960 lines
61 KiB
Plaintext
960 lines
61 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).exclude(historic_season=None).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": 2,
|
|
"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": 3,
|
|
"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 GradientBoostingRegressor\n",
|
|
"\n",
|
|
"\n",
|
|
"remove_columns = ['season_id', 'resultEntered', 'reversible', 'reschedule', 'homeGoals', 'awayGoals',\n",
|
|
" 'homeGoals2', 'awayGoals2', 'homeGoals3', 'awayGoals3', 'home', 'away', 'date', 'time',\n",
|
|
" '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": 4,
|
|
"id": "45e08026",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Random Forest Regression Accuracy: 0.6976274695189291\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"rf_regressor = GradientBoostingRegressor(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": 5,
|
|
"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": 6,
|
|
"id": "4c1f8b45",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"0 4000 6269.331273160804\n",
|
|
"1 3264 2212.472559220525\n",
|
|
"2 6000 4474.321544046366\n",
|
|
"3 4250 5853.876279843164\n",
|
|
"4 1200 2724.8479999971523\n",
|
|
"5 4300 12248.194405029995\n",
|
|
"6 3874 12179.504105471333\n",
|
|
"7 2800 7179.957816540268\n",
|
|
"8 5500 4343.454242132351\n",
|
|
"9 6000 15615.670772432313\n",
|
|
"10 3500 2142.013025198476\n",
|
|
"11 4500 6581.086576491373\n",
|
|
"12 2140 2787.000793225451\n",
|
|
"13 3146 7948.163004948026\n",
|
|
"14 2600 5277.360995538767\n",
|
|
"15 4875 4182.090794311384\n",
|
|
"16 5807 1298.0546590068432\n",
|
|
"17 5200 7641.152367897079\n",
|
|
"18 3500 12709.91385314257\n",
|
|
"19 2643 13796.44798966877\n",
|
|
"20 4000 2910.0979973906897\n",
|
|
"21 2500 11367.203439146624\n",
|
|
"22 2000 4578.676165761646\n",
|
|
"23 3198 11801.197736843704\n",
|
|
"24 3571 3256.2683962230926\n",
|
|
"25 2712 13963.658569196019\n",
|
|
"26 2100 3428.2076365623343\n",
|
|
"27 4525 3179.082309242862\n",
|
|
"28 6625 5319.700728374097\n",
|
|
"29 4966 4814.201898718184\n",
|
|
"30 2000 11668.774282716573\n",
|
|
"31 2100 16459.759735440115\n",
|
|
"32 2310 10424.049272604385\n",
|
|
"33 2600 3249.7584854521983\n",
|
|
"34 2000 4933.417332760704\n",
|
|
"35 4300 11330.47572253357\n",
|
|
"36 2734 4916.62667788047\n",
|
|
"37 3500 7616.108008546191\n",
|
|
"38 3050 5834.707115002423\n",
|
|
"39 5256 10956.873320747929\n",
|
|
"40 3012 1746.8793669215172\n",
|
|
"41 5060 4642.521259437015\n",
|
|
"42 1500 10636.260356819768\n",
|
|
"43 4000 6226.573175422698\n",
|
|
"44 1950 6253.5032474003465\n",
|
|
"45 2300 5174.122252417559\n",
|
|
"46 2300 2724.3318310328705\n",
|
|
"47 1950 9804.743199526134\n",
|
|
"48 3058 3874.7670154643415\n",
|
|
"49 2000 10001.45516441721\n",
|
|
"50 2345 5023.8562523965875\n",
|
|
"51 5500 5174.122252417559\n",
|
|
"52 5585 3817.7336514404283\n",
|
|
"53 4046 4720.395073786049\n",
|
|
"54 6000 4569.1440149122045\n",
|
|
"55 4384 4918.984377964906\n",
|
|
"56 3000 4674.700091188421\n",
|
|
"57 3500 3544.0179791758733\n",
|
|
"58 7000 18261.80159810542\n",
|
|
"59 10195 13504.402960006135\n",
|
|
"60 3476 2439.2710475675904\n",
|
|
"61 2120 6683.8055866507775\n",
|
|
"62 3800 4831.40407430577\n",
|
|
"63 2675 18297.8862272201\n",
|
|
"64 6603 5900.839366216985\n",
|
|
"65 1500 5547.896256687706\n",
|
|
"66 4000 15716.448413829125\n",
|
|
"67 4545 3510.458423897759\n",
|
|
"68 1396 4299.864451241506\n",
|
|
"69 5400 11340.993418688882\n",
|
|
"70 5041 13344.492446272585\n",
|
|
"71 1500 12842.717635745401\n",
|
|
"72 1800 2826.6198584357285\n",
|
|
"73 3189 2216.6069457661515\n",
|
|
"74 3800 7551.124847543301\n",
|
|
"75 2178 10854.693947136366\n",
|
|
"76 6169 3754.054098366518\n",
|
|
"77 2676 6227.444878272318\n",
|
|
"78 3510 4372.710484500164\n",
|
|
"79 3900 5899.365613773657\n",
|
|
"80 2120 9146.338397438585\n",
|
|
"81 8000 6598.214034291334\n",
|
|
"82 2500 4180.2412129479535\n",
|
|
"83 4016 1013.1917302016966\n",
|
|
"84 1500 13356.059667395755\n",
|
|
"85 2860 3859.7092616602254\n",
|
|
"86 2150 12546.493369090958\n",
|
|
"87 3336 13781.48736284787\n",
|
|
"88 8820 6867.843525597063\n",
|
|
"89 4792 9977.417685682762\n",
|
|
"90 1650 23425.898130663616\n",
|
|
"91 2000 6589.051500702451\n",
|
|
"92 2111 3109.4945264556886\n",
|
|
"93 4470 13408.035401832343\n",
|
|
"94 7000 5519.4148042321\n",
|
|
"95 3850 6089.400396461468\n",
|
|
"96 4366 9610.498715489919\n",
|
|
"97 2645 8316.017866765993\n",
|
|
"98 2384 6197.035790504067\n",
|
|
"99 4522 12779.77200503699\n",
|
|
"100 6328 14744.016207170152\n",
|
|
"101 3877 19458.90971123009\n",
|
|
"102 2000 4874.224861034961\n",
|
|
"103 4157 5208.703053494322\n",
|
|
"104 2942 13386.059288545708\n",
|
|
"105 3655 13257.062956113012\n",
|
|
"106 4500 2441.3256508749882\n",
|
|
"107 2964 2416.608601291409\n",
|
|
"108 2863 2553.749826440011\n",
|
|
"109 4935 11198.828086622805\n",
|
|
"110 2526 12041.524132520446\n",
|
|
"111 5679 8045.757009722594\n",
|
|
"112 7286 13009.125855582837\n",
|
|
"113 6055 12448.19779590953\n",
|
|
"114 1200 11188.064346395086\n",
|
|
"115 4110 4271.21931546039\n",
|
|
"116 1957 3087.6529257626803\n",
|
|
"117 1790 2569.376378289409\n",
|
|
"118 5422 6173.099807850697\n",
|
|
"119 4650 12505.12258551472\n",
|
|
"120 5297 12652.949405216323\n",
|
|
"121 3036 6268.840108714523\n",
|
|
"122 3233 9367.596675826102\n",
|
|
"123 12000 2337.3771641354024\n",
|
|
"124 7632 1819.0748787672667\n",
|
|
"125 3620 7428.132912505317\n",
|
|
"126 2000 2124.849872483271\n",
|
|
"127 2145 6073.702450147759\n",
|
|
"128 2227 15691.707999141212\n",
|
|
"129 2520 17731.752052395845\n",
|
|
"130 10000 9224.693549735079\n",
|
|
"131 2000 4333.254162853479\n",
|
|
"132 4052 5463.585852647499\n",
|
|
"133 2137 3385.7917346498257\n",
|
|
"134 2609 8949.299267841006\n",
|
|
"135 3256 5594.136151838447\n",
|
|
"136 8173 7321.006115614261\n",
|
|
"137 1250 6314.334305949203\n",
|
|
"138 7401 1841.6632769675457\n",
|
|
"139 4200 5913.92021057092\n",
|
|
"140 6100 5914.498891029855\n",
|
|
"141 5182 4485.159036791194\n",
|
|
"142 1300 3930.555183694012\n",
|
|
"143 2117 2087.6087105330876\n",
|
|
"144 6500 3129.218824655229\n",
|
|
"145 5174 19100.66955411291\n",
|
|
"146 3867 7381.962946265084\n",
|
|
"147 1918 3668.522599331723\n",
|
|
"148 2800 5984.122720695453\n",
|
|
"149 7648 5579.807332727605\n",
|
|
"150 5638 5000.766880937242\n",
|
|
"151 5262 8796.640913054198\n",
|
|
"152 1650 11038.130604200423\n",
|
|
"153 1657 3053.4760351098107\n",
|
|
"154 4086 16594.785800693066\n",
|
|
"155 9000 3561.181162895718\n",
|
|
"156 1500 5042.543289014211\n",
|
|
"157 8145 12723.53945595642\n",
|
|
"158 2625 7117.118765444254\n",
|
|
"159 6281 3092.870875123699\n",
|
|
"160 4520 1916.9886938042089\n",
|
|
"161 1200 3334.4719289950585\n",
|
|
"162 4829 4248.693921085489\n",
|
|
"163 1760 3065.4160555916155\n",
|
|
"164 3469 10943.4771813175\n",
|
|
"165 7500 4646.274655834034\n",
|
|
"166 5227 7247.410995462059\n",
|
|
"167 1765 12061.617431570865\n",
|
|
"168 5200 3906.7362684340383\n",
|
|
"169 6402 5802.18818098374\n",
|
|
"170 4832 3735.241252929307\n",
|
|
"171 1500 8821.101907961975\n",
|
|
"172 2342 3612.477133461679\n",
|
|
"173 2799 908.2647209562191\n",
|
|
"174 3850 3078.4680731926246\n",
|
|
"175 4200 3812.544744635514\n",
|
|
"176 4531 2705.9998772537424\n",
|
|
"177 1751 1967.6894728073173\n",
|
|
"178 4250 5924.714538352343\n",
|
|
"179 5705 6513.58136727248\n",
|
|
"180 3528 1966.268176067289\n",
|
|
"181 2496 4368.04545595507\n",
|
|
"182 4370 2024.6278361643576\n",
|
|
"183 1350 5634.107750848085\n",
|
|
"184 5334 10956.873320747929\n",
|
|
"185 1423 6752.977449121771\n",
|
|
"186 4129 4490.334901030411\n",
|
|
"187 5858 2706.770656503444\n",
|
|
"188 3300 4860.877678091679\n",
|
|
"189 3500 3893.6704796446884\n",
|
|
"190 10280 3778.999270841025\n",
|
|
"191 4500 7722.956089373864\n",
|
|
"192 10500 3823.8621517621054\n",
|
|
"193 3932 5270.396448543778\n",
|
|
"194 5500 7707.251425986663\n",
|
|
"195 2200 14389.91484985415\n",
|
|
"196 8206 6044.3459440998595\n",
|
|
"197 8000 4457.480201516091\n",
|
|
"198 6372 8890.061129175148\n",
|
|
"199 7900 13621.063242184871\n",
|
|
"200 1628 13169.821966149577\n",
|
|
"201 4142 3606.329399178841\n",
|
|
"202 1150 6794.3761007737885\n",
|
|
"203 1750 3527.0690712828796\n",
|
|
"204 4072 4870.13044656876\n",
|
|
"205 1176 14005.1555660555\n",
|
|
"206 3200 5021.013261618377\n",
|
|
"207 1715 10318.676591702464\n",
|
|
"208 7530 10459.245297486614\n",
|
|
"209 4600 8316.395535933456\n",
|
|
"210 1200 5095.800766098369\n",
|
|
"211 4452 17619.45148176299\n",
|
|
"212 2400 2917.6212527362745\n",
|
|
"213 4057 3523.9765596992324\n",
|
|
"214 5000 7568.184421468349\n",
|
|
"215 4147 2328.238657453435\n",
|
|
"216 3046 9816.145570783112\n",
|
|
"217 6215 2815.1845153039812\n",
|
|
"218 3350 2458.220350724582\n",
|
|
"219 3500 1143.4289915252082\n",
|
|
"220 5870 3349.522734018162\n",
|
|
"221 4113 6562.149215912538\n",
|
|
"222 3420 15899.046839388688\n",
|
|
"223 6116 9045.668754767581\n",
|
|
"224 5902 2748.9553238280632\n",
|
|
"225 3787 4271.853869225162\n",
|
|
"226 3600 7439.183383616434\n",
|
|
"227 7017 2210.108006140514\n",
|
|
"228 10280 4927.066871405887\n",
|
|
"229 5600 9564.813393139277\n",
|
|
"230 3656 3329.8106332184043\n",
|
|
"231 6480 17110.713931585673\n",
|
|
"232 1646 4677.074303376587\n",
|
|
"233 2600 4440.882887387751\n",
|
|
"234 4300 2118.7542046100843\n",
|
|
"235 7948 3214.332854949672\n",
|
|
"236 4579 7753.321604795282\n",
|
|
"237 4364 6460.580153817578\n",
|
|
"238 5000 3400.25051693908\n",
|
|
"239 2034 4284.09527513928\n",
|
|
"240 3500 5231.567751256019\n",
|
|
"241 5688 6856.195658240203\n",
|
|
"242 1800 12184.56551356091\n",
|
|
"243 10131 2598.8215825730167\n",
|
|
"244 5784 5943.724350980219\n",
|
|
"245 1813 4361.9722090698815\n",
|
|
"246 3700 5182.000108363626\n",
|
|
"247 6700 5850.0908748940765\n",
|
|
"248 3700 2399.8908348974273\n",
|
|
"249 7799 1934.7516235634123\n",
|
|
"250 1884 11765.60702403286\n",
|
|
"251 3042 4656.923175922518\n",
|
|
"252 5000 13415.8010505132\n",
|
|
"253 5112 13024.815014128593\n",
|
|
"254 1404 3154.9780459262442\n",
|
|
"255 2471 4309.257915103401\n",
|
|
"256 7749 10739.861964061618\n",
|
|
"257 6254 3516.7357048629206\n",
|
|
"258 2502 11979.741281816012\n",
|
|
"259 2300 3650.093842398789\n",
|
|
"260 6500 11606.67581375423\n",
|
|
"261 2646 2003.5526871642137\n",
|
|
"262 9546 3357.754620510872\n",
|
|
"263 7500 1954.3537407404217\n",
|
|
"264 11016 5754.557478633303\n",
|
|
"265 5763 9554.299673198937\n",
|
|
"266 2460 9835.688834354334\n",
|
|
"267 5511 3086.8504515496356\n",
|
|
"268 1857 12616.742317378286\n",
|
|
"269 7000 3003.607544650991\n",
|
|
"270 6333 2916.8409179975624\n",
|
|
"271 6107 4089.944537354285\n",
|
|
"272 1518 2032.35131917741\n",
|
|
"273 9310 13077.88651001709\n",
|
|
"274 3551 5538.163168934835\n",
|
|
"275 1700 5176.458388224016\n",
|
|
"276 2250 6964.458867728808\n",
|
|
"277 6000 3658.89357449004\n",
|
|
"278 2003 12082.974153438317\n",
|
|
"279 15183 3440.5699078224566\n",
|
|
"280 7113 3704.6762193220106\n",
|
|
"281 3818 7853.080965569755\n",
|
|
"282 12300 4595.931196930642\n",
|
|
"283 12488 4204.879371769705\n",
|
|
"284 8000 14443.56582836753\n",
|
|
"285 10832 6863.813679372925\n",
|
|
"286 2107 6377.807460294606\n",
|
|
"287 2100 11228.724002036284\n",
|
|
"288 14135 4931.900522781224\n",
|
|
"289 6115 -1359.8525015661405\n",
|
|
"290 9364 7649.975494379696\n",
|
|
"291 4773 9481.483722041376\n",
|
|
"292 3525 3903.097436115244\n",
|
|
"293 6126 3235.0879045787524\n",
|
|
"294 6487 6016.561185150087\n",
|
|
"295 3879 5963.8817259278985\n",
|
|
"296 4943 2022.7875651402155\n",
|
|
"297 1335 16308.04344887568\n",
|
|
"298 4125 3686.2401457869464\n",
|
|
"299 7986 19570.539544136012\n",
|
|
"300 5000 1647.3199417777416\n",
|
|
"301 3559 10844.540513558119\n",
|
|
"302 6573 6749.5080657275785\n",
|
|
"303 2300 7761.8563105694675\n",
|
|
"304 5117 6850.569455542703\n",
|
|
"305 5000 6749.5080657275785\n",
|
|
"306 7165 7562.513169172154\n",
|
|
"307 1406 4792.945224334402\n",
|
|
"308 12300 5438.795622303128\n",
|
|
"309 3573 4076.6328454195104\n",
|
|
"310 6500 6484.405267932639\n",
|
|
"311 4508 4454.794170262632\n",
|
|
"312 7546 3612.3231311814357\n",
|
|
"313 5413 6996.1341583386675\n",
|
|
"314 5754 3103.9097707577357\n",
|
|
"315 1307 3807.778748518246\n",
|
|
"316 5433 3244.8492954237204\n",
|
|
"317 2304 3555.7092877099694\n",
|
|
"318 4000 15777.67435290044\n",
|
|
"319 6425 7309.990757727653\n",
|
|
"320 7250 11630.888375951426\n",
|
|
"321 5500 8636.615133701885\n",
|
|
"322 1800 8768.014881915431\n",
|
|
"323 2240 4826.610795633974\n",
|
|
"324 9000 10890.956739194247\n",
|
|
"325 1266 5814.283121498163\n",
|
|
"326 3850 6443.817549805847\n",
|
|
"327 2122 4535.813236908496\n",
|
|
"328 6423 4517.133895929519\n",
|
|
"329 6455 12060.20986179786\n",
|
|
"330 2100 9687.757282298406\n",
|
|
"331 7843 5535.984701360246\n",
|
|
"332 9617 3697.898412456338\n",
|
|
"333 5033 13417.324343888626\n",
|
|
"334 1129 1900.0831637980234\n",
|
|
"335 1500 9359.137469515703\n",
|
|
"336 8932 4625.923230167911\n",
|
|
"337 4637 6298.491882691631\n",
|
|
"338 15327 8789.796058286662\n",
|
|
"339 1233 3349.3518456663473\n",
|
|
"340 2364 5489.380456222845\n",
|
|
"341 10316 10482.794651236341\n",
|
|
"342 13200 816.4886171815257\n",
|
|
"343 1303 2434.5700084840264\n",
|
|
"344 8687 1339.7548137515469\n",
|
|
"345 1653 14174.38517292645\n",
|
|
"346 7067 4769.726084812967\n",
|
|
"347 8265 6296.217972893376\n",
|
|
"348 1587 3735.0734883030373\n",
|
|
"349 2479 9172.144059825323\n",
|
|
"350 6366 2943.565074257849\n",
|
|
"351 5114 4607.856876244279\n",
|
|
"352 6138 6525.133501060625\n",
|
|
"353 1765 5306.257077971849\n",
|
|
"354 3129 1846.9373281342548\n",
|
|
"355 2295 3398.94891754905\n",
|
|
"356 5507 6787.25326071226\n",
|
|
"357 5200 11426.371872238831\n",
|
|
"358 6326 13253.411199790291\n",
|
|
"359 10804 12321.66926933166\n",
|
|
"360 6721 7784.752069975153\n",
|
|
"361 5574 3078.4680731926246\n",
|
|
"362 10020 5279.60487971954\n",
|
|
"363 3678 3703.664495952156\n",
|
|
"364 4342 6008.626463397685\n",
|
|
"365 8000 12218.65449210225\n",
|
|
"366 1687 4676.4051865459105\n",
|
|
"367 1967 6462.389387331538\n",
|
|
"368 27252 4596.524835292175\n",
|
|
"369 20520 10707.201673752295\n",
|
|
"370 10000 16951.68427825804\n",
|
|
"371 1661 5088.693439115733\n",
|
|
"372 1356 4243.341369783109\n",
|
|
"373 8000 13092.177864088904\n",
|
|
"374 6288 7390.894960432231\n",
|
|
"375 9979 13110.150275789669\n",
|
|
"376 3083 9102.1199952011\n",
|
|
"377 1574 12359.06343032517\n",
|
|
"378 10452 3163.5893828816875\n",
|
|
"379 4790 4611.20167092646\n",
|
|
"380 5563 4506.779900903538\n",
|
|
"381 1103 5630.433669946042\n",
|
|
"382 3846 14705.914690376329\n",
|
|
"383 3750 1678.928854632361\n",
|
|
"384 4309 4188.9078922720755\n",
|
|
"385 6254 2185.786798925416\n",
|
|
"386 2133 5240.602438128821\n",
|
|
"387 12800 6440.899480264031\n",
|
|
"388 5300 2202.973850976274\n",
|
|
"389 10102 3349.487579181281\n",
|
|
"390 9326 6276.255533517775\n",
|
|
"391 2613 10027.074674303978\n",
|
|
"392 11976 3863.6273789168304\n",
|
|
"393 12143 3393.520429895661\n",
|
|
"394 13200 3769.0173149934762\n",
|
|
"395 6320 4363.984786293422\n",
|
|
"396 1542 12775.267839770944\n",
|
|
"397 4560 4771.697760449484\n",
|
|
"398 8304 4637.7607774767075\n",
|
|
"399 10480 4525.181055134041\n",
|
|
"400 5352 6595.799090340065\n",
|
|
"401 2137 6969.014509412282\n",
|
|
"402 5169 4750.975859023578\n",
|
|
"403 2799 11080.256944317674\n",
|
|
"404 6000 2003.8289254279437\n",
|
|
"405 4986 4197.359917287667\n",
|
|
"406 1824 4332.950723087893\n",
|
|
"407 1562 5099.093054122171\n",
|
|
"408 5890 12081.878696518148\n",
|
|
"409 6077 9270.461413927558\n",
|
|
"410 1485 1496.4321088051206\n",
|
|
"411 1825 4246.200510854761\n",
|
|
"412 5340 3255.3624000842133\n",
|
|
"413 9237 1859.449426524448\n",
|
|
"414 6499 7976.974080873097\n",
|
|
"415 1240 4436.075476859059\n",
|
|
"416 4656 3617.4469653780425\n",
|
|
"417 2335 4997.427948460792\n",
|
|
"418 8000 8685.398728410411\n",
|
|
"419 5641 8353.951860004401\n",
|
|
"420 1444 8676.625836361147\n",
|
|
"421 12900 2321.255461480635\n",
|
|
"422 6500 4629.528409764403\n",
|
|
"423 7506 13467.919271136741\n",
|
|
"424 6438 7930.93744973132\n",
|
|
"425 2261 13370.226646925103\n",
|
|
"426 2121 6247.557132112603\n",
|
|
"427 5437 3592.393531883706\n",
|
|
"428 1536 3084.700451088983\n",
|
|
"429 9295 11590.303689144444\n",
|
|
"430 3252 6586.4530811986915\n",
|
|
"431 1331 2974.1520911988323\n",
|
|
"432 5442 10642.588370514337\n",
|
|
"433 4527 4146.7680464203095\n",
|
|
"434 6500 5535.980475001773\n",
|
|
"435 1238 4462.334896687268\n",
|
|
"436 10702 3998.7386180649096\n",
|
|
"437 8056 6374.403929120289\n",
|
|
"438 4517 3968.6353947354496\n",
|
|
"439 5108 11261.565350761522\n",
|
|
"440 6354 8418.830560829478\n",
|
|
"441 4322 3305.0788833275337\n",
|
|
"442 3129 5121.086170529202\n",
|
|
"443 2486 17408.009128939066\n",
|
|
"444 6200 4741.563419125667\n",
|
|
"445 10320 5313.293980930455\n",
|
|
"446 5204 11883.544862171222\n",
|
|
"447 7429 11135.61940509253\n",
|
|
"448 1837 5034.717550057033\n",
|
|
"449 3311 5410.885965608366\n",
|
|
"450 5425 4536.934131183477\n",
|
|
"451 1141 9035.881634012976\n",
|
|
"452 8142 3094.3729707170237\n",
|
|
"453 9630 5759.575130507457\n",
|
|
"454 3400 2189.066685634728\n",
|
|
"455 5991 8549.539484234854\n",
|
|
"456 4537 2673.967338892564\n",
|
|
"457 1389 4718.1351657552605\n",
|
|
"458 6560 12005.006870572877\n",
|
|
"459 5417 8357.181906814383\n",
|
|
"460 1326 11175.583517484314\n",
|
|
"461 1226 11704.844017633459\n",
|
|
"462 9439 7822.772984507\n",
|
|
"463 6075 3640.18245452465\n",
|
|
"464 4139 12954.348380007397\n",
|
|
"465 6921 4400.219099707537\n",
|
|
"466 1412 3860.7463982703334\n",
|
|
"467 1580 3516.678313124346\n",
|
|
"468 6480 3492.9019039587733\n",
|
|
"469 7740 8106.980110824186\n",
|
|
"470 9187 2395.5573418076624\n",
|
|
"471 5923 15299.331508342839\n",
|
|
"472 1690 10362.006379124352\n",
|
|
"473 1829 9487.82923378465\n",
|
|
"474 13132 12486.558265969294\n",
|
|
"475 5673 14305.952439617615\n",
|
|
"476 10143 14864.146909677464\n",
|
|
"477 2631 2331.8590852138564\n",
|
|
"478 16753 3373.2896869040014\n",
|
|
"479 8300 6524.596616257537\n",
|
|
"480 2541 9190.959336462798\n",
|
|
"481 1638 8198.234066736764\n",
|
|
"482 6097 15793.35353841358\n",
|
|
"483 8250 10749.527129598226\n",
|
|
"484 1638 2035.970947988072\n",
|
|
"485 1145 4696.342706754138\n",
|
|
"486 8300 4612.694230043811\n",
|
|
"487 9750 12698.282535481198\n",
|
|
"488 12532 16313.121000032259\n",
|
|
"489 10739 4867.058831371186\n",
|
|
"490 18230 8459.589175229492\n",
|
|
"491 6125 9447.347582186656\n",
|
|
"492 6225 4050.031770291008\n",
|
|
"493 16509 3119.674368698192\n",
|
|
"494 6782 15229.554761308495\n",
|
|
"495 6125 6761.654267834814\n",
|
|
"496 1681 16165.79694411498\n",
|
|
"497 1798 5819.205951991664\n",
|
|
"498 13385 4938.678542279483\n",
|
|
"499 12300 12501.32563965754\n",
|
|
"500 4100 6001.137996121012\n",
|
|
"501 6190 1475.6160798039427\n",
|
|
"502 9246 3958.254475739062\n",
|
|
"503 14322 5117.760831637467\n",
|
|
"504 7396 3351.115059928001\n",
|
|
"505 3851 11349.992398228365\n",
|
|
"506 4734 2254.841749289375\n",
|
|
"507 2058 4497.8123659139665\n",
|
|
"508 8869 6254.021135129916\n",
|
|
"509 11269 5503.560118534005\n",
|
|
"510 2506 3923.4245548541553\n",
|
|
"511 11730 13493.388189645208\n",
|
|
"512 8045 11793.907143350536\n",
|
|
"513 7500 19150.25573472374\n",
|
|
"514 9166 1503.1775986772748\n",
|
|
"515 5368 768.7325537958045\n",
|
|
"516 2395 11349.634056006476\n",
|
|
"517 9087 6323.225289121863\n",
|
|
"518 7407 12191.765888863798\n",
|
|
"519 5949 4662.532235685591\n",
|
|
"520 10216 1407.9870443357727\n",
|
|
"521 4731 2077.9540157496162\n",
|
|
"522 9248 7446.569623693983\n",
|
|
"523 18500 7152.090683775011\n",
|
|
"524 6308 2046.8540380357522\n",
|
|
"525 5748 7367.288137184615\n",
|
|
"526 3138 13384.130363257449\n",
|
|
"527 2012 10653.209976527616\n",
|
|
"528 8657 4045.4916586458935\n",
|
|
"529 7500 5925.985098665058\n",
|
|
"530 1463 16696.570686355375\n",
|
|
"531 7625 8283.901419829008\n",
|
|
"532 17260 1332.645261264805\n",
|
|
"533 7020 8097.968034350275\n",
|
|
"534 6592 12655.393492203057\n",
|
|
"535 1463 9686.161910222057\n",
|
|
"536 5112 14887.791991132963\n",
|
|
"537 9672 11980.464991642053\n",
|
|
"538 5360 3200.7882786135533\n",
|
|
"539 7338 4568.898795584218\n",
|
|
"540 4113 4548.0218263260895\n",
|
|
"541 5443 6464.617670339604\n",
|
|
"542 7368 3509.1119407497345\n",
|
|
"543 8017 14989.39181389453\n",
|
|
"544 8619 5951.016907958763\n",
|
|
"545 2651 2389.4447101228966\n",
|
|
"546 14840 5539.6976673052395\n",
|
|
"547 6041 5739.887811430656\n",
|
|
"548 8685 4713.845075975436\n",
|
|
"549 1252 10368.07303045706\n",
|
|
"550 2655 6283.479460803347\n",
|
|
"551 15140 10087.168600437875\n",
|
|
"552 7885 5075.27707615952\n",
|
|
"553 8685 8212.189918949982\n",
|
|
"554 7542 5046.9757285848955\n",
|
|
"555 4676 12393.032671740262\n",
|
|
"556 2450 4089.113201090722\n",
|
|
"557 7225 8668.848872884808\n",
|
|
"558 18500 3625.579521584379\n",
|
|
"559 8499 11175.583517484314\n",
|
|
"560 5057 11954.107603250402\n",
|
|
"561 8418 9871.105936883523\n",
|
|
"562 22885 4480.13584030295\n",
|
|
"563 2820 12949.498758756125\n",
|
|
"564 1868 9863.602442864936\n",
|
|
"565 2523 7594.763347975478\n",
|
|
"566 10058 4106.730423429261\n",
|
|
"567 7138 6882.786147983847\n",
|
|
"568 7610 8398.894749936191\n",
|
|
"569 2670 3175.8554412316626\n",
|
|
"570 2364 12814.987663022268\n",
|
|
"571 8435 5162.810338028615\n",
|
|
"572 8841 1036.8156262763082\n",
|
|
"573 15240 6022.001956780606\n",
|
|
"574 10180 10816.936615359207\n",
|
|
"575 12534 6207.885794866743\n",
|
|
"576 20520 4784.208803197017\n",
|
|
"577 13500 2214.8851307287455\n",
|
|
"578 5000 3893.0598787794556\n",
|
|
"579 12813 6018.340623795528\n",
|
|
"580 7050 4794.636511757451\n",
|
|
"581 6665 719.7944431444473\n",
|
|
"582 16350 12795.036594803416\n",
|
|
"583 25623 14184.768449790256\n",
|
|
"584 2063 9810.563762098913\n",
|
|
"585 3393 4663.9954601549625\n",
|
|
"586 2217 8773.41625094894\n",
|
|
"587 9003 3036.44495239287\n",
|
|
"588 14470 14674.536778589223\n",
|
|
"589 7603 7067.956282785245\n",
|
|
"590 8685 4819.903639940648\n",
|
|
"591 6436 3119.275984646008\n",
|
|
"592 6112 13083.263275048763\n",
|
|
"593 6127 4357.32710824466\n",
|
|
"594 1373 15735.44034049763\n",
|
|
"595 8046 9829.34768547027\n",
|
|
"596 6865 3214.003054487506\n",
|
|
"597 8286 5601.510414450099\n",
|
|
"598 6302 4623.138939835301\n",
|
|
"599 2208 11173.469568286891\n",
|
|
"600 3615 9099.567629826535\n",
|
|
"601 15940 1128.3453286817544\n",
|
|
"602 5010 12327.197143168583\n",
|
|
"603 8212 2358.011077505077\n",
|
|
"604 1272 3507.632555908359\n",
|
|
"605 2540 3225.323372800012\n",
|
|
"606 9600 4090.038562587634\n",
|
|
"607 26043 9172.521869455777\n",
|
|
"608 6103 9608.33642905634\n",
|
|
"609 2747 3357.020239766667\n",
|
|
"610 2960 6086.4085631268035\n",
|
|
"611 26043 7352.588636862007\n",
|
|
"612 11444 5749.178712087224\n",
|
|
"613 6608 7009.789042663147\n",
|
|
"614 8685 4860.877678091679\n",
|
|
"615 7809 2574.283478221735\n",
|
|
"616 10910 16177.070094482096\n",
|
|
"617 6015 7330.670097266955\n",
|
|
"618 5233 5874.080532458615\n",
|
|
"619 1425 13376.351664670756\n",
|
|
"620 11160 11489.591677776654\n",
|
|
"621 2105 8093.267208688064\n",
|
|
"622 7428 12065.410201066788\n",
|
|
"623 5204 4090.2545827148147\n",
|
|
"624 1851 4009.0006330108404\n",
|
|
"625 26043 3438.1572509050984\n",
|
|
"626 5426 2253.846596804711\n",
|
|
"627 6219 17137.025597423733\n",
|
|
"628 11212 8852.762121006672\n",
|
|
"629 8124 2764.2110979500026\n",
|
|
"630 1982 16415.374793629482\n",
|
|
"631 3694 8244.824334909112\n",
|
|
"632 6075 2457.9006791839233\n",
|
|
"633 4561 2321.330005122368\n",
|
|
"634 3042 4222.503424740425\n",
|
|
"635 19747 8223.799821601031\n",
|
|
"636 15145 1871.3160227674202\n",
|
|
"637 7072 4620.709006674663\n",
|
|
"638 2582 2230.203623949988\n",
|
|
"639 1425 5754.610018189932\n",
|
|
"640 5219 18795.832958315765\n",
|
|
"641 7182 2545.237704767577\n",
|
|
"642 8899 4793.967670294426\n",
|
|
"643 6313 3355.7302621566423\n",
|
|
"644 2435 1977.784883709689\n",
|
|
"645 3108 5286.771821824771\n",
|
|
"646 12198 6863.347568508702\n",
|
|
"647 5761 3753.5604597260553\n",
|
|
"648 8685 3501.396202209124\n",
|
|
"649 8141 6710.759936928313\n",
|
|
"650 9185 6449.711014158032\n",
|
|
"651 5331 6111.495678866401\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_56157/1621740581.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": 7,
|
|
"id": "bba1ad86",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"2171"
|
|
]
|
|
},
|
|
"execution_count": 7,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"len(y)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "970a3733",
|
|
"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
|
|
}
|