{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_csv(file):\n",
    "    return pd.read_csv(file)\n",
    "\n",
    "def cleanup_data(data):\n",
    "    # remove games when he didn't play\n",
    "    data = data.drop(data[data.G.isnull()].index)\n",
    "\n",
    "    # normalize age to years\n",
    "    data['Age'] = data['Age'].str.split('-', 1).str[0].astype(int)\n",
    "\n",
    "    # normalize minutes to exclude seconds\n",
    "    data['MP'] = data[\"MP\"].str.split(\":\").str[0].astype(int)\n",
    "    \n",
    "    data['PTS'] = data['PTS'].astype(int)\n",
    "    return data\n",
    "\n",
    "def read_and_clean_csv(file, columns=[]):\n",
    "    data = read_csv(file)\n",
    "    clean_data = cleanup_data(data)\n",
    "    return clean_data[columns]\n",
    "\n",
    "csv_label_name = \"PTS\" # points per game \"+/-\"\n",
    "feature_names = [\"G\", \"MP\", \"Age\"]\n",
    "csv_column_names = feature_names + [csv_label_name]\n",
    "\n",
    "feature_columns = [\n",
    "    tf.feature_column.numeric_column(feature) for feature in feature_names\n",
    "]\n",
    "\n",
    "def read_seasons(seasons=range(2002, 2018)):\n",
    "    stats = []\n",
    "    for season in seasons:\n",
    "        file = str(season) + \".csv\"\n",
    "        season_stat = read_and_clean_csv(file, columns=csv_column_names)\n",
    "        stats.append(season_stat)\n",
    "    return pd.concat(stats)\n",
    "\n",
    "stats = read_seasons()\n",
    "train_factor = 0.8\n",
    "train, test = stats[:int(len(stats)*train_factor)], stats[int(len(stats)*train_factor):]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test:(212, 4) train: (845, 4) stats: (1057, 4)\n"
     ]
    }
   ],
   "source": [
    "print(\"test:\" + str(test.shape) + \" train: \" + str(train.shape) + \" stats: \" + str(stats.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_fn_train = tf.estimator.inputs.pandas_input_fn(\n",
    "    x = pd.DataFrame({\n",
    "        \"Age\" : train[\"Age\"].values,\n",
    "        \"MP\" : train[\"MP\"].values,\n",
    "        \"G\" : train[\"G\"].values\n",
    "    }),\n",
    "    y = pd.Series(train.PTS.values),\n",
    "    shuffle=False,\n",
    "    num_epochs=20000\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Using default config.\n",
      "WARNING:tensorflow:Using temporary folder as model directory: /var/folders/55/qkrngbr949x1jzs0c0dt70wc0000gn/T/tmp6aleB_\n",
      "INFO:tensorflow:Using config: {'_save_checkpoints_secs': 600, '_session_config': None, '_keep_checkpoint_max': 5, '_task_type': 'worker', '_train_distribute': None, '_is_chief': True, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x119d64a90>, '_evaluation_master': '', '_save_checkpoints_steps': None, '_keep_checkpoint_every_n_hours': 10000, '_service': None, '_num_ps_replicas': 0, '_tf_random_seed': None, '_master': '', '_num_worker_replicas': 1, '_task_id': 0, '_log_step_count_steps': 100, '_model_dir': '/var/folders/55/qkrngbr949x1jzs0c0dt70wc0000gn/T/tmp6aleB_', '_global_id_in_cluster': 0, '_save_summary_steps': 100}\n"
     ]
    }
   ],
   "source": [
    "estimator = tf.estimator.DNNRegressor(hidden_units=[5, 5], feature_columns=feature_columns)\n",
    "# estimator = tf.estimator.LinearRegressor(feature_columns=feature_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Calling model_fn.\n",
      "INFO:tensorflow:Done calling model_fn.\n",
      "INFO:tensorflow:Create CheckpointSaverHook.\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Saving checkpoints for 1 into /var/folders/55/qkrngbr949x1jzs0c0dt70wc0000gn/T/tmp6aleB_/model.ckpt.\n",
      "INFO:tensorflow:loss = 11780.277, step = 1\n",
      "INFO:tensorflow:global_step/sec: 483.334\n",
      "INFO:tensorflow:loss = 4870.778, step = 101 (0.211 sec)\n",
      "INFO:tensorflow:global_step/sec: 542.573\n",
      "INFO:tensorflow:loss = 5737.624, step = 201 (0.182 sec)\n",
      "INFO:tensorflow:global_step/sec: 567.927\n",
      "INFO:tensorflow:loss = 6592.8374, step = 301 (0.178 sec)\n",
      "INFO:tensorflow:global_step/sec: 555.549\n",
      "INFO:tensorflow:loss = 4664.8516, step = 401 (0.178 sec)\n",
      "INFO:tensorflow:global_step/sec: 551.746\n",
      "INFO:tensorflow:loss = 3528.5503, step = 501 (0.181 sec)\n",
      "INFO:tensorflow:global_step/sec: 576.426\n",
      "INFO:tensorflow:loss = 3874.3115, step = 601 (0.174 sec)\n",
      "INFO:tensorflow:global_step/sec: 521.251\n",
      "INFO:tensorflow:loss = 5857.655, step = 701 (0.191 sec)\n",
      "INFO:tensorflow:global_step/sec: 513.901\n",
      "INFO:tensorflow:loss = 3966.4067, step = 801 (0.196 sec)\n",
      "INFO:tensorflow:global_step/sec: 547.201\n",
      "INFO:tensorflow:loss = 7186.493, step = 901 (0.182 sec)\n",
      "INFO:tensorflow:global_step/sec: 574.564\n",
      "INFO:tensorflow:loss = 5782.948, step = 1001 (0.174 sec)\n",
      "INFO:tensorflow:global_step/sec: 559.51\n",
      "INFO:tensorflow:loss = 4811.618, step = 1101 (0.180 sec)\n",
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      "INFO:tensorflow:loss = 3870.3435, step = 1201 (0.179 sec)\n",
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      "INFO:tensorflow:loss = 3749.3418, step = 1301 (0.175 sec)\n",
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      "INFO:tensorflow:loss = 5511.0107, step = 1401 (0.191 sec)\n",
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      "INFO:tensorflow:loss = 4344.091, step = 1501 (0.272 sec)\n",
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      "INFO:tensorflow:loss = 7747.7563, step = 1601 (0.297 sec)\n",
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      "INFO:tensorflow:loss = 5185.41, step = 1701 (0.295 sec)\n",
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      "INFO:tensorflow:loss = 4030.0234, step = 2001 (0.273 sec)\n",
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      "INFO:tensorflow:loss = 5182.0327, step = 2101 (0.275 sec)\n",
      "INFO:tensorflow:global_step/sec: 371.273\n",
      "INFO:tensorflow:loss = 4995.22, step = 2201 (0.271 sec)\n",
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      "INFO:tensorflow:loss = 6698.9805, step = 2301 (0.252 sec)\n",
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      "INFO:tensorflow:loss = 5240.0957, step = 2401 (0.183 sec)\n",
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      "INFO:tensorflow:loss = 3489.7217, step = 2501 (0.194 sec)\n",
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      "INFO:tensorflow:loss = 4645.6396, step = 2801 (0.213 sec)\n",
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      "INFO:tensorflow:loss = 3673.1335, step = 3901 (0.228 sec)\n",
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      "INFO:tensorflow:loss = 3722.8765, step = 4001 (0.276 sec)\n",
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      "INFO:tensorflow:global_step/sec: 611.098\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:loss = 4245.5166, step = 8001 (0.166 sec)\n",
      "INFO:tensorflow:global_step/sec: 512.004\n",
      "INFO:tensorflow:loss = 4224.4185, step = 8101 (0.194 sec)\n",
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      "INFO:tensorflow:global_step/sec: 445.957\n",
      "INFO:tensorflow:loss = 4349.967, step = 12601 (0.226 sec)\n",
      "INFO:tensorflow:global_step/sec: 468.011\n",
      "INFO:tensorflow:loss = 3832.1284, step = 12701 (0.213 sec)\n",
      "INFO:tensorflow:global_step/sec: 453.673\n",
      "INFO:tensorflow:loss = 3624.3105, step = 12801 (0.220 sec)\n",
      "INFO:tensorflow:global_step/sec: 555.028\n",
      "INFO:tensorflow:loss = 5314.712, step = 12901 (0.180 sec)\n",
      "INFO:tensorflow:global_step/sec: 522.793\n",
      "INFO:tensorflow:loss = 3963.271, step = 13001 (0.193 sec)\n",
      "INFO:tensorflow:global_step/sec: 427.635\n",
      "INFO:tensorflow:loss = 7261.0635, step = 13101 (0.232 sec)\n",
      "INFO:tensorflow:global_step/sec: 422.792\n",
      "INFO:tensorflow:loss = 5316.6904, step = 13201 (0.240 sec)\n",
      "INFO:tensorflow:global_step/sec: 398.584\n",
      "INFO:tensorflow:loss = 3509.518, step = 13301 (0.250 sec)\n",
      "INFO:tensorflow:global_step/sec: 477.676\n",
      "INFO:tensorflow:loss = 3950.3757, step = 13401 (0.208 sec)\n",
      "INFO:tensorflow:global_step/sec: 537.323\n",
      "INFO:tensorflow:loss = 4207.055, step = 13501 (0.187 sec)\n",
      "INFO:tensorflow:global_step/sec: 514.973\n",
      "INFO:tensorflow:loss = 4895.705, step = 13601 (0.195 sec)\n",
      "INFO:tensorflow:global_step/sec: 549.701\n",
      "INFO:tensorflow:loss = 4922.595, step = 13701 (0.182 sec)\n",
      "INFO:tensorflow:global_step/sec: 550.727\n",
      "INFO:tensorflow:loss = 6584.413, step = 13801 (0.181 sec)\n",
      "INFO:tensorflow:global_step/sec: 529.659\n",
      "INFO:tensorflow:loss = 4810.9863, step = 13901 (0.188 sec)\n",
      "INFO:tensorflow:global_step/sec: 522.122\n",
      "INFO:tensorflow:loss = 3672.9873, step = 14001 (0.192 sec)\n",
      "INFO:tensorflow:global_step/sec: 512.361\n",
      "INFO:tensorflow:loss = 3960.1, step = 14101 (0.194 sec)\n",
      "INFO:tensorflow:global_step/sec: 482.565\n",
      "INFO:tensorflow:loss = 4306.886, step = 14201 (0.213 sec)\n",
      "INFO:tensorflow:global_step/sec: 505.015\n",
      "INFO:tensorflow:loss = 4530.63, step = 14301 (0.195 sec)\n",
      "INFO:tensorflow:global_step/sec: 505.712\n",
      "INFO:tensorflow:loss = 5932.661, step = 14401 (0.196 sec)\n",
      "INFO:tensorflow:global_step/sec: 540.883\n",
      "INFO:tensorflow:loss = 6007.171, step = 14501 (0.184 sec)\n",
      "INFO:tensorflow:global_step/sec: 470.354\n",
      "INFO:tensorflow:loss = 4717.772, step = 14601 (0.213 sec)\n",
      "INFO:tensorflow:global_step/sec: 567.682\n",
      "INFO:tensorflow:loss = 3565.5312, step = 14701 (0.176 sec)\n",
      "INFO:tensorflow:global_step/sec: 546.726\n",
      "INFO:tensorflow:loss = 3784.0513, step = 14801 (0.184 sec)\n",
      "INFO:tensorflow:global_step/sec: 515.349\n",
      "INFO:tensorflow:loss = 5295.657, step = 14901 (0.194 sec)\n",
      "INFO:tensorflow:global_step/sec: 504.783\n",
      "INFO:tensorflow:loss = 3765.6045, step = 15001 (0.197 sec)\n",
      "INFO:tensorflow:global_step/sec: 551.953\n",
      "INFO:tensorflow:loss = 7169.092, step = 15101 (0.180 sec)\n",
      "INFO:tensorflow:global_step/sec: 515.185\n",
      "INFO:tensorflow:loss = 5117.5986, step = 15201 (0.197 sec)\n",
      "INFO:tensorflow:global_step/sec: 537.582\n",
      "INFO:tensorflow:loss = 4515.6133, step = 15301 (0.185 sec)\n",
      "INFO:tensorflow:global_step/sec: 481.512\n",
      "INFO:tensorflow:loss = 3737.886, step = 15401 (0.207 sec)\n",
      "INFO:tensorflow:global_step/sec: 432.814\n",
      "INFO:tensorflow:loss = 3527.7324, step = 15501 (0.232 sec)\n",
      "INFO:tensorflow:global_step/sec: 545.697\n",
      "INFO:tensorflow:loss = 5352.289, step = 15601 (0.183 sec)\n",
      "INFO:tensorflow:global_step/sec: 545.363\n",
      "INFO:tensorflow:loss = 3984.9607, step = 15701 (0.184 sec)\n",
      "INFO:tensorflow:global_step/sec: 614.711\n",
      "INFO:tensorflow:loss = 7259.448, step = 15801 (0.163 sec)\n",
      "INFO:tensorflow:global_step/sec: 433.372\n",
      "INFO:tensorflow:loss = 5304.165, step = 15901 (0.232 sec)\n",
      "INFO:tensorflow:global_step/sec: 478.396\n",
      "INFO:tensorflow:loss = 3635.9912, step = 16001 (0.207 sec)\n",
      "INFO:tensorflow:global_step/sec: 499.388\n",
      "INFO:tensorflow:loss = 4024.7393, step = 16101 (0.202 sec)\n",
      "INFO:tensorflow:global_step/sec: 582.245\n",
      "INFO:tensorflow:loss = 4102.973, step = 16201 (0.169 sec)\n",
      "INFO:tensorflow:global_step/sec: 558.176\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:loss = 4943.475, step = 16301 (0.179 sec)\n",
      "INFO:tensorflow:global_step/sec: 477.92\n",
      "INFO:tensorflow:loss = 4897.615, step = 16401 (0.210 sec)\n",
      "INFO:tensorflow:global_step/sec: 471.332\n",
      "INFO:tensorflow:loss = 6711.3145, step = 16501 (0.212 sec)\n",
      "INFO:tensorflow:global_step/sec: 501.063\n",
      "INFO:tensorflow:loss = 4922.823, step = 16601 (0.200 sec)\n",
      "INFO:tensorflow:global_step/sec: 511.312\n",
      "INFO:tensorflow:loss = 3605.7017, step = 16701 (0.196 sec)\n",
      "INFO:tensorflow:global_step/sec: 488.274\n",
      "INFO:tensorflow:loss = 4116.338, step = 16801 (0.204 sec)\n",
      "INFO:tensorflow:global_step/sec: 475.763\n",
      "INFO:tensorflow:loss = 4373.8354, step = 16901 (0.210 sec)\n",
      "INFO:tensorflow:global_step/sec: 505.359\n",
      "INFO:tensorflow:loss = 4523.6562, step = 17001 (0.198 sec)\n",
      "INFO:tensorflow:global_step/sec: 544.784\n",
      "INFO:tensorflow:loss = 5399.242, step = 17101 (0.186 sec)\n",
      "INFO:tensorflow:global_step/sec: 473.718\n",
      "INFO:tensorflow:loss = 6535.8184, step = 17201 (0.210 sec)\n",
      "INFO:tensorflow:global_step/sec: 489.707\n",
      "INFO:tensorflow:loss = 4712.4453, step = 17301 (0.203 sec)\n",
      "INFO:tensorflow:global_step/sec: 518.672\n",
      "INFO:tensorflow:loss = 3440.6655, step = 17401 (0.192 sec)\n",
      "INFO:tensorflow:global_step/sec: 473.913\n",
      "INFO:tensorflow:loss = 3774.4985, step = 17501 (0.211 sec)\n",
      "INFO:tensorflow:global_step/sec: 532.439\n",
      "INFO:tensorflow:loss = 5482.442, step = 17601 (0.188 sec)\n",
      "INFO:tensorflow:global_step/sec: 521.939\n",
      "INFO:tensorflow:loss = 3954.7568, step = 17701 (0.192 sec)\n",
      "INFO:tensorflow:global_step/sec: 520.82\n",
      "INFO:tensorflow:loss = 6967.4224, step = 17801 (0.193 sec)\n",
      "INFO:tensorflow:global_step/sec: 566.698\n",
      "INFO:tensorflow:loss = 5766.056, step = 17901 (0.176 sec)\n",
      "INFO:tensorflow:global_step/sec: 587.306\n",
      "INFO:tensorflow:loss = 4789.04, step = 18001 (0.170 sec)\n",
      "INFO:tensorflow:global_step/sec: 487.843\n",
      "INFO:tensorflow:loss = 3840.3052, step = 18101 (0.207 sec)\n",
      "INFO:tensorflow:global_step/sec: 360.971\n",
      "INFO:tensorflow:loss = 3528.114, step = 18201 (0.277 sec)\n",
      "INFO:tensorflow:global_step/sec: 389.903\n",
      "INFO:tensorflow:loss = 5389.16, step = 18301 (0.256 sec)\n",
      "INFO:tensorflow:global_step/sec: 470.386\n",
      "INFO:tensorflow:loss = 4320.253, step = 18401 (0.210 sec)\n",
      "INFO:tensorflow:global_step/sec: 478.822\n",
      "INFO:tensorflow:loss = 7631.6104, step = 18501 (0.212 sec)\n",
      "INFO:tensorflow:global_step/sec: 554.699\n",
      "INFO:tensorflow:loss = 5169.927, step = 18601 (0.177 sec)\n",
      "INFO:tensorflow:global_step/sec: 565.966\n",
      "INFO:tensorflow:loss = 3669.042, step = 18701 (0.177 sec)\n",
      "INFO:tensorflow:global_step/sec: 384.36\n",
      "INFO:tensorflow:loss = 4044.535, step = 18801 (0.262 sec)\n",
      "INFO:tensorflow:global_step/sec: 391.002\n",
      "INFO:tensorflow:loss = 3768.8938, step = 18901 (0.254 sec)\n",
      "INFO:tensorflow:global_step/sec: 499.62\n",
      "INFO:tensorflow:loss = 5151.9043, step = 19001 (0.203 sec)\n",
      "INFO:tensorflow:global_step/sec: 521.428\n",
      "INFO:tensorflow:loss = 4927.2476, step = 19101 (0.191 sec)\n",
      "INFO:tensorflow:global_step/sec: 456.784\n",
      "INFO:tensorflow:loss = 6653.1714, step = 19201 (0.216 sec)\n",
      "INFO:tensorflow:global_step/sec: 449.305\n",
      "INFO:tensorflow:loss = 5210.455, step = 19301 (0.223 sec)\n",
      "INFO:tensorflow:global_step/sec: 538.251\n",
      "INFO:tensorflow:loss = 3470.4531, step = 19401 (0.186 sec)\n",
      "INFO:tensorflow:global_step/sec: 419.939\n",
      "INFO:tensorflow:loss = 4187.2314, step = 19501 (0.240 sec)\n",
      "INFO:tensorflow:global_step/sec: 324.793\n",
      "INFO:tensorflow:loss = 4155.7354, step = 19601 (0.306 sec)\n",
      "INFO:tensorflow:global_step/sec: 458.222\n",
      "INFO:tensorflow:loss = 4642.956, step = 19701 (0.221 sec)\n",
      "INFO:tensorflow:global_step/sec: 539.276\n",
      "INFO:tensorflow:loss = 5173.722, step = 19801 (0.184 sec)\n",
      "INFO:tensorflow:global_step/sec: 463.3\n",
      "INFO:tensorflow:loss = 6550.7397, step = 19901 (0.215 sec)\n",
      "INFO:tensorflow:Saving checkpoints for 20000 into /var/folders/55/qkrngbr949x1jzs0c0dt70wc0000gn/T/tmp6aleB_/model.ckpt.\n",
      "INFO:tensorflow:Loss for final step: 6522.2236.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.estimator.canned.dnn.DNNRegressor at 0x11a60e1d0>"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train\n",
    "estimator.train(input_fn=input_fn_train, steps=20000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Calling model_fn.\n",
      "INFO:tensorflow:Done calling model_fn.\n",
      "INFO:tensorflow:Starting evaluation at 2018-05-09-23:46:48\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Restoring parameters from /var/folders/55/qkrngbr949x1jzs0c0dt70wc0000gn/T/tmp6aleB_/model.ckpt-20000\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Finished evaluation at 2018-05-09-23:46:48\n",
      "INFO:tensorflow:Saving dict for global step 20000: average_loss = 21.1875, global_step = 20000, loss = 2245.875\n",
      "Loss: 2245.875000\n"
     ]
    }
   ],
   "source": [
    "input_fn_test = tf.estimator.inputs.pandas_input_fn(\n",
    "    x = pd.DataFrame({\n",
    "        \"Age\" : test[\"Age\"].values,\n",
    "        \"MP\" : test[\"MP\"].values,\n",
    "        \"G\" : test[\"G\"].values\n",
    "    }),\n",
    "    y = pd.Series(test.PTS.values),\n",
    "    shuffle=False\n",
    ")\n",
    "evaluation = estimator.evaluate(input_fn=input_fn_test)\n",
    "loss_score = evaluation[\"loss\"]\n",
    "print(\"Loss: {0:f}\".format(loss_score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Calling model_fn.\n",
      "INFO:tensorflow:Done calling model_fn.\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Restoring parameters from /var/folders/55/qkrngbr949x1jzs0c0dt70wc0000gn/T/tmp6aleB_/model.ckpt-20000\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n"
     ]
    }
   ],
   "source": [
    "import itertools\n",
    "\n",
    "input_fn_predict = tf.estimator.inputs.pandas_input_fn(\n",
    "    x = pd.DataFrame({\n",
    "        \"Age\" : test[\"Age\"].values,\n",
    "        \"MP\" : test[\"MP\"].values,\n",
    "        \"G\" : test[\"G\"].values\n",
    "    }),\n",
    "    y=None, # we are predicting\n",
    "    shuffle=False\n",
    ")\n",
    "\n",
    "predictions = estimator.predict(input_fn=input_fn_predict)\n",
    "predictions = list(p[\"predictions\"][0] for p in predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x119f38210>]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kh0gvf62JHE7PO2C3mnZuTplqdj9XkBMIGtA3hjsJV3lapBrWF64pbi7f/iTE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\nu4tGSJkp7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A+W3mQ/O6jmAzAGXpkhTYszLHGEFWboRnNI2DTKAlIC2srALtIYxBN163Bd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dz5cLhkwV0iVZst34ZI+SpFmfZipKppwDeoxM1VjOlE+YrWYqnUyJjj6NR9V4wbVDplylbUh7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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1021050d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.plot(test.PTS.values)\n",
    "plt.plot(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delta=test.PTS-predictions\n",
    "np.abs(np.mean(delta)).round() # mean prediction error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+J4PRQ0O3XtCFRH2gL5w8mvEmjD99JusMOWfr+Nmdi5kWphuLkehEZSO5xfV8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\nm5BAF0IIm/j/L2bAa9lZK9sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119b1cf50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(stats.Age, stats.PTS)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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J+dYadR2KX3nH+zEJMa/hvQhbiHkDa53fOj5Y+xdiYWQWrgO69UIEwF6jrkPx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\nBnRCCGkIDOiEENIQ/h+ABVFIn7nMVwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a93b750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(stats[\"Age\"], stats[\"PTS\"])\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}