6
--- /dev/null
+++ b/model-py/cv-jobs/eta0.05_depth9_nboost1000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.05_depth9_nboost1000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.05 9 1000
diff --git a/model-py/cv-jobs/eta0.05_depth9_nboost2000.sh b/model-py/cv-jobs/eta0.05_depth9_nboost2000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..166424512b306ffda6afad024426c0371ac3efec
--- /dev/null
+++ b/model-py/cv-jobs/eta0.05_depth9_nboost2000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.05_depth9_nboost2000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.05 9 2000
diff --git a/model-py/cv-jobs/eta0.05_depth9_nboost3000.sh b/model-py/cv-jobs/eta0.05_depth9_nboost3000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..21f6475393938e4d66a8f20758952d44a728eb88
--- /dev/null
+++ b/model-py/cv-jobs/eta0.05_depth9_nboost3000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.05_depth9_nboost3000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.05 9 3000
diff --git a/model-py/cv-jobs/eta0.05_depth9_nboost4000.sh b/model-py/cv-jobs/eta0.05_depth9_nboost4000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..d98d0c62e73f30cb110292396d20945c9812ccc2
--- /dev/null
+++ b/model-py/cv-jobs/eta0.05_depth9_nboost4000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.05_depth9_nboost4000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.05 9 4000
diff --git a/model-py/cv-jobs/eta0.05_depth9_nboost5000.sh b/model-py/cv-jobs/eta0.05_depth9_nboost5000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..bc0ecb1c3704914617f27f012ac844b9b124c20d
--- /dev/null
+++ b/model-py/cv-jobs/eta0.05_depth9_nboost5000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.05_depth9_nboost5000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.05 9 5000
diff --git a/model-py/cv-jobs/eta0.1_depth10_nboost1000.sh b/model-py/cv-jobs/eta0.1_depth10_nboost1000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..8b237f7086fbe37db893dc610bfe025986622999
--- /dev/null
+++ b/model-py/cv-jobs/eta0.1_depth10_nboost1000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.1_depth10_nboost1000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.1 10 1000
diff --git a/model-py/cv-jobs/eta0.1_depth10_nboost2000.sh b/model-py/cv-jobs/eta0.1_depth10_nboost2000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..c556d15b034f0786dfbce9bc331bfec1f6b99d38
--- /dev/null
+++ b/model-py/cv-jobs/eta0.1_depth10_nboost2000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.1_depth10_nboost2000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.1 10 2000
diff --git a/model-py/cv-jobs/eta0.1_depth10_nboost3000.sh b/model-py/cv-jobs/eta0.1_depth10_nboost3000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..0df47eb5ea7d9ee02997c0733ee224872a9f71cb
--- /dev/null
+++ b/model-py/cv-jobs/eta0.1_depth10_nboost3000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.1_depth10_nboost3000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.1 10 3000
diff --git a/model-py/cv-jobs/eta0.1_depth10_nboost4000.sh b/model-py/cv-jobs/eta0.1_depth10_nboost4000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..9c44747d252b86db72ca7dd565f82c94592befa2
--- /dev/null
+++ b/model-py/cv-jobs/eta0.1_depth10_nboost4000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.1_depth10_nboost4000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.1 10 4000
diff --git a/model-py/cv-jobs/eta0.1_depth10_nboost5000.sh b/model-py/cv-jobs/eta0.1_depth10_nboost5000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..d5770dedb69009365813971d2ad727c96acaed11
--- /dev/null
+++ b/model-py/cv-jobs/eta0.1_depth10_nboost5000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.1_depth10_nboost5000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.1 10 5000
diff --git a/model-py/cv-jobs/eta0.1_depth3_nboost1000.sh b/model-py/cv-jobs/eta0.1_depth3_nboost1000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..8c9c840df7661dd1af7f03f842ff6c92b867c6c3
--- /dev/null
+++ b/model-py/cv-jobs/eta0.1_depth3_nboost1000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=1
+#SBATCH --mem-per-cpu=128
+#SBATCH --output=eta0.1_depth3_nboost1000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.1 3 1000
diff --git a/model-py/cv-jobs/eta0.1_depth3_nboost1000.sh.txt b/model-py/cv-jobs/eta0.1_depth3_nboost1000.sh.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/model-py/cv-jobs/eta0.1_depth3_nboost2000.sh b/model-py/cv-jobs/eta0.1_depth3_nboost2000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..92cebbfda662e92cbf26559b6dfcd59dd549b6ff
--- /dev/null
+++ b/model-py/cv-jobs/eta0.1_depth3_nboost2000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.1_depth3_nboost2000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.1 3 2000
diff --git a/model-py/cv-jobs/eta0.1_depth3_nboost3000.sh b/model-py/cv-jobs/eta0.1_depth3_nboost3000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..94b5745498e4008baa29fb99b5c917542c5633ea
--- /dev/null
+++ b/model-py/cv-jobs/eta0.1_depth3_nboost3000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.1_depth3_nboost3000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.1 3 3000
diff --git a/model-py/cv-jobs/eta0.1_depth3_nboost4000.sh b/model-py/cv-jobs/eta0.1_depth3_nboost4000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..01076afd48f8ff485b77dccecd49b9b4b947b1c6
--- /dev/null
+++ b/model-py/cv-jobs/eta0.1_depth3_nboost4000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.1_depth3_nboost4000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.1 3 4000
diff --git a/model-py/cv-jobs/eta0.1_depth3_nboost5000.sh b/model-py/cv-jobs/eta0.1_depth3_nboost5000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..d28366a247d74020b04cad8be48d88f1b9785b60
--- /dev/null
+++ b/model-py/cv-jobs/eta0.1_depth3_nboost5000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.1_depth3_nboost5000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.1 3 5000
diff --git a/model-py/cv-jobs/eta0.1_depth4_nboost1000.sh b/model-py/cv-jobs/eta0.1_depth4_nboost1000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..66d8fd4cd29c3bc3f98f652dda9012ebdebdd1be
--- /dev/null
+++ b/model-py/cv-jobs/eta0.1_depth4_nboost1000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.1_depth4_nboost1000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.1 4 1000
diff --git a/model-py/cv-jobs/eta0.1_depth4_nboost2000.sh b/model-py/cv-jobs/eta0.1_depth4_nboost2000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..dd6e28a3c0a8db7a4890f79a1cd1e7b35fd92810
--- /dev/null
+++ b/model-py/cv-jobs/eta0.1_depth4_nboost2000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.1_depth4_nboost2000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.1 4 2000
diff --git a/model-py/cv-jobs/eta0.1_depth4_nboost3000.sh b/model-py/cv-jobs/eta0.1_depth4_nboost3000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..3f4606207d5ee5edf2232425029ce8ebe1384d53
--- /dev/null
+++ b/model-py/cv-jobs/eta0.1_depth4_nboost3000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.1_depth4_nboost3000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.1 4 3000
diff --git a/model-py/cv-jobs/eta0.1_depth4_nboost4000.sh b/model-py/cv-jobs/eta0.1_depth4_nboost4000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..031a10e5676ab08671b2f7197bbfec5b8fb2f0d2
--- /dev/null
+++ b/model-py/cv-jobs/eta0.1_depth4_nboost4000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.1_depth4_nboost4000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.1 4 4000
diff --git a/model-py/cv-jobs/eta0.1_depth4_nboost5000.sh b/model-py/cv-jobs/eta0.1_depth4_nboost5000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..647dfc39db90859e268dc04ae68ea5952c0db283
--- /dev/null
+++ b/model-py/cv-jobs/eta0.1_depth4_nboost5000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.1_depth4_nboost5000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.1 4 5000
diff --git a/model-py/cv-jobs/eta0.1_depth5_nboost1000.sh b/model-py/cv-jobs/eta0.1_depth5_nboost1000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..a6080f28ba88d405361cb9199f5266a65c71b71f
--- /dev/null
+++ b/model-py/cv-jobs/eta0.1_depth5_nboost1000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.1_depth5_nboost1000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.1 5 1000
diff --git a/model-py/cv-jobs/eta0.1_depth5_nboost2000.sh b/model-py/cv-jobs/eta0.1_depth5_nboost2000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..75a694d32ebda14d71d6fa9fef2b861e000a6797
--- /dev/null
+++ b/model-py/cv-jobs/eta0.1_depth5_nboost2000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.1_depth5_nboost2000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.1 5 2000
diff --git a/model-py/cv-jobs/eta0.1_depth5_nboost3000.sh b/model-py/cv-jobs/eta0.1_depth5_nboost3000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..c273219a51a18b5842985e5adc4ff66ffefb691f
--- /dev/null
+++ b/model-py/cv-jobs/eta0.1_depth5_nboost3000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.1_depth5_nboost3000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.1 5 3000
diff --git a/model-py/cv-jobs/eta0.1_depth5_nboost4000.sh b/model-py/cv-jobs/eta0.1_depth5_nboost4000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..305075ac0ffbee7e5971afab82fc2938dec8a77f
--- /dev/null
+++ b/model-py/cv-jobs/eta0.1_depth5_nboost4000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.1_depth5_nboost4000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.1 5 4000
diff --git a/model-py/cv-jobs/eta0.1_depth5_nboost5000.sh b/model-py/cv-jobs/eta0.1_depth5_nboost5000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..d5035fdd4a29016991f2279c949c6e0678fef4bc
--- /dev/null
+++ b/model-py/cv-jobs/eta0.1_depth5_nboost5000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.1_depth5_nboost5000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.1 5 5000
diff --git a/model-py/cv-jobs/eta0.1_depth6_nboost1000.sh b/model-py/cv-jobs/eta0.1_depth6_nboost1000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..e6c68dfc4e2a2456cb2b33c79decd19eb1947d4a
--- /dev/null
+++ b/model-py/cv-jobs/eta0.1_depth6_nboost1000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.1_depth6_nboost1000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.1 6 1000
diff --git a/model-py/cv-jobs/eta0.1_depth6_nboost2000.sh b/model-py/cv-jobs/eta0.1_depth6_nboost2000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..510e4db539a99600a66fccf7cf07a9e5bb1aa379
--- /dev/null
+++ b/model-py/cv-jobs/eta0.1_depth6_nboost2000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.1_depth6_nboost2000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.1 6 2000
diff --git a/model-py/cv-jobs/eta0.1_depth6_nboost3000.sh b/model-py/cv-jobs/eta0.1_depth6_nboost3000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..d70038a93c9712fa9912b95e0524010ef25ac057
--- /dev/null
+++ b/model-py/cv-jobs/eta0.1_depth6_nboost3000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.1_depth6_nboost3000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.1 6 3000
diff --git a/model-py/cv-jobs/eta0.1_depth6_nboost4000.sh b/model-py/cv-jobs/eta0.1_depth6_nboost4000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..dd2e0e81cd125b28a2ba94bc7cb97bc7fbafa3e7
--- /dev/null
+++ b/model-py/cv-jobs/eta0.1_depth6_nboost4000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.1_depth6_nboost4000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.1 6 4000
diff --git a/model-py/cv-jobs/eta0.1_depth6_nboost5000.sh b/model-py/cv-jobs/eta0.1_depth6_nboost5000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..beaa77aea09b716d6cf45ed820632d7756df7d31
--- /dev/null
+++ b/model-py/cv-jobs/eta0.1_depth6_nboost5000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.1_depth6_nboost5000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.1 6 5000
diff --git a/model-py/cv-jobs/eta0.1_depth7_nboost1000.sh b/model-py/cv-jobs/eta0.1_depth7_nboost1000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..251c05b5e5bfaab9901d0650fa9f1179766206dd
--- /dev/null
+++ b/model-py/cv-jobs/eta0.1_depth7_nboost1000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.1_depth7_nboost1000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.1 7 1000
diff --git a/model-py/cv-jobs/eta0.1_depth7_nboost2000.sh b/model-py/cv-jobs/eta0.1_depth7_nboost2000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..777647d4b96e5c0a8b2a6346971cd25f4590d120
--- /dev/null
+++ b/model-py/cv-jobs/eta0.1_depth7_nboost2000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.1_depth7_nboost2000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.1 7 2000
diff --git a/model-py/cv-jobs/eta0.1_depth7_nboost3000.sh b/model-py/cv-jobs/eta0.1_depth7_nboost3000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..0154552ca9e4602851d5e6e8497762793583fbc9
--- /dev/null
+++ b/model-py/cv-jobs/eta0.1_depth7_nboost3000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.1_depth7_nboost3000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.1 7 3000
diff --git a/model-py/cv-jobs/eta0.1_depth7_nboost4000.sh b/model-py/cv-jobs/eta0.1_depth7_nboost4000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..f9c98fd29c35eb145af4b97180c6aa7368cf5b08
--- /dev/null
+++ b/model-py/cv-jobs/eta0.1_depth7_nboost4000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.1_depth7_nboost4000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.1 7 4000
diff --git a/model-py/cv-jobs/eta0.1_depth7_nboost5000.sh b/model-py/cv-jobs/eta0.1_depth7_nboost5000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..25a98a22da6daad90fb3b5b57d4b321523c92d65
--- /dev/null
+++ b/model-py/cv-jobs/eta0.1_depth7_nboost5000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.1_depth7_nboost5000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.1 7 5000
diff --git a/model-py/cv-jobs/eta0.1_depth8_nboost1000.sh b/model-py/cv-jobs/eta0.1_depth8_nboost1000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..06f17f7aecdd02880b3ad8815f4af036811f27c9
--- /dev/null
+++ b/model-py/cv-jobs/eta0.1_depth8_nboost1000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.1_depth8_nboost1000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.1 8 1000
diff --git a/model-py/cv-jobs/eta0.1_depth8_nboost2000.sh b/model-py/cv-jobs/eta0.1_depth8_nboost2000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..c83d6a58f95aa97226e312eeeeb76f2cdbb9a7f9
--- /dev/null
+++ b/model-py/cv-jobs/eta0.1_depth8_nboost2000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.1_depth8_nboost2000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.1 8 2000
diff --git a/model-py/cv-jobs/eta0.1_depth8_nboost3000.sh b/model-py/cv-jobs/eta0.1_depth8_nboost3000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..a69e523f081279dd720fe067ca800d121fdd653f
--- /dev/null
+++ b/model-py/cv-jobs/eta0.1_depth8_nboost3000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.1_depth8_nboost3000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.1 8 3000
diff --git a/model-py/cv-jobs/eta0.1_depth8_nboost4000.sh b/model-py/cv-jobs/eta0.1_depth8_nboost4000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..7c62b120a9555e515de887f07a68311d7c1bb92a
--- /dev/null
+++ b/model-py/cv-jobs/eta0.1_depth8_nboost4000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.1_depth8_nboost4000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.1 8 4000
diff --git a/model-py/cv-jobs/eta0.1_depth8_nboost5000.sh b/model-py/cv-jobs/eta0.1_depth8_nboost5000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..08a92eb4afe495335d92d50c93bad30d30543075
--- /dev/null
+++ b/model-py/cv-jobs/eta0.1_depth8_nboost5000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.1_depth8_nboost5000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.1 8 5000
diff --git a/model-py/cv-jobs/eta0.1_depth9_nboost1000.sh b/model-py/cv-jobs/eta0.1_depth9_nboost1000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..9c362c83d3a4118aa968567616c5207c025a3abb
--- /dev/null
+++ b/model-py/cv-jobs/eta0.1_depth9_nboost1000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.1_depth9_nboost1000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.1 9 1000
diff --git a/model-py/cv-jobs/eta0.1_depth9_nboost2000.sh b/model-py/cv-jobs/eta0.1_depth9_nboost2000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..447ef32e5c368df29c48f440240bd4873c3c90ff
--- /dev/null
+++ b/model-py/cv-jobs/eta0.1_depth9_nboost2000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.1_depth9_nboost2000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.1 9 2000
diff --git a/model-py/cv-jobs/eta0.1_depth9_nboost3000.sh b/model-py/cv-jobs/eta0.1_depth9_nboost3000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..379d9fb9573595fd37da4af05bd271d9daec6701
--- /dev/null
+++ b/model-py/cv-jobs/eta0.1_depth9_nboost3000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.1_depth9_nboost3000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.1 9 3000
diff --git a/model-py/cv-jobs/eta0.1_depth9_nboost4000.sh b/model-py/cv-jobs/eta0.1_depth9_nboost4000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..10f42793f2d3bd2b6b2e1dc9df235f2ce524f2f9
--- /dev/null
+++ b/model-py/cv-jobs/eta0.1_depth9_nboost4000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.1_depth9_nboost4000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.1 9 4000
diff --git a/model-py/cv-jobs/eta0.1_depth9_nboost5000.sh b/model-py/cv-jobs/eta0.1_depth9_nboost5000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..d6fa487e640a29840411a792bcaf567440c79b6f
--- /dev/null
+++ b/model-py/cv-jobs/eta0.1_depth9_nboost5000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.1_depth9_nboost5000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.1 9 5000
diff --git a/model-py/cv-jobs/eta0.2_depth10_nboost1000.sh b/model-py/cv-jobs/eta0.2_depth10_nboost1000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..ff150a9c63c05a13248609aa83722dddd3db8849
--- /dev/null
+++ b/model-py/cv-jobs/eta0.2_depth10_nboost1000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.2_depth10_nboost1000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.2 10 1000
diff --git a/model-py/cv-jobs/eta0.2_depth10_nboost2000.sh b/model-py/cv-jobs/eta0.2_depth10_nboost2000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..3d85bd770e0ca86b3267a33174df984d6e65e2b5
--- /dev/null
+++ b/model-py/cv-jobs/eta0.2_depth10_nboost2000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.2_depth10_nboost2000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.2 10 2000
diff --git a/model-py/cv-jobs/eta0.2_depth10_nboost3000.sh b/model-py/cv-jobs/eta0.2_depth10_nboost3000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..d9fb6752baac9cb60346ba9532c9c925ea71eea5
--- /dev/null
+++ b/model-py/cv-jobs/eta0.2_depth10_nboost3000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.2_depth10_nboost3000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.2 10 3000
diff --git a/model-py/cv-jobs/eta0.2_depth10_nboost4000.sh b/model-py/cv-jobs/eta0.2_depth10_nboost4000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..412dff61db6fea6521c734384d4c3de2dd7cb452
--- /dev/null
+++ b/model-py/cv-jobs/eta0.2_depth10_nboost4000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.2_depth10_nboost4000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.2 10 4000
diff --git a/model-py/cv-jobs/eta0.2_depth10_nboost5000.sh b/model-py/cv-jobs/eta0.2_depth10_nboost5000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..a1a3f3163994a1d88e60df788c54681e104b3fe1
--- /dev/null
+++ b/model-py/cv-jobs/eta0.2_depth10_nboost5000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.2_depth10_nboost5000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.2 10 5000
diff --git a/model-py/cv-jobs/eta0.2_depth3_nboost1000.sh b/model-py/cv-jobs/eta0.2_depth3_nboost1000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..09b60757815b72e400cc13bce661daa114efdf39
--- /dev/null
+++ b/model-py/cv-jobs/eta0.2_depth3_nboost1000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.2_depth3_nboost1000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.2 3 1000
diff --git a/model-py/cv-jobs/eta0.2_depth3_nboost1000.txt b/model-py/cv-jobs/eta0.2_depth3_nboost1000.txt
new file mode 100644
index 0000000000000000000000000000000000000000..331fc9b720914aab604dcf99728cdfb436c341b6
--- /dev/null
+++ b/model-py/cv-jobs/eta0.2_depth3_nboost1000.txt
@@ -0,0 +1,2 @@
+/cm/local/apps/slurm/var/spool/job2781023/slurm_script: line 13: 110872 Killed python model-indiv-cv.py 0.2 3 1000
+slurmstepd: error: Detected 2 oom-kill event(s) in step 2781023.batch cgroup. Some of your processes may have been killed by the cgroup out-of-memory handler.
diff --git a/model-py/cv-jobs/eta0.2_depth3_nboost2000.sh b/model-py/cv-jobs/eta0.2_depth3_nboost2000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..b3905a25bf8ccb7f82f1a87a8e1e7e66e34b767e
--- /dev/null
+++ b/model-py/cv-jobs/eta0.2_depth3_nboost2000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.2_depth3_nboost2000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.2 3 2000
diff --git a/model-py/cv-jobs/eta0.2_depth3_nboost3000.sh b/model-py/cv-jobs/eta0.2_depth3_nboost3000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..e1b8eacf5c09b5eccc7e9153fb1353e66674f79a
--- /dev/null
+++ b/model-py/cv-jobs/eta0.2_depth3_nboost3000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.2_depth3_nboost3000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.2 3 3000
diff --git a/model-py/cv-jobs/eta0.2_depth3_nboost4000.sh b/model-py/cv-jobs/eta0.2_depth3_nboost4000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..2a2794d3471d20d63bad17cc28ed39d0ccf15918
--- /dev/null
+++ b/model-py/cv-jobs/eta0.2_depth3_nboost4000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.2_depth3_nboost4000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.2 3 4000
diff --git a/model-py/cv-jobs/eta0.2_depth3_nboost5000.sh b/model-py/cv-jobs/eta0.2_depth3_nboost5000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..d460f5b0f52693f9344af46e8a7a6707ff35a79a
--- /dev/null
+++ b/model-py/cv-jobs/eta0.2_depth3_nboost5000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.2_depth3_nboost5000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.2 3 5000
diff --git a/model-py/cv-jobs/eta0.2_depth4_nboost1000.sh b/model-py/cv-jobs/eta0.2_depth4_nboost1000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..3cab56a80430e4ae4852feebb4f876b412aa2897
--- /dev/null
+++ b/model-py/cv-jobs/eta0.2_depth4_nboost1000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.2_depth4_nboost1000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.2 4 1000
diff --git a/model-py/cv-jobs/eta0.2_depth4_nboost2000.sh b/model-py/cv-jobs/eta0.2_depth4_nboost2000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..71df9215de63fe09e3c1b586092ed980b7add57e
--- /dev/null
+++ b/model-py/cv-jobs/eta0.2_depth4_nboost2000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.2_depth4_nboost2000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.2 4 2000
diff --git a/model-py/cv-jobs/eta0.2_depth4_nboost3000.sh b/model-py/cv-jobs/eta0.2_depth4_nboost3000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..aac34b01bf9035b51fb98d3ca85636315e64b3d3
--- /dev/null
+++ b/model-py/cv-jobs/eta0.2_depth4_nboost3000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.2_depth4_nboost3000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.2 4 3000
diff --git a/model-py/cv-jobs/eta0.2_depth4_nboost4000.sh b/model-py/cv-jobs/eta0.2_depth4_nboost4000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..4813b8e6437a9c42738456cffc9c05473445fe40
--- /dev/null
+++ b/model-py/cv-jobs/eta0.2_depth4_nboost4000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.2_depth4_nboost4000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.2 4 4000
diff --git a/model-py/cv-jobs/eta0.2_depth4_nboost5000.sh b/model-py/cv-jobs/eta0.2_depth4_nboost5000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..de0b59df26aafc36cc0debfeec81fea225b4c80a
--- /dev/null
+++ b/model-py/cv-jobs/eta0.2_depth4_nboost5000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.2_depth4_nboost5000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.2 4 5000
diff --git a/model-py/cv-jobs/eta0.2_depth5_nboost1000.sh b/model-py/cv-jobs/eta0.2_depth5_nboost1000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..b0e69d94d95d09269b6478626a2201c6e6d5411d
--- /dev/null
+++ b/model-py/cv-jobs/eta0.2_depth5_nboost1000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.2_depth5_nboost1000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.2 5 1000
diff --git a/model-py/cv-jobs/eta0.2_depth5_nboost2000.sh b/model-py/cv-jobs/eta0.2_depth5_nboost2000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..bfbdb2609b2a7b8ddda7cd8426ec1077792fe04c
--- /dev/null
+++ b/model-py/cv-jobs/eta0.2_depth5_nboost2000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.2_depth5_nboost2000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.2 5 2000
diff --git a/model-py/cv-jobs/eta0.2_depth5_nboost3000.sh b/model-py/cv-jobs/eta0.2_depth5_nboost3000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..a0a034d57018babeb23dfa4636deda3fa37718c9
--- /dev/null
+++ b/model-py/cv-jobs/eta0.2_depth5_nboost3000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.2_depth5_nboost3000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.2 5 3000
diff --git a/model-py/cv-jobs/eta0.2_depth5_nboost4000.sh b/model-py/cv-jobs/eta0.2_depth5_nboost4000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..1c741946c2ece896a462b4d1fcda4ccd10715b74
--- /dev/null
+++ b/model-py/cv-jobs/eta0.2_depth5_nboost4000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.2_depth5_nboost4000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.2 5 4000
diff --git a/model-py/cv-jobs/eta0.2_depth5_nboost5000.sh b/model-py/cv-jobs/eta0.2_depth5_nboost5000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..d7be0eb13de2fb1c1fa91bc428d38a3eab473aea
--- /dev/null
+++ b/model-py/cv-jobs/eta0.2_depth5_nboost5000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.2_depth5_nboost5000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.2 5 5000
diff --git a/model-py/cv-jobs/eta0.2_depth6_nboost1000.sh b/model-py/cv-jobs/eta0.2_depth6_nboost1000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..14b9cec4f57b494848d00aff4f05571bb41975fe
--- /dev/null
+++ b/model-py/cv-jobs/eta0.2_depth6_nboost1000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.2_depth6_nboost1000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.2 6 1000
diff --git a/model-py/cv-jobs/eta0.2_depth6_nboost2000.sh b/model-py/cv-jobs/eta0.2_depth6_nboost2000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..4c9308842c354cafd13d0cbdad99b48345552019
--- /dev/null
+++ b/model-py/cv-jobs/eta0.2_depth6_nboost2000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.2_depth6_nboost2000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.2 6 2000
diff --git a/model-py/cv-jobs/eta0.2_depth6_nboost3000.sh b/model-py/cv-jobs/eta0.2_depth6_nboost3000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..78471d8d7aa920f554d8ae04df7889d673186d7a
--- /dev/null
+++ b/model-py/cv-jobs/eta0.2_depth6_nboost3000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.2_depth6_nboost3000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.2 6 3000
diff --git a/model-py/cv-jobs/eta0.2_depth6_nboost4000.sh b/model-py/cv-jobs/eta0.2_depth6_nboost4000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..90766bf65a435d95fe3d20a675821448b275ccbe
--- /dev/null
+++ b/model-py/cv-jobs/eta0.2_depth6_nboost4000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.2_depth6_nboost4000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.2 6 4000
diff --git a/model-py/cv-jobs/eta0.2_depth6_nboost5000.sh b/model-py/cv-jobs/eta0.2_depth6_nboost5000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..6230e91c9476ccbc601a61a12e28f8b737b4fb0c
--- /dev/null
+++ b/model-py/cv-jobs/eta0.2_depth6_nboost5000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.2_depth6_nboost5000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.2 6 5000
diff --git a/model-py/cv-jobs/eta0.2_depth7_nboost1000.sh b/model-py/cv-jobs/eta0.2_depth7_nboost1000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..36cbead33ce6a7a616fbda6445645a944c30d679
--- /dev/null
+++ b/model-py/cv-jobs/eta0.2_depth7_nboost1000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.2_depth7_nboost1000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.2 7 1000
diff --git a/model-py/cv-jobs/eta0.2_depth7_nboost2000.sh b/model-py/cv-jobs/eta0.2_depth7_nboost2000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..d08c3252f763150f2cc3e6a5888a5f64f868757d
--- /dev/null
+++ b/model-py/cv-jobs/eta0.2_depth7_nboost2000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.2_depth7_nboost2000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.2 7 2000
diff --git a/model-py/cv-jobs/eta0.2_depth7_nboost3000.sh b/model-py/cv-jobs/eta0.2_depth7_nboost3000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..d43affd625095d7c6485e3a6c48ffae073a23f93
--- /dev/null
+++ b/model-py/cv-jobs/eta0.2_depth7_nboost3000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.2_depth7_nboost3000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.2 7 3000
diff --git a/model-py/cv-jobs/eta0.2_depth7_nboost4000.sh b/model-py/cv-jobs/eta0.2_depth7_nboost4000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..eac7b244acba7197c3f201bcd520548c7c8e9b33
--- /dev/null
+++ b/model-py/cv-jobs/eta0.2_depth7_nboost4000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.2_depth7_nboost4000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.2 7 4000
diff --git a/model-py/cv-jobs/eta0.2_depth7_nboost5000.sh b/model-py/cv-jobs/eta0.2_depth7_nboost5000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..20c584fd5e2ea218470593e3e38d9dd9d3daffbf
--- /dev/null
+++ b/model-py/cv-jobs/eta0.2_depth7_nboost5000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.2_depth7_nboost5000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.2 7 5000
diff --git a/model-py/cv-jobs/eta0.2_depth8_nboost1000.sh b/model-py/cv-jobs/eta0.2_depth8_nboost1000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..09f61080409d6251b007565be800594ac9774807
--- /dev/null
+++ b/model-py/cv-jobs/eta0.2_depth8_nboost1000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.2_depth8_nboost1000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.2 8 1000
diff --git a/model-py/cv-jobs/eta0.2_depth8_nboost2000.sh b/model-py/cv-jobs/eta0.2_depth8_nboost2000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..025d7f90672c1152eb6cf0011b875173a3808442
--- /dev/null
+++ b/model-py/cv-jobs/eta0.2_depth8_nboost2000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.2_depth8_nboost2000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.2 8 2000
diff --git a/model-py/cv-jobs/eta0.2_depth8_nboost3000.sh b/model-py/cv-jobs/eta0.2_depth8_nboost3000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..6445d16ee91cf555dc968af59d6cbc443cab4f25
--- /dev/null
+++ b/model-py/cv-jobs/eta0.2_depth8_nboost3000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.2_depth8_nboost3000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.2 8 3000
diff --git a/model-py/cv-jobs/eta0.2_depth8_nboost4000.sh b/model-py/cv-jobs/eta0.2_depth8_nboost4000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..3aa3fb7fadd9c0f85d892ad6e2ed29092b4eeeaa
--- /dev/null
+++ b/model-py/cv-jobs/eta0.2_depth8_nboost4000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.2_depth8_nboost4000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.2 8 4000
diff --git a/model-py/cv-jobs/eta0.2_depth8_nboost5000.sh b/model-py/cv-jobs/eta0.2_depth8_nboost5000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..9d040ba64fdfc69b14770e928f1cfd1fbf124a1d
--- /dev/null
+++ b/model-py/cv-jobs/eta0.2_depth8_nboost5000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.2_depth8_nboost5000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.2 8 5000
diff --git a/model-py/cv-jobs/eta0.2_depth9_nboost1000.sh b/model-py/cv-jobs/eta0.2_depth9_nboost1000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..41448c1a329c9cbd4d62a59eb3b60eebaa88842d
--- /dev/null
+++ b/model-py/cv-jobs/eta0.2_depth9_nboost1000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.2_depth9_nboost1000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.2 9 1000
diff --git a/model-py/cv-jobs/eta0.2_depth9_nboost2000.sh b/model-py/cv-jobs/eta0.2_depth9_nboost2000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..d9d2f8248663c92099f377fdc81d0a988d0d128e
--- /dev/null
+++ b/model-py/cv-jobs/eta0.2_depth9_nboost2000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.2_depth9_nboost2000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.2 9 2000
diff --git a/model-py/cv-jobs/eta0.2_depth9_nboost3000.sh b/model-py/cv-jobs/eta0.2_depth9_nboost3000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..68d05138ba3209eff4368745e350e71f4091f55b
--- /dev/null
+++ b/model-py/cv-jobs/eta0.2_depth9_nboost3000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.2_depth9_nboost3000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.2 9 3000
diff --git a/model-py/cv-jobs/eta0.2_depth9_nboost4000.sh b/model-py/cv-jobs/eta0.2_depth9_nboost4000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..5215085994377171373c214e394f8f6b52cbeb82
--- /dev/null
+++ b/model-py/cv-jobs/eta0.2_depth9_nboost4000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.2_depth9_nboost4000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.2 9 4000
diff --git a/model-py/cv-jobs/eta0.2_depth9_nboost5000.sh b/model-py/cv-jobs/eta0.2_depth9_nboost5000.sh
new file mode 100644
index 0000000000000000000000000000000000000000..228f97c8c7e5179605779042923c53930f1a877b
--- /dev/null
+++ b/model-py/cv-jobs/eta0.2_depth9_nboost5000.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=eta0.2_depth9_nboost5000.sh.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.2 9 5000
diff --git a/model-py/cv-jobs/trial-cv.txt b/model-py/cv-jobs/trial-cv.txt
new file mode 100644
index 0000000000000000000000000000000000000000..893a30a1d8a478d57651a81b01fea54c17b42848
--- /dev/null
+++ b/model-py/cv-jobs/trial-cv.txt
@@ -0,0 +1,49 @@
+slurmstepd: error: _oom_event_monitor: poll(): Bad address
+/cm/local/apps/slurm/var/spool/job2780931/slurm_script: line 13: 29750 Killed python model-indiv-cv.py 0.2 3 5000
+slurmstepd: error: Detected 2 oom-kill event(s) in step 2780931.batch cgroup. Some of your processes may have been killed by the cgroup out-of-memory handler.
+slurmstepd: error: _oom_event_monitor: poll(): Bad address
+/cm/local/apps/slurm/var/spool/job2780932/slurm_script: line 13: 84921 Killed python model-indiv-cv.py 0.01 4 5000
+slurmstepd: error: Detected 1 oom-kill event(s) in step 2780932.batch cgroup. Some of your processes may have been killed by the cgroup out-of-memory handler.
+slurmstepd: error: _oom_event_monitor: poll(): Bad address
+/cm/local/apps/slurm/var/spool/job2780956/slurm_script: line 13: 64597 Killed python model-indiv-cv.py 0.01 10 5000
+/cm/local/apps/slurm/var/spool/job2780943/slurm_script: line 13: 63066 Killed python model-indiv-cv.py 0.2 6 5000
+slurmstepd: error: Detected 2 oom-kill event(s) in step 2780943.batch cgroup. Some of your processes may have been killed by the cgroup out-of-memory handler.
+/cm/local/apps/slurm/var/spool/job2780945/slurm_script: line 13: 73895 Killed python model-indiv-cv.py 0.05 7 5000
+slurmstepd: error: Detected 2 oom-kill event(s) in step 2780945.batch cgroup. Some of your processes may have been killed by the cgroup out-of-memory handler.
+/cm/local/apps/slurm/var/spool/job2780934/slurm_script: line 13: 72499 Killed python model-indiv-cv.py 0.1 4 5000
+slurmstepd: error: Detected 18 oom-kill event(s) in step 2780934.batch cgroup. Some of your processes may have been killed by the cgroup out-of-memory handler.
+slurmstepd: error: _oom_event_monitor: poll(): Bad address
+/cm/local/apps/slurm/var/spool/job2780935/slurm_script: line 13: 85185 Killed python model-indiv-cv.py 0.2 4 5000
+slurmstepd: error: Detected 1 oom-kill event(s) in step 2780935.batch cgroup. Some of your processes may have been killed by the cgroup out-of-memory handler.
+/cm/local/apps/slurm/var/spool/job2780949/slurm_script: line 13: 63621 Killed python model-indiv-cv.py 0.05 8 5000
+slurmstepd: error: Detected 20 oom-kill event(s) in step 2780949.batch cgroup. Some of your processes may have been killed by the cgroup out-of-memory handler.
+/cm/local/apps/slurm/var/spool/job2780958/slurm_script: line 13: 32742 Killed python model-indiv-cv.py 0.1 10 5000
+slurmstepd: error: Detected 4 oom-kill event(s) in step 2780958.batch cgroup. Some of your processes may have been killed by the cgroup out-of-memory handler.
+/cm/local/apps/slurm/var/spool/job2780953/slurm_script: line 13: 32239 Killed python model-indiv-cv.py 0.05 9 5000
+/cm/local/apps/slurm/var/spool/job2780957/slurm_script: line 13: 75146 Killed python model-indiv-cv.py 0.05 10 5000
+slurmstepd: error: Detected 2 oom-kill event(s) in step 2780957.batch cgroup. Some of your processes may have been killed by the cgroup out-of-memory handler.
+slurmstepd: error: _oom_event_monitor: poll(): Bad address
+/cm/local/apps/slurm/var/spool/job2780951/slurm_script: line 13: 86532 Killed python model-indiv-cv.py 0.2 8 5000
+slurmstepd: error: Detected 1 oom-kill event(s) in step 2780951.batch cgroup. Some of your processes may have been killed by the cgroup out-of-memory handler.
+/cm/local/apps/slurm/var/spool/job2780955/slurm_script: line 13: 76960 Killed python model-indiv-cv.py 0.2 9 5000
+slurmstepd: error: Detected 2 oom-kill event(s) in step 2780955.batch cgroup. Some of your processes may have been killed by the cgroup out-of-memory handler.
+/cm/local/apps/slurm/var/spool/job2780924/slurm_script: line 13: 74591 Killed python model-indiv-cv.py 0.01 10 4000
+slurmstepd: error: Detected 2 oom-kill event(s) in step 2780924.batch cgroup. Some of your processes may have been killed by the cgroup out-of-memory handler.
+slurmstepd: error: _oom_event_monitor: poll(): Bad address
+/cm/local/apps/slurm/var/spool/job2780944/slurm_script: line 13: 31174 Killed python model-indiv-cv.py 0.01 7 5000
+/cm/local/apps/slurm/var/spool/job2780952/slurm_script: line 13: 74631 Killed python model-indiv-cv.py 0.01 9 5000
+slurmstepd: error: Detected 2 oom-kill event(s) in step 2780952.batch cgroup. Some of your processes may have been killed by the cgroup out-of-memory handler.
+/cm/local/apps/slurm/var/spool/job2780959/slurm_script: line 13: 87663 Killed python model-indiv-cv.py 0.2 10 5000
+slurmstepd: error: Detected 26 oom-kill event(s) in step 2780959.batch cgroup. Some of your processes may have been killed by the cgroup out-of-memory handler.
+slurmstepd: error: _oom_event_monitor: poll(): Bad address
+/cm/local/apps/slurm/var/spool/job2780950/slurm_script: line 13: 63620 Killed python model-indiv-cv.py 0.1 8 5000
+slurmstepd: error: Detected 1 oom-kill event(s) in step 2780950.batch cgroup. Some of your processes may have been killed by the cgroup out-of-memory handler.
+slurmstepd: error: *** JOB 2780946 ON c0099 CANCELLED AT 2019-09-05T16:53:10 ***
+slurmstepd: error: *** JOB 2780954 ON c0101 CANCELLED AT 2019-09-05T16:53:10 ***
+slurmstepd: error: *** JOB 2780800 ON c0113 CANCELLED AT 2019-09-05T16:53:10 ***
+slurmstepd: error: *** JOB 2780941 ON c0097 CANCELLED AT 2019-09-05T16:53:10 ***
+slurmstepd: error: *** JOB 2780933 ON c0098 CANCELLED AT 2019-09-05T16:53:10 ***
+slurmstepd: error: Detected 1 oom-kill event(s) in step 2780933.batch cgroup. Some of your processes may have been killed by the cgroup out-of-memory handler.
+slurmstepd: error: Detected 1 oom-kill event(s) in step 2780946.batch cgroup. Some of your processes may have been killed by the cgroup out-of-memory handler.
+slurmstepd: error: Detected 1 oom-kill event(s) in step 2780800.batch cgroup. Some of your processes may have been killed by the cgroup out-of-memory handler.
+slurmstepd: error: Detected 1 oom-kill event(s) in step 2780941.batch cgroup. Some of your processes may have been killed by the cgroup out-of-memory handler.
diff --git a/model-py/cv-jobs/trial-job.sh b/model-py/cv-jobs/trial-job.sh
new file mode 100644
index 0000000000000000000000000000000000000000..cd242de26aafbe70dd971e0e50c62ca7a3b53b2b
--- /dev/null
+++ b/model-py/cv-jobs/trial-job.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+#SBATCH --partition=pascalnodes
+#SBATCH --time=6:00:00
+#SBATCH --ntasks=4
+#SBATCH --mem-per-cpu=25
+#SBATCH --output=trial-cv.txt
+
+module load Anaconda3/5.3.0
+source activate ctmodel-ml
+
+cd /data/user/mdefende/Projects/ctmodel-ml/model-py
+
+python model-indiv-cv.py 0.2 3 1000
diff --git a/model-py/cv-out/eta0.2depth3nboost1000.csv b/model-py/cv-out/eta0.2depth3nboost1000.csv
new file mode 100644
index 0000000000000000000000000000000000000000..1a0b926c73daf9655c56c840aface7738cf72359
--- /dev/null
+++ b/model-py/cv-out/eta0.2depth3nboost1000.csv
@@ -0,0 +1,6 @@
+2.000000000000000111e-01
+3.000000000000000000e+00
+1.000000000000000000e+03
+9.990000000000000000e+02
+2.018326000000000009e-01
+2.021152999999999977e-01
diff --git a/model-py/model-indiv-cv.py b/model-py/model-indiv-cv.py
new file mode 100644
index 0000000000000000000000000000000000000000..568c2712730b6bc183f7a41d5ed5becf46723393
--- /dev/null
+++ b/model-py/model-indiv-cv.py
@@ -0,0 +1,26 @@
+import pandas as pd
+import numpy as np
+import xgboost as xgb
+import sys
+
+def main():
+ eta_val = float(sys.argv[1])
+ max_depth_val = int(sys.argv[2])
+ nboost_val = int(sys.argv[3])
+
+ df = pd.read_csv("/data/user/mdefende/Projects/ctmodel-ml/data/BigThick_Restricted_cleaned.csv")
+
+ x_feat = pd.concat([df[['Age','Ecc','Pol','Sulc0']],df.filter(regex='[^Thick1]5')], axis = 1)
+ y_feat = df[['Thick5']]
+
+ dfull = xgb.DMatrix(x_feat,y_feat)
+ param = {'tree_method': 'gpu_hist', 'max_depth': max_depth_val, 'eta': eta_val}
+
+ cv_results = xgb.cv(params = param, dtrain = dfull, num_boost_round = nboost_val, nfold = 10, metrics = 'mae', early_stopping_rounds=10, verbose_eval = True, as_pandas=True, seed = 123)
+ idx = cv_results['test-mae-mean'].idxmin()
+
+ cv_out = np.asarray([eta_val,max_depth_val, nboost_val, idx, cv_results['train-mae-mean'][idx], cv_results['test-mae-mean'][idx]])
+ file_name = 'eta' + str(eta_val) + '_depth' + str(max_depth_val) + '_nboost' + str(nboost_val) + '.csv'
+ np.savetxt('/data/user/mdefende/Projects/ctmodel-ml/model-py/cv-out/' + file_name, cv_out, delimiter = ',')
+
+main()
diff --git a/model-py/model-xgboost.ipynb b/model-py/model-xgboost.ipynb
index 59acb84ae045f3e6adbc3a2440d1a78f42df3c57..1fd66b6c6f3662f0cd47441df4553fe464391c74 100644
--- a/model-py/model-xgboost.ipynb
+++ b/model-py/model-xgboost.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
@@ -17,7 +17,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
@@ -27,55 +27,30 @@
},
{
"cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Split into test and train dataframes\n",
- "df_train = df[df.Group == \"Train\"]\n",
- "df_test = df[df.Group == \"Test\"]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
+ "execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"# df_x are your input features\n",
- "x_train = pd.concat([df_train[['Age','Ecc','Pol','Sulc0']],df_train.filter(regex='[^Thick1]5')], axis = 1)\n",
- "x_test = pd.concat([df_test[['Age','Ecc','Pol','Sulc0']],df_test.filter(regex='[^Thick1]5')], axis = 1)\n",
+ "x_feat = pd.concat([df[['Age','Ecc','Pol','Sulc0']],df.filter(regex='[^Thick1]5')], axis = 1)\n",
"\n",
"# df_y is your output feature (the one you want to predict)\n",
- "y_train = df_train[['Thick5']]\n",
- "y_test = df_test[['Thick5']]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "x_train = x_train.drop('Age',axis = 1)\n",
- "x_test = x_test.drop('Age',axis = 1)"
+ "y_feat = df[['Thick5']]"
]
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"# Convert input data from numpy to XGBoost format\n",
- "dtrain = xgb.DMatrix(x_train, y_train)\n",
- "dtest = xgb.DMatrix(x_test, y_test)\n",
- "dfull = xgb.DMatrix(pd.concat([x_train,x_test]),pd.concat([y_train,y_test]))"
+ "dfull = xgb.DMatrix(x_feat,y_feat)"
]
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
@@ -85,2110 +60,4252 @@
"param = {'tree_method': 'gpu_hist',\n",
" 'max_depth': 6,\n",
" 'eta': 0.05,\n",
- " 'verbosity': 2}\n",
- " #'eval_metric': ['mae','rmse'],\n",
- " #'early_stopping_rounds': 10}\n",
+ " 'verbosity': 2,\n",
+ " 'eval_metric': ['mae','rmse'],\n",
+ " 'early_stopping_rounds': 10}\n",
"\n",
- "watchlist = [(dtrain,'train'),(dtest,'eval')]"
+ "watchlist = [(dfull,'train')]"
]
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "[0]\ttrain-mae:1.20939\ttrain-rmse:1.26675\teval-mae:1.2162\teval-rmse:1.27346\n",
- "Multiple eval metrics have been passed: 'eval-rmse' will be used for early stopping.\n",
+ "[0]\ttrain-mae:1.27818\ttrain-rmse:1.33537\n",
+ "Multiple eval metrics have been passed: 'train-rmse' will be used for early stopping.\n",
"\n",
- "Will train until eval-rmse hasn't improved in 10 rounds.\n",
- "[1]\ttrain-mae:1.08846\ttrain-rmse:1.14769\teval-mae:1.09508\teval-rmse:1.15407\n",
- "[2]\ttrain-mae:0.979631\ttrain-rmse:1.04119\teval-mae:0.986081\teval-rmse:1.0473\n",
- "[3]\ttrain-mae:0.881687\ttrain-rmse:0.945979\teval-mae:0.888027\teval-rmse:0.951832\n",
- "[4]\ttrain-mae:0.793589\ttrain-rmse:0.860992\teval-mae:0.79984\teval-rmse:0.86664\n",
- "[5]\ttrain-mae:0.714485\ttrain-rmse:0.785209\teval-mae:0.720587\teval-rmse:0.790599\n",
- "[6]\ttrain-mae:0.64376\ttrain-rmse:0.717836\teval-mae:0.649675\teval-rmse:0.722992\n",
- "[7]\ttrain-mae:0.580869\ttrain-rmse:0.658094\teval-mae:0.586576\teval-rmse:0.663014\n",
- "[8]\ttrain-mae:0.525393\ttrain-rmse:0.605307\teval-mae:0.530857\teval-rmse:0.609979\n",
- "[9]\ttrain-mae:0.476745\ttrain-rmse:0.558715\teval-mae:0.482019\teval-rmse:0.563169\n",
- "[10]\ttrain-mae:0.434432\ttrain-rmse:0.517759\teval-mae:0.439509\teval-rmse:0.522001\n",
- "[11]\ttrain-mae:0.397872\ttrain-rmse:0.481713\teval-mae:0.402697\teval-rmse:0.485773\n",
- "[12]\ttrain-mae:0.366673\ttrain-rmse:0.450466\teval-mae:0.37128\teval-rmse:0.454333\n",
- "[13]\ttrain-mae:0.340053\ttrain-rmse:0.42325\teval-mae:0.344399\teval-rmse:0.426944\n",
- "[14]\ttrain-mae:0.317574\ttrain-rmse:0.399785\teval-mae:0.321656\teval-rmse:0.403332\n",
- "[15]\ttrain-mae:0.298646\ttrain-rmse:0.379535\teval-mae:0.302443\teval-rmse:0.382935\n",
- "[16]\ttrain-mae:0.282735\ttrain-rmse:0.362026\teval-mae:0.286255\teval-rmse:0.365281\n",
- "[17]\ttrain-mae:0.269368\ttrain-rmse:0.34702\teval-mae:0.272639\teval-rmse:0.350179\n",
- "[18]\ttrain-mae:0.258318\ttrain-rmse:0.334278\teval-mae:0.261354\teval-rmse:0.337343\n",
- "[19]\ttrain-mae:0.249065\ttrain-rmse:0.323424\teval-mae:0.25187\teval-rmse:0.326368\n",
- "[20]\ttrain-mae:0.241425\ttrain-rmse:0.31421\teval-mae:0.24405\teval-rmse:0.317094\n",
- "[21]\ttrain-mae:0.235024\ttrain-rmse:0.306298\teval-mae:0.237482\teval-rmse:0.309117\n",
- "[22]\ttrain-mae:0.229813\ttrain-rmse:0.299756\teval-mae:0.232087\teval-rmse:0.302479\n",
- "[23]\ttrain-mae:0.225486\ttrain-rmse:0.294172\teval-mae:0.227657\teval-rmse:0.296866\n",
- "[24]\ttrain-mae:0.221817\ttrain-rmse:0.289396\teval-mae:0.223897\teval-rmse:0.292063\n",
- "[25]\ttrain-mae:0.218776\ttrain-rmse:0.285324\teval-mae:0.220784\teval-rmse:0.287996\n",
- "[26]\ttrain-mae:0.216343\ttrain-rmse:0.281996\teval-mae:0.218285\teval-rmse:0.284668\n",
- "[27]\ttrain-mae:0.214336\ttrain-rmse:0.279235\teval-mae:0.216259\teval-rmse:0.28195\n",
- "[28]\ttrain-mae:0.212596\ttrain-rmse:0.276804\teval-mae:0.21452\teval-rmse:0.279555\n",
- "[29]\ttrain-mae:0.211314\ttrain-rmse:0.274911\teval-mae:0.213378\teval-rmse:0.277898\n",
- "[30]\ttrain-mae:0.210128\ttrain-rmse:0.273205\teval-mae:0.212172\teval-rmse:0.276201\n",
- "[31]\ttrain-mae:0.209233\ttrain-rmse:0.271836\teval-mae:0.211472\teval-rmse:0.275108\n",
- "[32]\ttrain-mae:0.208233\ttrain-rmse:0.270372\teval-mae:0.210482\teval-rmse:0.273705\n",
- "[33]\ttrain-mae:0.207409\ttrain-rmse:0.269142\teval-mae:0.209729\teval-rmse:0.272599\n",
- "[34]\ttrain-mae:0.206855\ttrain-rmse:0.268249\teval-mae:0.209395\teval-rmse:0.271978\n",
- "[35]\ttrain-mae:0.20637\ttrain-rmse:0.267466\teval-mae:0.209125\teval-rmse:0.271466\n",
- "[36]\ttrain-mae:0.205793\ttrain-rmse:0.266629\teval-mae:0.208557\teval-rmse:0.270644\n",
- "[37]\ttrain-mae:0.205245\ttrain-rmse:0.26581\teval-mae:0.208045\teval-rmse:0.269883\n",
- "[38]\ttrain-mae:0.204879\ttrain-rmse:0.265213\teval-mae:0.207937\teval-rmse:0.26961\n",
- "[39]\ttrain-mae:0.204535\ttrain-rmse:0.264705\teval-mae:0.207652\teval-rmse:0.269172\n",
- "[40]\ttrain-mae:0.204107\ttrain-rmse:0.264061\teval-mae:0.207303\teval-rmse:0.26865\n",
- "[41]\ttrain-mae:0.203922\ttrain-rmse:0.263745\teval-mae:0.207216\teval-rmse:0.26844\n",
- "[42]\ttrain-mae:0.203614\ttrain-rmse:0.263283\teval-mae:0.206984\teval-rmse:0.268077\n",
- "[43]\ttrain-mae:0.203375\ttrain-rmse:0.262904\teval-mae:0.207\teval-rmse:0.268017\n",
- "[44]\ttrain-mae:0.20314\ttrain-rmse:0.262585\teval-mae:0.206806\teval-rmse:0.267753\n",
- "[45]\ttrain-mae:0.202794\ttrain-rmse:0.262106\teval-mae:0.206542\teval-rmse:0.267382\n",
- "[46]\ttrain-mae:0.202569\ttrain-rmse:0.261778\teval-mae:0.206555\teval-rmse:0.267346\n",
- "[47]\ttrain-mae:0.202376\ttrain-rmse:0.261495\teval-mae:0.206418\teval-rmse:0.267137\n",
- "[48]\ttrain-mae:0.202047\ttrain-rmse:0.26108\teval-mae:0.206143\teval-rmse:0.266781\n",
- "[49]\ttrain-mae:0.201886\ttrain-rmse:0.260865\teval-mae:0.206006\teval-rmse:0.266588\n",
- "[50]\ttrain-mae:0.201694\ttrain-rmse:0.260598\teval-mae:0.206045\teval-rmse:0.266598\n",
- "[51]\ttrain-mae:0.201508\ttrain-rmse:0.260353\teval-mae:0.205907\teval-rmse:0.266407\n",
- "[52]\ttrain-mae:0.201274\ttrain-rmse:0.260035\teval-mae:0.205742\teval-rmse:0.266177\n",
- "[53]\ttrain-mae:0.201196\ttrain-rmse:0.259925\teval-mae:0.205705\teval-rmse:0.266111\n",
- "[54]\ttrain-mae:0.201034\ttrain-rmse:0.259734\teval-mae:0.205585\teval-rmse:0.265952\n",
- "[55]\ttrain-mae:0.200848\ttrain-rmse:0.259475\teval-mae:0.205455\teval-rmse:0.265764\n",
- "[56]\ttrain-mae:0.200639\ttrain-rmse:0.259223\teval-mae:0.205302\teval-rmse:0.265572\n",
- "[57]\ttrain-mae:0.200402\ttrain-rmse:0.258914\teval-mae:0.205322\teval-rmse:0.265591\n",
- "[58]\ttrain-mae:0.200214\ttrain-rmse:0.258668\teval-mae:0.205195\teval-rmse:0.265428\n",
- "[59]\ttrain-mae:0.200046\ttrain-rmse:0.258471\teval-mae:0.205055\teval-rmse:0.265262\n",
- "[60]\ttrain-mae:0.199907\ttrain-rmse:0.258289\teval-mae:0.204977\teval-rmse:0.265158\n",
- "[61]\ttrain-mae:0.199674\ttrain-rmse:0.257991\teval-mae:0.204974\teval-rmse:0.265158\n",
- "[62]\ttrain-mae:0.199457\ttrain-rmse:0.257743\teval-mae:0.204822\teval-rmse:0.264988\n",
- "[63]\ttrain-mae:0.199279\ttrain-rmse:0.257542\teval-mae:0.20469\teval-rmse:0.264835\n",
- "[64]\ttrain-mae:0.199078\ttrain-rmse:0.257295\teval-mae:0.204553\teval-rmse:0.264663\n",
- "[65]\ttrain-mae:0.198902\ttrain-rmse:0.25707\teval-mae:0.204585\teval-rmse:0.264699\n",
- "[66]\ttrain-mae:0.198684\ttrain-rmse:0.256827\teval-mae:0.204438\teval-rmse:0.264534\n",
- "[67]\ttrain-mae:0.198539\ttrain-rmse:0.25665\teval-mae:0.204357\teval-rmse:0.264449\n",
- "[68]\ttrain-mae:0.198363\ttrain-rmse:0.256428\teval-mae:0.204282\teval-rmse:0.264357\n",
- "[69]\ttrain-mae:0.198241\ttrain-rmse:0.256283\teval-mae:0.204275\teval-rmse:0.264346\n",
- "[70]\ttrain-mae:0.198061\ttrain-rmse:0.25608\teval-mae:0.204158\teval-rmse:0.264221\n",
- "[71]\ttrain-mae:0.197876\ttrain-rmse:0.255852\teval-mae:0.204042\teval-rmse:0.26409\n",
- "[72]\ttrain-mae:0.197747\ttrain-rmse:0.255706\teval-mae:0.203986\teval-rmse:0.264028\n",
- "[73]\ttrain-mae:0.19758\ttrain-rmse:0.25551\teval-mae:0.203876\teval-rmse:0.263897\n",
- "[74]\ttrain-mae:0.197375\ttrain-rmse:0.255264\teval-mae:0.203843\teval-rmse:0.263865\n",
- "[75]\ttrain-mae:0.197221\ttrain-rmse:0.255092\teval-mae:0.203748\teval-rmse:0.263758\n",
- "[76]\ttrain-mae:0.197131\ttrain-rmse:0.254986\teval-mae:0.203702\teval-rmse:0.263703\n",
- "[77]\ttrain-mae:0.196986\ttrain-rmse:0.25482\teval-mae:0.203626\teval-rmse:0.263624\n",
- "[78]\ttrain-mae:0.196782\ttrain-rmse:0.254568\teval-mae:0.2036\teval-rmse:0.263594\n",
- "[79]\ttrain-mae:0.196706\ttrain-rmse:0.254483\teval-mae:0.203564\teval-rmse:0.263549\n",
- "[80]\ttrain-mae:0.196547\ttrain-rmse:0.254298\teval-mae:0.203461\teval-rmse:0.263436\n",
- "[81]\ttrain-mae:0.196417\ttrain-rmse:0.254156\teval-mae:0.203393\teval-rmse:0.263362\n",
- "[82]\ttrain-mae:0.19628\ttrain-rmse:0.253987\teval-mae:0.203326\teval-rmse:0.263289\n",
- "[83]\ttrain-mae:0.196177\ttrain-rmse:0.253865\teval-mae:0.203336\teval-rmse:0.26331\n",
- "[84]\ttrain-mae:0.196062\ttrain-rmse:0.253728\teval-mae:0.203294\teval-rmse:0.263261\n",
- "[85]\ttrain-mae:0.19599\ttrain-rmse:0.253649\teval-mae:0.203259\teval-rmse:0.26323\n",
- "[86]\ttrain-mae:0.195885\ttrain-rmse:0.25352\teval-mae:0.203233\teval-rmse:0.263199\n",
- "[87]\ttrain-mae:0.195797\ttrain-rmse:0.253419\teval-mae:0.203182\teval-rmse:0.26313\n",
- "[88]\ttrain-mae:0.195692\ttrain-rmse:0.253293\teval-mae:0.203197\teval-rmse:0.263144\n",
- "[89]\ttrain-mae:0.195606\ttrain-rmse:0.253202\teval-mae:0.203153\teval-rmse:0.263092\n",
- "[90]\ttrain-mae:0.195484\ttrain-rmse:0.253059\teval-mae:0.203097\teval-rmse:0.263022\n",
- "[91]\ttrain-mae:0.195362\ttrain-rmse:0.252909\teval-mae:0.20303\teval-rmse:0.262944\n",
- "[92]\ttrain-mae:0.195212\ttrain-rmse:0.252724\teval-mae:0.203036\teval-rmse:0.262959\n",
- "[93]\ttrain-mae:0.195114\ttrain-rmse:0.252609\teval-mae:0.203026\teval-rmse:0.262951\n",
- "[94]\ttrain-mae:0.195035\ttrain-rmse:0.252517\teval-mae:0.20305\teval-rmse:0.26298\n",
- "[95]\ttrain-mae:0.194907\ttrain-rmse:0.252369\teval-mae:0.202986\teval-rmse:0.262915\n",
- "[96]\ttrain-mae:0.194779\ttrain-rmse:0.252205\teval-mae:0.202918\teval-rmse:0.262832\n",
- "[97]\ttrain-mae:0.194693\ttrain-rmse:0.252106\teval-mae:0.202889\teval-rmse:0.2628\n",
- "[98]\ttrain-mae:0.194645\ttrain-rmse:0.252058\teval-mae:0.202882\teval-rmse:0.262796\n",
- "[99]\ttrain-mae:0.194562\ttrain-rmse:0.251976\teval-mae:0.202841\teval-rmse:0.262754\n",
- "[100]\ttrain-mae:0.194432\ttrain-rmse:0.251813\teval-mae:0.202777\teval-rmse:0.262686\n"
+ "Will train until train-rmse hasn't improved in 10 rounds.\n",
+ "[1]\ttrain-mae:1.21427\ttrain-rmse:1.27265\n",
+ "[2]\ttrain-mae:1.15356\ttrain-rmse:1.21319\n",
+ "[3]\ttrain-mae:1.09588\ttrain-rmse:1.15684\n",
+ "[4]\ttrain-mae:1.04109\ttrain-rmse:1.10355\n",
+ "[5]\ttrain-mae:0.989038\ttrain-rmse:1.053\n",
+ "[6]\ttrain-mae:0.93959\ttrain-rmse:1.0052\n",
+ "[7]\ttrain-mae:0.892618\ttrain-rmse:0.95998\n",
+ "[8]\ttrain-mae:0.848008\ttrain-rmse:0.91731\n",
+ "[9]\ttrain-mae:0.805655\ttrain-rmse:0.87681\n",
+ "[10]\ttrain-mae:0.765471\ttrain-rmse:0.8386\n",
+ "[11]\ttrain-mae:0.727377\ttrain-rmse:0.802563\n",
+ "[12]\ttrain-mae:0.691315\ttrain-rmse:0.768655\n",
+ "[13]\ttrain-mae:0.657241\ttrain-rmse:0.736553\n",
+ "[14]\ttrain-mae:0.62508\ttrain-rmse:0.706337\n",
+ "[15]\ttrain-mae:0.594791\ttrain-rmse:0.677739\n",
+ "[16]\ttrain-mae:0.566371\ttrain-rmse:0.6511\n",
+ "[17]\ttrain-mae:0.539661\ttrain-rmse:0.625736\n",
+ "[18]\ttrain-mae:0.51471\ttrain-rmse:0.602048\n",
+ "[19]\ttrain-mae:0.491394\ttrain-rmse:0.579737\n",
+ "[20]\ttrain-mae:0.469768\ttrain-rmse:0.558842\n",
+ "[21]\ttrain-mae:0.449559\ttrain-rmse:0.539185\n",
+ "[22]\ttrain-mae:0.430875\ttrain-rmse:0.520909\n",
+ "[23]\ttrain-mae:0.413484\ttrain-rmse:0.503655\n",
+ "[24]\ttrain-mae:0.397391\ttrain-rmse:0.487545\n",
+ "[25]\ttrain-mae:0.38268\ttrain-rmse:0.472627\n",
+ "[26]\ttrain-mae:0.369046\ttrain-rmse:0.458567\n",
+ "[27]\ttrain-mae:0.356397\ttrain-rmse:0.445429\n",
+ "[28]\ttrain-mae:0.344767\ttrain-rmse:0.43325\n",
+ "[29]\ttrain-mae:0.334047\ttrain-rmse:0.421863\n",
+ "[30]\ttrain-mae:0.324169\ttrain-rmse:0.411191\n",
+ "[31]\ttrain-mae:0.315123\ttrain-rmse:0.401299\n",
+ "[32]\ttrain-mae:0.307081\ttrain-rmse:0.392425\n",
+ "[33]\ttrain-mae:0.299425\ttrain-rmse:0.38387\n",
+ "[34]\ttrain-mae:0.292473\ttrain-rmse:0.376017\n",
+ "[35]\ttrain-mae:0.286144\ttrain-rmse:0.368763\n",
+ "[36]\ttrain-mae:0.280314\ttrain-rmse:0.361977\n",
+ "[37]\ttrain-mae:0.274916\ttrain-rmse:0.355608\n",
+ "[38]\ttrain-mae:0.2702\ttrain-rmse:0.349943\n",
+ "[39]\ttrain-mae:0.265665\ttrain-rmse:0.3445\n",
+ "[40]\ttrain-mae:0.261606\ttrain-rmse:0.339542\n",
+ "[41]\ttrain-mae:0.257757\ttrain-rmse:0.334782\n",
+ "[42]\ttrain-mae:0.25436\ttrain-rmse:0.330566\n",
+ "[43]\ttrain-mae:0.251177\ttrain-rmse:0.326572\n",
+ "[44]\ttrain-mae:0.248309\ttrain-rmse:0.322924\n",
+ "[45]\ttrain-mae:0.245775\ttrain-rmse:0.319692\n",
+ "[46]\ttrain-mae:0.243513\ttrain-rmse:0.316721\n",
+ "[47]\ttrain-mae:0.241287\ttrain-rmse:0.31381\n",
+ "[48]\ttrain-mae:0.239504\ttrain-rmse:0.311439\n",
+ "[49]\ttrain-mae:0.237676\ttrain-rmse:0.30902\n",
+ "[50]\ttrain-mae:0.236045\ttrain-rmse:0.306827\n",
+ "[51]\ttrain-mae:0.234497\ttrain-rmse:0.304717\n",
+ "[52]\ttrain-mae:0.233053\ttrain-rmse:0.302753\n",
+ "[53]\ttrain-mae:0.231752\ttrain-rmse:0.300962\n",
+ "[54]\ttrain-mae:0.23072\ttrain-rmse:0.299484\n",
+ "[55]\ttrain-mae:0.229565\ttrain-rmse:0.297859\n",
+ "[56]\ttrain-mae:0.228504\ttrain-rmse:0.296376\n",
+ "[57]\ttrain-mae:0.227567\ttrain-rmse:0.29503\n",
+ "[58]\ttrain-mae:0.226712\ttrain-rmse:0.293831\n",
+ "[59]\ttrain-mae:0.225953\ttrain-rmse:0.292753\n",
+ "[60]\ttrain-mae:0.225205\ttrain-rmse:0.291662\n",
+ "[61]\ttrain-mae:0.224535\ttrain-rmse:0.290675\n",
+ "[62]\ttrain-mae:0.223793\ttrain-rmse:0.289614\n",
+ "[63]\ttrain-mae:0.22324\ttrain-rmse:0.28877\n",
+ "[64]\ttrain-mae:0.222682\ttrain-rmse:0.287939\n",
+ "[65]\ttrain-mae:0.222148\ttrain-rmse:0.287185\n",
+ "[66]\ttrain-mae:0.221717\ttrain-rmse:0.286536\n",
+ "[67]\ttrain-mae:0.221328\ttrain-rmse:0.285933\n",
+ "[68]\ttrain-mae:0.220862\ttrain-rmse:0.285278\n",
+ "[69]\ttrain-mae:0.220575\ttrain-rmse:0.284803\n",
+ "[70]\ttrain-mae:0.220177\ttrain-rmse:0.284235\n",
+ "[71]\ttrain-mae:0.219806\ttrain-rmse:0.283721\n",
+ "[72]\ttrain-mae:0.21944\ttrain-rmse:0.283189\n",
+ "[73]\ttrain-mae:0.219149\ttrain-rmse:0.282745\n",
+ "[74]\ttrain-mae:0.218795\ttrain-rmse:0.282221\n",
+ "[75]\ttrain-mae:0.218498\ttrain-rmse:0.281758\n",
+ "[76]\ttrain-mae:0.2183\ttrain-rmse:0.281427\n",
+ "[77]\ttrain-mae:0.218069\ttrain-rmse:0.281076\n",
+ "[78]\ttrain-mae:0.217868\ttrain-rmse:0.280768\n",
+ "[79]\ttrain-mae:0.217532\ttrain-rmse:0.280277\n",
+ "[80]\ttrain-mae:0.217364\ttrain-rmse:0.280026\n",
+ "[81]\ttrain-mae:0.217168\ttrain-rmse:0.279726\n",
+ "[82]\ttrain-mae:0.216875\ttrain-rmse:0.279316\n",
+ "[83]\ttrain-mae:0.216631\ttrain-rmse:0.278964\n",
+ "[84]\ttrain-mae:0.216514\ttrain-rmse:0.278789\n",
+ "[85]\ttrain-mae:0.21625\ttrain-rmse:0.278411\n",
+ "[86]\ttrain-mae:0.216089\ttrain-rmse:0.278189\n",
+ "[87]\ttrain-mae:0.215856\ttrain-rmse:0.27786\n",
+ "[88]\ttrain-mae:0.215734\ttrain-rmse:0.277669\n",
+ "[89]\ttrain-mae:0.215646\ttrain-rmse:0.277521\n",
+ "[90]\ttrain-mae:0.215429\ttrain-rmse:0.277222\n",
+ "[91]\ttrain-mae:0.215314\ttrain-rmse:0.277065\n",
+ "[92]\ttrain-mae:0.215161\ttrain-rmse:0.27685\n",
+ "[93]\ttrain-mae:0.21497\ttrain-rmse:0.27662\n",
+ "[94]\ttrain-mae:0.214772\ttrain-rmse:0.276362\n",
+ "[95]\ttrain-mae:0.214555\ttrain-rmse:0.276066\n",
+ "[96]\ttrain-mae:0.214487\ttrain-rmse:0.275956\n",
+ "[97]\ttrain-mae:0.214403\ttrain-rmse:0.275827\n",
+ "[98]\ttrain-mae:0.214187\ttrain-rmse:0.27554\n",
+ "[99]\ttrain-mae:0.214069\ttrain-rmse:0.275402\n",
+ "[100]\ttrain-mae:0.213898\ttrain-rmse:0.275171\n",
+ "[101]\ttrain-mae:0.213835\ttrain-rmse:0.275076\n",
+ "[102]\ttrain-mae:0.213629\ttrain-rmse:0.274806\n",
+ "[103]\ttrain-mae:0.213531\ttrain-rmse:0.274689\n",
+ "[104]\ttrain-mae:0.213374\ttrain-rmse:0.274486\n",
+ "[105]\ttrain-mae:0.213271\ttrain-rmse:0.274357\n",
+ "[106]\ttrain-mae:0.213072\ttrain-rmse:0.274101\n",
+ "[107]\ttrain-mae:0.213002\ttrain-rmse:0.274001\n",
+ "[108]\ttrain-mae:0.212895\ttrain-rmse:0.273873\n",
+ "[109]\ttrain-mae:0.212773\ttrain-rmse:0.273712\n",
+ "[110]\ttrain-mae:0.212658\ttrain-rmse:0.273563\n",
+ "[111]\ttrain-mae:0.212604\ttrain-rmse:0.273493\n",
+ "[112]\ttrain-mae:0.212519\ttrain-rmse:0.273397\n",
+ "[113]\ttrain-mae:0.212385\ttrain-rmse:0.273225\n",
+ "[114]\ttrain-mae:0.212341\ttrain-rmse:0.273162\n",
+ "[115]\ttrain-mae:0.212206\ttrain-rmse:0.273\n",
+ "[116]\ttrain-mae:0.212122\ttrain-rmse:0.272912\n",
+ "[117]\ttrain-mae:0.212043\ttrain-rmse:0.272811\n",
+ "[118]\ttrain-mae:0.211974\ttrain-rmse:0.272721\n",
+ "[119]\ttrain-mae:0.211874\ttrain-rmse:0.272597\n",
+ "[120]\ttrain-mae:0.21177\ttrain-rmse:0.272477\n",
+ "[121]\ttrain-mae:0.211698\ttrain-rmse:0.272382\n",
+ "[122]\ttrain-mae:0.211565\ttrain-rmse:0.272215\n",
+ "[123]\ttrain-mae:0.211474\ttrain-rmse:0.272094\n",
+ "[124]\ttrain-mae:0.211427\ttrain-rmse:0.272033\n",
+ "[125]\ttrain-mae:0.211342\ttrain-rmse:0.271922\n",
+ "[126]\ttrain-mae:0.211262\ttrain-rmse:0.271835\n",
+ "[127]\ttrain-mae:0.211165\ttrain-rmse:0.271727\n",
+ "[128]\ttrain-mae:0.211084\ttrain-rmse:0.271625\n",
+ "[129]\ttrain-mae:0.211012\ttrain-rmse:0.271542\n",
+ "[130]\ttrain-mae:0.210919\ttrain-rmse:0.271444\n",
+ "[131]\ttrain-mae:0.210848\ttrain-rmse:0.27136\n",
+ "[132]\ttrain-mae:0.210793\ttrain-rmse:0.271299\n",
+ "[133]\ttrain-mae:0.210702\ttrain-rmse:0.271182\n",
+ "[134]\ttrain-mae:0.210595\ttrain-rmse:0.271068\n",
+ "[135]\ttrain-mae:0.210514\ttrain-rmse:0.270975\n",
+ "[136]\ttrain-mae:0.210474\ttrain-rmse:0.270924\n",
+ "[137]\ttrain-mae:0.210407\ttrain-rmse:0.270859\n",
+ "[138]\ttrain-mae:0.210332\ttrain-rmse:0.270773\n",
+ "[139]\ttrain-mae:0.210236\ttrain-rmse:0.27066\n",
+ "[140]\ttrain-mae:0.210181\ttrain-rmse:0.270593\n",
+ "[141]\ttrain-mae:0.210103\ttrain-rmse:0.27051\n",
+ "[142]\ttrain-mae:0.210044\ttrain-rmse:0.270436\n",
+ "[143]\ttrain-mae:0.209969\ttrain-rmse:0.270352\n",
+ "[144]\ttrain-mae:0.209883\ttrain-rmse:0.270242\n",
+ "[145]\ttrain-mae:0.209805\ttrain-rmse:0.270141\n",
+ "[146]\ttrain-mae:0.209748\ttrain-rmse:0.270068\n",
+ "[147]\ttrain-mae:0.209673\ttrain-rmse:0.269998\n",
+ "[148]\ttrain-mae:0.209622\ttrain-rmse:0.269943\n",
+ "[149]\ttrain-mae:0.209549\ttrain-rmse:0.269859\n",
+ "[150]\ttrain-mae:0.209478\ttrain-rmse:0.269779\n",
+ "[151]\ttrain-mae:0.209404\ttrain-rmse:0.269702\n",
+ "[152]\ttrain-mae:0.20929\ttrain-rmse:0.269556\n",
+ "[153]\ttrain-mae:0.20921\ttrain-rmse:0.269473\n",
+ "[154]\ttrain-mae:0.20913\ttrain-rmse:0.269377\n",
+ "[155]\ttrain-mae:0.209082\ttrain-rmse:0.269319\n",
+ "[156]\ttrain-mae:0.209027\ttrain-rmse:0.269265\n",
+ "[157]\ttrain-mae:0.20897\ttrain-rmse:0.269203\n",
+ "[158]\ttrain-mae:0.20892\ttrain-rmse:0.269147\n",
+ "[159]\ttrain-mae:0.208825\ttrain-rmse:0.269038\n",
+ "[160]\ttrain-mae:0.208794\ttrain-rmse:0.269004\n",
+ "[161]\ttrain-mae:0.20874\ttrain-rmse:0.26894\n",
+ "[162]\ttrain-mae:0.208686\ttrain-rmse:0.26887\n",
+ "[163]\ttrain-mae:0.20858\ttrain-rmse:0.268739\n",
+ "[164]\ttrain-mae:0.208494\ttrain-rmse:0.26864\n",
+ "[165]\ttrain-mae:0.208421\ttrain-rmse:0.268568\n",
+ "[166]\ttrain-mae:0.208362\ttrain-rmse:0.268512\n",
+ "[167]\ttrain-mae:0.208314\ttrain-rmse:0.268455\n",
+ "[168]\ttrain-mae:0.208261\ttrain-rmse:0.268393\n",
+ "[169]\ttrain-mae:0.208197\ttrain-rmse:0.268313\n",
+ "[170]\ttrain-mae:0.208164\ttrain-rmse:0.268276\n",
+ "[171]\ttrain-mae:0.208113\ttrain-rmse:0.268213\n",
+ "[172]\ttrain-mae:0.208056\ttrain-rmse:0.268145\n",
+ "[173]\ttrain-mae:0.208008\ttrain-rmse:0.2681\n",
+ "[174]\ttrain-mae:0.207956\ttrain-rmse:0.268039\n",
+ "[175]\ttrain-mae:0.207909\ttrain-rmse:0.267989\n",
+ "[176]\ttrain-mae:0.207858\ttrain-rmse:0.26793\n",
+ "[177]\ttrain-mae:0.207815\ttrain-rmse:0.26788\n",
+ "[178]\ttrain-mae:0.207781\ttrain-rmse:0.267842\n",
+ "[179]\ttrain-mae:0.207738\ttrain-rmse:0.267789\n",
+ "[180]\ttrain-mae:0.207686\ttrain-rmse:0.267731\n",
+ "[181]\ttrain-mae:0.207633\ttrain-rmse:0.267679\n",
+ "[182]\ttrain-mae:0.207585\ttrain-rmse:0.267631\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "[101]\ttrain-mae:0.194354\ttrain-rmse:0.251716\teval-mae:0.202778\teval-rmse:0.262687\n",
- "[102]\ttrain-mae:0.19427\ttrain-rmse:0.251621\teval-mae:0.202736\teval-rmse:0.262638\n",
- "[103]\ttrain-mae:0.194142\ttrain-rmse:0.251455\teval-mae:0.20267\teval-rmse:0.262556\n",
- "[104]\ttrain-mae:0.19406\ttrain-rmse:0.251368\teval-mae:0.202629\teval-rmse:0.262515\n",
- "[105]\ttrain-mae:0.193964\ttrain-rmse:0.251252\teval-mae:0.202582\teval-rmse:0.262462\n",
- "[106]\ttrain-mae:0.193881\ttrain-rmse:0.251161\teval-mae:0.202555\teval-rmse:0.262433\n",
- "[107]\ttrain-mae:0.193793\ttrain-rmse:0.25106\teval-mae:0.202526\teval-rmse:0.262401\n",
- "[108]\ttrain-mae:0.193729\ttrain-rmse:0.250989\teval-mae:0.20251\teval-rmse:0.262384\n",
- "[109]\ttrain-mae:0.193602\ttrain-rmse:0.250832\teval-mae:0.202489\teval-rmse:0.262365\n",
- "[110]\ttrain-mae:0.193497\ttrain-rmse:0.250701\teval-mae:0.202443\teval-rmse:0.262311\n",
- "[111]\ttrain-mae:0.193469\ttrain-rmse:0.250675\teval-mae:0.202435\teval-rmse:0.262305\n",
- "[112]\ttrain-mae:0.193342\ttrain-rmse:0.25051\teval-mae:0.202372\teval-rmse:0.26223\n",
- "[113]\ttrain-mae:0.193169\ttrain-rmse:0.250291\teval-mae:0.202382\teval-rmse:0.262243\n",
- "[114]\ttrain-mae:0.193135\ttrain-rmse:0.250257\teval-mae:0.202374\teval-rmse:0.262233\n",
- "[115]\ttrain-mae:0.193084\ttrain-rmse:0.250203\teval-mae:0.202356\teval-rmse:0.262213\n",
- "[116]\ttrain-mae:0.193009\ttrain-rmse:0.25011\teval-mae:0.202376\teval-rmse:0.262234\n",
- "[117]\ttrain-mae:0.192974\ttrain-rmse:0.25008\teval-mae:0.202368\teval-rmse:0.262228\n",
- "[118]\ttrain-mae:0.192884\ttrain-rmse:0.24997\teval-mae:0.202366\teval-rmse:0.262228\n",
- "[119]\ttrain-mae:0.192772\ttrain-rmse:0.249828\teval-mae:0.202328\teval-rmse:0.262193\n",
- "[120]\ttrain-mae:0.192724\ttrain-rmse:0.249771\teval-mae:0.202335\teval-rmse:0.262195\n",
- "[121]\ttrain-mae:0.192661\ttrain-rmse:0.249692\teval-mae:0.202316\teval-rmse:0.262175\n",
- "[122]\ttrain-mae:0.192576\ttrain-rmse:0.249582\teval-mae:0.202291\teval-rmse:0.262147\n",
- "[123]\ttrain-mae:0.192504\ttrain-rmse:0.249507\teval-mae:0.202266\teval-rmse:0.262125\n",
- "[124]\ttrain-mae:0.192433\ttrain-rmse:0.249421\teval-mae:0.20223\teval-rmse:0.262081\n",
- "[125]\ttrain-mae:0.192364\ttrain-rmse:0.249336\teval-mae:0.202209\teval-rmse:0.262059\n",
- "[126]\ttrain-mae:0.192328\ttrain-rmse:0.2493\teval-mae:0.202207\teval-rmse:0.262053\n",
- "[127]\ttrain-mae:0.192265\ttrain-rmse:0.249231\teval-mae:0.202181\teval-rmse:0.26203\n",
- "[128]\ttrain-mae:0.192227\ttrain-rmse:0.249191\teval-mae:0.202179\teval-rmse:0.262028\n",
- "[129]\ttrain-mae:0.192129\ttrain-rmse:0.249064\teval-mae:0.202131\teval-rmse:0.261976\n",
- "[130]\ttrain-mae:0.19205\ttrain-rmse:0.248984\teval-mae:0.202107\teval-rmse:0.261955\n",
- "[131]\ttrain-mae:0.191977\ttrain-rmse:0.248895\teval-mae:0.202083\teval-rmse:0.261928\n",
- "[132]\ttrain-mae:0.191916\ttrain-rmse:0.24882\teval-mae:0.202064\teval-rmse:0.261909\n",
- "[133]\ttrain-mae:0.191849\ttrain-rmse:0.248739\teval-mae:0.202045\teval-rmse:0.261887\n",
- "[134]\ttrain-mae:0.191774\ttrain-rmse:0.248649\teval-mae:0.202042\teval-rmse:0.261885\n",
- "[135]\ttrain-mae:0.191728\ttrain-rmse:0.248593\teval-mae:0.202042\teval-rmse:0.261881\n",
- "[136]\ttrain-mae:0.191697\ttrain-rmse:0.248558\teval-mae:0.202055\teval-rmse:0.261897\n",
- "[137]\ttrain-mae:0.191662\ttrain-rmse:0.248525\teval-mae:0.202054\teval-rmse:0.261899\n",
- "[138]\ttrain-mae:0.191518\ttrain-rmse:0.248343\teval-mae:0.202057\teval-rmse:0.261905\n",
- "[139]\ttrain-mae:0.191475\ttrain-rmse:0.248298\teval-mae:0.202049\teval-rmse:0.261899\n",
- "[140]\ttrain-mae:0.191389\ttrain-rmse:0.248197\teval-mae:0.202074\teval-rmse:0.261936\n",
- "[141]\ttrain-mae:0.191318\ttrain-rmse:0.248103\teval-mae:0.202048\teval-rmse:0.261906\n",
- "[142]\ttrain-mae:0.19119\ttrain-rmse:0.247961\teval-mae:0.201992\teval-rmse:0.261838\n",
- "[143]\ttrain-mae:0.191134\ttrain-rmse:0.247909\teval-mae:0.201984\teval-rmse:0.261828\n",
- "[144]\ttrain-mae:0.191084\ttrain-rmse:0.24785\teval-mae:0.201964\teval-rmse:0.261806\n",
- "[145]\ttrain-mae:0.190931\ttrain-rmse:0.247662\teval-mae:0.201933\teval-rmse:0.261768\n",
- "[146]\ttrain-mae:0.190895\ttrain-rmse:0.247628\teval-mae:0.201931\teval-rmse:0.261773\n",
- "[147]\ttrain-mae:0.190864\ttrain-rmse:0.247599\teval-mae:0.201922\teval-rmse:0.261764\n",
- "[148]\ttrain-mae:0.190805\ttrain-rmse:0.247531\teval-mae:0.201919\teval-rmse:0.26176\n",
- "[149]\ttrain-mae:0.190725\ttrain-rmse:0.247428\teval-mae:0.201904\teval-rmse:0.261744\n",
- "[150]\ttrain-mae:0.190696\ttrain-rmse:0.247402\teval-mae:0.201898\teval-rmse:0.261736\n",
- "[151]\ttrain-mae:0.190634\ttrain-rmse:0.247333\teval-mae:0.201896\teval-rmse:0.261738\n",
- "[152]\ttrain-mae:0.190562\ttrain-rmse:0.247244\teval-mae:0.201891\teval-rmse:0.261733\n",
- "[153]\ttrain-mae:0.190428\ttrain-rmse:0.247079\teval-mae:0.201833\teval-rmse:0.261668\n",
- "[154]\ttrain-mae:0.190364\ttrain-rmse:0.247011\teval-mae:0.201825\teval-rmse:0.261663\n",
- "[155]\ttrain-mae:0.19033\ttrain-rmse:0.246974\teval-mae:0.201836\teval-rmse:0.261677\n",
- "[156]\ttrain-mae:0.190305\ttrain-rmse:0.246945\teval-mae:0.201828\teval-rmse:0.261669\n",
- "[157]\ttrain-mae:0.190258\ttrain-rmse:0.246901\teval-mae:0.201825\teval-rmse:0.261668\n",
- "[158]\ttrain-mae:0.190201\ttrain-rmse:0.246836\teval-mae:0.20183\teval-rmse:0.261664\n",
- "[159]\ttrain-mae:0.190124\ttrain-rmse:0.246742\teval-mae:0.201823\teval-rmse:0.261663\n",
- "[160]\ttrain-mae:0.190054\ttrain-rmse:0.246651\teval-mae:0.201812\teval-rmse:0.261649\n",
- "[161]\ttrain-mae:0.190005\ttrain-rmse:0.24659\teval-mae:0.201799\teval-rmse:0.261635\n",
- "[162]\ttrain-mae:0.189938\ttrain-rmse:0.246499\teval-mae:0.201796\teval-rmse:0.261631\n",
- "[163]\ttrain-mae:0.189889\ttrain-rmse:0.246444\teval-mae:0.201794\teval-rmse:0.261632\n",
- "[164]\ttrain-mae:0.189845\ttrain-rmse:0.246395\teval-mae:0.201776\teval-rmse:0.261611\n",
- "[165]\ttrain-mae:0.189768\ttrain-rmse:0.246304\teval-mae:0.20179\teval-rmse:0.261631\n",
- "[166]\ttrain-mae:0.189714\ttrain-rmse:0.246238\teval-mae:0.201789\teval-rmse:0.26163\n",
- "[167]\ttrain-mae:0.189661\ttrain-rmse:0.246176\teval-mae:0.201775\teval-rmse:0.261615\n",
- "[168]\ttrain-mae:0.189614\ttrain-rmse:0.24613\teval-mae:0.20177\teval-rmse:0.26161\n",
- "[169]\ttrain-mae:0.189538\ttrain-rmse:0.246039\teval-mae:0.201782\teval-rmse:0.261627\n",
- "[170]\ttrain-mae:0.18949\ttrain-rmse:0.245976\teval-mae:0.201775\teval-rmse:0.261619\n",
- "[171]\ttrain-mae:0.189449\ttrain-rmse:0.245929\teval-mae:0.201786\teval-rmse:0.26163\n",
- "[172]\ttrain-mae:0.189406\ttrain-rmse:0.245885\teval-mae:0.201789\teval-rmse:0.261633\n",
- "[173]\ttrain-mae:0.189367\ttrain-rmse:0.245846\teval-mae:0.201783\teval-rmse:0.26163\n",
- "[174]\ttrain-mae:0.189305\ttrain-rmse:0.245775\teval-mae:0.201785\teval-rmse:0.261638\n",
- "[175]\ttrain-mae:0.189245\ttrain-rmse:0.245707\teval-mae:0.201784\teval-rmse:0.26164\n",
- "[176]\ttrain-mae:0.189196\ttrain-rmse:0.245658\teval-mae:0.201781\teval-rmse:0.261636\n",
- "[177]\ttrain-mae:0.189135\ttrain-rmse:0.245586\teval-mae:0.201784\teval-rmse:0.261645\n",
- "[178]\ttrain-mae:0.189089\ttrain-rmse:0.245538\teval-mae:0.201793\teval-rmse:0.261654\n",
- "Stopping. Best iteration:\n",
- "[168]\ttrain-mae:0.189614\ttrain-rmse:0.24613\teval-mae:0.20177\teval-rmse:0.26161\n",
- "\n"
- ]
- }
- ],
- "source": [
- "# Train model\n",
- "ml = xgb.train(param,dtrain,num_round,watchlist,early_stopping_rounds=10)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[0]\ttrain-mae:1.27818+2.8974e-05\ttrain-rmse:1.33537+2.90799e-05\ttest-mae:1.27818+0.000271395\ttest-rmse:1.33537+0.000268618\n",
- "[1]\ttrain-mae:1.21428+2.77851e-05\ttrain-rmse:1.27262+3.16992e-05\ttest-mae:1.21427+0.000266761\ttest-rmse:1.27262+0.000264136\n",
- "[2]\ttrain-mae:1.15356+2.62032e-05\ttrain-rmse:1.21317+4.11642e-05\ttest-mae:1.15356+0.000262622\ttest-rmse:1.21317+0.00025719\n",
- "[3]\ttrain-mae:1.09589+2.4908e-05\ttrain-rmse:1.15687+4.76063e-05\ttest-mae:1.09588+0.000259764\ttest-rmse:1.15687+0.000268382\n",
- "[4]\ttrain-mae:1.04109+2.36324e-05\ttrain-rmse:1.10353+2.64403e-05\ttest-mae:1.04109+0.000255523\ttest-rmse:1.10354+0.000276831\n",
- "[5]\ttrain-mae:0.989039+2.24279e-05\ttrain-rmse:1.05303+4.18761e-05\ttest-mae:0.989036+0.000254064\ttest-rmse:1.05303+0.000265298\n",
- "[6]\ttrain-mae:0.93959+2.11587e-05\ttrain-rmse:1.00525+4.21665e-05\ttest-mae:0.939587+0.000252439\ttest-rmse:1.00525+0.000278933\n",
- "[7]\ttrain-mae:0.892618+2.04746e-05\ttrain-rmse:0.96001+4.26174e-05\ttest-mae:0.892618+0.000251937\ttest-rmse:0.960021+0.000278682\n",
- "[8]\ttrain-mae:0.848008+1.94085e-05\ttrain-rmse:0.917218+5.20211e-05\ttest-mae:0.84801+0.000253072\ttest-rmse:0.917231+0.000258172\n",
- "[9]\ttrain-mae:0.805655+1.86397e-05\ttrain-rmse:0.876819+7.563e-05\ttest-mae:0.805655+0.000253124\ttest-rmse:0.876831+0.000267406\n",
- "[10]\ttrain-mae:0.76547+1.84035e-05\ttrain-rmse:0.838638+8.36124e-05\ttest-mae:0.765472+0.000252844\ttest-rmse:0.838652+0.000260103\n",
- "[11]\ttrain-mae:0.727379+1.77426e-05\ttrain-rmse:0.802576+0.000102144\ttest-mae:0.72738+0.000249669\ttest-rmse:0.802593+0.000255923\n",
- "[12]\ttrain-mae:0.691319+1.74826e-05\ttrain-rmse:0.768548+0.000109038\ttest-mae:0.691321+0.000249752\ttest-rmse:0.768566+0.000271329\n",
- "[13]\ttrain-mae:0.657233+2.04705e-05\ttrain-rmse:0.73643+0.00016027\ttest-mae:0.657232+0.000251886\ttest-rmse:0.736449+0.000318387\n",
- "[14]\ttrain-mae:0.625072+2.6158e-05\ttrain-rmse:0.706175+0.000148751\ttest-mae:0.625073+0.000251806\ttest-rmse:0.706198+0.0003062\n",
- "[15]\ttrain-mae:0.594798+2.84823e-05\ttrain-rmse:0.677707+0.00014796\ttest-mae:0.5948+0.000244596\ttest-rmse:0.677732+0.000292586\n",
- "[16]\ttrain-mae:0.566355+2.56211e-05\ttrain-rmse:0.650903+0.000148786\ttest-mae:0.566359+0.000251452\ttest-rmse:0.650929+0.000292657\n",
- "[17]\ttrain-mae:0.539695+3.17836e-05\ttrain-rmse:0.625679+0.000157634\ttest-mae:0.539699+0.000235015\ttest-rmse:0.625709+0.000234979\n",
- "[18]\ttrain-mae:0.514738+4.878e-05\ttrain-rmse:0.601957+0.000205054\ttest-mae:0.514745+0.000231132\ttest-rmse:0.601993+0.000264341\n",
- "[19]\ttrain-mae:0.49145+7.01353e-05\ttrain-rmse:0.579654+0.000210599\ttest-mae:0.491456+0.00022857\ttest-rmse:0.579691+0.000286851\n",
- "[20]\ttrain-mae:0.469758+7.866e-05\ttrain-rmse:0.558729+0.000231465\ttest-mae:0.469766+0.000221805\ttest-rmse:0.558768+0.000294084\n",
- "[21]\ttrain-mae:0.449572+0.000115173\ttrain-rmse:0.539102+0.000251519\ttest-mae:0.449582+0.000232612\ttest-rmse:0.539142+0.000313988\n",
- "[22]\ttrain-mae:0.430851+0.00010749\ttrain-rmse:0.520755+0.000217872\ttest-mae:0.430865+0.000241505\ttest-rmse:0.520799+0.000303285\n",
- "[23]\ttrain-mae:0.41351+0.000151426\ttrain-rmse:0.503584+0.000243559\ttest-mae:0.413525+0.000258896\ttest-rmse:0.503629+0.000309708\n",
- "[24]\ttrain-mae:0.397475+0.000143887\ttrain-rmse:0.487504+0.000242047\ttest-mae:0.397491+0.000263354\ttest-rmse:0.487552+0.000320081\n",
- "[25]\ttrain-mae:0.38263+0.000141006\ttrain-rmse:0.472452+0.000228959\ttest-mae:0.382649+0.000259263\ttest-rmse:0.472502+0.000297104\n",
- "[26]\ttrain-mae:0.368957+0.000152788\ttrain-rmse:0.458432+0.000239684\ttest-mae:0.368979+0.000248554\ttest-rmse:0.458485+0.00030017\n",
- "[27]\ttrain-mae:0.356312+0.000160635\ttrain-rmse:0.445316+0.000228426\ttest-mae:0.356337+0.000239344\ttest-rmse:0.445373+0.000281938\n",
- "[28]\ttrain-mae:0.34465+0.000141261\ttrain-rmse:0.433055+0.00020188\ttest-mae:0.344679+0.000218477\ttest-rmse:0.433115+0.00025974\n",
- "[29]\ttrain-mae:0.333997+0.000171878\ttrain-rmse:0.421699+0.000238005\ttest-mae:0.334027+0.000188525\ttest-rmse:0.42176+0.000257747\n",
- "[30]\ttrain-mae:0.324204+0.000201062\ttrain-rmse:0.411139+0.000254387\ttest-mae:0.324237+0.000214001\ttest-rmse:0.411206+0.000269759\n",
- "[31]\ttrain-mae:0.315154+0.00018098\ttrain-rmse:0.401246+0.000232216\ttest-mae:0.31519+0.000148221\ttest-rmse:0.401314+0.000211454\n",
- "[32]\ttrain-mae:0.306833+0.000171941\ttrain-rmse:0.392042+0.000227768\ttest-mae:0.306871+0.000170076\ttest-rmse:0.392112+0.00025608\n",
- "[33]\ttrain-mae:0.299243+0.000157719\ttrain-rmse:0.383539+0.0002242\ttest-mae:0.299284+0.000206175\ttest-rmse:0.383614+0.000261823\n",
- "[34]\ttrain-mae:0.292273+0.000176468\ttrain-rmse:0.375651+0.000233382\ttest-mae:0.292318+0.0001947\ttest-rmse:0.375729+0.000242456\n",
- "[35]\ttrain-mae:0.285912+0.000182429\ttrain-rmse:0.368352+0.000249936\ttest-mae:0.28596+0.000214406\ttest-rmse:0.368434+0.00027114\n",
- "[36]\ttrain-mae:0.280074+0.000173217\ttrain-rmse:0.361588+0.000237817\ttest-mae:0.280124+0.000220837\ttest-rmse:0.361673+0.000276375\n",
- "[37]\ttrain-mae:0.274786+0.00018974\ttrain-rmse:0.355391+0.000265789\ttest-mae:0.27484+0.000218778\ttest-rmse:0.355478+0.00027942\n",
- "[38]\ttrain-mae:0.269903+0.000184657\ttrain-rmse:0.349597+0.000261428\ttest-mae:0.269961+0.000269171\ttest-rmse:0.349688+0.000322493\n",
- "[39]\ttrain-mae:0.265415+0.000167603\ttrain-rmse:0.344197+0.000232491\ttest-mae:0.265477+0.000278497\ttest-rmse:0.344292+0.000344244\n",
- "[40]\ttrain-mae:0.26135+0.000218187\ttrain-rmse:0.339255+0.000283041\ttest-mae:0.261416+0.000343354\ttest-rmse:0.339354+0.000406527\n",
- "[41]\ttrain-mae:0.257647+0.000206954\ttrain-rmse:0.334698+0.000265489\ttest-mae:0.257715+0.00029773\ttest-rmse:0.334799+0.000372828\n",
- "[42]\ttrain-mae:0.254237+0.000193244\ttrain-rmse:0.330463+0.000247959\ttest-mae:0.25431+0.000270686\ttest-rmse:0.330569+0.000362951\n",
- "[43]\ttrain-mae:0.251192+0.000202189\ttrain-rmse:0.326619+0.000242501\ttest-mae:0.251269+0.000213114\ttest-rmse:0.326729+0.000301778\n",
- "[44]\ttrain-mae:0.248415+0.000200798\ttrain-rmse:0.32308+0.000243773\ttest-mae:0.248494+0.000224228\ttest-rmse:0.323192+0.000314474\n",
- "[45]\ttrain-mae:0.245782+0.000204024\ttrain-rmse:0.319706+0.00024133\ttest-mae:0.245863+0.00023673\ttest-rmse:0.31982+0.000329836\n",
- "[46]\ttrain-mae:0.243414+0.000203603\ttrain-rmse:0.316627+0.000253791\ttest-mae:0.243498+0.000216283\ttest-rmse:0.316742+0.000319266\n",
- "[47]\ttrain-mae:0.241288+0.000183775\ttrain-rmse:0.313827+0.00020703\ttest-mae:0.241374+0.000175851\ttest-rmse:0.313945+0.000270338\n",
- "[48]\ttrain-mae:0.239353+0.000152093\ttrain-rmse:0.311257+0.000187938\ttest-mae:0.239441+0.000196857\ttest-rmse:0.311378+0.000283931\n",
- "[49]\ttrain-mae:0.237561+0.000140029\ttrain-rmse:0.308871+0.000187871\ttest-mae:0.23765+0.000219376\ttest-rmse:0.308995+0.000292985\n",
- "[50]\ttrain-mae:0.235929+0.000179949\ttrain-rmse:0.306666+0.000205719\ttest-mae:0.236021+0.000220431\ttest-rmse:0.306792+0.000290518\n",
- "[51]\ttrain-mae:0.234424+0.000219852\ttrain-rmse:0.304623+0.000265924\ttest-mae:0.234518+0.000233565\ttest-rmse:0.304751+0.000318946\n",
- "[52]\ttrain-mae:0.233024+0.000195358\ttrain-rmse:0.302721+0.000253537\ttest-mae:0.233119+0.000215232\ttest-rmse:0.302851+0.000317222\n",
- "[53]\ttrain-mae:0.231728+0.000194383\ttrain-rmse:0.300939+0.000252298\ttest-mae:0.231825+0.00019039\ttest-rmse:0.301072+0.000294339\n",
- "[54]\ttrain-mae:0.230523+0.000223915\ttrain-rmse:0.299261+0.000296083\ttest-mae:0.230623+0.00021577\ttest-rmse:0.299396+0.00033928\n",
- "[55]\ttrain-mae:0.229419+0.000198592\ttrain-rmse:0.297716+0.000276977\ttest-mae:0.229523+0.000241059\ttest-rmse:0.297854+0.000357172\n",
- "[56]\ttrain-mae:0.228479+0.000200506\ttrain-rmse:0.296379+0.000264914\ttest-mae:0.228586+0.000266544\ttest-rmse:0.296521+0.000351357\n",
- "[57]\ttrain-mae:0.227529+0.000228371\ttrain-rmse:0.295038+0.000298681\ttest-mae:0.227639+0.000252404\ttest-rmse:0.295184+0.000339844\n",
- "[58]\ttrain-mae:0.22667+0.00021191\ttrain-rmse:0.293804+0.00027145\ttest-mae:0.226781+0.000278454\ttest-rmse:0.293952+0.000370803\n",
- "[59]\ttrain-mae:0.225868+0.000205707\ttrain-rmse:0.292656+0.000266309\ttest-mae:0.225982+0.00027735\ttest-rmse:0.292807+0.000376481\n",
- "[60]\ttrain-mae:0.225094+0.000156061\ttrain-rmse:0.291543+0.000208068\ttest-mae:0.22521+0.000251367\ttest-rmse:0.291696+0.000329518\n",
- "[61]\ttrain-mae:0.224399+0.000149622\ttrain-rmse:0.290534+0.000206443\ttest-mae:0.224519+0.000257657\ttest-rmse:0.29069+0.00032621\n",
- "[62]\ttrain-mae:0.223779+0.000139205\ttrain-rmse:0.289617+0.000187686\ttest-mae:0.223901+0.000207259\ttest-rmse:0.289776+0.000282905\n",
- "[63]\ttrain-mae:0.223175+0.000124649\ttrain-rmse:0.288741+0.000163919\ttest-mae:0.223298+0.000199069\ttest-rmse:0.288902+0.000262953\n",
- "[64]\ttrain-mae:0.222599+0.000117422\ttrain-rmse:0.2879+0.000148922\ttest-mae:0.222724+0.000204581\ttest-rmse:0.288062+0.000252152\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[65]\ttrain-mae:0.222092+0.000117466\ttrain-rmse:0.287147+0.000155174\ttest-mae:0.222218+0.000221899\ttest-rmse:0.287312+0.00027282\n",
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{
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{
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{
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},
{
"name": "stdout",
"output_type": "stream",
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},
{
"name": "stdout",
"output_type": "stream",
"text": [
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},
{
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},
{
"name": "stdout",
"output_type": "stream",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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},
{
"name": "stdout",
"output_type": "stream",
"text": [
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+ "[1969]\ttrain-mae:0.192062\ttrain-rmse:0.249249\n",
+ "[1970]\ttrain-mae:0.192058\ttrain-rmse:0.249244\n",
+ "[1971]\ttrain-mae:0.192052\ttrain-rmse:0.249235\n",
+ "[1972]\ttrain-mae:0.192047\ttrain-rmse:0.24923\n",
+ "[1973]\ttrain-mae:0.192041\ttrain-rmse:0.249222\n",
+ "[1974]\ttrain-mae:0.192035\ttrain-rmse:0.249215\n",
+ "[1975]\ttrain-mae:0.19203\ttrain-rmse:0.249209\n",
+ "[1976]\ttrain-mae:0.192028\ttrain-rmse:0.249205\n",
+ "[1977]\ttrain-mae:0.192025\ttrain-rmse:0.249201\n",
+ "[1978]\ttrain-mae:0.192023\ttrain-rmse:0.249199\n",
+ "[1979]\ttrain-mae:0.192018\ttrain-rmse:0.249194\n",
+ "[1980]\ttrain-mae:0.192017\ttrain-rmse:0.249193\n",
+ "[1981]\ttrain-mae:0.192016\ttrain-rmse:0.249191\n",
+ "[1982]\ttrain-mae:0.19201\ttrain-rmse:0.249183\n",
+ "[1983]\ttrain-mae:0.192007\ttrain-rmse:0.249179\n",
+ "[1984]\ttrain-mae:0.192005\ttrain-rmse:0.249177\n",
+ "[1985]\ttrain-mae:0.192002\ttrain-rmse:0.249175\n",
+ "[1986]\ttrain-mae:0.192\ttrain-rmse:0.249173\n",
+ "[1987]\ttrain-mae:0.191998\ttrain-rmse:0.24917\n",
+ "[1988]\ttrain-mae:0.191996\ttrain-rmse:0.249167\n",
+ "[1989]\ttrain-mae:0.191993\ttrain-rmse:0.249165\n",
+ "[1990]\ttrain-mae:0.191992\ttrain-rmse:0.249163\n",
+ "[1991]\ttrain-mae:0.19199\ttrain-rmse:0.249161\n",
+ "[1992]\ttrain-mae:0.191988\ttrain-rmse:0.249159\n",
+ "[1993]\ttrain-mae:0.191985\ttrain-rmse:0.249154\n",
+ "[1994]\ttrain-mae:0.191983\ttrain-rmse:0.249152\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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+ "[2923]\ttrain-mae:0.189239\ttrain-rmse:0.245823\n",
+ "[2924]\ttrain-mae:0.189236\ttrain-rmse:0.245819\n",
+ "[2925]\ttrain-mae:0.189233\ttrain-rmse:0.245816\n",
+ "[2926]\ttrain-mae:0.18923\ttrain-rmse:0.245811\n",
+ "[2927]\ttrain-mae:0.189227\ttrain-rmse:0.245807\n",
+ "[2928]\ttrain-mae:0.189224\ttrain-rmse:0.245804\n",
+ "[2929]\ttrain-mae:0.18922\ttrain-rmse:0.245799\n",
+ "[2930]\ttrain-mae:0.189217\ttrain-rmse:0.245794\n",
+ "[2931]\ttrain-mae:0.189214\ttrain-rmse:0.245791\n",
+ "[2932]\ttrain-mae:0.189211\ttrain-rmse:0.245788\n",
+ "[2933]\ttrain-mae:0.189206\ttrain-rmse:0.245781\n",
+ "[2934]\ttrain-mae:0.189202\ttrain-rmse:0.245777\n",
+ "[2935]\ttrain-mae:0.1892\ttrain-rmse:0.245774\n",
+ "[2936]\ttrain-mae:0.189197\ttrain-rmse:0.245771\n",
+ "[2937]\ttrain-mae:0.189194\ttrain-rmse:0.245767\n",
+ "[2938]\ttrain-mae:0.189191\ttrain-rmse:0.245764\n",
+ "[2939]\ttrain-mae:0.189188\ttrain-rmse:0.24576\n",
+ "[2940]\ttrain-mae:0.189185\ttrain-rmse:0.245756\n",
+ "[2941]\ttrain-mae:0.189181\ttrain-rmse:0.24575\n",
+ "[2942]\ttrain-mae:0.189178\ttrain-rmse:0.245747\n",
+ "[2943]\ttrain-mae:0.189174\ttrain-rmse:0.24574\n",
+ "[2944]\ttrain-mae:0.18917\ttrain-rmse:0.245736\n",
+ "[2945]\ttrain-mae:0.189167\ttrain-rmse:0.245732\n",
+ "[2946]\ttrain-mae:0.189163\ttrain-rmse:0.245728\n",
+ "[2947]\ttrain-mae:0.189159\ttrain-rmse:0.245723\n",
+ "[2948]\ttrain-mae:0.189157\ttrain-rmse:0.24572\n",
+ "[2949]\ttrain-mae:0.189153\ttrain-rmse:0.245715\n",
+ "[2950]\ttrain-mae:0.18915\ttrain-rmse:0.245711\n",
+ "[2951]\ttrain-mae:0.189147\ttrain-rmse:0.245708\n",
+ "[2952]\ttrain-mae:0.189144\ttrain-rmse:0.245704\n",
+ "[2953]\ttrain-mae:0.189143\ttrain-rmse:0.245702\n",
+ "[2954]\ttrain-mae:0.189141\ttrain-rmse:0.245701\n",
+ "[2955]\ttrain-mae:0.18914\ttrain-rmse:0.245699\n",
+ "[2956]\ttrain-mae:0.189136\ttrain-rmse:0.245695\n",
+ "[2957]\ttrain-mae:0.189133\ttrain-rmse:0.245691\n",
+ "[2958]\ttrain-mae:0.189131\ttrain-rmse:0.245689\n",
+ "[2959]\ttrain-mae:0.189129\ttrain-rmse:0.245687\n",
+ "[2960]\ttrain-mae:0.189127\ttrain-rmse:0.245683\n",
+ "[2961]\ttrain-mae:0.189124\ttrain-rmse:0.24568\n",
+ "[2962]\ttrain-mae:0.189122\ttrain-rmse:0.245677\n",
+ "[2963]\ttrain-mae:0.189118\ttrain-rmse:0.245673\n",
+ "[2964]\ttrain-mae:0.189116\ttrain-rmse:0.24567\n",
+ "[2965]\ttrain-mae:0.189114\ttrain-rmse:0.245668\n",
+ "[2966]\ttrain-mae:0.189112\ttrain-rmse:0.245665\n",
+ "[2967]\ttrain-mae:0.189109\ttrain-rmse:0.245662\n",
+ "[2968]\ttrain-mae:0.189106\ttrain-rmse:0.245659\n",
+ "[2969]\ttrain-mae:0.189105\ttrain-rmse:0.245657\n",
+ "[2970]\ttrain-mae:0.189102\ttrain-rmse:0.245654\n",
+ "[2971]\ttrain-mae:0.1891\ttrain-rmse:0.245651\n",
+ "[2972]\ttrain-mae:0.189094\ttrain-rmse:0.245645\n",
+ "[2973]\ttrain-mae:0.189091\ttrain-rmse:0.245641\n",
+ "[2974]\ttrain-mae:0.189089\ttrain-rmse:0.245638\n",
+ "[2975]\ttrain-mae:0.189087\ttrain-rmse:0.245635\n",
+ "[2976]\ttrain-mae:0.189084\ttrain-rmse:0.245633\n",
+ "[2977]\ttrain-mae:0.189083\ttrain-rmse:0.245632\n",
+ "[2978]\ttrain-mae:0.189082\ttrain-rmse:0.24563\n",
+ "[2979]\ttrain-mae:0.18908\ttrain-rmse:0.245628\n",
+ "[2980]\ttrain-mae:0.189077\ttrain-rmse:0.245625\n",
+ "[2981]\ttrain-mae:0.189075\ttrain-rmse:0.245623\n",
+ "[2982]\ttrain-mae:0.189072\ttrain-rmse:0.245618\n",
+ "[2983]\ttrain-mae:0.189071\ttrain-rmse:0.245617\n",
+ "[2984]\ttrain-mae:0.189069\ttrain-rmse:0.245614\n",
+ "[2985]\ttrain-mae:0.189065\ttrain-rmse:0.24561\n",
+ "[2986]\ttrain-mae:0.189064\ttrain-rmse:0.245607\n",
+ "[2987]\ttrain-mae:0.189062\ttrain-rmse:0.245606\n",
+ "[2988]\ttrain-mae:0.189059\ttrain-rmse:0.245602\n",
+ "[2989]\ttrain-mae:0.189057\ttrain-rmse:0.245598\n",
+ "[2990]\ttrain-mae:0.189055\ttrain-rmse:0.245596\n",
+ "[2991]\ttrain-mae:0.189053\ttrain-rmse:0.245594\n",
+ "[2992]\ttrain-mae:0.189051\ttrain-rmse:0.245591\n",
+ "[2993]\ttrain-mae:0.189048\ttrain-rmse:0.245587\n",
+ "[2994]\ttrain-mae:0.189046\ttrain-rmse:0.245584\n",
+ "[2995]\ttrain-mae:0.189044\ttrain-rmse:0.245581\n",
+ "[2996]\ttrain-mae:0.189042\ttrain-rmse:0.245578\n",
+ "[2997]\ttrain-mae:0.189039\ttrain-rmse:0.245575\n",
+ "[2998]\ttrain-mae:0.189036\ttrain-rmse:0.245571\n",
+ "[2999]\ttrain-mae:0.189033\ttrain-rmse:0.245566\n"
]
- },
+ }
+ ],
+ "source": [
+ "# Train model\n",
+ "ml = xgb.train(param,dfull,num_round,watchlist,early_stopping_rounds=10, seed = 123)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "[1088]\ttrain-mae:0.195626+9.2918e-05\ttrain-rmse:0.253528+0.000118388\ttest-mae:0.196409+0.000131712\ttest-rmse:0.254544+0.000208372\n",
- "[1089]\ttrain-mae:0.195622+9.31649e-05\ttrain-rmse:0.253523+0.000118561\ttest-mae:0.196406+0.000130551\ttest-rmse:0.25454+0.000207264\n",
- "[1090]\ttrain-mae:0.195615+9.13263e-05\ttrain-rmse:0.253515+0.000115931\ttest-mae:0.196399+0.000129975\ttest-rmse:0.254532+0.000206236\n",
- "[1091]\ttrain-mae:0.195609+8.99189e-05\ttrain-rmse:0.253507+0.000114299\ttest-mae:0.196394+0.000131078\ttest-rmse:0.254526+0.000208045\n",
- "[1092]\ttrain-mae:0.195604+8.90926e-05\ttrain-rmse:0.253501+0.000113698\ttest-mae:0.196389+0.000131874\ttest-rmse:0.25452+0.000209603\n",
- "[1093]\ttrain-mae:0.195599+8.86479e-05\ttrain-rmse:0.253495+0.000112851\ttest-mae:0.196385+0.000131914\ttest-rmse:0.254514+0.000209169\n",
- "[1094]\ttrain-mae:0.195595+8.88954e-05\ttrain-rmse:0.25349+0.000113168\ttest-mae:0.196381+0.000131176\ttest-rmse:0.25451+0.000209052\n",
- "[1095]\ttrain-mae:0.195591+8.87733e-05\ttrain-rmse:0.253485+0.000113404\ttest-mae:0.196378+0.000131146\ttest-rmse:0.254506+0.000208949\n",
- "[1096]\ttrain-mae:0.195586+8.85982e-05\ttrain-rmse:0.25348+0.000113227\ttest-mae:0.196374+0.000131128\ttest-rmse:0.254501+0.000208652\n",
- "[1097]\ttrain-mae:0.195581+8.89146e-05\ttrain-rmse:0.253473+0.000113898\ttest-mae:0.196369+0.000130853\ttest-rmse:0.254495+0.000208385\n",
- "[1098]\ttrain-mae:0.195577+9.07458e-05\ttrain-rmse:0.253468+0.00011551\ttest-mae:0.196365+0.000129018\ttest-rmse:0.254491+0.000207394\n",
- "[1099]\ttrain-mae:0.195572+8.99889e-05\ttrain-rmse:0.253462+0.000114587\ttest-mae:0.19636+0.000130676\ttest-rmse:0.254485+0.00020769\n",
- "[1100]\ttrain-mae:0.195566+9.07216e-05\ttrain-rmse:0.253455+0.000116126\ttest-mae:0.196355+0.000130332\ttest-rmse:0.254479+0.000206575\n",
- "[1101]\ttrain-mae:0.195559+8.80775e-05\ttrain-rmse:0.253447+0.000113024\ttest-mae:0.196348+0.000131421\ttest-rmse:0.254471+0.0002085\n",
- "[1102]\ttrain-mae:0.195556+8.7998e-05\ttrain-rmse:0.253442+0.000112531\ttest-mae:0.196345+0.000132087\ttest-rmse:0.254467+0.000209523\n",
- "[1103]\ttrain-mae:0.195551+8.89196e-05\ttrain-rmse:0.253437+0.000113529\ttest-mae:0.196341+0.000132171\ttest-rmse:0.254462+0.000208502\n",
- "[1104]\ttrain-mae:0.195545+8.73203e-05\ttrain-rmse:0.253429+0.000111531\ttest-mae:0.196335+0.000134339\ttest-rmse:0.254455+0.000211055\n",
- "[1105]\ttrain-mae:0.19554+8.66511e-05\ttrain-rmse:0.253424+0.000110621\ttest-mae:0.196331+0.000134612\ttest-rmse:0.25445+0.000211755\n",
- "[1106]\ttrain-mae:0.195534+8.85745e-05\ttrain-rmse:0.253415+0.000112409\ttest-mae:0.196326+0.000133372\ttest-rmse:0.254442+0.000209515\n",
- "[1107]\ttrain-mae:0.195528+8.95346e-05\ttrain-rmse:0.253407+0.00011354\ttest-mae:0.19632+0.000133383\ttest-rmse:0.254435+0.000209378\n",
- "[1108]\ttrain-mae:0.195519+8.6983e-05\ttrain-rmse:0.253397+0.000110664\ttest-mae:0.196312+0.000134705\ttest-rmse:0.254426+0.000210627\n",
- "[1109]\ttrain-mae:0.195513+8.71624e-05\ttrain-rmse:0.25339+0.000111368\ttest-mae:0.196306+0.000135703\ttest-rmse:0.254418+0.000210673\n",
- "[1110]\ttrain-mae:0.195508+8.60431e-05\ttrain-rmse:0.253383+0.000109778\ttest-mae:0.196301+0.000136993\ttest-rmse:0.254413+0.000212859\n",
- "[1111]\ttrain-mae:0.195501+8.82298e-05\ttrain-rmse:0.253375+0.000113043\ttest-mae:0.196295+0.000135671\ttest-rmse:0.254405+0.000210102\n",
- "[1112]\ttrain-mae:0.195495+8.669e-05\ttrain-rmse:0.253369+0.000110877\ttest-mae:0.19629+0.000138156\ttest-rmse:0.254399+0.000212422\n",
- "[1113]\ttrain-mae:0.19549+8.65589e-05\ttrain-rmse:0.253362+0.000110268\ttest-mae:0.196285+0.000138564\ttest-rmse:0.254393+0.000212705\n",
- "[1114]\ttrain-mae:0.195484+8.7092e-05\ttrain-rmse:0.253356+0.000110737\ttest-mae:0.19628+0.00013815\ttest-rmse:0.254388+0.000211937\n",
- "[1115]\ttrain-mae:0.195481+8.79293e-05\ttrain-rmse:0.253351+0.000111923\ttest-mae:0.196277+0.000137568\ttest-rmse:0.254384+0.000211345\n",
- "[1116]\ttrain-mae:0.195477+8.83246e-05\ttrain-rmse:0.253347+0.000112396\ttest-mae:0.196273+0.000137028\ttest-rmse:0.25438+0.000210569\n",
- "[1117]\ttrain-mae:0.195471+8.87141e-05\ttrain-rmse:0.25334+0.000112553\ttest-mae:0.196268+0.000137794\ttest-rmse:0.254374+0.0002113\n",
- "[1118]\ttrain-mae:0.195466+8.79298e-05\ttrain-rmse:0.253334+0.000112079\ttest-mae:0.196264+0.000138036\ttest-rmse:0.254369+0.000211688\n",
- "[1119]\ttrain-mae:0.195459+8.90888e-05\ttrain-rmse:0.253326+0.000113198\ttest-mae:0.196258+0.000138576\ttest-rmse:0.254361+0.00021208\n",
- "[1120]\ttrain-mae:0.195455+8.95612e-05\ttrain-rmse:0.253322+0.00011396\ttest-mae:0.196254+0.000138294\ttest-rmse:0.254357+0.000211899\n",
- "[1121]\ttrain-mae:0.195449+8.92547e-05\ttrain-rmse:0.253314+0.000113568\ttest-mae:0.196248+0.000138668\ttest-rmse:0.25435+0.000211732\n",
- "[1122]\ttrain-mae:0.195444+8.9415e-05\ttrain-rmse:0.253308+0.000113704\ttest-mae:0.196244+0.000137965\ttest-rmse:0.254345+0.000210968\n",
- "[1123]\ttrain-mae:0.19544+9.00889e-05\ttrain-rmse:0.253303+0.000114676\ttest-mae:0.196241+0.000137226\ttest-rmse:0.254341+0.000210209\n",
- "[1124]\ttrain-mae:0.195436+9.067e-05\ttrain-rmse:0.253298+0.000115362\ttest-mae:0.196236+0.000137323\ttest-rmse:0.254336+0.000209332\n",
- "[1125]\ttrain-mae:0.195431+9.07169e-05\ttrain-rmse:0.253292+0.000115033\ttest-mae:0.196232+0.000136746\ttest-rmse:0.254331+0.000209264\n",
- "[1126]\ttrain-mae:0.195425+8.98151e-05\ttrain-rmse:0.253285+0.000114641\ttest-mae:0.196227+0.00013739\ttest-rmse:0.254324+0.000209271\n",
- "[1127]\ttrain-mae:0.19542+9.0722e-05\ttrain-rmse:0.253279+0.000115946\ttest-mae:0.196222+0.000136758\ttest-rmse:0.254319+0.000208155\n",
- "[1128]\ttrain-mae:0.195415+9.02995e-05\ttrain-rmse:0.253273+0.000115839\ttest-mae:0.196218+0.000137095\ttest-rmse:0.254313+0.000207772\n",
- "[1129]\ttrain-mae:0.195408+9.18346e-05\ttrain-rmse:0.253264+0.000117431\ttest-mae:0.196211+0.000135682\ttest-rmse:0.254305+0.000205675\n",
- "[1130]\ttrain-mae:0.195405+9.2197e-05\ttrain-rmse:0.253261+0.000117707\ttest-mae:0.196209+0.000134992\ttest-rmse:0.254302+0.000204907\n",
- "[1131]\ttrain-mae:0.1954+9.40508e-05\ttrain-rmse:0.253255+0.000119887\ttest-mae:0.196204+0.000133533\ttest-rmse:0.254297+0.000202951\n",
- "[1132]\ttrain-mae:0.195396+9.36923e-05\ttrain-rmse:0.253249+0.000119659\ttest-mae:0.1962+0.000133466\ttest-rmse:0.254291+0.000203787\n",
- "[1133]\ttrain-mae:0.195389+9.35492e-05\ttrain-rmse:0.253241+0.000118635\ttest-mae:0.196194+0.000134117\ttest-rmse:0.254284+0.000203983\n",
- "[1134]\ttrain-mae:0.195384+9.42406e-05\ttrain-rmse:0.253234+0.000119828\ttest-mae:0.196189+0.000133935\ttest-rmse:0.254278+0.000203508\n",
- "[1135]\ttrain-mae:0.195378+9.36995e-05\ttrain-rmse:0.253227+0.00011897\ttest-mae:0.196184+0.000135093\ttest-rmse:0.254272+0.000204432\n",
- "[1136]\ttrain-mae:0.195373+9.42446e-05\ttrain-rmse:0.253221+0.000119994\ttest-mae:0.196179+0.000134743\ttest-rmse:0.254266+0.000203518\n",
- "[1137]\ttrain-mae:0.195369+9.42166e-05\ttrain-rmse:0.253216+0.000119897\ttest-mae:0.196176+0.000135014\ttest-rmse:0.254262+0.000203629\n",
- "[1138]\ttrain-mae:0.195365+9.44936e-05\ttrain-rmse:0.253211+0.000120534\ttest-mae:0.196172+0.000135593\ttest-rmse:0.254257+0.000203577\n",
- "[1139]\ttrain-mae:0.195361+9.44373e-05\ttrain-rmse:0.253206+0.000120712\ttest-mae:0.196169+0.000135782\ttest-rmse:0.254254+0.000203487\n",
- "[1140]\ttrain-mae:0.195355+9.38821e-05\ttrain-rmse:0.253199+0.000119675\ttest-mae:0.196164+0.000137684\ttest-rmse:0.254247+0.00020546\n",
- "[1141]\ttrain-mae:0.195351+9.38557e-05\ttrain-rmse:0.253194+0.000120048\ttest-mae:0.19616+0.000138702\ttest-rmse:0.254243+0.000205602\n",
- "[1142]\ttrain-mae:0.195346+9.37149e-05\ttrain-rmse:0.253189+0.000120027\ttest-mae:0.196155+0.000139197\ttest-rmse:0.254238+0.000206359\n",
- "[1143]\ttrain-mae:0.195342+9.33193e-05\ttrain-rmse:0.253184+0.000118825\ttest-mae:0.196151+0.000140908\ttest-rmse:0.254233+0.000208759\n",
- "[1144]\ttrain-mae:0.195337+9.31118e-05\ttrain-rmse:0.253177+0.000118147\ttest-mae:0.196147+0.000140951\ttest-rmse:0.254228+0.000208886\n",
- "[1145]\ttrain-mae:0.195332+9.23474e-05\ttrain-rmse:0.253172+0.000116838\ttest-mae:0.196143+0.00014085\ttest-rmse:0.254223+0.000208954\n",
- "[1146]\ttrain-mae:0.195326+9.16517e-05\ttrain-rmse:0.253165+0.000115545\ttest-mae:0.196137+0.000140859\ttest-rmse:0.254216+0.000209265\n",
- "[1147]\ttrain-mae:0.19532+9.20122e-05\ttrain-rmse:0.253157+0.000115476\ttest-mae:0.196131+0.000140328\ttest-rmse:0.254209+0.000209388\n",
- "[1148]\ttrain-mae:0.195313+9.0597e-05\ttrain-rmse:0.253148+0.000114416\ttest-mae:0.196124+0.000142342\ttest-rmse:0.254201+0.000211737\n",
- "[1149]\ttrain-mae:0.195308+9.04821e-05\ttrain-rmse:0.253142+0.00011433\ttest-mae:0.196119+0.000142778\ttest-rmse:0.254194+0.000213144\n",
- "[1150]\ttrain-mae:0.195302+9.00098e-05\ttrain-rmse:0.253133+0.000113306\ttest-mae:0.196114+0.000143292\ttest-rmse:0.254187+0.000213873\n"
+ "[0]\ttrain-mae:1.07637+3.83192e-05\ttest-mae:1.07636+0.000437905\n",
+ "[1]\ttrain-mae:0.861133+3.04099e-05\ttest-mae:0.861131+0.000433258\n",
+ "[2]\ttrain-mae:0.689956+4.13042e-05\ttest-mae:0.689954+0.000421552\n",
+ "[3]\ttrain-mae:0.558286+9.94585e-05\ttest-mae:0.558287+0.000374818\n",
+ "[4]\ttrain-mae:0.461918+6.10279e-05\ttest-mae:0.461927+0.000365394\n",
+ "[5]\ttrain-mae:0.394531+0.000313394\ttest-mae:0.394536+0.000267816\n",
+ "[6]\ttrain-mae:0.348903+0.000123009\ttest-mae:0.348914+0.000338078\n",
+ "[7]\ttrain-mae:0.317162+0.000151297\ttest-mae:0.317178+0.000338945\n",
+ "[8]\ttrain-mae:0.295348+0.000186726\ttest-mae:0.295367+0.000302932\n",
+ "[9]\ttrain-mae:0.281331+0.00023548\ttest-mae:0.281348+0.00033731\n",
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},
{
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},
{
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"text": [
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},
{
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"output_type": "stream",
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{
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},
{
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"output_type": "stream",
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},
{
"name": "stdout",
"output_type": "stream",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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+ "[985]\ttrain-mae:0.201883+5.9656e-05\ttest-mae:0.202163+0.000113415\n",
+ "[986]\ttrain-mae:0.201878+5.92545e-05\ttest-mae:0.202159+0.00011441\n",
+ "[987]\ttrain-mae:0.201874+5.88218e-05\ttest-mae:0.202156+0.000114945\n",
+ "[988]\ttrain-mae:0.20187+5.87929e-05\ttest-mae:0.202151+0.000115319\n",
+ "[989]\ttrain-mae:0.201866+5.93363e-05\ttest-mae:0.202148+0.000115745\n",
+ "[990]\ttrain-mae:0.201861+5.94848e-05\ttest-mae:0.202142+0.000117345\n",
+ "[991]\ttrain-mae:0.201856+5.93495e-05\ttest-mae:0.202137+0.0001176\n",
+ "[992]\ttrain-mae:0.201853+5.95134e-05\ttest-mae:0.202134+0.000117213\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "[1882]\ttrain-mae:0.192162+5.65322e-05\ttrain-rmse:0.249321+9.14626e-05\ttest-mae:0.193306+0.000187658\ttest-rmse:0.250795+0.000268797\n",
- "[1883]\ttrain-mae:0.192159+5.62424e-05\ttrain-rmse:0.249317+9.07298e-05\ttest-mae:0.193303+0.000187676\ttest-rmse:0.250791+0.000268633\n",
- "[1884]\ttrain-mae:0.192154+5.65647e-05\ttrain-rmse:0.249312+9.10868e-05\ttest-mae:0.193299+0.000188137\ttest-rmse:0.250787+0.000269028\n",
- "[1885]\ttrain-mae:0.19215+5.69547e-05\ttrain-rmse:0.249307+9.15238e-05\ttest-mae:0.193296+0.00018791\ttest-rmse:0.250782+0.000268396\n",
- "[1886]\ttrain-mae:0.192147+5.60842e-05\ttrain-rmse:0.249302+9.00214e-05\ttest-mae:0.193292+0.000187515\ttest-rmse:0.250779+0.000268322\n",
- "[1887]\ttrain-mae:0.192143+5.52905e-05\ttrain-rmse:0.249299+8.92178e-05\ttest-mae:0.19329+0.00018673\ttest-rmse:0.250775+0.000267488\n",
- "[1888]\ttrain-mae:0.19214+5.49386e-05\ttrain-rmse:0.249295+8.85731e-05\ttest-mae:0.193287+0.000186529\ttest-rmse:0.250773+0.000267316\n",
- "[1889]\ttrain-mae:0.192136+5.51588e-05\ttrain-rmse:0.24929+8.85032e-05\ttest-mae:0.193283+0.000186066\ttest-rmse:0.250767+0.000266774\n",
- "[1890]\ttrain-mae:0.192133+5.52482e-05\ttrain-rmse:0.249286+8.89162e-05\ttest-mae:0.193281+0.000186012\ttest-rmse:0.250765+0.000266363\n",
- "[1891]\ttrain-mae:0.19213+5.56389e-05\ttrain-rmse:0.249282+8.94145e-05\ttest-mae:0.193278+0.000186227\ttest-rmse:0.250761+0.000266482\n",
- "[1892]\ttrain-mae:0.192127+5.59174e-05\ttrain-rmse:0.249278+8.95969e-05\ttest-mae:0.193275+0.000185903\ttest-rmse:0.250758+0.000266424\n",
- "[1893]\ttrain-mae:0.192124+5.57656e-05\ttrain-rmse:0.249275+8.95188e-05\ttest-mae:0.193273+0.000185819\ttest-rmse:0.250755+0.000266397\n",
- "[1894]\ttrain-mae:0.19212+5.57771e-05\ttrain-rmse:0.249271+8.93817e-05\ttest-mae:0.193269+0.000185063\ttest-rmse:0.250751+0.000265574\n",
- "[1895]\ttrain-mae:0.192117+5.53556e-05\ttrain-rmse:0.249266+8.84219e-05\ttest-mae:0.193266+0.000185157\ttest-rmse:0.250747+0.000265397\n",
- "[1896]\ttrain-mae:0.192113+5.53736e-05\ttrain-rmse:0.249263+8.82528e-05\ttest-mae:0.193264+0.000184765\ttest-rmse:0.250744+0.00026508\n",
- "[1897]\ttrain-mae:0.192109+5.64468e-05\ttrain-rmse:0.249258+8.96718e-05\ttest-mae:0.19326+0.000185537\ttest-rmse:0.25074+0.000265493\n",
- "[1898]\ttrain-mae:0.192106+5.67388e-05\ttrain-rmse:0.249254+8.92917e-05\ttest-mae:0.193257+0.000185717\ttest-rmse:0.250737+0.000265502\n",
- "[1899]\ttrain-mae:0.192103+5.67799e-05\ttrain-rmse:0.249251+8.88955e-05\ttest-mae:0.193254+0.000185771\ttest-rmse:0.250734+0.000265849\n",
- "[1900]\ttrain-mae:0.1921+5.71104e-05\ttrain-rmse:0.249247+8.91337e-05\ttest-mae:0.193251+0.000185468\ttest-rmse:0.25073+0.000265353\n",
- "[1901]\ttrain-mae:0.192096+5.77585e-05\ttrain-rmse:0.249241+8.94441e-05\ttest-mae:0.193248+0.000186002\ttest-rmse:0.250725+0.000266133\n",
- "[1902]\ttrain-mae:0.192092+5.74662e-05\ttrain-rmse:0.249237+8.90065e-05\ttest-mae:0.193245+0.000185664\ttest-rmse:0.250721+0.000265681\n",
- "[1903]\ttrain-mae:0.192089+5.7556e-05\ttrain-rmse:0.249234+8.8474e-05\ttest-mae:0.193242+0.000185439\ttest-rmse:0.250718+0.000266011\n",
- "[1904]\ttrain-mae:0.192086+5.80576e-05\ttrain-rmse:0.249229+8.9304e-05\ttest-mae:0.193239+0.000185147\ttest-rmse:0.250714+0.000265132\n",
- "[1905]\ttrain-mae:0.192082+5.91605e-05\ttrain-rmse:0.249225+9.03923e-05\ttest-mae:0.193236+0.000185005\ttest-rmse:0.250711+0.000264447\n",
- "[1906]\ttrain-mae:0.192079+5.92747e-05\ttrain-rmse:0.249221+9.01144e-05\ttest-mae:0.193233+0.000185138\ttest-rmse:0.250707+0.000264863\n",
- "[1907]\ttrain-mae:0.192076+5.91443e-05\ttrain-rmse:0.249217+8.99756e-05\ttest-mae:0.193231+0.000184973\ttest-rmse:0.250704+0.000264649\n",
- "[1908]\ttrain-mae:0.192073+5.8798e-05\ttrain-rmse:0.249214+8.93376e-05\ttest-mae:0.193228+0.000184743\ttest-rmse:0.250701+0.000264678\n",
- "[1909]\ttrain-mae:0.19207+5.88384e-05\ttrain-rmse:0.24921+8.91673e-05\ttest-mae:0.193226+0.00018471\ttest-rmse:0.250698+0.000265034\n",
- "[1910]\ttrain-mae:0.192067+5.87184e-05\ttrain-rmse:0.249206+8.90088e-05\ttest-mae:0.193222+0.00018537\ttest-rmse:0.250694+0.000265893\n",
- "[1911]\ttrain-mae:0.192064+5.84294e-05\ttrain-rmse:0.249203+8.91251e-05\ttest-mae:0.19322+0.000185932\ttest-rmse:0.250692+0.000266648\n",
- "[1912]\ttrain-mae:0.192061+5.9e-05\ttrain-rmse:0.249199+8.97171e-05\ttest-mae:0.193217+0.000185608\ttest-rmse:0.250688+0.000266248\n",
- "[1913]\ttrain-mae:0.192058+5.93145e-05\ttrain-rmse:0.249196+8.98846e-05\ttest-mae:0.193215+0.000185463\ttest-rmse:0.250686+0.000265815\n",
- "[1914]\ttrain-mae:0.192055+5.95991e-05\ttrain-rmse:0.249192+9.04058e-05\ttest-mae:0.193213+0.000185142\ttest-rmse:0.250683+0.00026539\n",
- "[1915]\ttrain-mae:0.192053+5.96309e-05\ttrain-rmse:0.249189+9.03668e-05\ttest-mae:0.19321+0.000184276\ttest-rmse:0.250679+0.000264687\n",
- "[1916]\ttrain-mae:0.19205+6.01831e-05\ttrain-rmse:0.249185+9.0563e-05\ttest-mae:0.193208+0.000184128\ttest-rmse:0.250677+0.00026458\n",
- "[1917]\ttrain-mae:0.192047+5.99317e-05\ttrain-rmse:0.249182+9.00822e-05\ttest-mae:0.193205+0.000184874\ttest-rmse:0.250673+0.000265397\n",
- "[1918]\ttrain-mae:0.192044+6.05194e-05\ttrain-rmse:0.249178+9.14066e-05\ttest-mae:0.193203+0.00018526\ttest-rmse:0.250671+0.000265196\n",
- "[1919]\ttrain-mae:0.192041+6.04987e-05\ttrain-rmse:0.249175+9.15778e-05\ttest-mae:0.1932+0.000184808\ttest-rmse:0.250668+0.000264779\n",
- "[1920]\ttrain-mae:0.192038+6.10754e-05\ttrain-rmse:0.249172+9.23225e-05\ttest-mae:0.193198+0.000184756\ttest-rmse:0.250665+0.000264191\n",
- "[1921]\ttrain-mae:0.192035+6.10734e-05\ttrain-rmse:0.249168+9.24624e-05\ttest-mae:0.193195+0.000183849\ttest-rmse:0.250661+0.000263407\n",
- "[1922]\ttrain-mae:0.192032+6.11882e-05\ttrain-rmse:0.249165+9.22703e-05\ttest-mae:0.193192+0.00018311\ttest-rmse:0.250659+0.000262458\n",
- "[1923]\ttrain-mae:0.19203+6.16866e-05\ttrain-rmse:0.249161+9.29108e-05\ttest-mae:0.19319+0.000182582\ttest-rmse:0.250656+0.0002621\n",
- "[1924]\ttrain-mae:0.192027+6.26307e-05\ttrain-rmse:0.249159+9.41531e-05\ttest-mae:0.193188+0.000183291\ttest-rmse:0.250653+0.000261856\n",
- "[1925]\ttrain-mae:0.192024+6.31592e-05\ttrain-rmse:0.249155+9.50853e-05\ttest-mae:0.193186+0.000183722\ttest-rmse:0.250651+0.000262454\n",
- "[1926]\ttrain-mae:0.19202+6.31265e-05\ttrain-rmse:0.249151+9.48591e-05\ttest-mae:0.193182+0.000182502\ttest-rmse:0.250647+0.000261311\n",
- "[1927]\ttrain-mae:0.192016+6.36573e-05\ttrain-rmse:0.249146+9.49276e-05\ttest-mae:0.193179+0.000181717\ttest-rmse:0.250643+0.000260314\n",
- "[1928]\ttrain-mae:0.192013+6.37649e-05\ttrain-rmse:0.249142+9.47922e-05\ttest-mae:0.193176+0.00018099\ttest-rmse:0.250639+0.000259548\n",
- "[1929]\ttrain-mae:0.192009+6.36446e-05\ttrain-rmse:0.249137+9.46873e-05\ttest-mae:0.193172+0.000180897\ttest-rmse:0.250635+0.00025938\n",
- "[1930]\ttrain-mae:0.192005+6.39872e-05\ttrain-rmse:0.249133+9.55348e-05\ttest-mae:0.193169+0.000181006\ttest-rmse:0.250631+0.000258875\n",
- "[1931]\ttrain-mae:0.192002+6.45724e-05\ttrain-rmse:0.249129+9.64336e-05\ttest-mae:0.193166+0.000181672\ttest-rmse:0.250628+0.000259108\n",
- "[1932]\ttrain-mae:0.191998+6.47685e-05\ttrain-rmse:0.249124+9.6281e-05\ttest-mae:0.193163+0.000181422\ttest-rmse:0.250623+0.000258581\n",
- "[1933]\ttrain-mae:0.191994+6.54385e-05\ttrain-rmse:0.249119+9.69678e-05\ttest-mae:0.193159+0.000181521\ttest-rmse:0.250619+0.000258511\n",
- "[1934]\ttrain-mae:0.191991+6.61756e-05\ttrain-rmse:0.249115+9.79927e-05\ttest-mae:0.193156+0.000181971\ttest-rmse:0.250615+0.000258394\n",
- "[1935]\ttrain-mae:0.191987+6.64832e-05\ttrain-rmse:0.249111+9.85479e-05\ttest-mae:0.193153+0.000182232\ttest-rmse:0.250612+0.000258441\n",
- "[1936]\ttrain-mae:0.191984+6.70167e-05\ttrain-rmse:0.249106+9.91789e-05\ttest-mae:0.19315+0.000182405\ttest-rmse:0.250608+0.000258259\n",
- "[1937]\ttrain-mae:0.19198+6.76778e-05\ttrain-rmse:0.249102+9.97351e-05\ttest-mae:0.193147+0.00018206\ttest-rmse:0.250604+0.0002575\n",
- "[1938]\ttrain-mae:0.191976+6.87287e-05\ttrain-rmse:0.249098+0.000100914\ttest-mae:0.193144+0.000181679\ttest-rmse:0.250601+0.0002568\n",
- "[1939]\ttrain-mae:0.191973+6.95069e-05\ttrain-rmse:0.249094+0.000102335\ttest-mae:0.193141+0.000181626\ttest-rmse:0.250597+0.000256316\n",
- "[1940]\ttrain-mae:0.19197+7.03677e-05\ttrain-rmse:0.24909+0.000103235\ttest-mae:0.193139+0.000181858\ttest-rmse:0.250594+0.000256255\n",
- "[1941]\ttrain-mae:0.191967+7.05295e-05\ttrain-rmse:0.249087+0.000103436\ttest-mae:0.193136+0.000181893\ttest-rmse:0.250591+0.000256149\n",
- "[1942]\ttrain-mae:0.191964+7.14468e-05\ttrain-rmse:0.249082+0.000103958\ttest-mae:0.193133+0.000181037\ttest-rmse:0.250587+0.000254852\n",
- "[1943]\ttrain-mae:0.191961+7.15273e-05\ttrain-rmse:0.24908+0.000104074\ttest-mae:0.193131+0.000181036\ttest-rmse:0.250585+0.000254783\n",
- "[1944]\ttrain-mae:0.191959+7.12646e-05\ttrain-rmse:0.249076+0.000103752\ttest-mae:0.193128+0.000180791\ttest-rmse:0.250582+0.000254409\n"
+ "[993]\ttrain-mae:0.201848+5.84394e-05\ttest-mae:0.202129+0.000115924\n",
+ "[994]\ttrain-mae:0.201843+5.94358e-05\ttest-mae:0.202124+0.000116047\n",
+ "[995]\ttrain-mae:0.201839+5.93721e-05\ttest-mae:0.20212+0.000115682\n",
+ "[996]\ttrain-mae:0.201836+5.89496e-05\ttest-mae:0.202119+0.000115308\n",
+ "[997]\ttrain-mae:0.201833+5.85834e-05\ttest-mae:0.202115+0.000115341\n",
+ "[998]\ttrain-mae:0.20183+5.83326e-05\ttest-mae:0.202112+0.000115529\n",
+ "[999]\ttrain-mae:0.201827+5.82255e-05\ttest-mae:0.202109+0.000115595\n"
]
- },
+ }
+ ],
+ "source": [
+ "param = {'tree_method': 'gpu_hist',\n",
+ " 'max_depth': 3,\n",
+ " 'eta': 0.2,\n",
+ " 'verbosity': 2}\n",
+ "\n",
+ "cv_results = xgb.cv(params = param, dtrain = dfull, num_boost_round=1000, nfold = 10, metrics = 'mae', \n",
+ " early_stopping_rounds=10,as_pandas=True, verbose_eval = True, seed = 123)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "eta_vals = [0.01,0.05,0.1,0.2]\n",
+ "max_depth_vals = np.arange(5,10)\n",
+ "nboost_vals = np.arange(1000,5000,1000)\n",
+ "\n",
+ "eval_metrics = pd.DataFrame(columns=['eta','max_depth','nboost','best_idx','train-mae','test-mae'])\n",
+ "\n",
+ "for ii in eta_vals:\n",
+ " for jj in max_depth_vals:\n",
+ " for kk in nboost_vals:\n",
+ " print([ii,jj,kk])\n",
+ " param = {'tree_method': 'gpu_hist',\n",
+ " 'max_depth': jj,\n",
+ " 'eta': ii}\n",
+ " \n",
+ " cv_results = xgb.cv(params = param, dtrain = dfull, num_boost_round = kk, nfold = 10, metrics = 'mae', early_stopping_rounds=10, as_pandas=True, seed = 123)\n",
+ "\n",
+ " idx = cv_results['test-mae-mean'].idxmin()\n",
+ " eval_metrics.append(pd.Series([ii, jj, kk, idx, cv_results['train-mae-mean'][idx], cv_results['test-mae-mean'][idx]], index=eval_metrics.columns), ignore_index=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[1945]\ttrain-mae:0.191956+7.19639e-05\ttrain-rmse:0.249073+0.000104331\ttest-mae:0.193126+0.000181157\ttest-rmse:0.250579+0.000254451\n",
- "[1946]\ttrain-mae:0.191952+7.18376e-05\ttrain-rmse:0.249069+0.000104208\ttest-mae:0.193123+0.000181718\ttest-rmse:0.250575+0.00025532\n",
- "[1947]\ttrain-mae:0.191949+7.25052e-05\ttrain-rmse:0.249065+0.000105231\ttest-mae:0.19312+0.000182115\ttest-rmse:0.250572+0.000255493\n",
- "[1948]\ttrain-mae:0.191946+7.29205e-05\ttrain-rmse:0.249061+0.000105595\ttest-mae:0.193117+0.000181657\ttest-rmse:0.250568+0.000254658\n",
- "[1949]\ttrain-mae:0.191943+7.38884e-05\ttrain-rmse:0.249058+0.000106356\ttest-mae:0.193115+0.000181025\ttest-rmse:0.250565+0.00025332\n",
- "[1950]\ttrain-mae:0.19194+7.39114e-05\ttrain-rmse:0.249054+0.000107081\ttest-mae:0.193112+0.000180422\ttest-rmse:0.250562+0.000252262\n",
- "[1951]\ttrain-mae:0.191936+7.48444e-05\ttrain-rmse:0.249049+0.000108115\ttest-mae:0.193109+0.000180132\ttest-rmse:0.250558+0.000251625\n",
- "[1952]\ttrain-mae:0.191933+7.47747e-05\ttrain-rmse:0.249046+0.000108144\ttest-mae:0.193106+0.000181042\ttest-rmse:0.250555+0.000252884\n",
- "[1953]\ttrain-mae:0.19193+7.55334e-05\ttrain-rmse:0.249042+0.000108898\ttest-mae:0.193103+0.000180606\ttest-rmse:0.250551+0.000251876\n",
- "[1954]\ttrain-mae:0.191928+7.57126e-05\ttrain-rmse:0.249039+0.000109129\ttest-mae:0.193101+0.000180626\ttest-rmse:0.250549+0.000252023\n",
- "[1955]\ttrain-mae:0.191925+7.60237e-05\ttrain-rmse:0.249036+0.000109964\ttest-mae:0.193098+0.00018105\ttest-rmse:0.250546+0.00025234\n",
- "[1956]\ttrain-mae:0.191923+7.6116e-05\ttrain-rmse:0.249033+0.000110198\ttest-mae:0.193097+0.00018117\ttest-rmse:0.250544+0.000252412\n",
- "[1957]\ttrain-mae:0.191919+7.63969e-05\ttrain-rmse:0.249029+0.000110787\ttest-mae:0.193094+0.000180378\ttest-rmse:0.25054+0.000251856\n",
- "[1958]\ttrain-mae:0.191915+7.694e-05\ttrain-rmse:0.249024+0.000111649\ttest-mae:0.19309+0.000180531\ttest-rmse:0.250536+0.000251646\n",
- "[1959]\ttrain-mae:0.191912+7.75861e-05\ttrain-rmse:0.24902+0.000112605\ttest-mae:0.193087+0.000181002\ttest-rmse:0.250532+0.000251911\n",
- "[1960]\ttrain-mae:0.191908+7.85138e-05\ttrain-rmse:0.249015+0.000114009\ttest-mae:0.193084+0.000181891\ttest-rmse:0.250528+0.00025265\n",
- "[1961]\ttrain-mae:0.191905+7.87665e-05\ttrain-rmse:0.249011+0.000114677\ttest-mae:0.193081+0.00018137\ttest-rmse:0.250525+0.000251768\n",
- "[1962]\ttrain-mae:0.191902+7.89934e-05\ttrain-rmse:0.249008+0.000114681\ttest-mae:0.193078+0.000180826\ttest-rmse:0.250521+0.00025126\n",
- "[1963]\ttrain-mae:0.191898+7.85735e-05\ttrain-rmse:0.249003+0.000113947\ttest-mae:0.193075+0.000181344\ttest-rmse:0.250517+0.000251879\n",
- "[1964]\ttrain-mae:0.191894+7.83594e-05\ttrain-rmse:0.248998+0.000114143\ttest-mae:0.193071+0.000180216\ttest-rmse:0.250513+0.000250247\n",
- "[1965]\ttrain-mae:0.19189+7.90633e-05\ttrain-rmse:0.248994+0.00011462\ttest-mae:0.193068+0.000180018\ttest-rmse:0.25051+0.000249824\n",
- "[1966]\ttrain-mae:0.191886+7.9326e-05\ttrain-rmse:0.24899+0.000115001\ttest-mae:0.193065+0.000180231\ttest-rmse:0.250506+0.000249609\n",
- "[1967]\ttrain-mae:0.191882+7.8485e-05\ttrain-rmse:0.248984+0.000113699\ttest-mae:0.193061+0.000179832\ttest-rmse:0.250501+0.00024941\n",
- "[1968]\ttrain-mae:0.191877+7.79618e-05\ttrain-rmse:0.248978+0.000113049\ttest-mae:0.193057+0.000180485\ttest-rmse:0.250496+0.000250659\n",
- "[1969]\ttrain-mae:0.191874+7.77753e-05\ttrain-rmse:0.248974+0.000113063\ttest-mae:0.193054+0.000181671\ttest-rmse:0.250492+0.000252405\n",
- "[1970]\ttrain-mae:0.19187+7.69572e-05\ttrain-rmse:0.248969+0.000112105\ttest-mae:0.19305+0.000181243\ttest-rmse:0.250488+0.000252726\n",
- "[1971]\ttrain-mae:0.191866+7.69444e-05\ttrain-rmse:0.248964+0.000111996\ttest-mae:0.193046+0.000181384\ttest-rmse:0.250483+0.000252427\n",
- "[1972]\ttrain-mae:0.191861+7.64811e-05\ttrain-rmse:0.248959+0.000111299\ttest-mae:0.193043+0.000181204\ttest-rmse:0.250479+0.000252684\n",
- "[1973]\ttrain-mae:0.191858+7.62716e-05\ttrain-rmse:0.248955+0.000110895\ttest-mae:0.19304+0.000181992\ttest-rmse:0.250476+0.000253774\n",
- "[1974]\ttrain-mae:0.191855+7.58179e-05\ttrain-rmse:0.248951+0.000110271\ttest-mae:0.193037+0.000182172\ttest-rmse:0.250472+0.000254056\n",
- "[1975]\ttrain-mae:0.191852+7.5301e-05\ttrain-rmse:0.248947+0.000110143\ttest-mae:0.193034+0.000182413\ttest-rmse:0.250469+0.000254539\n",
- "[1976]\ttrain-mae:0.191848+7.46233e-05\ttrain-rmse:0.248943+0.000109\ttest-mae:0.193031+0.000182213\ttest-rmse:0.250465+0.000254917\n",
- "[1977]\ttrain-mae:0.191845+7.4625e-05\ttrain-rmse:0.248939+0.000109158\ttest-mae:0.193028+0.00018262\ttest-rmse:0.250461+0.000255209\n",
- "[1978]\ttrain-mae:0.191841+7.45772e-05\ttrain-rmse:0.248934+0.000109018\ttest-mae:0.193024+0.000182706\ttest-rmse:0.250457+0.000255678\n",
- "[1979]\ttrain-mae:0.191837+7.49015e-05\ttrain-rmse:0.24893+0.000109099\ttest-mae:0.193021+0.000182794\ttest-rmse:0.250454+0.000255862\n",
- "[1980]\ttrain-mae:0.191833+7.46898e-05\ttrain-rmse:0.248925+0.000109104\ttest-mae:0.193018+0.000182657\ttest-rmse:0.250449+0.000255605\n",
- "[1981]\ttrain-mae:0.19183+7.49963e-05\ttrain-rmse:0.248921+0.000109715\ttest-mae:0.193014+0.000182206\ttest-rmse:0.250445+0.000255188\n",
- "[1982]\ttrain-mae:0.191826+7.58298e-05\ttrain-rmse:0.248916+0.000110193\ttest-mae:0.193011+0.000182199\ttest-rmse:0.250442+0.000255009\n",
- "[1983]\ttrain-mae:0.191822+7.50361e-05\ttrain-rmse:0.248911+0.000109523\ttest-mae:0.193008+0.000182796\ttest-rmse:0.250437+0.000255892\n",
- "[1984]\ttrain-mae:0.191819+7.45933e-05\ttrain-rmse:0.248907+0.000109444\ttest-mae:0.193005+0.000182873\ttest-rmse:0.250433+0.000256234\n",
- "[1985]\ttrain-mae:0.191816+7.47786e-05\ttrain-rmse:0.248903+0.000109865\ttest-mae:0.193002+0.000183798\ttest-rmse:0.25043+0.000257152\n",
- "[1986]\ttrain-mae:0.191812+7.4329e-05\ttrain-rmse:0.248898+0.000109424\ttest-mae:0.192999+0.000182942\ttest-rmse:0.250426+0.000256398\n",
- "[1987]\ttrain-mae:0.191808+7.41091e-05\ttrain-rmse:0.248893+0.000109302\ttest-mae:0.192995+0.000182835\ttest-rmse:0.250421+0.000256862\n",
- "[1988]\ttrain-mae:0.191804+7.47348e-05\ttrain-rmse:0.248889+0.000110181\ttest-mae:0.192992+0.000183312\ttest-rmse:0.250417+0.000257165\n",
- "[1989]\ttrain-mae:0.1918+7.45091e-05\ttrain-rmse:0.248885+0.000110056\ttest-mae:0.192988+0.000183659\ttest-rmse:0.250413+0.000256789\n",
- "[1990]\ttrain-mae:0.191797+7.53685e-05\ttrain-rmse:0.24888+0.000111154\ttest-mae:0.192985+0.000183353\ttest-rmse:0.250409+0.000256493\n",
- "[1991]\ttrain-mae:0.191793+7.4762e-05\ttrain-rmse:0.248875+0.000110367\ttest-mae:0.192982+0.000184064\ttest-rmse:0.250405+0.000257442\n",
- "[1992]\ttrain-mae:0.191789+7.43156e-05\ttrain-rmse:0.248871+0.00010971\ttest-mae:0.192978+0.000184539\ttest-rmse:0.250401+0.000258081\n",
- "[1993]\ttrain-mae:0.191786+7.37995e-05\ttrain-rmse:0.248867+0.000108999\ttest-mae:0.192976+0.000185287\ttest-rmse:0.250398+0.000258892\n",
- "[1994]\ttrain-mae:0.191783+7.29811e-05\ttrain-rmse:0.248864+0.00010839\ttest-mae:0.192973+0.00018601\ttest-rmse:0.250395+0.000259976\n",
- "[1995]\ttrain-mae:0.19178+7.29893e-05\ttrain-rmse:0.24886+0.000108417\ttest-mae:0.19297+0.00018631\ttest-rmse:0.250392+0.000260942\n",
- "[1996]\ttrain-mae:0.191777+7.27706e-05\ttrain-rmse:0.248855+0.000108881\ttest-mae:0.192967+0.00018697\ttest-rmse:0.250388+0.000261573\n",
- "[1997]\ttrain-mae:0.191773+7.3054e-05\ttrain-rmse:0.248851+0.000108972\ttest-mae:0.192964+0.000187221\ttest-rmse:0.250384+0.000262058\n",
- "[1998]\ttrain-mae:0.19177+7.2549e-05\ttrain-rmse:0.248847+0.000108694\ttest-mae:0.192961+0.000187788\ttest-rmse:0.25038+0.0002627\n",
- "[1999]\ttrain-mae:0.191766+7.21094e-05\ttrain-rmse:0.248842+0.000108203\ttest-mae:0.192958+0.000187844\ttest-rmse:0.250376+0.000262873\n"
- ]
+ "data": {
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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
}
],
"source": [
- "cv_results = xgb.cv(params = param, dtrain = dfull, num_boost_round=2000, nfold = 10, metrics = ['mae','rmse'], \n",
- " early_stopping_rounds=10,as_pandas=True, verbose_eval = True)"
+ "# Plot feature importance values\n",
+ "xgb.plot_importance(ml)\n",
+ "pyplot.show()"
]
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": 18,
"metadata": {},
"outputs": [
{
@@ -2212,841 +4329,38 @@
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
- " <th>train-mae-mean</th>\n",
- " <th>train-mae-std</th>\n",
- " <th>train-rmse-mean</th>\n",
- " <th>train-rmse-std</th>\n",
- " <th>test-mae-mean</th>\n",
- " <th>test-mae-std</th>\n",
- " <th>test-rmse-mean</th>\n",
- " <th>test-rmse-std</th>\n",
+ " <th>Area5</th>\n",
+ " <th>Thick5</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
- " <th>0</th>\n",
- " <td>1.278184</td>\n",
- " <td>0.000047</td>\n",
- " <td>1.335369</td>\n",
- " <td>0.000049</td>\n",
- " <td>1.278184</td>\n",
- " <td>0.000198</td>\n",
- " <td>1.335370</td>\n",
- " <td>0.000203</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>1.214276</td>\n",
- " <td>0.000044</td>\n",
- " <td>1.272620</td>\n",
- " <td>0.000053</td>\n",
- " <td>1.214274</td>\n",
- " <td>0.000198</td>\n",
- " <td>1.272621</td>\n",
- " <td>0.000199</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>1.153563</td>\n",
- " <td>0.000043</td>\n",
- " <td>1.213154</td>\n",
- " <td>0.000051</td>\n",
- " <td>1.153561</td>\n",
- " <td>0.000201</td>\n",
- " <td>1.213156</td>\n",
- " <td>0.000199</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>1.095886</td>\n",
- " <td>0.000040</td>\n",
- " <td>1.156875</td>\n",
- " <td>0.000069</td>\n",
- " <td>1.095886</td>\n",
- " <td>0.000203</td>\n",
- " <td>1.156880</td>\n",
- " <td>0.000181</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>1.041092</td>\n",
- " <td>0.000038</td>\n",
- " <td>1.103535</td>\n",
- " <td>0.000041</td>\n",
- " <td>1.041093</td>\n",
- " <td>0.000201</td>\n",
- " <td>1.103542</td>\n",
- " <td>0.000206</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>5</th>\n",
- " <td>0.989039</td>\n",
- " <td>0.000036</td>\n",
- " <td>1.053041</td>\n",
- " <td>0.000063</td>\n",
- " <td>0.989040</td>\n",
- " <td>0.000203</td>\n",
- " <td>1.053050</td>\n",
- " <td>0.000205</td>\n",
+ " <th>Area5</th>\n",
+ " <td>1.000000</td>\n",
+ " <td>-0.197128</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>6</th>\n",
- " <td>0.939590</td>\n",
- " <td>0.000034</td>\n",
- " <td>1.005250</td>\n",
- " <td>0.000072</td>\n",
- " <td>0.939593</td>\n",
- " <td>0.000208</td>\n",
- " <td>1.005262</td>\n",
- " <td>0.000184</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>7</th>\n",
- " <td>0.892619</td>\n",
- " <td>0.000032</td>\n",
- " <td>0.960029</td>\n",
- " <td>0.000098</td>\n",
- " <td>0.892623</td>\n",
- " <td>0.000209</td>\n",
- " <td>0.960045</td>\n",
- " <td>0.000207</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>8</th>\n",
- " <td>0.848009</td>\n",
- " <td>0.000031</td>\n",
- " <td>0.917313</td>\n",
- " <td>0.000066</td>\n",
- " <td>0.848014</td>\n",
- " <td>0.000210</td>\n",
- " <td>0.917330</td>\n",
- " <td>0.000201</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>9</th>\n",
- " <td>0.805656</td>\n",
- " <td>0.000030</td>\n",
- " <td>0.876901</td>\n",
- " <td>0.000074</td>\n",
- " <td>0.805660</td>\n",
- " <td>0.000210</td>\n",
- " <td>0.876920</td>\n",
- " <td>0.000185</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>10</th>\n",
- " <td>0.765471</td>\n",
- " <td>0.000028</td>\n",
- " <td>0.838725</td>\n",
- " <td>0.000042</td>\n",
- " <td>0.765477</td>\n",
- " <td>0.000212</td>\n",
- " <td>0.838747</td>\n",
- " <td>0.000214</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>11</th>\n",
- " <td>0.727379</td>\n",
- " <td>0.000027</td>\n",
- " <td>0.802701</td>\n",
- " <td>0.000089</td>\n",
- " <td>0.727383</td>\n",
- " <td>0.000214</td>\n",
- " <td>0.802722</td>\n",
- " <td>0.000265</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>12</th>\n",
- " <td>0.691319</td>\n",
- " <td>0.000026</td>\n",
- " <td>0.768659</td>\n",
- " <td>0.000106</td>\n",
- " <td>0.691325</td>\n",
- " <td>0.000212</td>\n",
- " <td>0.768683</td>\n",
- " <td>0.000282</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>13</th>\n",
- " <td>0.657234</td>\n",
- " <td>0.000025</td>\n",
- " <td>0.736617</td>\n",
- " <td>0.000107</td>\n",
- " <td>0.657241</td>\n",
- " <td>0.000213</td>\n",
- " <td>0.736644</td>\n",
- " <td>0.000271</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>14</th>\n",
- " <td>0.625069</td>\n",
- " <td>0.000033</td>\n",
- " <td>0.706269</td>\n",
- " <td>0.000077</td>\n",
- " <td>0.625079</td>\n",
- " <td>0.000204</td>\n",
- " <td>0.706301</td>\n",
- " <td>0.000226</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>15</th>\n",
- " <td>0.594787</td>\n",
- " <td>0.000037</td>\n",
- " <td>0.677808</td>\n",
- " <td>0.000070</td>\n",
- " <td>0.594798</td>\n",
- " <td>0.000198</td>\n",
- " <td>0.677843</td>\n",
- " <td>0.000236</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>16</th>\n",
- " <td>0.566350</td>\n",
- " <td>0.000062</td>\n",
- " <td>0.651016</td>\n",
- " <td>0.000090</td>\n",
- " <td>0.566362</td>\n",
- " <td>0.000171</td>\n",
- " <td>0.651055</td>\n",
- " <td>0.000151</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>17</th>\n",
- " <td>0.539692</td>\n",
- " <td>0.000061</td>\n",
- " <td>0.625794</td>\n",
- " <td>0.000098</td>\n",
- " <td>0.539703</td>\n",
- " <td>0.000178</td>\n",
- " <td>0.625833</td>\n",
- " <td>0.000216</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>18</th>\n",
- " <td>0.514729</td>\n",
- " <td>0.000052</td>\n",
- " <td>0.602028</td>\n",
- " <td>0.000076</td>\n",
- " <td>0.514740</td>\n",
- " <td>0.000199</td>\n",
- " <td>0.602068</td>\n",
- " <td>0.000216</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>19</th>\n",
- " <td>0.491423</td>\n",
- " <td>0.000050</td>\n",
- " <td>0.579725</td>\n",
- " <td>0.000065</td>\n",
- " <td>0.491434</td>\n",
- " <td>0.000213</td>\n",
- " <td>0.579767</td>\n",
- " <td>0.000249</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>20</th>\n",
- " <td>0.469736</td>\n",
- " <td>0.000079</td>\n",
- " <td>0.558859</td>\n",
- " <td>0.000123</td>\n",
- " <td>0.469748</td>\n",
- " <td>0.000206</td>\n",
- " <td>0.558904</td>\n",
- " <td>0.000245</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>21</th>\n",
- " <td>0.449566</td>\n",
- " <td>0.000091</td>\n",
- " <td>0.539234</td>\n",
- " <td>0.000077</td>\n",
- " <td>0.449577</td>\n",
- " <td>0.000190</td>\n",
- " <td>0.539280</td>\n",
- " <td>0.000209</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>22</th>\n",
- " <td>0.430835</td>\n",
- " <td>0.000111</td>\n",
- " <td>0.520850</td>\n",
- " <td>0.000081</td>\n",
- " <td>0.430849</td>\n",
- " <td>0.000176</td>\n",
- " <td>0.520900</td>\n",
- " <td>0.000240</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>23</th>\n",
- " <td>0.413468</td>\n",
- " <td>0.000139</td>\n",
- " <td>0.503630</td>\n",
- " <td>0.000122</td>\n",
- " <td>0.413486</td>\n",
- " <td>0.000158</td>\n",
- " <td>0.503683</td>\n",
- " <td>0.000234</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>24</th>\n",
- " <td>0.397462</td>\n",
- " <td>0.000117</td>\n",
- " <td>0.487564</td>\n",
- " <td>0.000097</td>\n",
- " <td>0.397484</td>\n",
- " <td>0.000145</td>\n",
- " <td>0.487621</td>\n",
- " <td>0.000204</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>25</th>\n",
- " <td>0.382640</td>\n",
- " <td>0.000096</td>\n",
- " <td>0.472565</td>\n",
- " <td>0.000071</td>\n",
- " <td>0.382667</td>\n",
- " <td>0.000179</td>\n",
- " <td>0.472626</td>\n",
- " <td>0.000217</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>26</th>\n",
- " <td>0.368969</td>\n",
- " <td>0.000102</td>\n",
- " <td>0.458506</td>\n",
- " <td>0.000102</td>\n",
- " <td>0.368997</td>\n",
- " <td>0.000179</td>\n",
- " <td>0.458570</td>\n",
- " <td>0.000232</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>27</th>\n",
- " <td>0.356384</td>\n",
- " <td>0.000067</td>\n",
- " <td>0.445443</td>\n",
- " <td>0.000063</td>\n",
- " <td>0.356418</td>\n",
- " <td>0.000185</td>\n",
- " <td>0.445513</td>\n",
- " <td>0.000230</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>28</th>\n",
- " <td>0.344788</td>\n",
- " <td>0.000086</td>\n",
- " <td>0.433253</td>\n",
- " <td>0.000059</td>\n",
- " <td>0.344826</td>\n",
- " <td>0.000126</td>\n",
- " <td>0.433326</td>\n",
- " <td>0.000145</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>29</th>\n",
- " <td>0.334087</td>\n",
- " <td>0.000087</td>\n",
- " <td>0.421860</td>\n",
- " <td>0.000100</td>\n",
- " <td>0.334129</td>\n",
- " <td>0.000137</td>\n",
- " <td>0.421936</td>\n",
- " <td>0.000225</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>...</th>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1970</th>\n",
- " <td>0.191757</td>\n",
- " <td>0.000060</td>\n",
- " <td>0.248828</td>\n",
- " <td>0.000081</td>\n",
- " <td>0.193078</td>\n",
- " <td>0.000178</td>\n",
- " <td>0.250518</td>\n",
- " <td>0.000287</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1971</th>\n",
- " <td>0.191753</td>\n",
- " <td>0.000060</td>\n",
- " <td>0.248823</td>\n",
- " <td>0.000080</td>\n",
- " <td>0.193074</td>\n",
- " <td>0.000178</td>\n",
- " <td>0.250514</td>\n",
- " <td>0.000287</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1972</th>\n",
- " <td>0.191750</td>\n",
- " <td>0.000060</td>\n",
- " <td>0.248819</td>\n",
- " <td>0.000081</td>\n",
- " <td>0.193071</td>\n",
- " <td>0.000177</td>\n",
- " <td>0.250510</td>\n",
- " <td>0.000286</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1973</th>\n",
- " <td>0.191746</td>\n",
- " <td>0.000061</td>\n",
- " <td>0.248814</td>\n",
- " <td>0.000082</td>\n",
- " <td>0.193068</td>\n",
- " <td>0.000177</td>\n",
- " <td>0.250506</td>\n",
- " <td>0.000286</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1974</th>\n",
- " <td>0.191743</td>\n",
- " <td>0.000062</td>\n",
- " <td>0.248809</td>\n",
- " <td>0.000083</td>\n",
- " <td>0.193065</td>\n",
- " <td>0.000176</td>\n",
- " <td>0.250502</td>\n",
- " <td>0.000286</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1975</th>\n",
- " <td>0.191740</td>\n",
- " <td>0.000062</td>\n",
- " <td>0.248806</td>\n",
- " <td>0.000083</td>\n",
- " <td>0.193063</td>\n",
- " <td>0.000177</td>\n",
- " <td>0.250499</td>\n",
- " <td>0.000286</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1976</th>\n",
- " <td>0.191736</td>\n",
- " <td>0.000062</td>\n",
- " <td>0.248801</td>\n",
- " <td>0.000083</td>\n",
- " <td>0.193060</td>\n",
- " <td>0.000177</td>\n",
- " <td>0.250495</td>\n",
- " <td>0.000287</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1977</th>\n",
- " <td>0.191734</td>\n",
- " <td>0.000062</td>\n",
- " <td>0.248798</td>\n",
- " <td>0.000084</td>\n",
- " <td>0.193058</td>\n",
- " <td>0.000177</td>\n",
- " <td>0.250493</td>\n",
- " <td>0.000286</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1978</th>\n",
- " <td>0.191729</td>\n",
- " <td>0.000062</td>\n",
- " <td>0.248794</td>\n",
- " <td>0.000083</td>\n",
- " <td>0.193054</td>\n",
- " <td>0.000178</td>\n",
- " <td>0.250489</td>\n",
- " <td>0.000287</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1979</th>\n",
- " <td>0.191726</td>\n",
- " <td>0.000062</td>\n",
- " <td>0.248789</td>\n",
- " <td>0.000083</td>\n",
- " <td>0.193051</td>\n",
- " <td>0.000178</td>\n",
- " <td>0.250485</td>\n",
- " <td>0.000286</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1980</th>\n",
- " <td>0.191723</td>\n",
- " <td>0.000062</td>\n",
- " <td>0.248786</td>\n",
- " <td>0.000084</td>\n",
- " <td>0.193048</td>\n",
- " <td>0.000177</td>\n",
- " <td>0.250482</td>\n",
- " <td>0.000285</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1981</th>\n",
- " <td>0.191720</td>\n",
- " <td>0.000063</td>\n",
- " <td>0.248782</td>\n",
- " <td>0.000085</td>\n",
- " <td>0.193046</td>\n",
- " <td>0.000176</td>\n",
- " <td>0.250478</td>\n",
- " <td>0.000284</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1982</th>\n",
- " <td>0.191717</td>\n",
- " <td>0.000063</td>\n",
- " <td>0.248778</td>\n",
- " <td>0.000086</td>\n",
- " <td>0.193043</td>\n",
- " <td>0.000176</td>\n",
- " <td>0.250476</td>\n",
- " <td>0.000284</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1983</th>\n",
- " <td>0.191713</td>\n",
- " <td>0.000063</td>\n",
- " <td>0.248774</td>\n",
- " <td>0.000086</td>\n",
- " <td>0.193041</td>\n",
- " <td>0.000175</td>\n",
- " <td>0.250472</td>\n",
- " <td>0.000282</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1984</th>\n",
- " <td>0.191711</td>\n",
- " <td>0.000064</td>\n",
- " <td>0.248771</td>\n",
- " <td>0.000086</td>\n",
- " <td>0.193038</td>\n",
- " <td>0.000176</td>\n",
- " <td>0.250470</td>\n",
- " <td>0.000284</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1985</th>\n",
- " <td>0.191707</td>\n",
- " <td>0.000063</td>\n",
- " <td>0.248767</td>\n",
- " <td>0.000086</td>\n",
- " <td>0.193035</td>\n",
- " <td>0.000177</td>\n",
- " <td>0.250466</td>\n",
- " <td>0.000284</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1986</th>\n",
- " <td>0.191703</td>\n",
- " <td>0.000063</td>\n",
- " <td>0.248761</td>\n",
- " <td>0.000085</td>\n",
- " <td>0.193032</td>\n",
- " <td>0.000177</td>\n",
- " <td>0.250461</td>\n",
- " <td>0.000285</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1987</th>\n",
- " <td>0.191701</td>\n",
- " <td>0.000062</td>\n",
- " <td>0.248759</td>\n",
- " <td>0.000084</td>\n",
- " <td>0.193030</td>\n",
- " <td>0.000177</td>\n",
- " <td>0.250459</td>\n",
- " <td>0.000284</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1988</th>\n",
- " <td>0.191697</td>\n",
- " <td>0.000063</td>\n",
- " <td>0.248754</td>\n",
- " <td>0.000085</td>\n",
- " <td>0.193026</td>\n",
- " <td>0.000177</td>\n",
- " <td>0.250455</td>\n",
- " <td>0.000286</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1989</th>\n",
- " <td>0.191693</td>\n",
- " <td>0.000063</td>\n",
- " <td>0.248749</td>\n",
- " <td>0.000085</td>\n",
- " <td>0.193024</td>\n",
- " <td>0.000177</td>\n",
- " <td>0.250451</td>\n",
- " <td>0.000285</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1990</th>\n",
- " <td>0.191690</td>\n",
- " <td>0.000063</td>\n",
- " <td>0.248746</td>\n",
- " <td>0.000085</td>\n",
- " <td>0.193021</td>\n",
- " <td>0.000178</td>\n",
- " <td>0.250448</td>\n",
- " <td>0.000285</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1991</th>\n",
- " <td>0.191686</td>\n",
- " <td>0.000064</td>\n",
- " <td>0.248740</td>\n",
- " <td>0.000087</td>\n",
- " <td>0.193017</td>\n",
- " <td>0.000176</td>\n",
- " <td>0.250444</td>\n",
- " <td>0.000284</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1992</th>\n",
- " <td>0.191684</td>\n",
- " <td>0.000064</td>\n",
- " <td>0.248738</td>\n",
- " <td>0.000087</td>\n",
- " <td>0.193016</td>\n",
- " <td>0.000177</td>\n",
- " <td>0.250441</td>\n",
- " <td>0.000285</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1993</th>\n",
- " <td>0.191679</td>\n",
- " <td>0.000064</td>\n",
- " <td>0.248732</td>\n",
- " <td>0.000087</td>\n",
- " <td>0.193011</td>\n",
- " <td>0.000178</td>\n",
- " <td>0.250436</td>\n",
- " <td>0.000286</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1994</th>\n",
- " <td>0.191675</td>\n",
- " <td>0.000063</td>\n",
- " <td>0.248728</td>\n",
- " <td>0.000086</td>\n",
- " <td>0.193008</td>\n",
- " <td>0.000177</td>\n",
- " <td>0.250432</td>\n",
- " <td>0.000285</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1995</th>\n",
- " <td>0.191671</td>\n",
- " <td>0.000062</td>\n",
- " <td>0.248723</td>\n",
- " <td>0.000085</td>\n",
- " <td>0.193005</td>\n",
- " <td>0.000177</td>\n",
- " <td>0.250428</td>\n",
- " <td>0.000285</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1996</th>\n",
- " <td>0.191668</td>\n",
- " <td>0.000063</td>\n",
- " <td>0.248719</td>\n",
- " <td>0.000086</td>\n",
- " <td>0.193001</td>\n",
- " <td>0.000176</td>\n",
- " <td>0.250424</td>\n",
- " <td>0.000284</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1997</th>\n",
- " <td>0.191665</td>\n",
- " <td>0.000063</td>\n",
- " <td>0.248715</td>\n",
- " <td>0.000085</td>\n",
- " <td>0.192999</td>\n",
- " <td>0.000176</td>\n",
- " <td>0.250422</td>\n",
- " <td>0.000284</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1998</th>\n",
- " <td>0.191662</td>\n",
- " <td>0.000062</td>\n",
- " <td>0.248712</td>\n",
- " <td>0.000084</td>\n",
- " <td>0.192996</td>\n",
- " <td>0.000176</td>\n",
- " <td>0.250418</td>\n",
- " <td>0.000284</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1999</th>\n",
- " <td>0.191659</td>\n",
- " <td>0.000062</td>\n",
- " <td>0.248708</td>\n",
- " <td>0.000084</td>\n",
- " <td>0.192994</td>\n",
- " <td>0.000176</td>\n",
- " <td>0.250416</td>\n",
- " <td>0.000284</td>\n",
+ " <th>Thick5</th>\n",
+ " <td>-0.197128</td>\n",
+ " <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
- "<p>2000 rows × 8 columns</p>\n",
"</div>"
],
"text/plain": [
- " train-mae-mean train-mae-std train-rmse-mean train-rmse-std \\\n",
- "0 1.278184 0.000047 1.335369 0.000049 \n",
- "1 1.214276 0.000044 1.272620 0.000053 \n",
- "2 1.153563 0.000043 1.213154 0.000051 \n",
- "3 1.095886 0.000040 1.156875 0.000069 \n",
- "4 1.041092 0.000038 1.103535 0.000041 \n",
- "5 0.989039 0.000036 1.053041 0.000063 \n",
- "6 0.939590 0.000034 1.005250 0.000072 \n",
- "7 0.892619 0.000032 0.960029 0.000098 \n",
- "8 0.848009 0.000031 0.917313 0.000066 \n",
- "9 0.805656 0.000030 0.876901 0.000074 \n",
- "10 0.765471 0.000028 0.838725 0.000042 \n",
- "11 0.727379 0.000027 0.802701 0.000089 \n",
- "12 0.691319 0.000026 0.768659 0.000106 \n",
- "13 0.657234 0.000025 0.736617 0.000107 \n",
- "14 0.625069 0.000033 0.706269 0.000077 \n",
- "15 0.594787 0.000037 0.677808 0.000070 \n",
- "16 0.566350 0.000062 0.651016 0.000090 \n",
- "17 0.539692 0.000061 0.625794 0.000098 \n",
- "18 0.514729 0.000052 0.602028 0.000076 \n",
- "19 0.491423 0.000050 0.579725 0.000065 \n",
- "20 0.469736 0.000079 0.558859 0.000123 \n",
- "21 0.449566 0.000091 0.539234 0.000077 \n",
- "22 0.430835 0.000111 0.520850 0.000081 \n",
- "23 0.413468 0.000139 0.503630 0.000122 \n",
- "24 0.397462 0.000117 0.487564 0.000097 \n",
- "25 0.382640 0.000096 0.472565 0.000071 \n",
- "26 0.368969 0.000102 0.458506 0.000102 \n",
- "27 0.356384 0.000067 0.445443 0.000063 \n",
- "28 0.344788 0.000086 0.433253 0.000059 \n",
- "29 0.334087 0.000087 0.421860 0.000100 \n",
- "... ... ... ... ... \n",
- "1970 0.191757 0.000060 0.248828 0.000081 \n",
- "1971 0.191753 0.000060 0.248823 0.000080 \n",
- "1972 0.191750 0.000060 0.248819 0.000081 \n",
- "1973 0.191746 0.000061 0.248814 0.000082 \n",
- "1974 0.191743 0.000062 0.248809 0.000083 \n",
- "1975 0.191740 0.000062 0.248806 0.000083 \n",
- "1976 0.191736 0.000062 0.248801 0.000083 \n",
- "1977 0.191734 0.000062 0.248798 0.000084 \n",
- "1978 0.191729 0.000062 0.248794 0.000083 \n",
- "1979 0.191726 0.000062 0.248789 0.000083 \n",
- "1980 0.191723 0.000062 0.248786 0.000084 \n",
- "1981 0.191720 0.000063 0.248782 0.000085 \n",
- "1982 0.191717 0.000063 0.248778 0.000086 \n",
- "1983 0.191713 0.000063 0.248774 0.000086 \n",
- "1984 0.191711 0.000064 0.248771 0.000086 \n",
- "1985 0.191707 0.000063 0.248767 0.000086 \n",
- "1986 0.191703 0.000063 0.248761 0.000085 \n",
- "1987 0.191701 0.000062 0.248759 0.000084 \n",
- "1988 0.191697 0.000063 0.248754 0.000085 \n",
- "1989 0.191693 0.000063 0.248749 0.000085 \n",
- "1990 0.191690 0.000063 0.248746 0.000085 \n",
- "1991 0.191686 0.000064 0.248740 0.000087 \n",
- "1992 0.191684 0.000064 0.248738 0.000087 \n",
- "1993 0.191679 0.000064 0.248732 0.000087 \n",
- "1994 0.191675 0.000063 0.248728 0.000086 \n",
- "1995 0.191671 0.000062 0.248723 0.000085 \n",
- "1996 0.191668 0.000063 0.248719 0.000086 \n",
- "1997 0.191665 0.000063 0.248715 0.000085 \n",
- "1998 0.191662 0.000062 0.248712 0.000084 \n",
- "1999 0.191659 0.000062 0.248708 0.000084 \n",
- "\n",
- " test-mae-mean test-mae-std test-rmse-mean test-rmse-std \n",
- "0 1.278184 0.000198 1.335370 0.000203 \n",
- "1 1.214274 0.000198 1.272621 0.000199 \n",
- "2 1.153561 0.000201 1.213156 0.000199 \n",
- "3 1.095886 0.000203 1.156880 0.000181 \n",
- "4 1.041093 0.000201 1.103542 0.000206 \n",
- "5 0.989040 0.000203 1.053050 0.000205 \n",
- "6 0.939593 0.000208 1.005262 0.000184 \n",
- "7 0.892623 0.000209 0.960045 0.000207 \n",
- "8 0.848014 0.000210 0.917330 0.000201 \n",
- "9 0.805660 0.000210 0.876920 0.000185 \n",
- "10 0.765477 0.000212 0.838747 0.000214 \n",
- "11 0.727383 0.000214 0.802722 0.000265 \n",
- "12 0.691325 0.000212 0.768683 0.000282 \n",
- "13 0.657241 0.000213 0.736644 0.000271 \n",
- "14 0.625079 0.000204 0.706301 0.000226 \n",
- "15 0.594798 0.000198 0.677843 0.000236 \n",
- "16 0.566362 0.000171 0.651055 0.000151 \n",
- "17 0.539703 0.000178 0.625833 0.000216 \n",
- "18 0.514740 0.000199 0.602068 0.000216 \n",
- "19 0.491434 0.000213 0.579767 0.000249 \n",
- "20 0.469748 0.000206 0.558904 0.000245 \n",
- "21 0.449577 0.000190 0.539280 0.000209 \n",
- "22 0.430849 0.000176 0.520900 0.000240 \n",
- "23 0.413486 0.000158 0.503683 0.000234 \n",
- "24 0.397484 0.000145 0.487621 0.000204 \n",
- "25 0.382667 0.000179 0.472626 0.000217 \n",
- "26 0.368997 0.000179 0.458570 0.000232 \n",
- "27 0.356418 0.000185 0.445513 0.000230 \n",
- "28 0.344826 0.000126 0.433326 0.000145 \n",
- "29 0.334129 0.000137 0.421936 0.000225 \n",
- "... ... ... ... ... \n",
- "1970 0.193078 0.000178 0.250518 0.000287 \n",
- "1971 0.193074 0.000178 0.250514 0.000287 \n",
- "1972 0.193071 0.000177 0.250510 0.000286 \n",
- "1973 0.193068 0.000177 0.250506 0.000286 \n",
- "1974 0.193065 0.000176 0.250502 0.000286 \n",
- "1975 0.193063 0.000177 0.250499 0.000286 \n",
- "1976 0.193060 0.000177 0.250495 0.000287 \n",
- "1977 0.193058 0.000177 0.250493 0.000286 \n",
- "1978 0.193054 0.000178 0.250489 0.000287 \n",
- "1979 0.193051 0.000178 0.250485 0.000286 \n",
- "1980 0.193048 0.000177 0.250482 0.000285 \n",
- "1981 0.193046 0.000176 0.250478 0.000284 \n",
- "1982 0.193043 0.000176 0.250476 0.000284 \n",
- "1983 0.193041 0.000175 0.250472 0.000282 \n",
- "1984 0.193038 0.000176 0.250470 0.000284 \n",
- "1985 0.193035 0.000177 0.250466 0.000284 \n",
- "1986 0.193032 0.000177 0.250461 0.000285 \n",
- "1987 0.193030 0.000177 0.250459 0.000284 \n",
- "1988 0.193026 0.000177 0.250455 0.000286 \n",
- "1989 0.193024 0.000177 0.250451 0.000285 \n",
- "1990 0.193021 0.000178 0.250448 0.000285 \n",
- "1991 0.193017 0.000176 0.250444 0.000284 \n",
- "1992 0.193016 0.000177 0.250441 0.000285 \n",
- "1993 0.193011 0.000178 0.250436 0.000286 \n",
- "1994 0.193008 0.000177 0.250432 0.000285 \n",
- "1995 0.193005 0.000177 0.250428 0.000285 \n",
- "1996 0.193001 0.000176 0.250424 0.000284 \n",
- "1997 0.192999 0.000176 0.250422 0.000284 \n",
- "1998 0.192996 0.000176 0.250418 0.000284 \n",
- "1999 0.192994 0.000176 0.250416 0.000284 \n",
- "\n",
- "[2000 rows x 8 columns]"
+ " Area5 Thick5\n",
+ "Area5 1.000000 -0.197128\n",
+ "Thick5 -0.197128 1.000000"
]
},
- "execution_count": 25,
+ "execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "cv_results"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Plot feature importance values\n",
- "xgb.plot_importance(ml)\n",
- "pyplot.show()"
+ "df[['Area5','Thick5']].corr()"
]
},
{