939 lines
54 KiB
Plaintext
939 lines
54 KiB
Plaintext
{
|
|
"cells": [
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "4d2a8b6c",
|
|
"metadata": {},
|
|
"source": [
|
|
"#### Database"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 1,
|
|
"id": "7be9eeff",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"PROJECT_PATH = '/home/md/Work/ligalytics/leagues_stable/'\n",
|
|
"import os, sys\n",
|
|
"sys.path.insert(0, PROJECT_PATH)\n",
|
|
"os.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"leagues.settings\")\n",
|
|
"\n",
|
|
"from leagues import settings\n",
|
|
"settings.DATABASES['default']['NAME'] = PROJECT_PATH+'/db.sqlite3'\n",
|
|
"\n",
|
|
"import django\n",
|
|
"django.setup()\n",
|
|
"\n",
|
|
"from scheduler.models import *\n",
|
|
"from common.functions import distanceInKmByGPS\n",
|
|
"season = Season.objects.filter(nicename=\"Imported: Benchmark Season\").first()\n",
|
|
"import pandas as pd\n",
|
|
"import numpy as np\n",
|
|
"from django.db.models import F\n",
|
|
"games = Game.objects.filter(season=season)\n",
|
|
"df = pd.DataFrame.from_records(games.values())\n",
|
|
"games = Game.objects.filter(season=season).annotate(\n",
|
|
" home=F('homeTeam__shortname'),\n",
|
|
" away=F('awayTeam__shortname'),\n",
|
|
" home_lat=F('homeTeam__latitude'),\n",
|
|
" home_lon=F('homeTeam__longitude'),\n",
|
|
" home_attr=F('homeTeam__attractivity'),\n",
|
|
" away_lat=F('awayTeam__latitude'),\n",
|
|
" away_lon=F('awayTeam__longitude'),\n",
|
|
" away_attr=F('awayTeam__attractivity'),\n",
|
|
" home_country=F('homeTeam__country'),\n",
|
|
" away_country=F('awayTeam__country'),\n",
|
|
").values()\n",
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "bc191792",
|
|
"metadata": {},
|
|
"source": [
|
|
"#### Dataframe"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 31,
|
|
"id": "1e404cf8",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from sklearn.preprocessing import OneHotEncoder\n",
|
|
"\n",
|
|
"# create dataset\n",
|
|
"df = pd.DataFrame.from_records(games.values())\n",
|
|
"\n",
|
|
"# data cleaning\n",
|
|
"df['time'] = df['time'].replace('','0')\n",
|
|
"df = df[df['attendance'] != 0]\n",
|
|
"\n",
|
|
"# remove outliers\n",
|
|
"out_fields = ['attendance']\n",
|
|
"for field in out_fields:\n",
|
|
" q_low = df[field].quantile(0.01)\n",
|
|
" q_hi = df[field].quantile(0.99)\n",
|
|
" df = df[(df[field] < q_hi) & (df[field] > q_low)]\n",
|
|
"\n",
|
|
"\n",
|
|
"# pivots\n",
|
|
"pivot_homeTeam_mean = df.pivot_table('attendance','homeTeam_id',aggfunc='mean')\n",
|
|
"pivot_homeTeam_max = df.pivot_table('attendance','homeTeam_id',aggfunc='max')\n",
|
|
"\n",
|
|
"# add more features\n",
|
|
"df['weekday'] = df.apply(lambda r: r['date'].weekday(), axis=1)\n",
|
|
"df['day'] = df.apply(lambda r: r['date'].day, axis=1)\n",
|
|
"df['month'] = df.apply(lambda r: r['date'].month, axis=1)\n",
|
|
"df['year'] = df.apply(lambda r: r['date'].year, axis=1)\n",
|
|
"df['distance'] = df.apply(lambda r: distanceInKmByGPS(r['home_lon'],r['home_lat'],r['away_lon'],r['away_lat']), axis=1)\n",
|
|
"df['weekend'] = df.apply(lambda r: int(r['weekday'] in [6,7]), axis=1)\n",
|
|
"df['winter_season'] = df.apply(lambda r: int(r['month'] in [1,2,3,10,11,12]), axis=1)\n",
|
|
"df['home_base'] = df.apply(lambda r: pivot_homeTeam_mean.loc[r['homeTeam_id'],'attendance'], axis=1)\n",
|
|
"df['stadium_size'] = df.apply(lambda r: pivot_homeTeam_max.loc[r['homeTeam_id'],'attendance'], axis=1)\n",
|
|
"df['early'] = df.apply(lambda r: r['time'].replace(':','') < \"1800\", axis=1)\n",
|
|
"df['before2010'] = df.apply(lambda r: r['historic_season'].split('-')[0] < \"2010\", axis=1)\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"# one hot encoding\n",
|
|
"ohe_fields = ['home_country']\n",
|
|
"\n",
|
|
"for field in ohe_fields:\n",
|
|
" ohe = OneHotEncoder()\n",
|
|
" transformed = ohe.fit_transform(df[[field]])\n",
|
|
" df[ohe.categories_[0]] = transformed.toarray()\n",
|
|
"\n",
|
|
"# sort label to last index\n",
|
|
"cols = list(df.columns)\n",
|
|
"cols.append(cols.pop(cols.index('attendance')))\n",
|
|
"df = df[cols]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "e2ea08e5",
|
|
"metadata": {},
|
|
"source": [
|
|
"#### Train/Test Data - Normalization"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 32,
|
|
"id": "74e12f87",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"import numpy as np \n",
|
|
"import pandas as pd \n",
|
|
"import matplotlib.pyplot as plt\n",
|
|
"import seaborn as sns\n",
|
|
"from sklearn.model_selection import train_test_split, cross_val_predict\n",
|
|
"from sklearn import metrics\n",
|
|
"from sklearn.ensemble import RandomForestRegressor\n",
|
|
"\n",
|
|
"\n",
|
|
"remove_columns = ['season_id', 'resultEntered', 'reversible', 'reschedule', 'homeGoals', 'awayGoals',\n",
|
|
" 'homeGoals2', 'awayGoals2', 'homeGoals3', 'awayGoals3', 'home', 'away', 'date', 'time',\n",
|
|
" 'id', 'homeTeam_id', 'awayTeam_id', 'historic_season',\n",
|
|
" 'home_country','home_lat','home_lon','away_lat','away_lon','away_country','year']\n",
|
|
"feature_cols = list(set(df.columns[:-1]) - set(remove_columns))\n",
|
|
"# feature_cols = ['weekday','weekend','home_base','distance','winter_season']\n",
|
|
"label = 'attendance'\n",
|
|
"\n",
|
|
"\n",
|
|
"data = df[feature_cols+[label]]\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"X = df[feature_cols] # Features\n",
|
|
"y = df[label] # Target variable\n",
|
|
"\n",
|
|
"X_train, X_test, y_train, y_test = train_test_split(\n",
|
|
" X, y, test_size=0.3, random_state=1) # 70% training and 30% test"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 60,
|
|
"id": "45e08026",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Random Forest Regression Accuracy: 0.701779484610914\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"rf_regressor = RandomForestRegressor(n_estimators = 200 , random_state = 42)\n",
|
|
"rf_regressor.fit(X_train,y_train)\n",
|
|
"\n",
|
|
"# #Predicting the SalePrices using test set \n",
|
|
"y_pred_rf = rf_regressor.predict(X_test)\n",
|
|
"\n",
|
|
"# #Random Forest Regression Accuracy with test set\n",
|
|
"print('Random Forest Regression Accuracy: ', rf_regressor.score(X_test,y_test))\n",
|
|
"\n",
|
|
"# #Predicting the SalePrice using cross validation (KFold method)\n",
|
|
"# y_pred_rf = cross_val_predict(rf_regressor, X, y, cv=10 )\n",
|
|
"\n",
|
|
"# #Random Forest Regression Accuracy with cross validation\n",
|
|
"# accuracy_rf = metrics.r2_score(y, y_pred_rf)\n",
|
|
"# print('Cross-Predicted(KFold) Random Forest Regression Accuracy: ', accuracy_rf)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 43,
|
|
"id": "0de49b8a",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": "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",
|
|
"text/plain": [
|
|
"<Figure size 1080x720 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"ranking = np.argsort(-rf_regressor.feature_importances_)\n",
|
|
"f, ax = plt.subplots(figsize=(15, 10))\n",
|
|
"sns.barplot(x=rf_regressor.feature_importances_[ranking], y=X_train.columns.values[ranking], orient='h')\n",
|
|
"ax.set_xlabel(\"feature importance\")\n",
|
|
"plt.tight_layout()\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 61,
|
|
"id": "4c1f8b45",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"0 4000 6580.545\n",
|
|
"1 3264 1930.28\n",
|
|
"2 6000 4557.7\n",
|
|
"3 4250 5707.365\n",
|
|
"4 1200 2293.235\n",
|
|
"5 4300 14245.4\n",
|
|
"6 3874 10814.615\n",
|
|
"7 2800 7051.27\n",
|
|
"8 5500 5780.415\n",
|
|
"9 6000 16578.36\n",
|
|
"10 3500 3465.88\n",
|
|
"11 4500 6437.125\n",
|
|
"12 2140 2539.445\n",
|
|
"13 3146 7725.725\n",
|
|
"14 2600 4115.015\n",
|
|
"15 4875 3892.94\n",
|
|
"16 5807 2310.32\n",
|
|
"17 5200 5284.785\n",
|
|
"18 3500 14448.53\n",
|
|
"19 2643 13368.715\n",
|
|
"20 4000 5098.405\n",
|
|
"21 2500 11200.69\n",
|
|
"22 2000 4440.93\n",
|
|
"23 3198 13613.71\n",
|
|
"24 3571 2143.51\n",
|
|
"25 2712 14239.07\n",
|
|
"26 2100 2803.565\n",
|
|
"27 4525 3025.8\n",
|
|
"28 6625 5005.1\n",
|
|
"29 4966 4908.325\n",
|
|
"30 2000 11584.62\n",
|
|
"31 2100 17840.985\n",
|
|
"32 2310 11679.06\n",
|
|
"33 2600 3441.75\n",
|
|
"34 2000 4676.61\n",
|
|
"35 4300 12716.24\n",
|
|
"36 2734 4625.195\n",
|
|
"37 3500 8200.89\n",
|
|
"38 3050 5654.81\n",
|
|
"39 5256 9792.665\n",
|
|
"40 3012 2310.83\n",
|
|
"41 5060 4974.58\n",
|
|
"42 1500 9684.55\n",
|
|
"43 4000 7074.835\n",
|
|
"44 1950 9311.275\n",
|
|
"45 2300 5989.395\n",
|
|
"46 2300 2172.745\n",
|
|
"47 1950 9854.69\n",
|
|
"48 3058 3366.505\n",
|
|
"49 2000 9185.505\n",
|
|
"50 2345 5486.87\n",
|
|
"51 5500 6112.29\n",
|
|
"52 5585 3817.555\n",
|
|
"53 4046 5211.04\n",
|
|
"54 6000 5262.485\n",
|
|
"55 4384 4752.54\n",
|
|
"56 3000 4680.4\n",
|
|
"57 3500 3730.125\n",
|
|
"58 7000 19604.555\n",
|
|
"59 10195 12155.54\n",
|
|
"60 3476 2316.185\n",
|
|
"61 2120 6507.535\n",
|
|
"62 3800 4992.07\n",
|
|
"63 2675 17715.46\n",
|
|
"64 6603 5947.32\n",
|
|
"65 1500 6841.915\n",
|
|
"66 4000 14031.8475\n",
|
|
"67 4545 3450.985\n",
|
|
"68 1396 3998.6\n",
|
|
"69 5400 12293.125\n",
|
|
"70 5041 10571.61\n",
|
|
"71 1500 13793.105\n",
|
|
"72 1800 3019.735\n",
|
|
"73 3189 2286.085\n",
|
|
"74 3800 8072.33\n",
|
|
"75 2178 14740.815\n",
|
|
"76 6169 3358.27\n",
|
|
"77 2676 6266.61\n",
|
|
"78 3510 4507.855\n",
|
|
"79 3900 5281.48\n",
|
|
"80 2120 6582.6\n",
|
|
"81 8000 7046.1\n",
|
|
"82 2500 5538.39\n",
|
|
"83 4016 1779.045\n",
|
|
"84 1500 14032.36875\n",
|
|
"85 2860 3886.32\n",
|
|
"86 2150 12050.75\n",
|
|
"87 3336 13855.32\n",
|
|
"88 8820 6569.785\n",
|
|
"89 4792 8718.28\n",
|
|
"90 1650 23018.465\n",
|
|
"91 2000 7827.75\n",
|
|
"92 2111 3149.185\n",
|
|
"93 4470 12892.46\n",
|
|
"94 7000 6091.705\n",
|
|
"95 3850 6530.255\n",
|
|
"96 4366 10065.345\n",
|
|
"97 2645 9916.91\n",
|
|
"98 2384 6651.595\n",
|
|
"99 4522 14023.15\n",
|
|
"100 6328 16536.015\n",
|
|
"101 3877 22520.085\n",
|
|
"102 2000 5087.5858333333335\n",
|
|
"103 4157 4995.895\n",
|
|
"104 2942 14835.2\n",
|
|
"105 3655 11868.02\n",
|
|
"106 4500 2356.345\n",
|
|
"107 2964 3001.075\n",
|
|
"108 2863 2862.48\n",
|
|
"109 4935 11530.06\n",
|
|
"110 2526 13431.275\n",
|
|
"111 5679 8583.235\n",
|
|
"112 7286 12046.48\n",
|
|
"113 6055 12324.2175\n",
|
|
"114 1200 10979.595\n",
|
|
"115 4110 3696.885\n",
|
|
"116 1957 1784.81\n",
|
|
"117 1790 2310.685\n",
|
|
"118 5422 5891.27\n",
|
|
"119 4650 12989.17\n",
|
|
"120 5297 13769.85\n",
|
|
"121 3036 7880.975\n",
|
|
"122 3233 10235.395\n",
|
|
"123 12000 2019.825\n",
|
|
"124 7632 1784.93\n",
|
|
"125 3620 8620.28\n",
|
|
"126 2000 2247.685\n",
|
|
"127 2145 5211.695\n",
|
|
"128 2227 16537.25\n",
|
|
"129 2520 16835.395\n",
|
|
"130 10000 8674.06\n",
|
|
"131 2000 5183.62\n",
|
|
"132 4052 5794.44\n",
|
|
"133 2137 3961.6\n",
|
|
"134 2609 10219.755\n",
|
|
"135 3256 3750.165\n",
|
|
"136 8173 7718.525\n",
|
|
"137 1250 5951.49\n",
|
|
"138 7401 2454.21\n",
|
|
"139 4200 9402.87\n",
|
|
"140 6100 6322.165\n",
|
|
"141 5182 4632.07\n",
|
|
"142 1300 3705.42\n",
|
|
"143 2117 2149.76\n",
|
|
"144 6500 5198.585\n",
|
|
"145 5174 24179.485\n",
|
|
"146 3867 7086.495\n",
|
|
"147 1918 3776.675\n",
|
|
"148 2800 7035.84\n",
|
|
"149 7648 6624.045\n",
|
|
"150 5638 4533.465\n",
|
|
"151 5262 8931.215\n",
|
|
"152 1650 10611.175\n",
|
|
"153 1657 5332.755\n",
|
|
"154 4086 16697.77\n",
|
|
"155 9000 4076.79\n",
|
|
"156 1500 4362.885\n",
|
|
"157 8145 15920.005\n",
|
|
"158 2625 8195.605\n",
|
|
"159 6281 2241.7\n",
|
|
"160 4520 2029.15\n",
|
|
"161 1200 2951.745\n",
|
|
"162 4829 4604.96\n",
|
|
"163 1760 3564.905\n",
|
|
"164 3469 9817.235\n",
|
|
"165 7500 5750.205\n",
|
|
"166 5227 6575.79\n",
|
|
"167 1765 12158.355\n",
|
|
"168 5200 3810.195\n",
|
|
"169 6402 7280.82\n",
|
|
"170 4832 3182.905\n",
|
|
"171 1500 9403.08\n",
|
|
"172 2342 3885.02\n",
|
|
"173 2799 2147.4\n",
|
|
"174 3850 3098.345\n",
|
|
"175 4200 3161.31\n",
|
|
"176 4531 1797.735\n",
|
|
"177 1751 1703.18\n",
|
|
"178 4250 5936.4125\n",
|
|
"179 5705 7906.34\n",
|
|
"180 3528 1578.835\n",
|
|
"181 2496 5813.035\n",
|
|
"182 4370 1658.86\n",
|
|
"183 1350 3784.05\n",
|
|
"184 5334 9792.665\n",
|
|
"185 1423 6670.74\n",
|
|
"186 4129 3725.405\n",
|
|
"187 5858 2032.275\n",
|
|
"188 3300 4597.795\n",
|
|
"189 3500 4803.27\n",
|
|
"190 10280 3359.415\n",
|
|
"191 4500 8830.49625\n",
|
|
"192 10500 3642.33\n",
|
|
"193 3932 5338.775\n",
|
|
"194 5500 5575.605\n",
|
|
"195 2200 16004.22\n",
|
|
"196 8206 4797.275\n",
|
|
"197 8000 5357.22\n",
|
|
"198 6372 8878.1\n",
|
|
"199 7900 13958.58\n",
|
|
"200 1628 13360.12\n",
|
|
"201 4142 3265.425\n",
|
|
"202 1150 8374.72\n",
|
|
"203 1750 4471.255\n",
|
|
"204 4072 4537.83\n",
|
|
"205 1176 14088.795\n",
|
|
"206 3200 4798.96\n",
|
|
"207 1715 12487.48\n",
|
|
"208 7530 10884.455\n",
|
|
"209 4600 7020.67\n",
|
|
"210 1200 5571.775\n",
|
|
"211 4452 16737.72\n",
|
|
"212 2400 3627.235\n",
|
|
"213 4057 3048.885\n",
|
|
"214 5000 7612.795\n",
|
|
"215 4147 1906.285\n",
|
|
"216 3046 11647.86\n",
|
|
"217 6215 2800.26\n",
|
|
"218 3350 2044.6\n",
|
|
"219 3500 1575.61\n",
|
|
"220 5870 3940.91\n",
|
|
"221 4113 6889.23\n",
|
|
"222 3420 17471.3\n",
|
|
"223 6116 7448.86\n",
|
|
"224 5902 3766.12\n",
|
|
"225 3787 3984.03\n",
|
|
"226 3600 6773.825\n",
|
|
"227 7017 2120.505\n",
|
|
"228 10280 5304.46\n",
|
|
"229 5600 9301.37\n",
|
|
"230 3656 3520.035\n",
|
|
"231 6480 14618.295\n",
|
|
"232 1646 5412.37\n",
|
|
"233 2600 4642.425\n",
|
|
"234 4300 2358.205\n",
|
|
"235 7948 3387.4\n",
|
|
"236 4579 6892.56\n",
|
|
"237 4364 6032.51\n",
|
|
"238 5000 3701.665\n",
|
|
"239 2034 2459.645\n",
|
|
"240 3500 4322.685\n",
|
|
"241 5688 7433.355\n",
|
|
"242 1800 11416.59\n",
|
|
"243 10131 3018.53\n",
|
|
"244 5784 5802.455\n",
|
|
"245 1813 4643.985\n",
|
|
"246 3700 4748.285\n",
|
|
"247 6700 7172.535\n",
|
|
"248 3700 2260.81\n",
|
|
"249 7799 2543.05\n",
|
|
"250 1884 11840.945\n",
|
|
"251 3042 5026.895\n",
|
|
"252 5000 12697.255\n",
|
|
"253 5112 12816.485\n",
|
|
"254 1404 2985.83\n",
|
|
"255 2471 4937.79\n",
|
|
"256 7749 8946.405\n",
|
|
"257 6254 3540.255\n",
|
|
"258 2502 11979.99\n",
|
|
"259 2300 3865.78\n",
|
|
"260 6500 10954.815\n",
|
|
"261 2646 2107.79\n",
|
|
"262 9546 5031.68\n",
|
|
"263 7500 2161.645\n",
|
|
"264 11016 6702.55\n",
|
|
"265 5763 9477.27\n",
|
|
"266 2460 9735.075\n",
|
|
"267 5511 1635.865\n",
|
|
"268 1857 11158.305\n",
|
|
"269 7000 1856.91\n",
|
|
"270 6333 2795.195\n",
|
|
"271 6107 3487.66\n",
|
|
"272 1518 2304.03\n",
|
|
"273 9310 16034.98\n",
|
|
"274 3551 6612.595\n",
|
|
"275 1700 4897.935\n",
|
|
"276 2250 8678.285\n",
|
|
"277 6000 2745.995\n",
|
|
"278 2003 10819.06\n",
|
|
"279 15183 4209.865\n",
|
|
"280 7113 2508.52\n",
|
|
"281 3818 8803.065\n",
|
|
"282 12300 3207.05\n",
|
|
"283 12488 5297.225\n",
|
|
"284 8000 13887.795\n",
|
|
"285 10832 6841.995\n",
|
|
"286 2107 5646.825\n",
|
|
"287 2100 10862.95\n",
|
|
"288 14135 4668.9625\n",
|
|
"289 6115 3493.45\n",
|
|
"290 9364 6440.755\n",
|
|
"291 4773 12457.905\n",
|
|
"292 3525 4335.24\n",
|
|
"293 6126 3478.67\n",
|
|
"294 6487 6093.975\n",
|
|
"295 3879 5981.07\n",
|
|
"296 4943 2130.5\n",
|
|
"297 1335 18139.0\n",
|
|
"298 4125 2991.4\n",
|
|
"299 7986 20566.615\n",
|
|
"300 5000 1574.75\n",
|
|
"301 3559 12854.765\n",
|
|
"302 6573 6217.415\n",
|
|
"303 2300 7889.96\n",
|
|
"304 5117 7310.34\n",
|
|
"305 5000 6879.69\n",
|
|
"306 7165 6601.825\n",
|
|
"307 1406 4288.72\n",
|
|
"308 12300 5307.19\n",
|
|
"309 3573 4497.045\n",
|
|
"310 6500 6256.32\n",
|
|
"311 4508 5486.1\n",
|
|
"312 7546 3663.385\n",
|
|
"313 5413 7938.35\n",
|
|
"314 5754 3178.54\n",
|
|
"315 1307 3337.145\n",
|
|
"316 5433 2951.285\n",
|
|
"317 2304 4515.835\n",
|
|
"318 4000 10191.245\n",
|
|
"319 6425 6563.885\n",
|
|
"320 7250 14309.845\n",
|
|
"321 5500 8663.35\n",
|
|
"322 1800 6115.675\n",
|
|
"323 2240 3341.57\n",
|
|
"324 9000 9561.66\n",
|
|
"325 1266 6050.45\n",
|
|
"326 3850 5461.605\n",
|
|
"327 2122 5666.745\n",
|
|
"328 6423 4170.395\n",
|
|
"329 6455 12976.51\n",
|
|
"330 2100 9193.3\n",
|
|
"331 7843 6734.765\n",
|
|
"332 9617 3180.015\n",
|
|
"333 5033 10773.97125\n",
|
|
"334 1129 1717.375\n",
|
|
"335 1500 9708.96\n",
|
|
"336 8932 4352.705\n",
|
|
"337 4637 8567.97\n",
|
|
"338 15327 7835.35\n",
|
|
"339 1233 3639.265\n",
|
|
"340 2364 6209.395\n",
|
|
"341 10316 11016.235\n",
|
|
"342 13200 2119.405\n",
|
|
"343 1303 2709.155\n",
|
|
"344 8687 1634.185\n",
|
|
"345 1653 13753.1375\n",
|
|
"346 7067 4695.66\n",
|
|
"347 8265 8292.81\n",
|
|
"348 1587 4810.64\n",
|
|
"349 2479 7368.865\n",
|
|
"350 6366 3286.005\n",
|
|
"351 5114 4085.725\n",
|
|
"352 6138 9741.92\n",
|
|
"353 1765 5426.53\n",
|
|
"354 3129 1673.915\n",
|
|
"355 2295 3511.86\n",
|
|
"356 5507 4669.1925\n",
|
|
"357 5200 12605.555\n",
|
|
"358 6326 15411.865\n",
|
|
"359 10804 11185.52\n",
|
|
"360 6721 5787.13\n",
|
|
"361 5574 3080.71\n",
|
|
"362 10020 5907.88\n",
|
|
"363 3678 3327.455\n",
|
|
"364 4342 6535.975\n",
|
|
"365 8000 14431.15\n",
|
|
"366 1687 5395.356666666667\n",
|
|
"367 1967 5023.89\n",
|
|
"368 27252 5910.655\n",
|
|
"369 20520 9579.255\n",
|
|
"370 10000 17502.835\n",
|
|
"371 1661 5887.475\n",
|
|
"372 1356 4524.945\n",
|
|
"373 8000 13422.775\n",
|
|
"374 6288 8526.295\n",
|
|
"375 9979 12948.585\n",
|
|
"376 3083 7691.755\n",
|
|
"377 1574 11412.42\n",
|
|
"378 10452 2908.155\n",
|
|
"379 4790 3480.47\n",
|
|
"380 5563 4298.075\n",
|
|
"381 1103 4372.96\n",
|
|
"382 3846 9778.52\n",
|
|
"383 3750 2400.16\n",
|
|
"384 4309 5070.415\n",
|
|
"385 6254 1577.175\n",
|
|
"386 2133 4828.135\n",
|
|
"387 12800 7410.605\n",
|
|
"388 5300 1702.315\n",
|
|
"389 10102 2563.91\n",
|
|
"390 9326 6329.725\n",
|
|
"391 2613 7333.205\n",
|
|
"392 11976 4998.75\n",
|
|
"393 12143 2995.995\n",
|
|
"394 13200 4269.515\n",
|
|
"395 6320 5055.135\n",
|
|
"396 1542 13829.665\n",
|
|
"397 4560 4800.095\n",
|
|
"398 8304 5038.89\n",
|
|
"399 10480 3193.88\n",
|
|
"400 5352 4633.325\n",
|
|
"401 2137 7124.72\n",
|
|
"402 5169 6736.805\n",
|
|
"403 2799 13044.645\n",
|
|
"404 6000 2021.2\n",
|
|
"405 4986 3783.67\n",
|
|
"406 1824 6328.3\n",
|
|
"407 1562 4818.96\n",
|
|
"408 5890 12351.575\n",
|
|
"409 6077 8368.7\n",
|
|
"410 1485 2307.815\n",
|
|
"411 1825 4152.695\n",
|
|
"412 5340 3259.93\n",
|
|
"413 9237 1645.765\n",
|
|
"414 6499 7667.76125\n",
|
|
"415 1240 4280.79\n",
|
|
"416 4656 4129.7\n",
|
|
"417 2335 5560.904166666666\n",
|
|
"418 8000 11956.94\n",
|
|
"419 5641 7928.66\n",
|
|
"420 1444 7371.75\n",
|
|
"421 12900 2452.485\n",
|
|
"422 6500 3510.74\n",
|
|
"423 7506 13593.255\n",
|
|
"424 6438 10959.67\n",
|
|
"425 2261 13815.8\n",
|
|
"426 2121 7264.35\n",
|
|
"427 5437 3966.71\n",
|
|
"428 1536 3881.21\n",
|
|
"429 9295 14548.765\n",
|
|
"430 3252 6801.995\n",
|
|
"431 1331 2413.215\n",
|
|
"432 5442 9457.055\n",
|
|
"433 4527 3871.07\n",
|
|
"434 6500 5900.165\n",
|
|
"435 1238 4079.73\n",
|
|
"436 10702 4984.83\n",
|
|
"437 8056 6596.595\n",
|
|
"438 4517 3666.385\n",
|
|
"439 5108 12268.525\n",
|
|
"440 6354 9083.105\n",
|
|
"441 4322 3158.935\n",
|
|
"442 3129 6390.115\n",
|
|
"443 2486 16959.01\n",
|
|
"444 6200 3927.465\n",
|
|
"445 10320 6169.805\n",
|
|
"446 5204 11800.575\n",
|
|
"447 7429 17239.05\n",
|
|
"448 1837 4729.9\n",
|
|
"449 3311 6117.47\n",
|
|
"450 5425 4203.0\n",
|
|
"451 1141 7236.865\n",
|
|
"452 8142 4073.855\n",
|
|
"453 9630 6304.535\n",
|
|
"454 3400 2734.205\n",
|
|
"455 5991 7359.03\n",
|
|
"456 4537 2602.975\n",
|
|
"457 1389 8594.86\n",
|
|
"458 6560 13770.56\n",
|
|
"459 5417 7300.155\n",
|
|
"460 1326 12498.02\n",
|
|
"461 1226 12151.51\n",
|
|
"462 9439 7763.215\n",
|
|
"463 6075 2155.91\n",
|
|
"464 4139 11945.41\n",
|
|
"465 6921 4866.605\n",
|
|
"466 1412 4655.13\n",
|
|
"467 1580 3638.64\n",
|
|
"468 6480 3190.87\n",
|
|
"469 7740 8526.32\n",
|
|
"470 9187 3697.575\n",
|
|
"471 5923 16333.9175\n",
|
|
"472 1690 9985.465\n",
|
|
"473 1829 8347.22\n",
|
|
"474 13132 13420.35\n",
|
|
"475 5673 14952.235\n",
|
|
"476 10143 15199.465\n",
|
|
"477 2631 2170.875\n",
|
|
"478 16753 3160.5\n",
|
|
"479 8300 6444.085\n",
|
|
"480 2541 9135.885\n",
|
|
"481 1638 9089.395\n",
|
|
"482 6097 14731.42\n",
|
|
"483 8250 10358.07\n",
|
|
"484 1638 2472.25\n",
|
|
"485 1145 4099.34\n",
|
|
"486 8300 4606.435\n",
|
|
"487 9750 12722.03\n",
|
|
"488 12532 15722.44\n",
|
|
"489 10739 5063.045\n",
|
|
"490 18230 9996.75\n",
|
|
"491 6125 8251.825\n",
|
|
"492 6225 3833.575\n",
|
|
"493 16509 2648.755\n",
|
|
"494 6782 13865.8\n",
|
|
"495 6125 6070.855\n",
|
|
"496 1681 15837.77\n",
|
|
"497 1798 5082.45\n",
|
|
"498 13385 4033.305\n",
|
|
"499 12300 11861.3\n",
|
|
"500 4100 8838.96\n",
|
|
"501 6190 1632.66\n",
|
|
"502 9246 3630.915\n",
|
|
"503 14322 5414.425\n",
|
|
"504 7396 3048.95\n",
|
|
"505 3851 12195.32\n",
|
|
"506 4734 1627.625\n",
|
|
"507 2058 5133.655\n",
|
|
"508 8869 5051.91\n",
|
|
"509 11269 7680.75\n",
|
|
"510 2506 4868.0\n",
|
|
"511 11730 11872.185\n",
|
|
"512 8045 10004.22\n",
|
|
"513 7500 22047.495\n",
|
|
"514 9166 2411.96\n",
|
|
"515 5368 1678.645\n",
|
|
"516 2395 9312.065\n",
|
|
"517 9087 7342.77\n",
|
|
"518 7407 8699.05\n",
|
|
"519 5949 4514.755\n",
|
|
"520 10216 2410.22\n",
|
|
"521 4731 2032.19\n",
|
|
"522 9248 6795.98\n",
|
|
"523 18500 8829.36\n",
|
|
"524 6308 6920.595\n",
|
|
"525 5748 7288.28\n",
|
|
"526 3138 12551.665\n",
|
|
"527 2012 9096.635\n",
|
|
"528 8657 3791.66\n",
|
|
"529 7500 5467.56\n",
|
|
"530 1463 18574.32\n",
|
|
"531 7625 8138.19\n",
|
|
"532 17260 2172.135\n",
|
|
"533 7020 7228.85\n",
|
|
"534 6592 14669.08\n",
|
|
"535 1463 9407.33\n",
|
|
"536 5112 15252.19\n",
|
|
"537 9672 13353.205\n",
|
|
"538 5360 2470.61\n",
|
|
"539 7338 4989.74\n",
|
|
"540 4113 4484.305\n",
|
|
"541 5443 7201.3\n",
|
|
"542 7368 3404.155\n",
|
|
"543 8017 14148.48\n",
|
|
"544 8619 6160.425\n",
|
|
"545 2651 2679.64\n",
|
|
"546 14840 5971.57\n",
|
|
"547 6041 6304.95\n",
|
|
"548 8685 4880.825\n",
|
|
"549 1252 10886.525\n",
|
|
"550 2655 6104.335\n",
|
|
"551 15140 11179.75\n",
|
|
"552 7885 7929.78\n",
|
|
"553 8685 7818.845\n",
|
|
"554 7542 5478.925\n",
|
|
"555 4676 12477.1055\n",
|
|
"556 2450 5207.06\n",
|
|
"557 7225 9006.11\n",
|
|
"558 18500 2964.14\n",
|
|
"559 8499 12498.02\n",
|
|
"560 5057 13908.8225\n",
|
|
"561 8418 8635.185\n",
|
|
"562 22885 4714.2\n",
|
|
"563 2820 15387.885\n",
|
|
"564 1868 6215.625\n",
|
|
"565 2523 7000.925\n",
|
|
"566 10058 4604.805\n",
|
|
"567 7138 7506.575\n",
|
|
"568 7610 9124.655\n",
|
|
"569 2670 2921.225\n",
|
|
"570 2364 13164.26\n",
|
|
"571 8435 4459.1\n",
|
|
"572 8841 2059.765\n",
|
|
"573 15240 6943.635\n",
|
|
"574 10180 12877.445\n",
|
|
"575 12534 5554.46\n",
|
|
"576 20520 5569.985\n",
|
|
"577 13500 2075.635\n",
|
|
"578 5000 5377.4\n",
|
|
"579 12813 6347.0\n",
|
|
"580 7050 4979.655\n",
|
|
"581 6665 1697.77\n",
|
|
"582 16350 12666.47\n",
|
|
"583 25623 14755.155\n",
|
|
"584 2063 7468.775\n",
|
|
"585 3393 3799.25\n",
|
|
"586 2217 7613.54\n",
|
|
"587 9003 4315.82\n",
|
|
"588 14470 14461.775\n",
|
|
"589 7603 8187.18\n",
|
|
"590 8685 5318.81\n",
|
|
"591 6436 3784.09\n",
|
|
"592 6112 14373.265\n",
|
|
"593 6127 5815.515\n",
|
|
"594 1373 15596.585\n",
|
|
"595 8046 7578.585\n",
|
|
"596 6865 2831.665\n",
|
|
"597 8286 8018.355\n",
|
|
"598 6302 6389.695\n",
|
|
"599 2208 10284.765\n",
|
|
"600 3615 8835.65\n",
|
|
"601 15940 1988.025\n",
|
|
"602 5010 13787.54\n",
|
|
"603 8212 1622.005\n",
|
|
"604 1272 3604.73\n",
|
|
"605 2540 2657.585\n",
|
|
"606 9600 4641.735\n",
|
|
"607 26043 9152.84\n",
|
|
"608 6103 10734.435\n",
|
|
"609 2747 4064.025\n",
|
|
"610 2960 6617.965\n",
|
|
"611 26043 7470.415\n",
|
|
"612 11444 6470.97\n",
|
|
"613 6608 6619.14\n",
|
|
"614 8685 4750.37\n",
|
|
"615 7809 2736.045\n",
|
|
"616 10910 16122.215\n",
|
|
"617 6015 8841.82\n",
|
|
"618 5233 5604.625\n",
|
|
"619 1425 18549.55\n",
|
|
"620 11160 15778.39\n",
|
|
"621 2105 7980.145\n",
|
|
"622 7428 9160.515\n",
|
|
"623 5204 4859.245\n",
|
|
"624 1851 3186.285\n",
|
|
"625 26043 3114.36\n",
|
|
"626 5426 3059.535\n",
|
|
"627 6219 16506.235\n",
|
|
"628 11212 10394.045\n",
|
|
"629 8124 2754.425\n",
|
|
"630 1982 17523.185\n",
|
|
"631 3694 5186.16\n",
|
|
"632 6075 2738.09\n",
|
|
"633 4561 2731.69\n",
|
|
"634 3042 4152.18\n",
|
|
"635 19747 6661.585\n",
|
|
"636 15145 2865.705\n",
|
|
"637 7072 3242.73\n",
|
|
"638 2582 2405.715\n",
|
|
"639 1425 4791.017083333334\n",
|
|
"640 5219 18050.315\n",
|
|
"641 7182 2914.96\n",
|
|
"642 8899 5130.16\n",
|
|
"643 6313 1917.815\n",
|
|
"644 2435 2274.995\n",
|
|
"645 3108 4957.4\n",
|
|
"646 12198 6797.66\n",
|
|
"647 5761 4253.455\n",
|
|
"648 8685 3508.57\n",
|
|
"649 8141 7389.745\n",
|
|
"650 9185 6143.315\n",
|
|
"651 5331 5715.955\n"
|
|
]
|
|
},
|
|
{
|
|
"ename": "IndexError",
|
|
"evalue": "index 652 is out of bounds for axis 0 with size 652",
|
|
"output_type": "error",
|
|
"traceback": [
|
|
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
|
"\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)",
|
|
"\u001b[0;32m/tmp/ipykernel_11017/1621740581.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_pred_rf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
|
|
"\u001b[0;31mIndexError\u001b[0m: index 652 is out of bounds for axis 0 with size 652"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"for i,v in enumerate(y):\n",
|
|
" print(i,v,y_pred_rf[i])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "bba1ad86",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3.7.13 ('leagues')",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.7.13"
|
|
},
|
|
"vscode": {
|
|
"interpreter": {
|
|
"hash": "a07b7f3079ca8c056705d3c757c4f3f92f9509f33eeab9ad5420dacec37bc01a"
|
|
}
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|