kaggle_passnyc6.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## &#x1F310; &nbsp; Code Library, Style, and Links\n",
    "\n",
    "The previous notebooks:\n",
    "\n",
    "&#x1F4D8; &nbsp; [PASSNYC. Data Exploration](https://www.kaggle.com/olgabelitskaya/passnyc-data-exploration)\n",
    "\n",
    "&#x1F4D8; &nbsp; [PASSNYC. Numeric and Categorical Variables](https://www.kaggle.com/olgabelitskaya/passnyc-numeric-and-categorical-variables)\n",
    "\n",
    "&#x1F4D8; &nbsp; [PASSNYC. Comparing All Districts with 5th District](passnyc-comparing-all-districts-with-5th-district)\n",
    "\n",
    "&#x1F4D8; &nbsp; [PASSNYC. Regression Methods](https://www.kaggle.com/olgabelitskaya/passnyc-regression-methods)\n",
    "\n",
    "&#x1F4D8; &nbsp; [PASSNYC. Neural Networks](https://www.kaggle.com/olgabelitskaya/passnyc-neural-networks)\n",
    "\n",
    "Useful `LINKS`:\n",
    "\n",
    "&#x1F4E1; &nbsp; [School Quality Reports. Educator Guide](http://schools.nyc.gov/NR/rdonlyres/967E0EE1-7E5D-4E47-BC21-573FEEE23AE2/0/201516EducatorGuideHS9252017.pdf)\n",
    "\n",
    "&#x1F4E1; &nbsp; [New York City Department of Education](https://www.schools.nyc.gov)\n",
    "\n",
    "&#x1F4E1; &nbsp; [NYC OpenData](https://opendata.cityofnewyork.us/)\n",
    "\n",
    "&#x1F4E1; &nbsp; [Pandas Visualization](https://pandas.pydata.org/pandas-docs/stable/visualization.html)\n",
    "\n",
    "&#x1F4E1; &nbsp; [Pandas Styling](https://pandas.pydata.org/pandas-docs/stable/style.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style> \n",
       "@import url('https://fonts.googleapis.com/css?family=Orbitron|Roboto');\n",
       "body {background-color: gainsboro;} \n",
       "a {color: #37c9e1; font-family: 'Roboto';} \n",
       "h1 {color: #37c9e1; font-family: 'Orbitron'; text-shadow: 4px 4px 4px #aaa;} \n",
       "h2, h3 {color: slategray; font-family: 'Orbitron'; text-shadow: 4px 4px 4px #aaa;}\n",
       "h4 {color: #37c9e1; font-family: 'Roboto';}\n",
       "span {text-shadow: 4px 4px 4px #aaa;}\n",
       "div.output_prompt, div.output_area pre {color: slategray;}\n",
       "div.input_prompt, div.output_subarea {color: #37c9e1;}      \n",
       "div.output_stderr pre {background-color: gainsboro;}  \n",
       "div.output_stderr {background-color: slategrey;}               \n",
       "</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%html\n",
    "<style> \n",
    "@import url('https://fonts.googleapis.com/css?family=Orbitron|Roboto');\n",
    "body {background-color: gainsboro;} \n",
    "a {color: #37c9e1; font-family: 'Roboto';} \n",
    "h1 {color: #37c9e1; font-family: 'Orbitron'; text-shadow: 4px 4px 4px #aaa;} \n",
    "h2, h3 {color: slategray; font-family: 'Orbitron'; text-shadow: 4px 4px 4px #aaa;}\n",
    "h4 {color: #37c9e1; font-family: 'Roboto';}\n",
    "span {text-shadow: 4px 4px 4px #aaa;}\n",
    "div.output_prompt, div.output_area pre {color: slategray;}\n",
    "div.input_prompt, div.output_subarea {color: #37c9e1;}      \n",
    "div.output_stderr pre {background-color: gainsboro;}  \n",
    "div.output_stderr {background-color: slategrey;}               \n",
    "</style>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib.colors as mcolors\n",
    "from descartes import PolygonPatch\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=FutureWarning)\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error, median_absolute_error, mean_absolute_error\n",
    "from sklearn.metrics import r2_score, explained_variance_score\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "from keras.models import Sequential, load_model, Model\n",
    "from keras.optimizers import SGD, RMSprop\n",
    "from keras.layers import Dense, Dropout, LSTM\n",
    "from keras.layers import Activation, Flatten, Input, BatchNormalization\n",
    "from keras.layers import Conv1D, MaxPooling1D, Conv2D, MaxPooling2D\n",
    "from keras.layers.embeddings import Embedding\n",
    "\n",
    "from keras.wrappers.scikit_learn import KerasRegressor\n",
    "from sklearn.preprocessing import StandardScaler, RobustScaler, MinMaxScaler\n",
    "from keras.callbacks import EarlyStopping, ReduceLROnPlateau\n",
    "from keras.layers.advanced_activations import PReLU, LeakyReLU\n",
    "from keras.callbacks import ModelCheckpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function for metrics\n",
    "def scores(regressor, y_train, y_valid, y_test, y_train_reg, y_valid_reg, y_test_reg):\n",
    "    print(\"_______________________________________\")\n",
    "    print(regressor)\n",
    "    print(\"_______________________________________\")\n",
    "    print(\"EV score. Train: \", explained_variance_score(y_train, y_train_reg))\n",
    "    print(\"EV score. Valid: \", explained_variance_score(y_valid, y_valid_reg))\n",
    "    print(\"EV score. Test: \", explained_variance_score(y_test, y_test_reg))\n",
    "    print(\"---------\")\n",
    "    print(\"R2 score. Train: \", r2_score(y_train, y_train_reg))\n",
    "    print(\"R2 score. Valid: \", r2_score(y_valid, y_valid_reg))\n",
    "    print(\"R2 score. Test: \", r2_score(y_test, y_test_reg))\n",
    "    print(\"---------\")\n",
    "    print(\"MSE score. Train: \", mean_squared_error(y_train, y_train_reg))\n",
    "    print(\"MSE score. Valid: \", mean_squared_error(y_valid, y_valid_reg))\n",
    "    print(\"MSE score. Test: \", mean_squared_error(y_test, y_test_reg))\n",
    "    print(\"---------\")\n",
    "    print(\"MAE score. Train: \", mean_absolute_error(y_train, y_train_reg))\n",
    "    print(\"MAE score. Valid: \", mean_absolute_error(y_valid, y_valid_reg))\n",
    "    print(\"MAE score. Test: \", mean_absolute_error(y_test, y_test_reg))\n",
    "    print(\"---------\")\n",
    "    print(\"MdAE score. Train: \", median_absolute_error(y_train, y_train_reg))\n",
    "    print(\"MdAE score. Valid: \", median_absolute_error(y_valid, y_valid_reg))\n",
    "    print(\"MdAE score. Test: \", median_absolute_error(y_test, y_test_reg))\n",
    "\n",
    "# Functions for neural networks fitting\n",
    "def loss_plot(fit_history, n):\n",
    "    plt.figure(figsize=(14,6))\n",
    "\n",
    "    plt.plot(fit_history.history['loss'][n:], \n",
    "             color='#3636ff',label='train')\n",
    "    plt.plot(fit_history.history['val_loss'][n:], \n",
    "             color='#ff3636',label='valid')\n",
    "\n",
    "    plt.legend()\n",
    "    plt.title('Loss Function');  \n",
    "    \n",
    "def mae_plot(fit_history, n):\n",
    "    plt.figure(figsize=(14,6))\n",
    "\n",
    "    plt.plot(fit_history.history['mean_absolute_error'][n:], \n",
    "             color='#3636ff',label='train')\n",
    "    plt.plot(fit_history.history['val_mean_absolute_error'][n:], \n",
    "             color='#ff3636',label='valid')\n",
    "\n",
    "    plt.legend()\n",
    "    plt.title('Mean Absolute Error');\n",
    "    \n",
    "# Dataframe style\n",
    "style_dict = {'background-color':'slategray',\n",
    "              'color':'#37c9e1',\n",
    "              'border-color': 'white',\n",
    "              'font-family':'Roboto'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## &#x1F310; &nbsp; Load and Preprocess the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1272, 161), (140, 7))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "school_explorer = pd.read_csv('2016 School Explorer.csv')\n",
    "d5_shsat = pd.read_csv('D5 SHSAT Registrations and Testers.csv')\n",
    "school_explorer.shape, d5_shsat.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Missing Values:  0\n"
     ]
    }
   ],
   "source": [
    "drop_list = ['Adjusted Grade','New?','Other Location Code in LCGMS']\n",
    "school_explorer = school_explorer.drop(drop_list, axis=1)\n",
    "# Replace the same values\n",
    "school_explorer.loc[[427,1023,712,908],'School Name'] = \\\n",
    "['P.S. 212 D12','P.S. 212 D30','P.S. 253 D21','P.S. 253 D27']\n",
    "# Transform from string to numeric values \n",
    "school_explorer['School Income Estimate'] = \\\n",
    "school_explorer['School Income Estimate'].astype('str') \n",
    "for s in [\",\",\"$\",\" \"]:\n",
    "    school_explorer['School Income Estimate'] = \\\n",
    "    school_explorer['School Income Estimate'].str.replace(s, \"\")\n",
    "school_explorer['School Income Estimate'] = \\\n",
    "school_explorer['School Income Estimate'].str.replace(\"nan\", \"0\")\n",
    "school_explorer['School Income Estimate'] = \\\n",
    "school_explorer['School Income Estimate'].astype(float)\n",
    "school_explorer['School Income Estimate'].replace(0, np.NaN, inplace=True)\n",
    "\n",
    "percent_list = ['Percent ELL','Percent Asian','Percent Black',\n",
    "                'Percent Hispanic','Percent Black / Hispanic',\n",
    "                'Percent White','Student Attendance Rate',\n",
    "                'Percent of Students Chronically Absent',\n",
    "                'Rigorous Instruction %','Collaborative Teachers %',\n",
    "                'Supportive Environment %','Effective School Leadership %',\n",
    "                'Strong Family-Community Ties %','Trust %']\n",
    "\n",
    "target_list = ['Average ELA Proficiency','Average Math Proficiency']\n",
    "economic_list = ['Economic Need Index','School Income Estimate']\n",
    "rating_list = ['Rigorous Instruction Rating','Collaborative Teachers Rating',\n",
    "               'Supportive Environment Rating','Effective School Leadership Rating',\n",
    "               'Strong Family-Community Ties Rating','Trust Rating',\n",
    "               'Student Achievement Rating']\n",
    "# Transform to numeric variables and fillna missing values\n",
    "for el in percent_list:\n",
    "    school_explorer[el] = school_explorer[el].astype('str')\n",
    "    school_explorer[el] = school_explorer[el].str.replace(\"%\", \"\")\n",
    "    school_explorer[el] = school_explorer[el].str.replace(\"nan\", \"0\")\n",
    "    school_explorer[el] = school_explorer[el].astype(float)\n",
    "    school_explorer[el].replace(0, np.NaN, inplace=True)\n",
    "    school_explorer[el] = school_explorer[el].interpolate()\n",
    "\n",
    "for el in target_list+economic_list:\n",
    "    school_explorer[el] = school_explorer[el].interpolate()\n",
    "    \n",
    "for el in rating_list:\n",
    "    moda_value = school_explorer[el].value_counts().idxmax()\n",
    "    school_explorer[el] = school_explorer[el].fillna(moda_value)\n",
    "    \n",
    "# Preprocess categorical features      \n",
    "category_list = ['District','Community School?','City','Grades']\n",
    "                 \n",
    "for feature in category_list:\n",
    "    feature_cat = pd.factorize(school_explorer[feature])\n",
    "    school_explorer[feature] = feature_cat[0]\n",
    "    \n",
    "for feature in rating_list:\n",
    "    feature_pairs = dict(zip(['Not Meeting Target', 'Meeting Target', \n",
    "                              'Approaching Target', 'Exceeding Target'],\n",
    "                             ['0','2','1','3']))\n",
    "    school_explorer[feature].replace(feature_pairs,inplace=True)\n",
    "    school_explorer[feature] = school_explorer[feature].astype(int)\n",
    "    \n",
    "category_list = list(category_list+rating_list)\n",
    "\n",
    "numeric_list = list(school_explorer\\\n",
    ".columns[[4,5]+list(range(13,24))+[25,27,29,31,33]+list(range(38,158))])    \n",
    "    \n",
    "print('Number of Missing Values: ', sum(school_explorer.isna().sum()))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21, 155)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a new dataframe\n",
    "sat_list = ['DBN','Number of students who registered for the SHSAT',\n",
    "            'Number of students who took the SHSAT']\n",
    "d5_shsat_2016 = d5_shsat[sat_list][d5_shsat['Year of SHST']==2016]\\\n",
    ".groupby(['DBN'],as_index=False).agg(np.sum)\n",
    "# Create a new variable\n",
    "d5_shsat_2016['Took SHSAT %'] = \\\n",
    "d5_shsat_2016['Number of students who took the SHSAT']\\\n",
    "/ d5_shsat_2016['Number of students who registered for the SHSAT']\n",
    "\n",
    "d5_shsat_2016['Took SHSAT %'] = \\\n",
    "d5_shsat_2016['Took SHSAT %'].fillna(0).apply(lambda x: round(x,3))\n",
    "d5_shsat_2016.rename(columns={'DBN':'Location Code'}, inplace=True)\n",
    "# Merge dataframes\n",
    "d5_shsat_2016 = \\\n",
    "pd.merge(school_explorer[['Location Code']+numeric_list+category_list+target_list],\n",
    "                         d5_shsat_2016, on='Location Code')\n",
    "\n",
    "d5_shsat_2016.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## &#x1F310; &nbsp; Split the Data for Neural Networks' Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "features1 = school_explorer[numeric_list].values\n",
    "features2 = school_explorer[numeric_list+category_list]\n",
    "targets1 = school_explorer['Average Math Proficiency'].values\n",
    "targets2 = school_explorer['Average ELA Proficiency'].values\n",
    "\n",
    "features_enc = features2\n",
    "encode = OneHotEncoder(sparse=False)\n",
    "\n",
    "for column in category_list:\n",
    "    encode.fit(features2[[column]])\n",
    "    transform = encode.transform(features2[[column]])\n",
    "    \n",
    "    transform = \\\n",
    "    pd.DataFrame(transform, \n",
    "                 columns=[(column+\"_\"+str(i)) for i in features2[column].value_counts().index])\n",
    "    transform = transform.set_index(features2.index.values)\n",
    "    \n",
    "    features_enc = pd.concat([features_enc, transform], axis=1)\n",
    "    features_enc = features_enc.drop(column, 1)\n",
    "    \n",
    "features2 = features2.values\n",
    "features_enc = features_enc.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train1, X_test1, y_train1, y_test1 = \\\n",
    "train_test_split(features1, targets1, test_size=0.3, random_state=1)\n",
    "\n",
    "n = int(len(X_test1)/2)\n",
    "X_valid1, y_valid1 = X_test1[:n], y_test1[:n]\n",
    "X_test1, y_test1 = X_test1[n:], y_test1[n:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((890, 138), (191, 138), (191, 138), (890,), (191,), (191,))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data = school_explorer\n",
    "# features = numeric variables\n",
    "# targets = Average Math Proficiency\n",
    "X_train1.shape, X_test1.shape, X_valid1.shape, \\\n",
    "y_train1.shape, y_test1.shape, y_valid1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train2, X_test2, y_train2, y_test2 = \\\n",
    "train_test_split(features1, targets2, test_size=0.3, random_state=1)\n",
    "\n",
    "n = int(len(X_test2)/2)\n",
    "X_valid2, y_valid2 = X_test2[:n], y_test2[:n]\n",
    "X_test2, y_test2 = X_test2[n:], y_test2[n:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((890, 138), (191, 138), (191, 138), (890,), (191,), (191,))"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data = school_explorer\n",
    "# eatures = numeric variables\n",
    "# targets = Average ELA Proficiency\n",
    "X_train2.shape, X_test2.shape, X_valid2.shape, \\\n",
    "y_train2.shape, y_test2.shape, y_valid2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train3, X_test3, y_train3, y_test3 = \\\n",
    "train_test_split(features_enc, targets1, test_size=0.3, random_state=1)\n",
    "\n",
    "n = int(len(X_test3)/2)\n",
    "X_valid3, y_valid3 = X_test3[:n], y_test3[:n]\n",
    "X_test3, y_test3 = X_test3[n:], y_test3[n:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((890, 286), (191, 286), (191, 286), (890,), (191,), (191,))"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data = school_explorer, \n",
    "# features = numeric & encoded categorical variables \n",
    "# targets = Average Math Proficiency\n",
    "\n",
    "X_train3.shape, X_test3.shape, X_valid3.shape, \\\n",
    "y_train3.shape, y_test3.shape, y_valid3.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train4, X_test4, y_train4, y_test4 = \\\n",
    "train_test_split(features_enc, targets2, test_size=0.3, random_state=1)\n",
    "\n",
    "n = int(len(X_test4)/2)\n",
    "X_valid4, y_valid4 = X_test4[:n], y_test4[:n]\n",
    "X_test4, y_test4 = X_test4[n:], y_test4[n:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((890, 286), (191, 286), (191, 286), (890,), (191,), (191,))"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data = school_explorer\n",
    "# features = numeric & encoded categorical variables\n",
    "# targets = Average ELA Proficiency\n",
    "\n",
    "X_train4.shape, X_test4.shape, X_valid4.shape, \\\n",
    "y_train4.shape, y_test4.shape, y_valid4.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## &#x1F310; &nbsp; Neural Network Models (MLP)\n",
    "#### Numeric features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mlp_model1():\n",
    "    model = Sequential()\n",
    "    \n",
    "    model.add(Dense(138, input_dim=138))\n",
    "    model.add(LeakyReLU(alpha=0.02))\n",
    "    model.add(Dense(138*4))\n",
    "    model.add(LeakyReLU(alpha=0.02))\n",
    "    \n",
    "    model.add(Dense(138*16))\n",
    "    model.add(LeakyReLU(alpha=0.02))\n",
    "    model.add(Dense(138*16))\n",
    "    model.add(LeakyReLU(alpha=0.02))\n",
    "    \n",
    "    model.add(Dense(1))\n",
    "    \n",
    "    model.compile(loss='mse', optimizer='rmsprop', metrics=['mae'])\n",
    "    return model\n",
    "\n",
    "mlp_model1 = mlp_model1()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "mlp_checkpointer1 = ModelCheckpoint(filepath='mlp_model1.passnyc.hdf5', \n",
    "                                    verbose=2, save_best_only=True)\n",
    "mlp_lr_reduction1 = ReduceLROnPlateau(monitor='val_loss', \n",
    "                                      patience=5, verbose=2, factor=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 890 samples, validate on 191 samples\n",
      "Epoch 1/100\n",
      " - 10s - loss: 398650807.3806 - mean_absolute_error: 3625.8463 - val_loss: 6.0715 - val_mean_absolute_error: 2.1390\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 6.07149, saving model to mlp_model1.passnyc.hdf5\n",
      "Epoch 2/100\n",
      " - 5s - loss: 3391.3517 - mean_absolute_error: 33.8122 - val_loss: 21.5130 - val_mean_absolute_error: 4.4839\n",
      "\n",
      "Epoch 00002: val_loss did not improve from 6.07149\n",
      "Epoch 3/100\n",
      " - 6s - loss: 831045.1101 - mean_absolute_error: 211.5333 - val_loss: 1.1303 - val_mean_absolute_error: 0.7794\n",
      "\n",
      "Epoch 00003: val_loss improved from 6.07149 to 1.13028, saving model to mlp_model1.passnyc.hdf5\n",
      "Epoch 4/100\n",
      " - 5s - loss: 2729.8199 - mean_absolute_error: 22.4524 - val_loss: 7035.7861 - val_mean_absolute_error: 76.2411\n",
      "\n",
      "Epoch 00004: val_loss did not improve from 1.13028\n",
      "Epoch 5/100\n",
      " - 5s - loss: 1599.9094 - mean_absolute_error: 19.6288 - val_loss: 4824.5349 - val_mean_absolute_error: 62.5141\n",
      "\n",
      "Epoch 00005: val_loss did not improve from 1.13028\n",
      "Epoch 6/100\n",
      " - 5s - loss: 4239089.3554 - mean_absolute_error: 307.6867 - val_loss: 143.1573 - val_mean_absolute_error: 10.3580\n",
      "\n",
      "Epoch 00006: val_loss did not improve from 1.13028\n",
      "Epoch 7/100\n",
      " - 6s - loss: 7779.3898 - mean_absolute_error: 58.7970 - val_loss: 45.2908 - val_mean_absolute_error: 5.6626\n",
      "\n",
      "Epoch 00007: val_loss did not improve from 1.13028\n",
      "Epoch 8/100\n",
      " - 6s - loss: 97305.0229 - mean_absolute_error: 91.6135 - val_loss: 16.0017 - val_mean_absolute_error: 3.9076\n",
      "\n",
      "Epoch 00008: val_loss did not improve from 1.13028\n",
      "\n",
      "Epoch 00008: ReduceLROnPlateau reducing learning rate to 0.00020000000949949026.\n",
      "Epoch 9/100\n",
      " - 6s - loss: 85.2144 - mean_absolute_error: 4.9760 - val_loss: 32.5623 - val_mean_absolute_error: 5.4892\n",
      "\n",
      "Epoch 00009: val_loss did not improve from 1.13028\n",
      "Epoch 10/100\n",
      " - 6s - loss: 383.7327 - mean_absolute_error: 8.5989 - val_loss: 1011.0513 - val_mean_absolute_error: 29.1339\n",
      "\n",
      "Epoch 00010: val_loss did not improve from 1.13028\n",
      "Epoch 11/100\n",
      " - 6s - loss: 295.1712 - mean_absolute_error: 10.7959 - val_loss: 0.9800 - val_mean_absolute_error: 0.7691\n",
      "\n",
      "Epoch 00011: val_loss improved from 1.13028 to 0.98000, saving model to mlp_model1.passnyc.hdf5\n",
      "Epoch 12/100\n",
      " - 5s - loss: 91.5129 - mean_absolute_error: 4.8470 - val_loss: 0.9898 - val_mean_absolute_error: 0.7843\n",
      "\n",
      "Epoch 00012: val_loss did not improve from 0.98000\n",
      "Epoch 13/100\n",
      " - 6s - loss: 84.8457 - mean_absolute_error: 3.9777 - val_loss: 14.7568 - val_mean_absolute_error: 2.9653\n",
      "\n",
      "Epoch 00013: val_loss did not improve from 0.98000\n",
      "Epoch 14/100\n",
      " - 5s - loss: 10972.8553 - mean_absolute_error: 16.2867 - val_loss: 2.1490 - val_mean_absolute_error: 1.3895\n",
      "\n",
      "Epoch 00014: val_loss did not improve from 0.98000\n",
      "Epoch 15/100\n",
      " - 5s - loss: 29.5612 - mean_absolute_error: 3.7663 - val_loss: 1.3126 - val_mean_absolute_error: 1.0116\n",
      "\n",
      "Epoch 00015: val_loss did not improve from 0.98000\n",
      "Epoch 16/100\n",
      " - 6s - loss: 26.1617 - mean_absolute_error: 3.3299 - val_loss: 0.9509 - val_mean_absolute_error: 0.7727\n",
      "\n",
      "Epoch 00016: val_loss improved from 0.98000 to 0.95086, saving model to mlp_model1.passnyc.hdf5\n",
      "Epoch 17/100\n",
      " - 5s - loss: 46.3089 - mean_absolute_error: 3.8165 - val_loss: 1.1599 - val_mean_absolute_error: 0.7636\n",
      "\n",
      "Epoch 00017: val_loss did not improve from 0.95086\n",
      "Epoch 18/100\n",
      " - 5s - loss: 46.1042 - mean_absolute_error: 3.0536 - val_loss: 19.0883 - val_mean_absolute_error: 4.2370\n",
      "\n",
      "Epoch 00018: val_loss did not improve from 0.95086\n",
      "Epoch 19/100\n",
      " - 6s - loss: 81.6985 - mean_absolute_error: 3.8779 - val_loss: 2.1924 - val_mean_absolute_error: 1.4124\n",
      "\n",
      "Epoch 00019: val_loss did not improve from 0.95086\n",
      "Epoch 20/100\n",
      " - 6s - loss: 15.7076 - mean_absolute_error: 2.6148 - val_loss: 16.6424 - val_mean_absolute_error: 3.9627\n",
      "\n",
      "Epoch 00020: val_loss did not improve from 0.95086\n",
      "Epoch 21/100\n",
      " - 5s - loss: 158.0182 - mean_absolute_error: 5.2481 - val_loss: 85.9040 - val_mean_absolute_error: 7.9570\n",
      "\n",
      "Epoch 00021: val_loss did not improve from 0.95086\n",
      "\n",
      "Epoch 00021: ReduceLROnPlateau reducing learning rate to 4.0000001899898055e-05.\n",
      "Epoch 22/100\n",
      " - 5s - loss: 4.8558 - mean_absolute_error: 1.2216 - val_loss: 5.6784 - val_mean_absolute_error: 2.3500\n",
      "\n",
      "Epoch 00022: val_loss did not improve from 0.95086\n",
      "Epoch 23/100\n",
      " - 6s - loss: 3.9292 - mean_absolute_error: 1.4245 - val_loss: 1.4279 - val_mean_absolute_error: 0.8088\n",
      "\n",
      "Epoch 00023: val_loss did not improve from 0.95086\n",
      "Epoch 24/100\n",
      " - 6s - loss: 2.3333 - mean_absolute_error: 1.1402 - val_loss: 2.3466 - val_mean_absolute_error: 1.0213\n",
      "\n",
      "Epoch 00024: val_loss did not improve from 0.95086\n",
      "Epoch 25/100\n",
      " - 5s - loss: 1.7123 - mean_absolute_error: 0.9843 - val_loss: 0.7556 - val_mean_absolute_error: 0.6838\n",
      "\n",
      "Epoch 00025: val_loss improved from 0.95086 to 0.75564, saving model to mlp_model1.passnyc.hdf5\n",
      "Epoch 26/100\n",
      " - 5s - loss: 1.2526 - mean_absolute_error: 0.8680 - val_loss: 0.8318 - val_mean_absolute_error: 0.6688\n",
      "\n",
      "Epoch 00026: val_loss did not improve from 0.75564\n",
      "Epoch 27/100\n",
      " - 5s - loss: 1.0177 - mean_absolute_error: 0.8076 - val_loss: 1.3455 - val_mean_absolute_error: 0.7928\n",
      "\n",
      "Epoch 00027: val_loss did not improve from 0.75564\n",
      "Epoch 28/100\n",
      " - 6s - loss: 1.0662 - mean_absolute_error: 0.8073 - val_loss: 0.6644 - val_mean_absolute_error: 0.6387\n",
      "\n",
      "Epoch 00028: val_loss improved from 0.75564 to 0.66437, saving model to mlp_model1.passnyc.hdf5\n",
      "Epoch 29/100\n",
      " - 6s - loss: 0.7930 - mean_absolute_error: 0.6968 - val_loss: 0.8204 - val_mean_absolute_error: 0.8239\n",
      "\n",
      "Epoch 00029: val_loss did not improve from 0.66437\n",
      "Epoch 30/100\n",
      " - 5s - loss: 0.8569 - mean_absolute_error: 0.7094 - val_loss: 0.6308 - val_mean_absolute_error: 0.6237\n",
      "\n",
      "Epoch 00030: val_loss improved from 0.66437 to 0.63078, saving model to mlp_model1.passnyc.hdf5\n",
      "Epoch 31/100\n",
      " - 6s - loss: 0.7326 - mean_absolute_error: 0.6646 - val_loss: 0.7485 - val_mean_absolute_error: 0.5805\n",
      "\n",
      "Epoch 00031: val_loss did not improve from 0.63078\n",
      "Epoch 32/100\n",
      " - 5s - loss: 0.7876 - mean_absolute_error: 0.6584 - val_loss: 0.3838 - val_mean_absolute_error: 0.5056\n",
      "\n",
      "Epoch 00032: val_loss improved from 0.63078 to 0.38376, saving model to mlp_model1.passnyc.hdf5\n",
      "Epoch 33/100\n",
      " - 5s - loss: 0.7540 - mean_absolute_error: 0.6713 - val_loss: 0.6524 - val_mean_absolute_error: 0.5785\n",
      "\n",
      "Epoch 00033: val_loss did not improve from 0.38376\n",
      "Epoch 34/100\n",
      " - 6s - loss: 1.2045 - mean_absolute_error: 0.7543 - val_loss: 0.6169 - val_mean_absolute_error: 0.5440\n",
      "\n",
      "Epoch 00034: val_loss did not improve from 0.38376\n",
      "Epoch 35/100\n",
      " - 6s - loss: 0.5202 - mean_absolute_error: 0.5558 - val_loss: 1.5889 - val_mean_absolute_error: 1.1996\n",
      "\n",
      "Epoch 00035: val_loss did not improve from 0.38376\n",
      "Epoch 36/100\n",
      " - 5s - loss: 0.9127 - mean_absolute_error: 0.7022 - val_loss: 0.8698 - val_mean_absolute_error: 0.8551\n",
      "\n",
      "Epoch 00036: val_loss did not improve from 0.38376\n",
      "Epoch 37/100\n",
      " - 5s - loss: 0.6374 - mean_absolute_error: 0.6216 - val_loss: 0.5513 - val_mean_absolute_error: 0.5111\n",
      "\n",
      "Epoch 00037: val_loss did not improve from 0.38376\n",
      "\n",
      "Epoch 00037: ReduceLROnPlateau reducing learning rate to 8.000000525498762e-06.\n",
      "Epoch 38/100\n",
      " - 6s - loss: 0.2317 - mean_absolute_error: 0.3594 - val_loss: 0.2585 - val_mean_absolute_error: 0.3868\n",
      "\n",
      "Epoch 00038: val_loss improved from 0.38376 to 0.25850, saving model to mlp_model1.passnyc.hdf5\n",
      "Epoch 39/100\n",
      " - 5s - loss: 0.1944 - mean_absolute_error: 0.3361 - val_loss: 0.1734 - val_mean_absolute_error: 0.3153\n",
      "\n",
      "Epoch 00039: val_loss improved from 0.25850 to 0.17344, saving model to mlp_model1.passnyc.hdf5\n",
      "Epoch 40/100\n",
      " - 6s - loss: 0.1810 - mean_absolute_error: 0.3202 - val_loss: 0.1335 - val_mean_absolute_error: 0.2818\n",
      "\n",
      "Epoch 00040: val_loss improved from 0.17344 to 0.13353, saving model to mlp_model1.passnyc.hdf5\n",
      "Epoch 41/100\n",
      " - 6s - loss: 0.1804 - mean_absolute_error: 0.3242 - val_loss: 0.2233 - val_mean_absolute_error: 0.3764\n",
      "\n",
      "Epoch 00041: val_loss did not improve from 0.13353\n",
      "Epoch 42/100\n",
      " - 5s - loss: 0.1729 - mean_absolute_error: 0.3203 - val_loss: 0.1305 - val_mean_absolute_error: 0.2658\n",
      "\n",
      "Epoch 00042: val_loss improved from 0.13353 to 0.13047, saving model to mlp_model1.passnyc.hdf5\n",
      "Epoch 43/100\n",
      " - 5s - loss: 0.1713 - mean_absolute_error: 0.3178 - val_loss: 0.1847 - val_mean_absolute_error: 0.3086\n",
      "\n",
      "Epoch 00043: val_loss did not improve from 0.13047\n",
      "Epoch 44/100\n",
      " - 6s - loss: 0.1675 - mean_absolute_error: 0.3182 - val_loss: 0.1299 - val_mean_absolute_error: 0.2862\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 00044: val_loss improved from 0.13047 to 0.12994, saving model to mlp_model1.passnyc.hdf5\n",
      "Epoch 45/100\n",
      " - 5s - loss: 0.1658 - mean_absolute_error: 0.3062 - val_loss: 0.1157 - val_mean_absolute_error: 0.2630\n",
      "\n",
      "Epoch 00045: val_loss improved from 0.12994 to 0.11567, saving model to mlp_model1.passnyc.hdf5\n",
      "Epoch 46/100\n",
      " - 6s - loss: 0.1670 - mean_absolute_error: 0.3185 - val_loss: 0.1181 - val_mean_absolute_error: 0.2616\n",
      "\n",
      "Epoch 00046: val_loss did not improve from 0.11567\n",
      "Epoch 47/100\n",
      " - 6s - loss: 0.1640 - mean_absolute_error: 0.3080 - val_loss: 0.2856 - val_mean_absolute_error: 0.4259\n",
      "\n",
      "Epoch 00047: val_loss did not improve from 0.11567\n",
      "Epoch 48/100\n",
      " - 6s - loss: 0.1657 - mean_absolute_error: 0.3125 - val_loss: 0.1241 - val_mean_absolute_error: 0.2766\n",
      "\n",
      "Epoch 00048: val_loss did not improve from 0.11567\n",
      "Epoch 49/100\n",
      " - 5s - loss: 0.1660 - mean_absolute_error: 0.3117 - val_loss: 0.1776 - val_mean_absolute_error: 0.3392\n",
      "\n",
      "Epoch 00049: val_loss did not improve from 0.11567\n",
      "Epoch 50/100\n",
      " - 5s - loss: 0.1599 - mean_absolute_error: 0.3133 - val_loss: 0.1114 - val_mean_absolute_error: 0.2497\n",
      "\n",
      "Epoch 00050: val_loss improved from 0.11567 to 0.11145, saving model to mlp_model1.passnyc.hdf5\n",
      "Epoch 51/100\n",
      " - 6s - loss: 0.1577 - mean_absolute_error: 0.3056 - val_loss: 0.1141 - val_mean_absolute_error: 0.2622\n",
      "\n",
      "Epoch 00051: val_loss did not improve from 0.11145\n",
      "Epoch 52/100\n",
      " - 6s - loss: 0.1627 - mean_absolute_error: 0.3064 - val_loss: 0.1942 - val_mean_absolute_error: 0.3205\n",
      "\n",
      "Epoch 00052: val_loss did not improve from 0.11145\n",
      "Epoch 53/100\n",
      " - 6s - loss: 0.1521 - mean_absolute_error: 0.2975 - val_loss: 0.3160 - val_mean_absolute_error: 0.4548\n",
      "\n",
      "Epoch 00053: val_loss did not improve from 0.11145\n",
      "Epoch 54/100\n",
      " - 6s - loss: 0.1507 - mean_absolute_error: 0.2970 - val_loss: 0.1486 - val_mean_absolute_error: 0.2741\n",
      "\n",
      "Epoch 00054: val_loss did not improve from 0.11145\n",
      "Epoch 55/100\n",
      " - 6s - loss: 0.1567 - mean_absolute_error: 0.3033 - val_loss: 0.1196 - val_mean_absolute_error: 0.2496\n",
      "\n",
      "Epoch 00055: val_loss did not improve from 0.11145\n",
      "\n",
      "Epoch 00055: ReduceLROnPlateau reducing learning rate to 1.6000001778593287e-06.\n",
      "Epoch 56/100\n",
      " - 6s - loss: 0.1083 - mean_absolute_error: 0.2487 - val_loss: 0.1063 - val_mean_absolute_error: 0.2594\n",
      "\n",
      "Epoch 00056: val_loss improved from 0.11145 to 0.10626, saving model to mlp_model1.passnyc.hdf5\n",
      "Epoch 57/100\n",
      " - 6s - loss: 0.1005 - mean_absolute_error: 0.2427 - val_loss: 0.0952 - val_mean_absolute_error: 0.2441\n",
      "\n",
      "Epoch 00057: val_loss improved from 0.10626 to 0.09520, saving model to mlp_model1.passnyc.hdf5\n",
      "Epoch 58/100\n",
      " - 5s - loss: 0.0984 - mean_absolute_error: 0.2423 - val_loss: 0.0900 - val_mean_absolute_error: 0.2312\n",
      "\n",
      "Epoch 00058: val_loss improved from 0.09520 to 0.09001, saving model to mlp_model1.passnyc.hdf5\n",
      "Epoch 59/100\n",
      " - 6s - loss: 0.0948 - mean_absolute_error: 0.2380 - val_loss: 0.0925 - val_mean_absolute_error: 0.2433\n",
      "\n",
      "Epoch 00059: val_loss did not improve from 0.09001\n",
      "Epoch 60/100\n",
      " - 5s - loss: 0.0927 - mean_absolute_error: 0.2328 - val_loss: 0.0937 - val_mean_absolute_error: 0.2459\n",
      "\n",
      "Epoch 00060: val_loss did not improve from 0.09001\n",
      "Epoch 61/100\n",
      " - 6s - loss: 0.0926 - mean_absolute_error: 0.2326 - val_loss: 0.1144 - val_mean_absolute_error: 0.2740\n",
      "\n",
      "Epoch 00061: val_loss did not improve from 0.09001\n",
      "Epoch 62/100\n",
      " - 5s - loss: 0.0886 - mean_absolute_error: 0.2272 - val_loss: 0.0844 - val_mean_absolute_error: 0.2334\n",
      "\n",
      "Epoch 00062: val_loss improved from 0.09001 to 0.08444, saving model to mlp_model1.passnyc.hdf5\n",
      "Epoch 63/100\n",
      " - 6s - loss: 0.0879 - mean_absolute_error: 0.2266 - val_loss: 0.0778 - val_mean_absolute_error: 0.2166\n",
      "\n",
      "Epoch 00063: val_loss improved from 0.08444 to 0.07779, saving model to mlp_model1.passnyc.hdf5\n",
      "Epoch 64/100\n",
      " - 8s - loss: 0.0889 - mean_absolute_error: 0.2280 - val_loss: 0.0897 - val_mean_absolute_error: 0.2406\n",
      "\n",
      "Epoch 00064: val_loss did not improve from 0.07779\n",
      "Epoch 65/100\n",
      " - 7s - loss: 0.0842 - mean_absolute_error: 0.2220 - val_loss: 0.0944 - val_mean_absolute_error: 0.2195\n",
      "\n",
      "Epoch 00065: val_loss did not improve from 0.07779\n",
      "Epoch 66/100\n",
      " - 6s - loss: 0.0858 - mean_absolute_error: 0.2248 - val_loss: 0.0962 - val_mean_absolute_error: 0.2503\n",
      "\n",
      "Epoch 00066: val_loss did not improve from 0.07779\n",
      "Epoch 67/100\n",
      " - 5s - loss: 0.0856 - mean_absolute_error: 0.2235 - val_loss: 0.0753 - val_mean_absolute_error: 0.2054\n",
      "\n",
      "Epoch 00067: val_loss improved from 0.07779 to 0.07526, saving model to mlp_model1.passnyc.hdf5\n",
      "Epoch 68/100\n",
      " - 6s - loss: 0.0828 - mean_absolute_error: 0.2209 - val_loss: 0.0722 - val_mean_absolute_error: 0.2071\n",
      "\n",
      "Epoch 00068: val_loss improved from 0.07526 to 0.07224, saving model to mlp_model1.passnyc.hdf5\n",
      "Epoch 69/100\n",
      " - 5s - loss: 0.0817 - mean_absolute_error: 0.2205 - val_loss: 0.0709 - val_mean_absolute_error: 0.2045\n",
      "\n",
      "Epoch 00069: val_loss improved from 0.07224 to 0.07092, saving model to mlp_model1.passnyc.hdf5\n",
      "Epoch 70/100\n",
      " - 5s - loss: 0.0820 - mean_absolute_error: 0.2190 - val_loss: 0.0825 - val_mean_absolute_error: 0.2061\n",
      "\n",
      "Epoch 00070: val_loss did not improve from 0.07092\n",
      "Epoch 71/100\n",
      " - 5s - loss: 0.0823 - mean_absolute_error: 0.2176 - val_loss: 0.0722 - val_mean_absolute_error: 0.1991\n",
      "\n",
      "Epoch 00071: val_loss did not improve from 0.07092\n",
      "Epoch 72/100\n",
      " - 5s - loss: 0.0803 - mean_absolute_error: 0.2169 - val_loss: 0.0714 - val_mean_absolute_error: 0.1972\n",
      "\n",
      "Epoch 00072: val_loss did not improve from 0.07092\n",
      "Epoch 73/100\n",
      " - 6s - loss: 0.0803 - mean_absolute_error: 0.2175 - val_loss: 0.0784 - val_mean_absolute_error: 0.2222\n",
      "\n",
      "Epoch 00073: val_loss did not improve from 0.07092\n",
      "Epoch 74/100\n",
      " - 6s - loss: 0.0791 - mean_absolute_error: 0.2149 - val_loss: 0.0663 - val_mean_absolute_error: 0.1990\n",
      "\n",
      "Epoch 00074: val_loss improved from 0.07092 to 0.06634, saving model to mlp_model1.passnyc.hdf5\n",
      "Epoch 75/100\n",
      " - 6s - loss: 0.0787 - mean_absolute_error: 0.2138 - val_loss: 0.0672 - val_mean_absolute_error: 0.1941\n",
      "\n",
      "Epoch 00075: val_loss did not improve from 0.06634\n",
      "Epoch 76/100\n",
      " - 6s - loss: 0.0770 - mean_absolute_error: 0.2121 - val_loss: 0.0680 - val_mean_absolute_error: 0.2039\n",
      "\n",
      "Epoch 00076: val_loss did not improve from 0.06634\n",
      "Epoch 77/100\n",
      " - 5s - loss: 0.0778 - mean_absolute_error: 0.2162 - val_loss: 0.0648 - val_mean_absolute_error: 0.1967\n",
      "\n",
      "Epoch 00077: val_loss improved from 0.06634 to 0.06481, saving model to mlp_model1.passnyc.hdf5\n",
      "Epoch 78/100\n",
      " - 5s - loss: 0.0759 - mean_absolute_error: 0.2106 - val_loss: 0.0788 - val_mean_absolute_error: 0.2231\n",
      "\n",
      "Epoch 00078: val_loss did not improve from 0.06481\n",
      "Epoch 79/100\n",
      " - 5s - loss: 0.0778 - mean_absolute_error: 0.2141 - val_loss: 0.0638 - val_mean_absolute_error: 0.1962\n",
      "\n",
      "Epoch 00079: val_loss improved from 0.06481 to 0.06380, saving model to mlp_model1.passnyc.hdf5\n",
      "Epoch 80/100\n",
      " - 5s - loss: 0.0738 - mean_absolute_error: 0.2078 - val_loss: 0.0722 - val_mean_absolute_error: 0.1952\n",
      "\n",
      "Epoch 00080: val_loss did not improve from 0.06380\n",
      "Epoch 81/100\n",
      " - 5s - loss: 0.0734 - mean_absolute_error: 0.2088 - val_loss: 0.0824 - val_mean_absolute_error: 0.2328\n",
      "\n",
      "Epoch 00081: val_loss did not improve from 0.06380\n",
      "Epoch 82/100\n",
      " - 5s - loss: 0.0737 - mean_absolute_error: 0.2082 - val_loss: 0.0793 - val_mean_absolute_error: 0.2037\n",
      "\n",
      "Epoch 00082: val_loss did not improve from 0.06380\n",
      "Epoch 83/100\n",
      " - 5s - loss: 0.0720 - mean_absolute_error: 0.2064 - val_loss: 0.0912 - val_mean_absolute_error: 0.2426\n",
      "\n",
      "Epoch 00083: val_loss did not improve from 0.06380\n",
      "Epoch 84/100\n",
      " - 5s - loss: 0.0739 - mean_absolute_error: 0.2070 - val_loss: 0.0613 - val_mean_absolute_error: 0.1884\n",
      "\n",
      "Epoch 00084: val_loss improved from 0.06380 to 0.06126, saving model to mlp_model1.passnyc.hdf5\n",
      "Epoch 85/100\n",
      " - 5s - loss: 0.0729 - mean_absolute_error: 0.2072 - val_loss: 0.0604 - val_mean_absolute_error: 0.1907\n",
      "\n",
      "Epoch 00085: val_loss improved from 0.06126 to 0.06040, saving model to mlp_model1.passnyc.hdf5\n",
      "Epoch 86/100\n",
      " - 5s - loss: 0.0714 - mean_absolute_error: 0.2029 - val_loss: 0.0616 - val_mean_absolute_error: 0.1840\n",
      "\n",
      "Epoch 00086: val_loss did not improve from 0.06040\n",
      "Epoch 87/100\n",
      " - 5s - loss: 0.0706 - mean_absolute_error: 0.2009 - val_loss: 0.0724 - val_mean_absolute_error: 0.1962\n",
      "\n",
      "Epoch 00087: val_loss did not improve from 0.06040\n",
      "Epoch 88/100\n",
      " - 5s - loss: 0.0719 - mean_absolute_error: 0.2033 - val_loss: 0.0788 - val_mean_absolute_error: 0.2271\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 00088: val_loss did not improve from 0.06040\n",
      "Epoch 89/100\n",
      " - 6s - loss: 0.0705 - mean_absolute_error: 0.2015 - val_loss: 0.0612 - val_mean_absolute_error: 0.1801\n",
      "\n",
      "Epoch 00089: val_loss did not improve from 0.06040\n",
      "Epoch 90/100\n",
      " - 5s - loss: 0.0709 - mean_absolute_error: 0.2015 - val_loss: 0.1146 - val_mean_absolute_error: 0.2815\n",
      "\n",
      "Epoch 00090: val_loss did not improve from 0.06040\n",
      "\n",
      "Epoch 00090: ReduceLROnPlateau reducing learning rate to 3.200000264769187e-07.\n",
      "Epoch 91/100\n",
      " - 5s - loss: 0.0645 - mean_absolute_error: 0.1932 - val_loss: 0.0567 - val_mean_absolute_error: 0.1812\n",
      "\n",
      "Epoch 00091: val_loss improved from 0.06040 to 0.05666, saving model to mlp_model1.passnyc.hdf5\n",
      "Epoch 92/100\n",
      " - 5s - loss: 0.0619 - mean_absolute_error: 0.1887 - val_loss: 0.0578 - val_mean_absolute_error: 0.1869\n",
      "\n",
      "Epoch 00092: val_loss did not improve from 0.05666\n",
      "Epoch 93/100\n",
      " - 5s - loss: 0.0621 - mean_absolute_error: 0.1886 - val_loss: 0.0576 - val_mean_absolute_error: 0.1858\n",
      "\n",
      "Epoch 00093: val_loss did not improve from 0.05666\n",
      "Epoch 94/100\n",
      " - 5s - loss: 0.0619 - mean_absolute_error: 0.1876 - val_loss: 0.0572 - val_mean_absolute_error: 0.1844\n",
      "\n",
      "Epoch 00094: val_loss did not improve from 0.05666\n",
      "Epoch 95/100\n",
      " - 5s - loss: 0.0616 - mean_absolute_error: 0.1864 - val_loss: 0.0564 - val_mean_absolute_error: 0.1799\n",
      "\n",
      "Epoch 00095: val_loss improved from 0.05666 to 0.05637, saving model to mlp_model1.passnyc.hdf5\n",
      "Epoch 96/100\n",
      " - 5s - loss: 0.0617 - mean_absolute_error: 0.1877 - val_loss: 0.0567 - val_mean_absolute_error: 0.1785\n",
      "\n",
      "Epoch 00096: val_loss did not improve from 0.05637\n",
      "Epoch 97/100\n",
      " - 5s - loss: 0.0617 - mean_absolute_error: 0.1874 - val_loss: 0.0567 - val_mean_absolute_error: 0.1833\n",
      "\n",
      "Epoch 00097: val_loss did not improve from 0.05637\n",
      "Epoch 98/100\n",
      " - 5s - loss: 0.0609 - mean_absolute_error: 0.1860 - val_loss: 0.0567 - val_mean_absolute_error: 0.1778\n",
      "\n",
      "Epoch 00098: val_loss did not improve from 0.05637\n",
      "Epoch 99/100\n",
      " - 5s - loss: 0.0608 - mean_absolute_error: 0.1867 - val_loss: 0.0583 - val_mean_absolute_error: 0.1773\n",
      "\n",
      "Epoch 00099: val_loss did not improve from 0.05637\n",
      "Epoch 100/100\n",
      " - 5s - loss: 0.0611 - mean_absolute_error: 0.1858 - val_loss: 0.0596 - val_mean_absolute_error: 0.1918\n",
      "\n",
      "Epoch 00100: val_loss did not improve from 0.05637\n",
      "\n",
      "Epoch 00100: ReduceLROnPlateau reducing learning rate to 6.400000529538374e-08.\n"
     ]
    }
   ],
   "source": [
    "mlp_history1 = mlp_model1.fit(X_train1, y_train1, \n",
    "                              epochs=100, batch_size=16, verbose=2,\n",
    "                              validation_data=(X_valid1, y_valid1),\n",
    "                              callbacks=[mlp_checkpointer1,mlp_lr_reduction1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss_plot(mlp_history1,50)\n",
    "mae_plot(mlp_history1,50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_______________________________________\n",
      "MLP; \n",
      "Numeric Features; \n",
      "Average Math Proficiency\n",
      "_______________________________________\n",
      "EV score. Train:  0.7278370753086256\n",
      "EV score. Valid:  0.7333998416354985\n",
      "EV score. Test:  0.7706826198736405\n",
      "---------\n",
      "R2 score. Train:  0.7255806550424555\n",
      "R2 score. Valid:  0.7327965464193871\n",
      "R2 score. Test:  0.7705303447815445\n",
      "---------\n",
      "MSE score. Train:  0.06045880661273547\n",
      "MSE score. Valid:  0.056366405278860075\n",
      "MSE score. Test:  0.04770487881710968\n",
      "---------\n",
      "MAE score. Train:  0.18431656159533097\n",
      "MAE score. Valid:  0.17986067575517953\n",
      "MAE score. Test:  0.1745087014942269\n",
      "---------\n",
      "MdAE score. Train:  0.1455758428573608\n",
      "MdAE score. Valid:  0.1499717807769776\n",
      "MdAE score. Test:  0.13744794845581065\n"
     ]
    }
   ],
   "source": [
    "mlp_model1.load_weights('mlp_model1.passnyc.hdf5')\n",
    "y_train_mlp1 = mlp_model1.predict(X_train1)\n",
    "y_valid_mlp1 = mlp_model1.predict(X_valid1)\n",
    "y_test_mlp1 = mlp_model1.predict(X_test1)\n",
    "\n",
    "scores('MLP; \\nNumeric Features; \\nAverage Math Proficiency', \n",
    "       y_train1, y_valid1, y_test1, y_train_mlp1, y_valid_mlp1, y_test_mlp1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mlp_model2():\n",
    "    model = Sequential()\n",
    "    \n",
    "    model.add(Dense(138, input_dim=138))\n",
    "    model.add(LeakyReLU(alpha=0.02))\n",
    "    model.add(Dense(138*4))\n",
    "    model.add(LeakyReLU(alpha=0.02))\n",
    "    \n",
    "    model.add(Dense(138*16))\n",
    "    model.add(LeakyReLU(alpha=0.02))\n",
    "    model.add(Dense(138*16))\n",
    "    model.add(LeakyReLU(alpha=0.02))\n",
    "    \n",
    "    model.add(Dense(1))\n",
    "    \n",
    "    model.compile(loss='mse', optimizer='rmsprop', metrics=['mae'])\n",
    "    return model\n",
    "\n",
    "mlp_model2 = mlp_model2()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "mlp_checkpointer2 = ModelCheckpoint(filepath='mlp_model2.passnyc.hdf5', \n",
    "                                    verbose=2, save_best_only=True)\n",
    "mlp_lr_reduction2 = ReduceLROnPlateau(monitor='val_loss', \n",
    "                                      patience=5, verbose=2, factor=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 890 samples, validate on 191 samples\n",
      "Epoch 1/100\n",
      " - 8s - loss: 523402340.6239 - mean_absolute_error: 4165.8775 - val_loss: 2.9557 - val_mean_absolute_error: 1.6600\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 2.95572, saving model to mlp_model2.passnyc.hdf5\n",
      "Epoch 2/100\n",
      " - 5s - loss: 6576.4692 - mean_absolute_error: 33.3147 - val_loss: 53.4247 - val_mean_absolute_error: 6.2808\n",
      "\n",
      "Epoch 00002: val_loss did not improve from 2.95572\n",
      "Epoch 3/100\n",
      " - 5s - loss: 22900.5147 - mean_absolute_error: 58.3853 - val_loss: 1442060.5281 - val_mean_absolute_error: 1086.9957\n",
      "\n",
      "Epoch 00003: val_loss did not improve from 2.95572\n",
      "Epoch 4/100\n",
      " - 5s - loss: 3309247.5614 - mean_absolute_error: 399.8143 - val_loss: 800.1427 - val_mean_absolute_error: 26.0026\n",
      "\n",
      "Epoch 00004: val_loss did not improve from 2.95572\n",
      "Epoch 5/100\n",
      " - 5s - loss: 3629.4617 - mean_absolute_error: 30.9967 - val_loss: 1089.8754 - val_mean_absolute_error: 29.4859\n",
      "\n",
      "Epoch 00005: val_loss did not improve from 2.95572\n",
      "Epoch 6/100\n",
      " - 5s - loss: 91914.1101 - mean_absolute_error: 94.4105 - val_loss: 88.3227 - val_mean_absolute_error: 8.8473\n",
      "\n",
      "Epoch 00006: val_loss did not improve from 2.95572\n",
      "\n",
      "Epoch 00006: ReduceLROnPlateau reducing learning rate to 0.00020000000949949026.\n",
      "Epoch 7/100\n",
      " - 5s - loss: 62.3387 - mean_absolute_error: 3.6142 - val_loss: 532.8735 - val_mean_absolute_error: 20.4946\n",
      "\n",
      "Epoch 00007: val_loss did not improve from 2.95572\n",
      "Epoch 8/100\n",
      " - 5s - loss: 173.9097 - mean_absolute_error: 7.4007 - val_loss: 1.5983 - val_mean_absolute_error: 0.8360\n",
      "\n",
      "Epoch 00008: val_loss improved from 2.95572 to 1.59833, saving model to mlp_model2.passnyc.hdf5\n",
      "Epoch 9/100\n",
      " - 5s - loss: 106.0702 - mean_absolute_error: 5.2160 - val_loss: 1.1102 - val_mean_absolute_error: 0.7290\n",
      "\n",
      "Epoch 00009: val_loss improved from 1.59833 to 1.11020, saving model to mlp_model2.passnyc.hdf5\n",
      "Epoch 10/100\n",
      " - 5s - loss: 27.6160 - mean_absolute_error: 3.4097 - val_loss: 64.9150 - val_mean_absolute_error: 6.8832\n",
      "\n",
      "Epoch 00010: val_loss did not improve from 1.11020\n",
      "Epoch 11/100\n",
      " - 5s - loss: 26.6194 - mean_absolute_error: 3.0333 - val_loss: 26.7392 - val_mean_absolute_error: 4.2438\n",
      "\n",
      "Epoch 00011: val_loss did not improve from 1.11020\n",
      "Epoch 12/100\n",
      " - 5s - loss: 28.7787 - mean_absolute_error: 3.2299 - val_loss: 7.2165 - val_mean_absolute_error: 2.6525\n",
      "\n",
      "Epoch 00012: val_loss did not improve from 1.11020\n",
      "Epoch 13/100\n",
      " - 5s - loss: 32.0982 - mean_absolute_error: 2.3534 - val_loss: 3.1708 - val_mean_absolute_error: 1.7564\n",
      "\n",
      "Epoch 00013: val_loss did not improve from 1.11020\n",
      "Epoch 14/100\n",
      " - 5s - loss: 6.2321 - mean_absolute_error: 1.6276 - val_loss: 4.8158 - val_mean_absolute_error: 1.5543\n",
      "\n",
      "Epoch 00014: val_loss did not improve from 1.11020\n",
      "\n",
      "Epoch 00014: ReduceLROnPlateau reducing learning rate to 4.0000001899898055e-05.\n",
      "Epoch 15/100\n",
      " - 5s - loss: 0.8348 - mean_absolute_error: 0.7102 - val_loss: 1.3796 - val_mean_absolute_error: 0.7868\n",
      "\n",
      "Epoch 00015: val_loss did not improve from 1.11020\n",
      "Epoch 16/100\n",
      " - 5s - loss: 1.0587 - mean_absolute_error: 0.7513 - val_loss: 0.8819 - val_mean_absolute_error: 0.6305\n",
      "\n",
      "Epoch 00016: val_loss improved from 1.11020 to 0.88192, saving model to mlp_model2.passnyc.hdf5\n",
      "Epoch 17/100\n",
      " - 6s - loss: 0.7254 - mean_absolute_error: 0.6800 - val_loss: 0.5373 - val_mean_absolute_error: 0.5494\n",
      "\n",
      "Epoch 00017: val_loss improved from 0.88192 to 0.53731, saving model to mlp_model2.passnyc.hdf5\n",
      "Epoch 18/100\n",
      " - 5s - loss: 0.7161 - mean_absolute_error: 0.6639 - val_loss: 0.8141 - val_mean_absolute_error: 0.6058\n",
      "\n",
      "Epoch 00018: val_loss did not improve from 0.53731\n",
      "Epoch 19/100\n",
      " - 5s - loss: 0.8025 - mean_absolute_error: 0.7076 - val_loss: 0.5485 - val_mean_absolute_error: 0.6293\n",
      "\n",
      "Epoch 00019: val_loss did not improve from 0.53731\n",
      "Epoch 20/100\n",
      " - 5s - loss: 0.5707 - mean_absolute_error: 0.5989 - val_loss: 0.3722 - val_mean_absolute_error: 0.4642\n",
      "\n",
      "Epoch 00020: val_loss improved from 0.53731 to 0.37216, saving model to mlp_model2.passnyc.hdf5\n",
      "Epoch 21/100\n",
      " - 5s - loss: 0.6238 - mean_absolute_error: 0.6013 - val_loss: 0.7926 - val_mean_absolute_error: 0.8272\n",
      "\n",
      "Epoch 00021: val_loss did not improve from 0.37216\n",
      "Epoch 22/100\n",
      " - 5s - loss: 0.5684 - mean_absolute_error: 0.5825 - val_loss: 0.5554 - val_mean_absolute_error: 0.6570\n",
      "\n",
      "Epoch 00022: val_loss did not improve from 0.37216\n",
      "Epoch 23/100\n",
      " - 5s - loss: 0.5808 - mean_absolute_error: 0.5789 - val_loss: 1.6235 - val_mean_absolute_error: 1.0799\n",
      "\n",
      "Epoch 00023: val_loss did not improve from 0.37216\n",
      "Epoch 24/100\n",
      " - 5s - loss: 0.4980 - mean_absolute_error: 0.5606 - val_loss: 0.7759 - val_mean_absolute_error: 0.6786\n",
      "\n",
      "Epoch 00024: val_loss did not improve from 0.37216\n",
      "Epoch 25/100\n",
      " - 5s - loss: 0.4872 - mean_absolute_error: 0.5425 - val_loss: 1.5987 - val_mean_absolute_error: 1.2258\n",
      "\n",
      "Epoch 00025: val_loss did not improve from 0.37216\n",
      "\n",
      "Epoch 00025: ReduceLROnPlateau reducing learning rate to 8.000000525498762e-06.\n",
      "Epoch 26/100\n",
      " - 5s - loss: 0.2366 - mean_absolute_error: 0.3589 - val_loss: 0.1220 - val_mean_absolute_error: 0.2387\n",
      "\n",
      "Epoch 00026: val_loss improved from 0.37216 to 0.12196, saving model to mlp_model2.passnyc.hdf5\n",
      "Epoch 27/100\n",
      " - 5s - loss: 0.1404 - mean_absolute_error: 0.2824 - val_loss: 0.0963 - val_mean_absolute_error: 0.2177\n",
      "\n",
      "Epoch 00027: val_loss improved from 0.12196 to 0.09634, saving model to mlp_model2.passnyc.hdf5\n",
      "Epoch 28/100\n",
      " - 5s - loss: 0.1226 - mean_absolute_error: 0.2604 - val_loss: 0.1094 - val_mean_absolute_error: 0.2605\n",
      "\n",
      "Epoch 00028: val_loss did not improve from 0.09634\n",
      "Epoch 29/100\n",
      " - 5s - loss: 0.1306 - mean_absolute_error: 0.2702 - val_loss: 0.0994 - val_mean_absolute_error: 0.2187\n",
      "\n",
      "Epoch 00029: val_loss did not improve from 0.09634\n",
      "Epoch 30/100\n",
      " - 5s - loss: 0.1262 - mean_absolute_error: 0.2706 - val_loss: 0.0813 - val_mean_absolute_error: 0.2041\n",
      "\n",
      "Epoch 00030: val_loss improved from 0.09634 to 0.08127, saving model to mlp_model2.passnyc.hdf5\n",
      "Epoch 31/100\n",
      " - 5s - loss: 0.1112 - mean_absolute_error: 0.2511 - val_loss: 0.0978 - val_mean_absolute_error: 0.2120\n",
      "\n",
      "Epoch 00031: val_loss did not improve from 0.08127\n",
      "Epoch 32/100\n",
      " - 5s - loss: 0.1187 - mean_absolute_error: 0.2587 - val_loss: 0.0852 - val_mean_absolute_error: 0.2192\n",
      "\n",
      "Epoch 00032: val_loss did not improve from 0.08127\n",
      "Epoch 33/100\n",
      " - 5s - loss: 0.1154 - mean_absolute_error: 0.2531 - val_loss: 0.1020 - val_mean_absolute_error: 0.2351\n",
      "\n",
      "Epoch 00033: val_loss did not improve from 0.08127\n",
      "Epoch 34/100\n",
      " - 5s - loss: 0.1152 - mean_absolute_error: 0.2564 - val_loss: 0.0823 - val_mean_absolute_error: 0.2054\n",
      "\n",
      "Epoch 00034: val_loss did not improve from 0.08127\n",
      "Epoch 35/100\n",
      " - 5s - loss: 0.1088 - mean_absolute_error: 0.2454 - val_loss: 0.1117 - val_mean_absolute_error: 0.2613\n",
      "\n",
      "Epoch 00035: val_loss did not improve from 0.08127\n",
      "\n",
      "Epoch 00035: ReduceLROnPlateau reducing learning rate to 1.6000001778593287e-06.\n",
      "Epoch 36/100\n",
      " - 5s - loss: 0.0738 - mean_absolute_error: 0.2024 - val_loss: 0.0753 - val_mean_absolute_error: 0.2096\n",
      "\n",
      "Epoch 00036: val_loss improved from 0.08127 to 0.07529, saving model to mlp_model2.passnyc.hdf5\n",
      "Epoch 37/100\n",
      " - 5s - loss: 0.0673 - mean_absolute_error: 0.1907 - val_loss: 0.0714 - val_mean_absolute_error: 0.1979\n",
      "\n",
      "Epoch 00037: val_loss improved from 0.07529 to 0.07141, saving model to mlp_model2.passnyc.hdf5\n",
      "Epoch 38/100\n",
      " - 5s - loss: 0.0653 - mean_absolute_error: 0.1871 - val_loss: 0.0639 - val_mean_absolute_error: 0.1734\n",
      "\n",
      "Epoch 00038: val_loss improved from 0.07141 to 0.06392, saving model to mlp_model2.passnyc.hdf5\n",
      "Epoch 39/100\n",
      " - 7s - loss: 0.0633 - mean_absolute_error: 0.1845 - val_loss: 0.0600 - val_mean_absolute_error: 0.1766\n",
      "\n",
      "Epoch 00039: val_loss improved from 0.06392 to 0.05999, saving model to mlp_model2.passnyc.hdf5\n",
      "Epoch 40/100\n",
      " - 6s - loss: 0.0606 - mean_absolute_error: 0.1812 - val_loss: 0.0573 - val_mean_absolute_error: 0.1688\n",
      "\n",
      "Epoch 00040: val_loss improved from 0.05999 to 0.05726, saving model to mlp_model2.passnyc.hdf5\n",
      "Epoch 41/100\n",
      " - 6s - loss: 0.0576 - mean_absolute_error: 0.1769 - val_loss: 0.0539 - val_mean_absolute_error: 0.1690\n",
      "\n",
      "Epoch 00041: val_loss improved from 0.05726 to 0.05388, saving model to mlp_model2.passnyc.hdf5\n",
      "Epoch 42/100\n",
      " - 6s - loss: 0.0573 - mean_absolute_error: 0.1754 - val_loss: 0.0570 - val_mean_absolute_error: 0.1656\n",
      "\n",
      "Epoch 00042: val_loss did not improve from 0.05388\n",
      "Epoch 43/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " - 6s - loss: 0.0580 - mean_absolute_error: 0.1779 - val_loss: 0.0659 - val_mean_absolute_error: 0.2000\n",
      "\n",
      "Epoch 00043: val_loss did not improve from 0.05388\n",
      "Epoch 44/100\n",
      " - 5s - loss: 0.0572 - mean_absolute_error: 0.1736 - val_loss: 0.0493 - val_mean_absolute_error: 0.1592\n",
      "\n",
      "Epoch 00044: val_loss improved from 0.05388 to 0.04930, saving model to mlp_model2.passnyc.hdf5\n",
      "Epoch 45/100\n",
      " - 5s - loss: 0.0557 - mean_absolute_error: 0.1744 - val_loss: 0.0558 - val_mean_absolute_error: 0.1778\n",
      "\n",
      "Epoch 00045: val_loss did not improve from 0.04930\n",
      "Epoch 46/100\n",
      " - 5s - loss: 0.0550 - mean_absolute_error: 0.1715 - val_loss: 0.0565 - val_mean_absolute_error: 0.1703\n",
      "\n",
      "Epoch 00046: val_loss did not improve from 0.04930\n",
      "Epoch 47/100\n",
      " - 5s - loss: 0.0534 - mean_absolute_error: 0.1665 - val_loss: 0.0489 - val_mean_absolute_error: 0.1604\n",
      "\n",
      "Epoch 00047: val_loss improved from 0.04930 to 0.04894, saving model to mlp_model2.passnyc.hdf5\n",
      "Epoch 48/100\n",
      " - 5s - loss: 0.0528 - mean_absolute_error: 0.1689 - val_loss: 0.0450 - val_mean_absolute_error: 0.1530\n",
      "\n",
      "Epoch 00048: val_loss improved from 0.04894 to 0.04500, saving model to mlp_model2.passnyc.hdf5\n",
      "Epoch 49/100\n",
      " - 5s - loss: 0.0534 - mean_absolute_error: 0.1658 - val_loss: 0.0642 - val_mean_absolute_error: 0.1823\n",
      "\n",
      "Epoch 00049: val_loss did not improve from 0.04500\n",
      "Epoch 50/100\n",
      " - 6s - loss: 0.0518 - mean_absolute_error: 0.1665 - val_loss: 0.0496 - val_mean_absolute_error: 0.1690\n",
      "\n",
      "Epoch 00050: val_loss did not improve from 0.04500\n",
      "Epoch 51/100\n",
      " - 5s - loss: 0.0502 - mean_absolute_error: 0.1658 - val_loss: 0.0433 - val_mean_absolute_error: 0.1507\n",
      "\n",
      "Epoch 00051: val_loss improved from 0.04500 to 0.04328, saving model to mlp_model2.passnyc.hdf5\n",
      "Epoch 52/100\n",
      " - 5s - loss: 0.0507 - mean_absolute_error: 0.1630 - val_loss: 0.0401 - val_mean_absolute_error: 0.1440\n",
      "\n",
      "Epoch 00052: val_loss improved from 0.04328 to 0.04008, saving model to mlp_model2.passnyc.hdf5\n",
      "Epoch 53/100\n",
      " - 5s - loss: 0.0475 - mean_absolute_error: 0.1579 - val_loss: 0.0444 - val_mean_absolute_error: 0.1492\n",
      "\n",
      "Epoch 00053: val_loss did not improve from 0.04008\n",
      "Epoch 54/100\n",
      " - 6s - loss: 0.0470 - mean_absolute_error: 0.1587 - val_loss: 0.0521 - val_mean_absolute_error: 0.1617\n",
      "\n",
      "Epoch 00054: val_loss did not improve from 0.04008\n",
      "Epoch 55/100\n",
      " - 5s - loss: 0.0488 - mean_absolute_error: 0.1641 - val_loss: 0.0390 - val_mean_absolute_error: 0.1408\n",
      "\n",
      "Epoch 00055: val_loss improved from 0.04008 to 0.03902, saving model to mlp_model2.passnyc.hdf5\n",
      "Epoch 56/100\n",
      " - 5s - loss: 0.0472 - mean_absolute_error: 0.1567 - val_loss: 0.0390 - val_mean_absolute_error: 0.1410\n",
      "\n",
      "Epoch 00056: val_loss improved from 0.03902 to 0.03898, saving model to mlp_model2.passnyc.hdf5\n",
      "Epoch 57/100\n",
      " - 6s - loss: 0.0469 - mean_absolute_error: 0.1568 - val_loss: 0.0395 - val_mean_absolute_error: 0.1418\n",
      "\n",
      "Epoch 00057: val_loss did not improve from 0.03898\n",
      "Epoch 58/100\n",
      " - 5s - loss: 0.0452 - mean_absolute_error: 0.1558 - val_loss: 0.0399 - val_mean_absolute_error: 0.1449\n",
      "\n",
      "Epoch 00058: val_loss did not improve from 0.03898\n",
      "Epoch 59/100\n",
      " - 6s - loss: 0.0475 - mean_absolute_error: 0.1591 - val_loss: 0.0416 - val_mean_absolute_error: 0.1467\n",
      "\n",
      "Epoch 00059: val_loss did not improve from 0.03898\n",
      "Epoch 60/100\n",
      " - 6s - loss: 0.0459 - mean_absolute_error: 0.1578 - val_loss: 0.0657 - val_mean_absolute_error: 0.1969\n",
      "\n",
      "Epoch 00060: val_loss did not improve from 0.03898\n",
      "\n",
      "Epoch 00060: ReduceLROnPlateau reducing learning rate to 3.200000264769187e-07.\n",
      "Epoch 61/100\n",
      " - 6s - loss: 0.0399 - mean_absolute_error: 0.1449 - val_loss: 0.0365 - val_mean_absolute_error: 0.1345\n",
      "\n",
      "Epoch 00061: val_loss improved from 0.03898 to 0.03650, saving model to mlp_model2.passnyc.hdf5\n",
      "Epoch 62/100\n",
      " - 5s - loss: 0.0381 - mean_absolute_error: 0.1403 - val_loss: 0.0355 - val_mean_absolute_error: 0.1368\n",
      "\n",
      "Epoch 00062: val_loss improved from 0.03650 to 0.03546, saving model to mlp_model2.passnyc.hdf5\n",
      "Epoch 63/100\n",
      " - 6s - loss: 0.0379 - mean_absolute_error: 0.1408 - val_loss: 0.0375 - val_mean_absolute_error: 0.1404\n",
      "\n",
      "Epoch 00063: val_loss did not improve from 0.03546\n",
      "Epoch 64/100\n",
      " - 6s - loss: 0.0380 - mean_absolute_error: 0.1405 - val_loss: 0.0350 - val_mean_absolute_error: 0.1352\n",
      "\n",
      "Epoch 00064: val_loss improved from 0.03546 to 0.03501, saving model to mlp_model2.passnyc.hdf5\n",
      "Epoch 65/100\n",
      " - 5s - loss: 0.0377 - mean_absolute_error: 0.1402 - val_loss: 0.0429 - val_mean_absolute_error: 0.1566\n",
      "\n",
      "Epoch 00065: val_loss did not improve from 0.03501\n",
      "Epoch 66/100\n",
      " - 5s - loss: 0.0380 - mean_absolute_error: 0.1413 - val_loss: 0.0352 - val_mean_absolute_error: 0.1329\n",
      "\n",
      "Epoch 00066: val_loss did not improve from 0.03501\n",
      "Epoch 67/100\n",
      " - 5s - loss: 0.0377 - mean_absolute_error: 0.1403 - val_loss: 0.0354 - val_mean_absolute_error: 0.1331\n",
      "\n",
      "Epoch 00067: val_loss did not improve from 0.03501\n",
      "Epoch 68/100\n",
      " - 5s - loss: 0.0373 - mean_absolute_error: 0.1402 - val_loss: 0.0360 - val_mean_absolute_error: 0.1381\n",
      "\n",
      "Epoch 00068: val_loss did not improve from 0.03501\n",
      "Epoch 69/100\n",
      " - 5s - loss: 0.0376 - mean_absolute_error: 0.1400 - val_loss: 0.0348 - val_mean_absolute_error: 0.1319\n",
      "\n",
      "Epoch 00069: val_loss improved from 0.03501 to 0.03481, saving model to mlp_model2.passnyc.hdf5\n",
      "Epoch 70/100\n",
      " - 5s - loss: 0.0371 - mean_absolute_error: 0.1381 - val_loss: 0.0344 - val_mean_absolute_error: 0.1343\n",
      "\n",
      "Epoch 00070: val_loss improved from 0.03481 to 0.03436, saving model to mlp_model2.passnyc.hdf5\n",
      "Epoch 71/100\n",
      " - 5s - loss: 0.0374 - mean_absolute_error: 0.1377 - val_loss: 0.0359 - val_mean_absolute_error: 0.1388\n",
      "\n",
      "Epoch 00071: val_loss did not improve from 0.03436\n",
      "Epoch 72/100\n",
      " - 6s - loss: 0.0368 - mean_absolute_error: 0.1383 - val_loss: 0.0339 - val_mean_absolute_error: 0.1309\n",
      "\n",
      "Epoch 00072: val_loss improved from 0.03436 to 0.03390, saving model to mlp_model2.passnyc.hdf5\n",
      "Epoch 73/100\n",
      " - 5s - loss: 0.0369 - mean_absolute_error: 0.1379 - val_loss: 0.0336 - val_mean_absolute_error: 0.1318\n",
      "\n",
      "Epoch 00073: val_loss improved from 0.03390 to 0.03358, saving model to mlp_model2.passnyc.hdf5\n",
      "Epoch 74/100\n",
      " - 5s - loss: 0.0365 - mean_absolute_error: 0.1374 - val_loss: 0.0343 - val_mean_absolute_error: 0.1311\n",
      "\n",
      "Epoch 00074: val_loss did not improve from 0.03358\n",
      "Epoch 75/100\n",
      " - 5s - loss: 0.0369 - mean_absolute_error: 0.1390 - val_loss: 0.0344 - val_mean_absolute_error: 0.1310\n",
      "\n",
      "Epoch 00075: val_loss did not improve from 0.03358\n",
      "Epoch 76/100\n",
      " - 5s - loss: 0.0362 - mean_absolute_error: 0.1368 - val_loss: 0.0337 - val_mean_absolute_error: 0.1304\n",
      "\n",
      "Epoch 00076: val_loss did not improve from 0.03358\n",
      "Epoch 77/100\n",
      " - 5s - loss: 0.0364 - mean_absolute_error: 0.1370 - val_loss: 0.0336 - val_mean_absolute_error: 0.1332\n",
      "\n",
      "Epoch 00077: val_loss did not improve from 0.03358\n",
      "Epoch 78/100\n",
      " - 5s - loss: 0.0360 - mean_absolute_error: 0.1371 - val_loss: 0.0332 - val_mean_absolute_error: 0.1301\n",
      "\n",
      "Epoch 00078: val_loss improved from 0.03358 to 0.03318, saving model to mlp_model2.passnyc.hdf5\n",
      "Epoch 79/100\n",
      " - 5s - loss: 0.0356 - mean_absolute_error: 0.1355 - val_loss: 0.0337 - val_mean_absolute_error: 0.1295\n",
      "\n",
      "Epoch 00079: val_loss did not improve from 0.03318\n",
      "Epoch 80/100\n",
      " - 5s - loss: 0.0356 - mean_absolute_error: 0.1349 - val_loss: 0.0324 - val_mean_absolute_error: 0.1292\n",
      "\n",
      "Epoch 00080: val_loss improved from 0.03318 to 0.03240, saving model to mlp_model2.passnyc.hdf5\n",
      "Epoch 81/100\n",
      " - 5s - loss: 0.0360 - mean_absolute_error: 0.1363 - val_loss: 0.0331 - val_mean_absolute_error: 0.1294\n",
      "\n",
      "Epoch 00081: val_loss did not improve from 0.03240\n",
      "Epoch 82/100\n",
      " - 5s - loss: 0.0354 - mean_absolute_error: 0.1350 - val_loss: 0.0327 - val_mean_absolute_error: 0.1290\n",
      "\n",
      "Epoch 00082: val_loss did not improve from 0.03240\n",
      "Epoch 83/100\n",
      " - 5s - loss: 0.0355 - mean_absolute_error: 0.1359 - val_loss: 0.0328 - val_mean_absolute_error: 0.1289\n",
      "\n",
      "Epoch 00083: val_loss did not improve from 0.03240\n",
      "Epoch 84/100\n",
      " - 5s - loss: 0.0354 - mean_absolute_error: 0.1355 - val_loss: 0.0317 - val_mean_absolute_error: 0.1283\n",
      "\n",
      "Epoch 00084: val_loss improved from 0.03240 to 0.03166, saving model to mlp_model2.passnyc.hdf5\n",
      "Epoch 85/100\n",
      " - 6s - loss: 0.0355 - mean_absolute_error: 0.1351 - val_loss: 0.0325 - val_mean_absolute_error: 0.1284\n",
      "\n",
      "Epoch 00085: val_loss did not improve from 0.03166\n",
      "Epoch 86/100\n",
      " - 6s - loss: 0.0354 - mean_absolute_error: 0.1347 - val_loss: 0.0325 - val_mean_absolute_error: 0.1317\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 00086: val_loss did not improve from 0.03166\n",
      "Epoch 87/100\n",
      " - 6s - loss: 0.0353 - mean_absolute_error: 0.1356 - val_loss: 0.0321 - val_mean_absolute_error: 0.1270\n",
      "\n",
      "Epoch 00087: val_loss did not improve from 0.03166\n",
      "Epoch 88/100\n",
      " - 6s - loss: 0.0350 - mean_absolute_error: 0.1342 - val_loss: 0.0320 - val_mean_absolute_error: 0.1308\n",
      "\n",
      "Epoch 00088: val_loss did not improve from 0.03166\n",
      "Epoch 89/100\n",
      " - 6s - loss: 0.0347 - mean_absolute_error: 0.1341 - val_loss: 0.0318 - val_mean_absolute_error: 0.1274\n",
      "\n",
      "Epoch 00089: val_loss did not improve from 0.03166\n",
      "\n",
      "Epoch 00089: ReduceLROnPlateau reducing learning rate to 6.400000529538374e-08.\n",
      "Epoch 90/100\n",
      " - 6s - loss: 0.0338 - mean_absolute_error: 0.1315 - val_loss: 0.0316 - val_mean_absolute_error: 0.1271\n",
      "\n",
      "Epoch 00090: val_loss improved from 0.03166 to 0.03156, saving model to mlp_model2.passnyc.hdf5\n",
      "Epoch 91/100\n",
      " - 5s - loss: 0.0337 - mean_absolute_error: 0.1318 - val_loss: 0.0316 - val_mean_absolute_error: 0.1271\n",
      "\n",
      "Epoch 00091: val_loss did not improve from 0.03156\n",
      "Epoch 92/100\n",
      " - 6s - loss: 0.0337 - mean_absolute_error: 0.1313 - val_loss: 0.0315 - val_mean_absolute_error: 0.1270\n",
      "\n",
      "Epoch 00092: val_loss improved from 0.03156 to 0.03149, saving model to mlp_model2.passnyc.hdf5\n",
      "Epoch 93/100\n",
      " - 5s - loss: 0.0336 - mean_absolute_error: 0.1314 - val_loss: 0.0314 - val_mean_absolute_error: 0.1275\n",
      "\n",
      "Epoch 00093: val_loss improved from 0.03149 to 0.03145, saving model to mlp_model2.passnyc.hdf5\n",
      "Epoch 94/100\n",
      " - 5s - loss: 0.0335 - mean_absolute_error: 0.1315 - val_loss: 0.0313 - val_mean_absolute_error: 0.1271\n",
      "\n",
      "Epoch 00094: val_loss improved from 0.03145 to 0.03130, saving model to mlp_model2.passnyc.hdf5\n",
      "Epoch 95/100\n",
      " - 5s - loss: 0.0335 - mean_absolute_error: 0.1312 - val_loss: 0.0313 - val_mean_absolute_error: 0.1272\n",
      "\n",
      "Epoch 00095: val_loss improved from 0.03130 to 0.03127, saving model to mlp_model2.passnyc.hdf5\n",
      "Epoch 96/100\n",
      " - 5s - loss: 0.0335 - mean_absolute_error: 0.1314 - val_loss: 0.0314 - val_mean_absolute_error: 0.1284\n",
      "\n",
      "Epoch 00096: val_loss did not improve from 0.03127\n",
      "Epoch 97/100\n",
      " - 5s - loss: 0.0334 - mean_absolute_error: 0.1312 - val_loss: 0.0312 - val_mean_absolute_error: 0.1268\n",
      "\n",
      "Epoch 00097: val_loss improved from 0.03127 to 0.03117, saving model to mlp_model2.passnyc.hdf5\n",
      "Epoch 98/100\n",
      " - 5s - loss: 0.0333 - mean_absolute_error: 0.1307 - val_loss: 0.0316 - val_mean_absolute_error: 0.1291\n",
      "\n",
      "Epoch 00098: val_loss did not improve from 0.03117\n",
      "Epoch 99/100\n",
      " - 5s - loss: 0.0334 - mean_absolute_error: 0.1310 - val_loss: 0.0312 - val_mean_absolute_error: 0.1284\n",
      "\n",
      "Epoch 00099: val_loss did not improve from 0.03117\n",
      "Epoch 100/100\n",
      " - 5s - loss: 0.0334 - mean_absolute_error: 0.1307 - val_loss: 0.0315 - val_mean_absolute_error: 0.1292\n",
      "\n",
      "Epoch 00100: val_loss did not improve from 0.03117\n"
     ]
    }
   ],
   "source": [
    "mlp_history2 = mlp_model2.fit(X_train2, y_train2, \n",
    "                              epochs=100, batch_size=16, verbose=2,\n",
    "                              validation_data=(X_valid2, y_valid2),\n",
    "                              callbacks=[mlp_checkpointer2,mlp_lr_reduction2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NPz8h/ni+mf3GzCxTP5RI1jQVhEpK/LhxY9uOR0RERETS0mIQMrN84BbgWGA08CUzG93gtK8Da0MIewK/An4ef28BcB9wQQhhDHAEsDX+nluB84ER8dsxrf1hRLKuuYoQaJ2QiIiISI5IpiJ0ADA/hPBBCGELMA04qcE5JwH3xO9PBybHKzxHA2+HEN4CCCGsDiFsM7OBQI8QwqshhADcC5ycgZ9HJLuaapaQqAgpCImIiIjkhGSC0CBgcb3HS+LPNXpOCKEWqAL6ACOBYGZPmtkbZnZlvfOXtHBNAMxsqpnNNrPZK1euTGK4IlkUi0GXLlBUtOPziYqQGiaIiIiI5ISCNrj+IcD+wEbgGTN7HQ9KSQkh3A7cDjBx4sSQjUGKJC0W82lxDZe0aWqciIiISE5JpiK0FBhS7/Hg+HONnhNfF9QTWI1Xel4IIawKIWwEZgLj4+cPbuGaItFTVbXz+iComxqnipCIiIhITkgmCM0CRpjZcDPrApwBzGhwzgzg7Pj9U4Fn42t/ngTGmllxPCAdDswJISwDYmY2Kb6W6KvAIxn4eUSyq7q68SCkipCIiIhITmlxalwIodbMLsZDTT5wdwih0syuB2aHEGYAdwF/MrP5wBo8LBFCWGtmN+FhKgAzQwiPxS/9TeCPQDfg8fhNJNpiMRg4cOfnFYREREREckpSa4RCCDPxaW31n7u23v0a4LQm3nsf3kK74fOzgfJUBivS7mIx2GuvnZ8vKoL8fE2NExEREckRSW2oKiJxiWYJDZl5VUgVIREREZGcoCAkkqwtW2DTpsaDEHjDBFWERERERHKCgpBIsqqr/dhUEFJFSERERCRnKAiJJKsqvv2VgpCIiIhIzlMQEklWLOZHTY0TERERyXkKQiLJaikIqSIkIiIikjMUhESSlQhCPXs2/roqQiIiIiI5Q0FIJFnJVIQ2bIAQ2m5MIiIiIpIWBSGRZMVivmlqSUnjr5eWwrZt3mJbRERERCJNQUgkWbEYdO/um6c2JhGQND1OREREJPIUhESSFYs1PS0OvCIEapggIiIikgMUhESS1VIQSlSEFIREREREIk9BSCRZialxTUlUhDQ1TkRERCTyFIREkhWLNd06GzQ1TkRERCSHKAiJJCvZNUKqCImIiIhEnoKQSDK2bYPqaq0REhEREekgFIREkpEIN80FoeJib62tICQiIiISeQpCIsmoqvJjc0EoL8/DkIKQiIiISOQpCIkkIxbzY3NBCHydkNYIiYiIiESegpBIMlIJQqoIiYiIiESegpBIMqqr/dhc+2zwhgmqCImIiIhEnoKQSDKSrQiVlKgiJCIiIpIDFIREkpFolpDYK6gpmhonIiIikhMUhESSEYt5taegoPnz1CxBREREJCcoCIkkIxZreVocqCIkIiIikiMUhESSkUoQ2roVtmzJ/phEREREJG0KQiLJiMVa7hgHPn0OVBUSERERiTgFIZFkJFsRUhASERERyQkKQiLJSGVqHKhhgoiIiEjEKQiJtCSE1IOQKkIiIiIikaYgJNKSDRtg2zZVhEREREQ6EAUhkZZUV/tRa4REREREOgwFIZGWxGJ+1NQ4ERERkQ5DQUikJakEIVWERERERHJCUkHIzI4xs3lmNt/Mrmrk9SIzeyD++mtmNiz+/DAz22Rmb8Zvt9V7z/PxayZe2zVTP5RIRqUShAoKoGtXrRESERERibiClk4ws3zgFuAoYAkwy8xmhBDm1Dvt68DaEMKeZnYG8HNgSvy1BSGEfZu4/JkhhNnpD1+kDVRV+TGZIAQ+PU5BSERERCTSkqkIHQDMDyF8EELYAkwDTmpwzknAPfH704HJZmaZG6ZIO0pUhLp3T+780lJNjRMRERGJuGSC0CBgcb3HS+LPNXpOCKEWqAL6xF8bbmb/MbN/mtmhDd73h/i0uGuaCk5mNtXMZpvZ7JUrVyYxXJEMi8WgqMinvCWjpERBSERERCTist0sYRkwNISwH3A5cL+ZJeYXnRlCGAscGr+d1dgFQgi3hxAmhhAm9uvXL8vDFWlEspupJigIiYiIiEReMkFoKTCk3uPB8ecaPcfMCoCewOoQwuYQwmqAEMLrwAJgZPzx0vixGrgfn4InEj2xGPTsmfz5WiMkIiIiEnnJBKFZwAgzG25mXYAzgBkNzpkBnB2/fyrwbAghmFm/eLMFzGx3YATwgZkVmFnf+POFwAlARet/HJEsSLUipCAkIiIiEnktdo0LIdSa2cXAk0A+cHcIodLMrgdmhxBmAHcBfzKz+cAaPCwBHAZcb2Zbge3ABSGENWZWAjwZD0H5wNPAHZn+4UQyIhaDIUNaPi9BzRJEREREIq/FIAQQQpgJzGzw3LX17tcApzXyvoeAhxp5fgMwIdXBirSLdNYIbdoEtbW+r5CIiIiIRE62myWI5L50ghBoepyIiIhIhCkIiTSnpgY2b059jRAoCImIiIhEmIKQSHOqq/2Y7GaqUBeEtE5IREREJLIUhESaE4v5MdX22aCKkIiIiEiEKQiJNCcRhNJZI6SKkIiIiEhkKQiJNCedIKSpcSIiIiKRpyAk0pyqKj+qIiQiIiLSoSgIiTSnNRUhrRESERERiSwFIZHmxGKQnw/Fxcm/p6gICgsVhEREREQiTEFIpDmJzVTNUntfSYmmxomIiIhEmIKQSHNisdRaZyeUlioIiYiIiESYgpBIc6qrU1sflKCKkIiIiEikKQiJNKeqKr0gVFqqNUIiIiIiEaYgJNKcWAy6d0/9fQpCIiIiIpGmICTSnESzhFRpapyIiIhIpCkIiTSlttarOulOjVMQEhEREYksBSGRplRX+zGdrnElJR6itm/P7JhEREREJCMUhESaEov5Md2KUAiwaVNmxyQiIiIiGaEg1NHV1MCUKfDSS+09ktzT2iAEmh4nIiIiElEKQh3dnDmwcCG88UZ7jyT3VFX5Md1mCaDOcSIiIiIRpSDU0VVU+PHjj9t3HLlIFSERERGRDktBqKNTEEqfgpCIiIhIh6Ug1JGFUBeEli1r37HkoupqMKsLNanQ1DgRERGRSFMQ6sg++QRWr4aBA726oepEamIx6N4d8vNTf68qQiIiIiKRpiDUkSWqQUcd5UdNj0tNIgilI1ERUhASERERiSQFoY6sogKKiuCww/yxpselpqoqvfVBAMXFkJenqXEiIiIiEaUg1JFVVMCoUTB0qD9WRSg1sVj6QcjMq0KqCImIiIhEkoJQR7VlC8ybB+Xl/mW+uFhBKFWtCULg64RUERIRERGJJAWhjuq992DrVg9CZlBWpiCUqtYGIVWERERERCJLQaijSjRKKC/3o4JQarZv9/bZCkIiIiIiHZKCUBuoroYnn4T/+R845hiYObMNPrSiAvr3h379/PHAgR6EQmiDD+8ANmzwMNSzZ/rX0NQ4ERERkcgqaO8BdFQffwwvvAAvvgj/+Q9s2wa9enkTt1tugSOPhK5dsziAioq6ahB4RaimBtauhd69s/jBHUQs5sfWrhFauDAjwxERERGRzFIQypDt22HuXA8+L74I8+f788OHw5lnegfrMWPgrbfgggtg+nT4yleyNJjVq71V9umn1z1XVubHjz9WEEpGpoKQKkIiIiIikaQg1Ao1NTB7tgefl16CVat865h994VLL4VDD4UhQ3Z8z377wUEHwT33wMkn+3fljEusDxo7tu65+kGofqVIGldV5cdMrBEKwRtWiIiIiEhkJBWEzOwY4GYgH7gzhHBDg9eLgHuBCcBqYEoIYaGZDQPmAvPip74aQrgg/p4JwB+BbsBM4NIQcmMBy7/+BY88Aq+95mGouBgmTfKqz2c+0/Kykm98A845B/7yFzj//CwMsKICCgpg5Mi65xJBSJuqJqe62o+tDUK1tbB5c5bnQYqIiIhIqloMQmaWD9wCHAUsAWaZ2YwQwpx6p30dWBtC2NPMzgB+DkyJv7YghLBvI5e+FTgfeA0PQscAj6f9k7ShefNgzhw4/ngPP+PHQ5cuyb9/1ChfI3T//XDaabDLLhkeYEUF7LWXL0hKKC72D1LnuORkamoc+PQ4BSERERGRSEmma9wBwPwQwgchhC3ANOCkBuecBNwTvz8dmGzW9FwgMxsI9AghvBqvAt0LnJzy6NvJmWfCo4/ClVd6JSiVEJQwdapXk+65p+VzU1Jb6yltzJidX1ML7eRlMgiphbaIiIhI5CQThAYBi+s9XhJ/rtFzQgi1QBXQJ/7acDP7j5n908wOrXf+khauCYCZTTWz2WY2e+XKlUkMN/uKilq/5GP4cDj2WG+asHx5ZsYFwAcfeMJqbB2QglDyYjHo1g0KC9O/hoKQiIiISGRlex+hZcDQEMJ+wOXA/WaW0p/YQwi3hxAmhhAm9kvsidNBnHeed5v7wx8yeNHGGiUklJXBJ594L29pXlVV66pB4GuEQJ3jRERERCIomSC0FKjf+2xw/LlGzzGzAqAnsDqEsDmEsBoghPA6sAAYGT9/cAvX7PDKyuCUU2DGDFi8uOXzk1JR4RsWDRy482sDB/rUuVWrMvRhHVgslrkgpIqQiIiISOQkE4RmASPMbLiZdQHOAGY0OGcGcHb8/qnAsyGEYGb94s0WMLPdgRHAByGEZUDMzCbF1xJ9FXgkAz9Pzjn3XG/wduedGbpgYiPVxubu1W+hLc3LRBDS1DgRERGRyGoxCMXX/FwMPIm3wn4whFBpZteb2Ynx0+4C+pjZfHwK3FXx5w8D3jazN/EmCheEENbEX/smcCcwH68U5UTHuEzr0wemTIEnn6zbhDVtVVWwaFHT+wQpCCUvk0FIU+NEREREIiepfYRCCDPxFtf1n7u23v0a4LRG3vcQ8FAT15wNaGdP4Ctfgb/9DX7/e7jxxlZcaE68o3lTQWjAAK8UKQi1TFPjRERERDq0bDdLkCT07Olh6IUX6nodpKWiAvLyfKOixnTpAn37Kgi1JAQPQi3tjNuS/Hzfv0kVIREREZHIURCKiClTvMfBbbe14iIVFbD77nWViMaohXbLampg69bWV4TA/y1UERIRERGJHAWhiCguhnPOgVmz/Jay7duhsrLpaXEJZWWwbFk6Q+w8MrGZaoKCkIiIiEgkKQhFyCmnwK67wq23+uyslHz0EVRXJxeEVqzwikcLliyBl15KcRwdQSaDUGmppsaJiIiIRJCCUIQUFfkmq5WV8OKLKb45sbgomSAUgm+s2ozFi+H88+Hb34Y//jHFseS6RBDq3r311yotVUVIREREJIIUhCLm+ONhyBDvILd9ewpvrKjwL9277db8eUm00F6xAv77v/3zDz/cK1R33JFGlSpXqSIkIiIi0uEpCEVMQQFMnep7Cv3jHym8saICxozxrnHNaSEIVVXBJZf48de/hp/9zMPZnXd6I4dOEYYSQai1XeNAa4REREREIkpBKIL+679gzz3h9tuhtjaJN2zcCAsWtDwtDqBfP09bjQShDRvgsstg6VL45S+9C3d+Pnz/+3DSST5F7re/7QRhSM0SRERERDo8BaEIysuDCy/0ZgV//3sSb5g71+exJROE8vN9Y9UGQWjzZrjySpg3D376U5gwYcfxXHUVnHoq/PnP8KtfdfAwFItBYSF07dr6a5WW+i83qUQrIiIiIm1FQSiiDj4Yxo71KWmbN7dw8jvv+HHMmOQu3qCFdm0tXHMNzJ7tx0MP3fkteXlwxRVwxhnwwANw440prmHKJVVVXg0ya/21Skv9qKqQiIiISKQoCEWUmVeFVq6Ev/2thZMrKmDo0OTXtAwc+GlFaPt2rwD985/eIe7YY5sf02WXwVlnwUMPwQ03dNAwFItlZloc1AUhNUwQERERiRQFoQibMAEOOMDX5jT5PToED0JjxyZ/4bIyWLuWsGEjN98Mjz3mrbJPP73lt5rBRRfBuefCI4/Aj38M27Yl/9E5IZNBqKTEj6oIiYiIiESKglDEXXghrFsH06Y1ccKyZbB2bXLrgxIGDgTg4d8tY9o0mDIFvv715N9uBhdc4OHpscfghz/sYEtgslERUhASERERiRQFoYgbPRqOOMKbFFRVNXJCYn1QKkEo3kL7penLOP54n+6WznKY887zoPbkk3DttR0oDFVXqyIkIiIi0sEpCOWAb3zDO2TfeWcj09CwmUqFAAAgAElEQVQqKry72e67J329Z+d5EDpsj4+5+uqWtx5qzjnn+L5DzzwD//M/sHVr+tdqTzt0wYvFMrOHEGiNkIiIiEhEKQjlgN13h+OOgwcf9OP118Nzz3k4orLSy0YFBUld68UX4fv/25vNeV05YcLHyb6tWWee6Y0Wnn/e22xv2dL6a7alG26AL34RPvwQT3IbN0L37pm5uIKQiIiISCQpCOWIq67yxgQHHggvvOCPTzhqM7Vz5jGvsJzly1u+xuuvw9VXw157G4VDBlKwfOdNVdN1+unw3e/CSy/5fkQ1NRm7dFb9/e/w8MOwYoVX3t6dlcHNVEFT40REREQiSkEoR3TpAkcd5dWgJ56A3/0OvvHZ9ygItdz5Wjknnghf/SrccYfvr9pww9O5c30foEGDfEPUvMFlO22q2lpf+IJPj3v1Vf+s996LdiD64AP4xS+8O9/993vx5qdXZTgIdeniNwUhERERkUjJwMQoaWsFBf7lfcJ7FfAP+O9bxzCu0itFd9/ta4n69YNDDvHNUfv184YIPXvCb34Du+yCN0x4801PTJnYODTuxBN9fD/6ke83BLDrrr7NUeI2ZIgfy8qSntGXcZs2eXWsuNjDZd++cPvtcMvUGCyFNxb0YHymPqykREFIREREJGIUhHJZRQUMHMjQ8X05a7wHj7Vr4eWXfS3QE0/4tC+A3r3ht7/1UAJ4CtmwIbONAeKOO863NZo3Dz76qO72j394Q7aE/HyvUDUMSCNHZq4g05Rf/hIWLoSbb/YQBH78zgUxuAZ+88eenNAPTj01Ax9WWqogJCIiIhIxCkK5rKJip7bZvXrB8cf7bcsWeOMNmDXLw8mQIfVOjLfQ5uOPMx6EwD9rh8/Di09VVTuGo48+gsWLfYybN/t5PXp4aNt774wPC4CZM31t0Lnn+pqr+oq3+tS4kRN7cOONvofT17/eyqJZaamaJYiIiIhEjIJQrlq5Ej75BM44o8lTunSBSZP8tpP4pqp8/DGMGpWdMTZg5tPydtkFxo3b8bXt271hwYcfws9/Dv/939kJQ4nr77ef74O0k5gHoSt/3IPa3/qaq7VrvSte2m3GS0oUhEREREQiRs0SclVlpR9T2Ui1vvoVoQjIy4MBA+Cgg7wRREmJh6F3383cZ9TUeDOHrl19XVCj65OqqiAvj4KeJVxzjbcGnz4drrmmFXskaWqciIiISOQoCOWqigooLIS99krv/d27+23ZssyOKwPKyrIThm66CRYsgB/+sN5aqYZiMf+95OVh5pvFXnwxPP20V4U2bkzjg9UsQURERCRyFIRyVUWFdxXo0iX9a5RlvoV2pmQ6DD3xBDzyCJx9dhNTBRNisZ06NZx1Fnz/+76O6eKLvWiUElWERERERCJHQSgX1dbCnDnpT4tLGDgwkhWhhEyFoUWL4IYbYJ99YOrUFk6urm60Zd3nP+/XeP99v0YyG9h+qrTUS0nbt6c0bhERERHJHgWhXLRggbdYa20QKivzINRw99UIaW0Yqqnx/YK6dPG9jVrct6iRilDC4Yf7PkwrV8L553v77aSUlPgxrXl1IiIiIpINCkK56J13/JiJILR5M6xe3foxZVFrwtCvfw3z58MPfgD9+yfxhmaCEHi3udtu88YJU6d6Ya5FpaV+1PQ4ERERkchQEMpFFRW+Q2qiBXa6ItY5rjnphKGnnvINZc86Cw4+OMkPaiEIgS/NuuMOH8s3vwnvvdfCNRWERERERCJHQSgXJTZSbdUun+RUEILUwtBHH8HPfub7FV1wQZIfsG1bk2uEGho82MPQ9u3w6KMtnJyYGqcgJCIiIhIZCkK5pqoKFi9u/bQ42HFT1RyRTBjavNn3CyooSHJdUML69b5eKokgBNC3L4wfD6++2sKJiYqQNlUVERERiQwFoVxTUeHHTAShrl2hT5+cCkLQchi6+Wafrnbttb5Ja9JiMT8mGYQADjzQq0/N/gpVERIRERGJHAWhXFNRAXl5MHp0Zq43cGDOBSFoOgw98ww89BB8+ctw6KEpXjSNIHTQQX5stiqkipCIiIhI5CgI5ZqKCthzT+jWLTPXi/Cmqi1pGIaeeQZ+8hMYMwYuuiiNCyaCUM+eSb9lt9286pRUEFJFSERERCQykgpCZnaMmc0zs/lmdlUjrxeZ2QPx118zs2ENXh9qZuvN7Ip6zy00s3fM7E0zm93aH6RT2L4dKiszMy0uoawMVqzwTVpzUP0wdPXVXiz78Y9TWBdUXxoVITOfHjdrVjO/wq5dIT9fQUhEREQkQloMQmaWD9wCHAuMBr5kZg3nZX0dWBtC2BP4FfDzBq/fBDzeyOU/G0LYN4QwMeWRd0YLF/r0qkwHoW3bPAzlqEQYOuAAb46QaIaXsjSCEMCkSb5XamJ7p52YeVLT1DgRERGRyEimInQAMD+E8EEIYQswDTipwTknAffE708HJpt5b2czOxn4EKjMzJA7sUw2SkjIsRbaTSkrg9/+tm7NTloSQah795Tetv/+XvB57bVmTiopUUVIREREJEKSCUKDgMX1Hi+JP9foOSGEWqAK6GNmpcB3gR82ct0APGVmr5vZ1KY+3MymmtlsM5u9cuXKJIbbgVVUeLViyJDMXbODBKGMiMWguDjleXXdu/u6pFdeaeak0lJVhEREREQiJNvNEq4DfhVCaOxP4YeEEMbjU+4uMrPDGrtACOH2EMLEEMLEfv36ZXGoOaCy0r9x52Xwn61/f7+egpAHoRQaJdQ3aRLMmwdr1zZxQmmpKkIiIiIiEZLMN+qlQP0SxOD4c42eY2YFQE9gNXAg8AszWwhcBlxtZhcDhBCWxo8rgIfxKXjSlA0bYMGCzE6LA69+9O8Py5Zl9rq5KBZLeX1QwqRJvhfrv//dxAkKQiIiIiKRkkwQmgWMMLPhZtYFOAOY0eCcGcDZ8funAs8Gd2gIYVgIYRjwa+CnIYT/M7MSM+sOYGYlwNFARQZ+no5r7lz/pp3pIAQ5u5dQxrUiCO29t7+1yTbaapYgIiIiEiktBqH4mp+LgSeBucCDIYRKM7vezE6Mn3YXviZoPnA5sFOL7Qb6Ay+Z2VvAv4HHQghPpPtDdApvv+3HTG2kWl8O7yWUUa0IQvn53kb71Ve9y/lO1CxBREREJFKSWhUeQpgJzGzw3LX17tcAp7Vwjevq3f8A2CeVgXZqIcA//uHrg9L8ot6ssjJYtQpqanzPm86qqqpVv99Jk/yfaf58GDmywYuJZgkheDttEREREWlX2W6WIJlQWQkffAAnntjyuekYONCPn3ySnevnghBaVRECrwhBE9PjSkt9v6aamrSvLyIiIiKZoyCUC2bM8ErNf/1Xdq6vFtq+I+q2ba0KQv36wZ57NhOEQNPjRERERCJCQSjqNm3y+VaTJ9d9mc60RBDqzJ3jEpuptnLq4YEHwltvea7aQUmJH9UwQURERCQSFISi7pln/Ft1tqbFAfTtC126dO6KUCIIpbmPUMJBB0FtLbzxRoMXVBESERERiRQFoaibMQN22w32yWJvibw8GDCgcweh6mo/trIitM8+PovxlVcavJCoCCkIiYiIiESCglCULVrk86w+//nsdxrr7C20q6r82L17qy7TpQuMH9/IOqFERUhT40REREQiQUEoyh591DeoOe647H9WZw9CGVojBN5Ge8kSWLq03pOaGiciIiISKQpC6aqtzf71H3sMDj4Y+vTJ7meBB6FYrPN+Uc9wEIIGVSFVhEREREQiRUEoHffcA2efnd0w9K9/wZo12W2SUF9nb6Edi0FRUUY2lB061Ldm2iEIFRf7sbMGTREREZGIURBKx9ChMH8+PP549j7j0Ue9m9tBB2XvM+rr7C20W7mZan1mXhWaPRu2bo0/mZfnYUhBSERERCQSFITSccQRMGoU3HknbNmS+euvWgUvv+xrgwoKMn/9xqgilLEgBB6ENm6Ed96p92RpqYKQiIiISEQoCKXDDC68ED75BP72t8xf/7HHYNs27xbXVnr08IqFglBGTJzofS52WifU2dYILV8O773X3qMQERER2YmCULoOOAAmTIA//tH/9J8pIfi0uP328yl4bcWsc3eOy3AQKi2F8vJGglBnqwj9+tdw6aX+37WIiIhIhCgIpStRFVq7FqZNy9x133wTFi9uuyYJ9Q0c2HnXCFVVZTQIgS/vmjfPe14AvqlqZ6sIvfOO/wJ26CUuIiIi0v4UhFpj7Fg47DC47766DTlba8YM/8J85JGZuV4qEhWhzvjX+wxXhKCujfa//x1/oqSkc1WEli+HlSv9fkVF+45FREREpAEFoda64AKfGnfvva2/1vr18MwzcPTRGWnjnLKyMti0Cdata/vPbk+bN/stw0For71gl13glVfiT3S2qXGVlXX3FYREREQkYhSEWmuPPeBzn4O//rXur9/peuop/0LeHtPioPN2jquu9mOGg1Beni8le+012L6dztcsobISCgu9cqogJCIiIhGjIJQJU6f65qp339266zz6KOy5p7fmbg+dNQjFYn7McBACnx63di28/z4ehLZsyU7L9SiqrISRI73xx3vvQU1Ne49IRERE5FMKQpkwaBCcfDI88ggsWZLeNebPhzlzvGW2WWbHl6zOGoQS67uyEIQOPNCPr76KByHoHFWh2lqYOxfGjPH2edu2eecIERERkYhQEMqUr33NNz+9/fb03j9jhk8jOvbYzI4rFcXFvqilswWhLFaE+vaFESPiQaikxJ/sDOuEPvzQK0Dl5X4DTY8TERGRSFEQypS+fWHKFF/n8/77qb13yxZ4/HE4/HDo2TM740tWZ9xLKItBCHx63FtvQU1BJwpCiUYJo0dDnz7eml1BSERERCJEQSiTzjrL/+r/+9+n9r4XXvAv45//fHbGlQoFoYybNCk+M2xpJ5oaV1npoX7wYH+shgkiIiISMQpCmdSjh4ehF1+Et99O/n0zZsCAAd5irL0NHAiffOLf3DuLWAzy8+umrmXYuHHeDf0/78eDUGepCI0eXbferbwcVqzwvYVEREREIkBBKNOmTIHeveHWW5PbmHTZMt9x84QTvN9yeysr84Xuq1a190jaxsaN3t+6d++sNano0gUmToR/V3aSitCGDfDBB3Vrg6Dufv29hURERETaUQS+eXcw3bp544Q33vAv2C157DE/nnBCdseVrM7UOW7TJrj8cm/t/K1vZfWjJk2C+Z90kjVCc+f6HwHGjKl7buRIT4SaHiciIiIRoSCUDSef7FPMfve7+E6aTdi2zfcO2n9/Pz8KEkFo2bL2HUe21dTAFVd4F4Mf/AAmT87qxx14IKynk0yNq98oIaGwEPbaS0FIREREIkNBKBsKC+H8833flOeea/q8WbN8Pc6JJ7bd2FoyYIBPEevIFaHNm+E734HXX4drr4XPfS7rHzlkCPQvK2BLXlHHD0Jz5niThIYdEMvL4d13feqliIiISDtTEMqWY46B4cO9g1xTX/wefdQbLBx+eNuOrTldungr8I4ahLZsge9+10Po97/fZvs2mfn0uOrtpWyv7uBrhCord1wflFBe7iE01fbyIiIiIlmgIJQt+flwwQWwaBHMnLnz61VV8M9/emDq0qXtx9ecjtpCe+tW+N734JVX/NjG67ImTYJqSlm3tAMHoeXLYeXKHdcHJWhjVREREYkQBaFsOvxw/0J4553+l/D6Hn/cv5hHaVpcQkcMQrW18D//Ay+95BWhk05q8yFMmAAbKaH6kw48NS6xPqixINS/v1cbFYREREQkAhSEsskMLrzQ/0r+t7/VPR+CT4sbNQpGjGi/8TWlrMz3fNm6tb1Hkhm1tT4N7p//9AYJX/hCuwyjtBSseylb1nTwIFRY2Ph/12ZeFVIQEhERkQhQEMq2/ff3TWTuuadu/5i5c2H+/GhWg8CDUAjeyCHX1dZ6V7jnnoPLLoPTTmvX4XTrV0JBzXpWr27XYWRPZWVdq+zGlJfDkiWwdm3bjktERESkAQWhtnDhhf7Fb9o0fzxjBhQVwdFHt++4mtJR9hLatg1+9CN4+mm4+GL40pfae0TsMriUEjYktcVUzqmt9ZDf2LS4BG2sKiIiIhGhINQWyst9vdCf/+zT5J56Cj77WZ8rFUWJPY1yOQht3w4/+Qk88YQH0bPOau8RAdCzrJTSjhqEPvzQ92dqLgjtvbc3EtH0OBEREWlnSQUhMzvGzOaZ2Xwzu6qR14vM7IH466+Z2bAGrw81s/VmdkWy1+xwLrgANm6ESy7xKXJRnRYHsOuu/mU1V4PQ9u3ws5/BY4/5fk7nnNPeI/qUlZZQzEZmvbqt2b12c1JzjRISunWDPfdUEBIREZF212IQMrN84BbgWGA08CUzG93gtK8Da0MIewK/An7e4PWbgMdTvGbHsvvuvmfNwoW+2eT48e09oqbl5/vGqsuWtfdIUhcC/OIXPv3wa1+D885r7xHtKF4F3LJuA/PmtfNYMq2y0jdRHTy4+fPKy/3cbdvaZlwiIiIijUimInQAMD+E8EEIYQswDWjYe/gk4J74/enAZDMzADM7GfgQqL8oIJlrdjznnw9du3rXMv/1RFcuttAOAX75S3j4YfjqV2Hq1PYe0c5KSgAoZX3Hmx5XWQmjR7f833Z5uVdHFy5sk2GJiIiINCaZIDQIWFzv8ZL4c42eE0KoBaqAPmZWCnwX+GEa1wTAzKaa2Wwzm71y5cokhhthZWVeqYjAov0W5VIQ2r4dPvjAK0HTp8OXvwzf/GY0w2a8IjRmt/W8+KJntw5hwwb/N0g0Q2iONlYVERGRCCjI8vWvA34VQlhvaX4pDSHcDtwOMHHixNz/2tizZ3uPIDllZd7pbtMmX9cRJRs2ePXhnXf8VlEB1dX+2hln+DqsKIYg+DQIHXngBq5+EG66CS6/PLrDTdrcuZ7qRicxw3XIEOjRw//d2mFjWxERERFILggtBYbUezw4/lxj5ywxswKgJ7AaOBA41cx+AewCbDezGuD1JK4p7SnROW7ZMl/f1F5CgKVL4e2364LPggVeBTLzsR15JIwb57ehQ9tvrMmIT4078oANfLkA7r/ft9y5+OIcD0Nz5vixuUYJCdpYVURERCIgmSA0CxhhZsPxsHIG8OUG58wAzgZeAU4Fng0hBODQxAlmdh2wPoTwf/Gw1NI1pT3V30uoLYPQli1e7amoqAs/ic03i4v9C/TXvgZjx/qX7u7d225smRCvCNmG9VxyCWzeDPfd59tKRXFJU9IqK71JQrIVz/JyeOUVWL8+um3kRUREpENrMQiFEGrN7GLgSSAfuDuEUGlm1wOzQwgzgLuAP5nZfGANHmxSvmYrfxbJpLbcVHX5cnj5Zb/NmuXT8cCnUH3mM/6ledw4GD7cO9rlssSX/vXrMYMrrvDsd9ddXhmKUKfv1FRWwoQJyZ9fXu7Vvjlz4IADsjcuERERkSYktUYohDATmNnguWvr3a8BTmvhGte1dE2JkN69vcNdNoLQtm3+xflf//Lb++/78wMGwHHHwYEHevDp1Svzn93e4lPjWL8egLw8+N73YOtWuPVWD0NfzrXa6PLlsHJlctPiEsaM8SlyFRUKQiIiItIust0sQXKVma8TylQQqqqCV1/14PPKKxCLeXVn3DhfIHPwwV7xyemFMkkoKoKCAm/4EJefD9dc45Whm2+GwkI4rdk/K0RMMhupNlRaCsOGaZ2QiIiItBsFIWnawIHw4Yfw5pteqigo8G/p9e/Xv9WfthaCV3peftnDT0WFNzjo1QsOOcSnvB14oHcP60zMvCpULwiB/zp/9COvDP3yl56XTjyxncaYqspK//cfMSK195WXwwsv+H8rHT0Ai4iISOQoCEnThg/3IPONbyR3fl5eXSgKoe7L/t57++KXgw/29sp5yWxf1YGVln46Na6+ggL46U/hyiv9WFgIxx7bDuNLVWUljBzpATkV5eXw6KOweHH0u/2JiIhIh6MgJE2bOhUOPdTLFFu2QG2t30/mVlvrAegzn4G+fdv7J4mWJoIQeJa44QbfW+j66/3x5MltPL5U1Nb6HkLplK/qb6yqICQiIiJtTEFImta1K+y3X3uPouMpKWkyCIH/2v/3f+HSS33tUEEBHH54G44vFR9+CDU1qa0PShg+3FuiV1R4kwwRERGRNtTJ5yiJtIPS0p3WCDXUrRvcdJMX1a6+2mcoRlI6jRIS8vN9qqQaJoiIiEg7UBASaWuNNEtoTGkp/PrXsMcecNVVvsVS5FRWesOLwYPTe395Ocyf71UlERERkTakICTS1ppZI9RQjx7wm994zrjiCvjPf7I8tlRVVtbtCZSO8nLfV2ru3MyOS0RERKQFCkIibS0xNS6EpE7fZRf47W+hf39vovDOO1keX7I2bIAPPkhvWlxC/YYJIiIiIm1IQUikrZWU+J5KGzcm/ZY+feD//g9694bLLvP1Q9On+3S55cuTzlSZNXeuf3BrglCvXl7uUhASERGRNqaucSJtrbTUjxs2eChK0q67wi23wHXXwYwZsGlT3WvdunkH6qFDYbfddjwWF6c2vNpan7m3fr0Pcf16/6y+fWHIkHpDnjPHj60JQgDl5YTZs/loYeD9+cb77/tevB995Blp7FgYN877KqTw6xIRERFploKQSFtLfJtfv97TTQoGDIDbbvNCzIoVHhY++ggWLfJjZSU8/fSOFaJ+/eqC0S671IWbDRvq7td/vHlz82Po3dsD0bc+qWRQz8HMmt2TIUM8tCQTutav9/4IicAz9M1yvrLqCS6esoIV9Cc/H4YN8yYRH30Er77qP09eHuy+u4eisWP9Nnhw+suTREREpHNTEBJpa/UrQmky8zVD/fvD/vvv+NrmzbBkyY4BadEiD0jV1Z7DSkp8GCUlHo4GDap7XFq64/2SEigqgpUrYfFivy1ZAv1WVvKv7RO47uq6z+7Tx0NSIhgNGeJdshOh5/334eOP687v0QMml5XDJ3DdFysoPak/w4f7RrIJ1dUe8N55x29PPgl/+5u/1quXLzNKVI1GjfJ9mERERERaoiAk0tYSQSjJznGpKiryasoee+z4fAh1lZVWW74cTlzJ5ItHs8eBOwakxYt936PVq+tON/Oq1OjRcNJJsOeeMGKEF8Ssdk+YXMSEogrYa/JOH9W9O0ya5DfwJnMLF8Lbb9eFoxdf9Nfy8/26Y8f6jL0xYzyMqWokIiIiDSkIibS1LAehptia1diaNZ4UWiu+kWqX/cYwciSMHLnzKRs3ejCqrfUpbU1WagoLYa+9km6YkJ9fF/ROOcWfq6qqC0Vvvw1//zv89a/+Wo8eHojKy/04ejT07Jniz9vAunUexhYtqjvm58NnPgOHHOLTEUVERCTaFIRE2lpijVArpsalbN06OO88L9P86U++YKg1Kis9wDSWgOKKi5t9eUfl5d4Gb+tWv26Kevb0AHLIIf542zbv7F1ZWXe78866tVNDhuwYjkaM2Plja2th2bIdw07iWFVVd15RkVe71q+HF17w5/beGw491Mez116qSImIiESRgpBIW6vfLKEt1NbC1VfDqlX+rf1HP4Lf/95LGOmqrPSUU38xT2uUl8P99/siotGjW325xBS5ESPg5JP9uQ0bvON3RYUPf9YseOIJf61LF/9x9toL1qzxsLN4seeyhF69YPhwOPJIz5HDhvlxwACfbhiCh6+XXvLbnXfCHXd4dSgR0iZOTG8N0/btvrZqwQL/jMSxoAAOOww++1mvuilwiYiIJE9BSKStFRf7N9a2CkK/+hW8/jr84AeeEK69Fv7yF/jKV9K7Xm2tJ4oTT8zcGOtvrJqBINSYkhIPIhMn+uMQfKlTZWVdOHriCW/4sNtucPDBdYFn6NCWp9OZ1U3ZO/tsWLvW10q9+KI3eHj4YQ9B++9fVy3q02fHayS6AdYPOwsWwIcf7tjNr6zMg08sVhe4hg6FI47woLb33gpFIiIiLVEQEmlreXn+rbwtpsY9/LBPOTvzTDjuOP+m/cwzXhE6+GAvcaTqww+hpqb1+wfV17+/l04qKuD00zN33WaYeTVnwACYvHOPhlbr1QuOP95vW7bAG2/UVYsSzR1Gj4YDDvCZix984Lf6+bhvXw88X/iCH/fYw//J6rcpX7XKp+Q99xz8+c9w773+Mx1xhN/GjWtd8U9ERKSjUhASaQ+lpdmvCP3nP3DjjXDQQXDRRf6cGXz3u/ClL/kUudtv9/lVqYg3Ssh45aa8POmGCbmmS5e6znff/rZXeRKB6J57vDPeHnvA5z7nx91391syTR369vWg9IUv+NqlF1+E55/3FuPTpvm+T4cf7qFo4sTU/7m3bPHKUyzm1y8p8a5/Gek+2IIQfJpiQYG3YxcREckkBSGR9lBamt2K0LJl8L3v+bfHH/1ox5JAnz5wxRVwzTW+LuerX03t2pWV3optyJDMjrm83Msaa9b4t/cOysyDxJ57wjnn+JS3Ll0yM5WtZ0844QS/bdjgU/Oef96n/D38sAeuxLS8ggIPNomAkwg7DR9v2rTz5+yyC0yY4MFq//0zu7Ht8uUwe7av4Zo92/evAjjwQC8WfuYzbRPCRESk41MQEmkPxcXZqwht2gTf+Y6v9L/xRv/229BRR8Gzz3pF6JBDvPyQrMpKnxaX6UUo9dcJHXZYZq8dYUVF2bluSYn/Mx91lM9k/Pe/PWe++CLMnLnjuQUFnm179vTjgAHePKJnz7rnErdVq+qCyjPP+PsHDPBAtP/+Ho4arn1qTlWVTxucNctvH33kz/fq5WFr//09Gz/0kFfTBg/2QHT88XWd6EVERNKhICTSHkpLd9xxNFNCgOuv97lXN93UdJtsM7jySp8+d/31vuI+mTlTGzb4QpbPfjaz4wZf4Z+f3+mCUFvo2tV/pYcd5r0u3n3X24UnQk63bqnl2sRys8WLPWDNmgX//Cc8+qi/vvvuHogOOAD222/HwFJTA2++WRem5s3za3Xr5ueecmGnkLQAAB+ESURBVIqHnz322LHyc9ZZHuQefND/077tNq98nXaaN4oQERFJlYKQSHsoLfVW0TU16fVTbsrdd3ul55JLfG1Qc3r39jB09dVw330+T6sl777r31oz2SghoWtXL0N00HVCUVFQUFd8aw0zDyBDh8Kpp/reTe+9V1fZeeQRDy35+TBqlC8pmz/fN73durVuHOed58FnzJjms3hhIRx9tN/mzIEHHvB1UA8+6NPlpkzx4KVpcyIikiwLiR0Gc8DEiRPD7Nmz23sYIq03Ywb85CcwcKCv10nsBNoazz/vjRCOPdZbZSf7J/6rr/Y/5997r/8Zvjn33gu33AJPPZXcSv5U/fKX8Pe/+5wrtTrLaVu2eOhJrPWZO9f/80qsK9p3X68Ctcbq1b726aGHfPrcbrt5hej443fsrNeSELzYuWaNX3PNGg9lI0f6tD+1IhcRyS1m9noIYWKL5ykIibSTN96An/8cFi70ll6XX+5tpNMxf77/aX333eHWW1NbeLJ2rXeR698f7rqr+T/Lf/e7/lkPPZTeOFvyxBMe4u67z3dDlQ4jhOwFiq1bPTs/8IBXi0pKfJurk0/2RhSrV9cFnMT9+o/XrNlxn6b6unf3/xRHjvTbiBHewrywMDs/i4iItJ6CkEgu2LrVN3+5+26vgEyd6n/STqXH8bp1cO65/if4P/7R9+NJ1bPPepe5b3wDvva1ps874QQYP97XFWXDkiXwxS/CVVf5YhGRFITgMysffNCD0bZtO59j5l3v+vTx2aG9e9fd79On7v6mTT579b33/DZ/fl1YKijwvzmMGFEXkkaM8PVWIiLS/pINQlojJNKeCgt9bc5RR3mHt1//2lt6XXVVcutwams9wKxa5avH0wlBAEce6WO46y7vr9xYNWb5cu9lnI31QQmDBvm31IqKtg9CVVXw9ts7r+6XnGEGY8f67ZJLfLZo1647BpxevZL/O8PYsXX3t23z5hDvvVcXkF59FR57rO6cgQM9FO27r08BbKv9llK1fr3/L/bWW148Puoo/19PRKSzUUVIJCpC8LZYN93kweYLX4ALL2y8/XXCjTfC9Ok+ney441r3+evWwRlneJj6wx92/raYqBrddVdmVts35dvf9srQAw9k7zMSNm2CF17wNU+vvOLfdsvKvOJV/1twFG3a5FXAbKzVkqStXr1j5WjuXP/PF7xCNH68h6IJE3xKXVuvNwoBPvnEM/5bb/ltwQJ/Pi8Ptm/388rLfUPfyZNTa38uIhJFmhonkqvWr/f9ff76V6+OXHaZt8pq+A3q4YfhhhvgzDP9z9+ZkGi4cP75vuaovt/+1sPJs8/6wots+cMfvLr19NPNh8B01db6n/KfesqbRNTUePg7+mhvb3bLLbBihU8RPOec1KYptpWPPoJLL/UwdPfdHt4kMlasgNdf9yYRr7/u+xuDV6QmTKjbjDaTG9Em1Nb6NL633qoLP4lNaYuLPfDssw+MG+fF3epq+Mc/4MknPdDl5Xkzi899Dg4/XMVREclNCkIiue7dd72Zwpw53hf4O9+p2zDlP/+Biy7y5//3fzPbYe3aaz2E/PGPPs8n4cILfZHE3Xdn7rMaM2sWXHwx3HwzTJqUmWtu3+7fCp96yn+2qir/c/2RR/o3vn33rZvDtH69V9qeeMK/Lf7wh9EKGnPnwre+9f/bu/foqMpzDeDPmzuTCOEuEuQugoqINIBab1QLtT1IcVW0Xqq01Ho89rSnWrW2WBQ5apeiq9LlBVY5emxtvVJvgK3Vtouryj1CAgQhXBMCuV8m854/3r3PngmTMElmMjPk+a211+y9Z2dmJ+yQ/cz3fe9nH+k3NQF9+gAvvBCb0EhRsX+/F4rWr7cGXwDo188CkdtidPrptj8QsOGDfn/LS/Dz1dU2z/GmTdblrbbWXqd/f7uE3eAzfHjruX7XLvsVWb7czjkzE7j4YvsVmTw5dpP/EhFFG4MQ0amgqclafhYtsjufW2+1losf/MC6RC1eHP0b4OPHrYpcz54WhtLT7TymTLFiCT/7WXTfr7mqKuBrX7MWqeatUm1VWGh3ditWWP+gzEwbAzV1qoWs1kp/ffAB8Pjjtn7vvfY18bZ6tY0f69nTguLhw9YaeOGFwFNPJWbrFYVQBfbs8ULRZ59Zr1TAGlr9fq+7WlukpNiYJDf4nH9++4tQukUnVqyw1qLycmsZuuIKC0XjxydHdfuGBmvxqqy0n6nPZyXbu3WzX32WRSc6dTEIEZ1KSkutkMLKlXbH4/NZy8zgwbF5v08+sRao2bOtkl1hIXDTTcBDD9k8RbF24432cfnChW37uuPHbYDGunV2F7dzp92x5efbHdyll1pt5Ujt32/jrzZtsq+/99749RV6/33g4YftY/2FC72BHO+8Y/unT7cxXLy7SyqBgF2mbktRerrl2eZLa/szM+2yiMWl6ffbuS1fbj1na2qsEfKyy6wQRX29LQ0N3nq4fcHrKSn2a+jzhT42X2++LzPT3r+iwgs4ra23VBIdsP8W3FDkLj6ffU/Bgcnnsy6NffrYr5z72JZ5qoio8zEIEZ2K1qwBli61sSv5+bF9r4cesjCxZIl101uwwMYtud3zYunRR61wxIoVJ97YV1RY2Nm798SlosI7buxYb/R3z57tPxe/337mixdbOHvoIetK15leftnGaE2YYN0lm9/x/u531np3113AzTd37rlRl1FXB/zrX15tERFrxcrMDF2C97nrWVneeiBg3fmqqy3YhFuvq4vsnHw+6+V62mm2tLSekmJdBoOXmhp7n5qaE59zl+rq8GXYfb7QYOSuBwemAQMYmIjiJapBSESmAngaQCqAF1X1v5s9nwngfwBcCKAMwPWqWiwi+QCedw8D8JCqvul8TTGASgBNAPyRnCyDEFEnqqiwLnK5uVZO270D6owWh2XLgPnzbSxMVVVo8Dl+3DtOxPr/5OUBgwbZkpcHjBrlDbiIls2bbfzUwYMWRGfPjn1XtEDAAtArr1h3wblzwxeqCASABx+0QhYLFlgfJqIk5vd7YSU4HPl8XsjJyemcX8HKSmutKy21KoEtPdbUnPj1eXnWWjdihLcMHJgcXQuJklnUgpCIpALYAeAqAPsArANwg6puCzrmTgBjVfUOEZkFYIaqXi8iPgANquoXkQEANgI4w9kuBjBBVUsj/aYYhIg62T//aeWsARst3dauau21Zw/wne942/37eyHHDTyDBlkRg6yszjknwELZk0/a5DHnnmuFFPLyYvNejY3W5W35cvtZ/OQnrU9KU1dnBTQKC63q3pgxsTkvIgqrpiY0GO3daxX8ioqs0KM79isryybkDQ5HI0awEj5RNEUzCE2GteR83dm+HwBUdUHQMcudY1aJSBqAgwD6atCLi8hQAKsBDGQQIkoi8+bZjf/3v29FGjpLQYH1o+nssBOJlSutdHkgYGOppk2LbktZdbUVRVi7FrjzTuCWWyJ7/aNHrex3Q4N1aYx2qxgRtUtdHbB7t4WinTvtsbDQK5QBWBX/ESOsBWnoUFsGD2YJc6L2iGYQug7AVFX9vrN9M4CJqnpX0DFbnGP2Ods7nWNKRWQigCUABgO4Oahr3G4A5QAUwHOq+jzCEJE5AOYAwJlnnnnhnj17TvY9EVE0VVYCv/kNcNttwJAh8T6bxHHwoHVV27DBuq39/OfWX6ejysqs9aeoCHjgAavU1xa7dllg7d/f5qPiXRRRQlK1zy4KC71wVFRkgamx0Tuub18LRUOGeI9DhtjQR9ZGIQovYYJQ0DGjASwFcKmq1onIQFUtEZF+AFYC+A9V/aS1c2GLEBEllKYm4KWXLHCkpwMXXABMnGiFLIYNa/tdyt69NlFqWZmN9bnoovad19q1NhFvfr6FWJbVJkoafr8VrNy9GygutmX3busxHDwOqXv3EwPSuedySjEiIPIgFMlfxxIAg4K285x94Y7Z53SN6wErmvD/VLVARKoAnAtgvaqWOPsPi8ibAPIBtBqEiIgSSmqqFU6YNMnKWK9d642j6tMH+MpXLIzk59t2a9yJUgMB4Nln7Y6mvfLzrYXq0UdtTNM99/CjY6IkkZZmxTnPPNPKlLtUbeqw3btDQ9LHHwNvv23H5OTYTAfXX8+KdUSRiCQIrQMw0hnjUwJgFoAbmx2zDMCtAFYBuA7A31RVna/Z64wJGgzgbADFIpINIEVVK531qwHMi863RETUyc4+2xbAusytXWvLqlU2/w9gLURuKBo/3iYpca1ZY2OCevSwiVKjMT/U9OnWwvTSS1ZY4oYbOv6aRBQ3bpHM/v3ts5dg5eXWve7VV61WyquvWmHLa69tfd5ooq4u0vLZ3wCwEFY+e4mqzheRebCWnWUikgXgJQAXADgKYJaq7nK60d0HoBFAAMA8VX1LRIYBeNN5+TQAr6jq/JOdB7vGEVFSCQRsAIAbjDZssEIGaWk2z1F+vn1s+8wz1q9l4UIbEBDN97//fvvI+LHHQj9eThRufeKjR+1urrzc1o8d89Z797Y7upEj4322RAlv82Zg0SLgs89sLqM5c2xKNZbspq6EE6oSESWaujpg40YvGO3YYfvHjweeeCI2hQ3q6oAf/ciKKDz3nNdy1ZkOHgQ+/ND69bhhxw05x4+Hn7FSxFrIcnOBAweA+nrg/POBmTOBK6/kx9xErVC1huZFi4Dt261B+o47gEsvZS9Z6hoYhIiIEt3RoxZQxo4NP1FqtJSVWVntpiZg8WLrWxNrgYCFvddes8l4AwEgO9tKXfXqZY+5ud568/09enhFHo4ftxLur79uk+v27Gld/2bMYIlwolYEAsBHH1l3uS+/tKGHd94JXHhhvM+MKLYYhIiIyLNzp80FNXCgtQxlZ8fmfVoKLddea/10OqJ5uAKASy6xVqL8/NYnnCXqwvx+4L33gBdesIbZiROtoXj06HifGVFsMAgREVGo1auBn/7URlrff79VsotWP5mCAgsoK1eGdmO74orYtHYdOAC89ZaVyyovt4IQM2cC11wTnfmciE5B9fX2GcXvf2+fWUyZAvzwh9Gpz0KUSBiEiIjoRG+8YYUTAGutOfts4KyzgFGj7HHgwMhbVurqbOzP668D27ZZJbxp04Bvf7vzChs0NFjfn9dft/FXmZnA1VdbKOLH3URhVVUBr7wC/OEPFo6uugoYPtzmIOre3Vvc7exsji2i5MIgRERE4W3bBmzaZMUatm+3SUncggXZ2RaIgsPR0KGhk7Lu3WuB6p13gIoKe37mTAtBsSj4EKkdO+y8PvgAqK218x80CMjKsup83brZY/C2u89dd7ezszu3q53fb2W9eLdJnai83FqH3n7bfmVakppqoSg4KLnrWVn2fEqK/TfhrqemekvzbXfJzPSWrKzw65wPmtqDQYiIiCJTX29FG7Zv98JRYaHtB6xr2/DhFooOHbIudqmp1u1t5kzgggsS6wa+qsrmb1qxwoJaTY3d5dXUhK9QF0737jYh7qRJNqAiFgUmSkpsrqnVq4H1661M+MMPA2PGRP+9iFqhar/uFRW2VFaGXw+3XV9vw/eamuwx2tLSTgxMPp995nLaafaZhRvS3PWcHO95d93n67z/ptyfZ329fcaRlWWfsXAYY+dhECIiovZrarIyU9u3ewFpxw67E7n2Wlv69In3WbZdY2NoMKqt9Zbg/UVFVpjhyBH7uqFDLRBNmmTBLyur7e9dWwt8+qkFn1WrrJgEAJxxhr32qlVAaSlw113ArFmJFS6JIqBq/3W4ixuQ/H5v3V38fi8s1NdbT9tItuvq7FepshKorrbHqirvc5uWpKRYGMrIsCUtzR7T020JXg9e3P2BgL138Hm528Hn5q43J2JhKDvba3T2+ULXs7ND97thzm19O+20zg10yYxBiIiIosv9e9FV/gqrWkvZ6tW2uBPiZmQA48Z5wWj48PA/E/fr3VafDRssiGVmAhMm2NdOmmTd90Rs9Pr8+TYB7le/Cvzyl1ZGnIhOqqHBApG7uAEpeL262o5rbLQleD14Cbc/JcXrspeV5S3BLVXhnk9Ls+BWXW2fsdTU2Lq73fyxsbH179PtppiT44Wj4KDkhqWGhtAgGemiemIXRrfLY0vb7nLPPYnz+RiDEBERUTTV1QGff26hZs0aG1sFAH37WvnuiRNtopYvvvDCj9uiNGyYhZ7Jk62iXmZm+PdQBf78Z+CZZ6yYxSOP2PFE1CU0NHiBqbIydHG7Jra2Hq73b3DXwuZjsJovIqEtd81b8YIfmy9PPdU509RFgkGIiIgolg4dskC0ejWwbp3djbhyciwcTZ7cvjFGBQXAgw9amfA5c4Bbbon+AIP6ert78fmi+7pEFBeqXu/ejAwLNhkZXacRPxiDEBERUWdparLwUlBgRSXOOafj5a6qqoAFC6xE+cSJwNy5VlCho3bssDmY3Op6I0daq9P55wNjxwL9+nX8PYiI4ohBiIiIKNmpWm3jJ5+0VqZ582x8UVvV1tpkt2+9BWzdah8TT5kCDBhgpdS3bLGuf4AVbxg71oLRuHHAkCEsd0VESYVBiIiI6FRRVAT84hfAnj3A7bcDs2fb6OSTcVt/3n/f+ssMHWoV/6ZNCy3E4PdbyfQNG2xi2o0bgaNH7bnu3YHzzvNajUaPDh3j5PbHOX685aWiwlsfMcIq47EQBBHFCIMQERHRqaS2FnjiCeDdd4Hx44Ff/zp8N7aaGutO17z1Z8YMa+mJZMCAqs1z5IaijRuB4mJ7Lj3dKuU1NnrhprVSVzk5Fnp69LD1Tz8FevUCHngAuOiidv0oiIhawyBERER0KnrvPeDxx61VZu5cL0yEa/2ZMQOYOjU6rS/Hjlk3uo0b7b26dfMCTktL9+4njpX64gsLcbt2WevU3Xfb5CmxoAp89pmFuJbKZLlltNzR5ZmZFva64ghzolMEgxAREdGpqrjYusoVFQHf+paFiq1b7SZ+yhQLGJG2/sRDQwPw/PPAyy8Dp58O/OpX1soVTZ9/Djz3nD22lYj9LLOzgVGj7Gc5diwwZowFQCJKaAxCREREp7K6OuDpp4E33vBaf6ZNs1aYZLFpk7UOlZQAs2YBd9xhrTMdsXWrBaA1a6zK3m23AZddZt33IplRsqHBfrb19dbtb9s2b86o1FSrCnjeeV44SpSJU4jo/zEIERERdQWVlTb2JlFbf06mthb47W+B116zCnVz51rLS1vt2GGtTP/4h3XLu+UW4LrrOh6sACv2sHmzLZs2Wdhyq+z17+8VkzjvPCtH3tHS6UTUIQxCRERElDzWrgUeeQQoLbUQM3u2jdU5meJi4IUXrEBETg7w3e8C118fu3FHgFdlb9MmWzZvtgl2AQte55xjrXS5uaFjptzt3Fw7LlnDK1GCYxAiIiKi5FJVZXMmvfuudUGbO9fKbYdTUgK8+KJNDJuZaV3rbrwxfl0DDx3ygtGmTcD+/daS1JKMjBPDkRuYeve2pU8fe+zVKzotW0RdBIMQERERJaePPwYWLLBuf3PmADfd5M2bdOgQsGQJ8Je/WBe0mTOtBalnz/ieczh+v30Px46FzqvkbofbX1Fh1e6ay8mxYNSrlxeQgpc+faybXk5O53+fRAkm0iDETqxERESUWC67zMbcPPYYsGiRjfu5+25g5UrgzTctKMyYAXzve0DfvvE+25alpVlAa0tI8/stFJWWAmVl9nj0qLdeVmYFHEpLvXFKwXr0APLybBk4MPSxd292xyMKwhYhIiIiSkyqwIoVNpFsZaW1Cl1zDXD77cCAAfE+u/irqfHCUVkZcOCAdRksKQH27rXWs0DAO75bNwtF7uIGpgEDvBY3ILRFqvl9YvPt9HTA5/PmYmLQiq9AwFoVe/To0v8WbBEiIiKi5CYCfP3rNsfQhx8CF18MnHlmvM8qcfh89vNo6WfS2OiFo337bCkpAb78Eli92kqER1NqqoWt4MXn8x6zsrztbt0sRLlLWlrL2+56RoatZ2fbjT6r81mrYEGBTXS8cSOwZYsFoZ49gdGjbRkzxh57947Oe6raGLgdO2wpLLRl6VIb65ZEeAURERFRYuvbF7jhhnifRfJJT285KAUCwJEjFowOHQo/LsnVvGUheLux0Uqg19R4j3V13nZtrXXt278/9Jimpo59byIWhtyuh7162RK87a737GnhKZotJKr2fTYf73XsWOh6ba2N3QpuhTvjjPaP5Sor8wpybNwIbN9u3SkBKz9/+eXA4ME2yXJBgQVet1WwXz8vFLnLyYqL1NfbaxUWhoae6mp7PiXFrq+xY8N31UxwDEJEREREXU1Kit2gx2NCWFW7eW9stCV4vfl28+caG626YHm5LUeP2rJjh21XVoZ/T7dKn9vC1NKSmhr66C7uBLvBQaelFjU3pOXmWkXDrVvt+GC5uaHdFINDUt++9u8TCFh5+ODgs2+f9/2MHm2VEt3JfXv0OPFcamosLH3xhY0t27YN+Pvfvefz8kJbjRobQwNPcbEXWn0+q+I4dapVdTzrLGDYsKSuaMgxQkRERER0amhs9EJScFAqL7cw4oYrv99u8N311vb5/RZocnNDy5wHb7vrubnW2hM85gqw8OZ2Udy/P/Tx0KHQFrKMDBu3VV7ulWDPzfUCz7hxwKhRdlx7VFR4waigwBZ3HixXv34WdEaOtOWssyyspaS07z07GctnExERERElOr8fOHgwNByVlFi3tbFjrYLioEGxLX5QVmbhKCPDQk+41qUkwmIJRERERESJLi3Nq+AXL717WzGSLiY52reIiIiIiIiiiEGIiIiIiIi6HAYhIiIiIiLqchiEiIiIiIioy2EQIiIiIiKiLieiICQiU0Vku4gUich9YZ7PFJFXnefXiMgQZ3++iGxwlo0iMiPS1yQiIiIiIoqVkwYhEUkF8CyAaQDGALhBRMY0O2w2gHJVHQHgKQCPOfu3AJigquMATAXwnIikRfiaREREREREMRFJi1A+gCJV3aWqDQD+CGB6s2OmA1jqrL8GYIqIiKrWqKrf2Z8FwJ29NZLXJCIiIiIiiolIgtBAAHuDtvc5+8Ie4wSf4wB6A4CITBSRrQA2A7jDeT6S14Tz9XNEZL2IrD9y5EgEp0tERERERNS6mBdLUNU1qnoOgK8AuF9Estr49c+r6gRVndC3b9/YnCQREREREXUpkQShEgCDgrbznH1hjxGRNAA9AJQFH6CqBQCqAJwb4WsSERERERHFRCRBaB2AkSIyVEQyAMwCsKzZMcsA3OqsXwfgb6qqztekAYCIDAZwNoDiCF+TiIiIiIgoJtJOdoCq+kXkLgDLAaQCWKKqW0VkHoD1qroMwGIAL4lIEYCjsGADAJcAuE9EGgEEANypqqUAEO41o/y9ERERERERhSWqevKjEoSIHAGwJ97n4egDoDTeJ0FJjdcQdRSvIeooXkPUUbyGqKNicQ0NVtWTFhdIqiCUSERkvapOiPd5UPLiNUQdxWuIOorXEHUUryHqqHheQzGvGkdERERERJRoGISIiIiIiKjLYRBqv+fjfQKU9HgNUUfxGqKO4jVEHcVriDoqbtcQxwgREREREVGXwxYhIiIiIiLqchiE2kFEporIdhEpEpH74n0+lPhEZImIHBaRLUH7eonIShEpdB57xvMcKbGJyCAR+UhEtonIVhH5sbOf1xGdlIhkichaEdnoXD+/dvYPFZE1zt+zV51JzolaJCKpIvK5iLzjbPMaooiJSLGIbBaRDSKy3tkXt79jDEJtJCKpAJ4FMA3AGAA3iMiY+J4VJYHfA5jabN99AP6qqiMB/NXZJmqJH8B/qeoYAJMA/Lvzfw+vI4pEPYArVfV8AOMATBWRSQAeA/CUqo4AUA5gdhzPkZLDjwEUBG3zGqK2ukJVxwWVzI7b3zEGobbLB1CkqrtUtQHAHwFMj/M5UYJT1U8AHG22ezqApc76UgDXdupJUVJR1QOq+pmzXgm7ERkIXkcUATVVzma6syiAKwG85uzn9UOtEpE8ANcAeNHZFvAaoo6L298xBqG2Gwhgb9D2PmcfUVv1V9UDzvpBAP3jeTKUPERkCIALAKwBryOKkNOlaQOAwwBWAtgJ4Jiq+p1D+PeMTmYhgHsBBJzt3uA1RG2jAFaIyKciMsfZF7e/Y2md9UZE1DJVVRFhCUc6KRHJAfA6gP9U1Qr7QNbwOqLWqGoTgHEikgvgTQBnx/mUKImIyDcBHFbVT0Xk8nifDyWtS1S1RET6AVgpIl8EP9nZf8fYItR2JQAGBW3nOfuI2uqQiAwAAOfxcJzPhxKciKTDQtD/quobzm5eR9QmqnoMwEcAJgPIFRH3Q1H+PaPWXAzg30SkGDYs4EoAT4PXELWBqpY4j4dhH8jkI45/xxiE2m4dgJFOlZQMALMALIvzOVFyWgbgVmf9VgBvx/FcKME5ffEXAyhQ1SeDnuJ1RCclIn2dliCISDcAV8HGmX0E4DrnMF4/1CJVvV9V81R1COze52+q+l3wGqIIiUi2iJzmrgO4GsAWxPHvGCdUbQcR+Qasn2wqgCWqOj/Op0QJTkT+AOByAH0AHAIwF8BbAP4E4EwAewB8R1WbF1QgAgCIyCUA/gFgM7z++Q/AxgnxOqJWichY2CDkVNiHoH9S1XkiMgz26X4vAJ8DuElV6+N3ppQMnK5xP1PVb/Iaokg518qbzmYagFdUdb6I9Eac/o4xCBERERERUZfDrnFERERERNTlMAgREREREVGXwyBERERERERdDoMQERERERF1OQxCRERERETU5TAIERERERFRl8MgREREREREXQ6DEBERERERdTn/B+61uu6NSdjJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss_plot(mlp_history2,50)\n",
    "mae_plot(mlp_history2,50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_______________________________________\n",
      "MLP; \n",
      "Numeric Features; \n",
      "Average ELA Proficiency\n",
      "_______________________________________\n",
      "EV score. Train:  0.7403658568667874\n",
      "EV score. Valid:  0.7741556084420419\n",
      "EV score. Test:  0.7491493638098652\n",
      "---------\n",
      "R2 score. Train:  0.7390911621256269\n",
      "R2 score. Valid:  0.7732458376184376\n",
      "R2 score. Test:  0.748834992062962\n",
      "---------\n",
      "MSE score. Train:  0.03324378548921448\n",
      "MSE score. Valid:  0.031170787738620993\n",
      "MSE score. Test:  0.032677299350403734\n",
      "---------\n",
      "MAE score. Train:  0.13030933825532148\n",
      "MAE score. Valid:  0.12680118367726056\n",
      "MAE score. Test:  0.14134071246907767\n",
      "---------\n",
      "MdAE score. Train:  0.09346426010131847\n",
      "MdAE score. Valid:  0.09689438819885243\n",
      "MdAE score. Test:  0.11625458717346193\n"
     ]
    }
   ],
   "source": [
    "mlp_model2.load_weights('mlp_model2.passnyc.hdf5')\n",
    "y_train_mlp2 = mlp_model2.predict(X_train2)\n",
    "y_valid_mlp2 = mlp_model2.predict(X_valid2)\n",
    "y_test_mlp2 = mlp_model2.predict(X_test2)\n",
    "\n",
    "scores('MLP; \\nNumeric Features; \\nAverage ELA Proficiency', \n",
    "       y_train2, y_valid2, y_test2, y_train_mlp2, y_valid_mlp2, y_test_mlp2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Numeric & encoded categorical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mlp_model3():\n",
    "    model = Sequential()\n",
    "    \n",
    "    model.add(Dense(286, input_dim=286))\n",
    "    model.add(LeakyReLU(alpha=0.02))\n",
    "\n",
    "    model.add(Dense(286*4))\n",
    "    model.add(LeakyReLU(alpha=0.02))\n",
    "    \n",
    "    model.add(Dense(286*16))\n",
    "    model.add(LeakyReLU(alpha=0.02))\n",
    "#    model.add(Dense(286*16))\n",
    "#    model.add(LeakyReLU(alpha=0.02))\n",
    "    \n",
    "    model.add(Dense(1))\n",
    "    \n",
    "    model.compile(loss='mse', optimizer='rmsprop', metrics=['mae'])\n",
    "    return model\n",
    "\n",
    "mlp_model3 = mlp_model3()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "mlp_checkpointer3 = ModelCheckpoint(filepath='mlp_model3.passnyc.hdf5', \n",
    "                                    verbose=2, save_best_only=True)\n",
    "mlp_lr_reduction3 = ReduceLROnPlateau(monitor='val_loss', \n",
    "                                      patience=5, verbose=2, factor=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 890 samples, validate on 191 samples\n",
      "Epoch 1/100\n",
      " - 7s - loss: 202503560.5196 - mean_absolute_error: 2708.8109 - val_loss: 46327.2700 - val_mean_absolute_error: 194.2658\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 46327.27000, saving model to mlp_model3.passnyc.hdf5\n",
      "Epoch 2/100\n",
      " - 5s - loss: 50786.7405 - mean_absolute_error: 89.7909 - val_loss: 1797.0536 - val_mean_absolute_error: 37.8393\n",
      "\n",
      "Epoch 00002: val_loss improved from 46327.27000 to 1797.05359, saving model to mlp_model3.passnyc.hdf5\n",
      "Epoch 3/100\n",
      " - 5s - loss: 7416098.6679 - mean_absolute_error: 609.2749 - val_loss: 1022.5302 - val_mean_absolute_error: 28.5583\n",
      "\n",
      "Epoch 00003: val_loss improved from 1797.05359 to 1022.53024, saving model to mlp_model3.passnyc.hdf5\n",
      "Epoch 4/100\n",
      " - 5s - loss: 19838.3646 - mean_absolute_error: 61.6326 - val_loss: 12.7264 - val_mean_absolute_error: 2.6658\n",
      "\n",
      "Epoch 00004: val_loss improved from 1022.53024 to 12.72639, saving model to mlp_model3.passnyc.hdf5\n",
      "Epoch 5/100\n",
      " - 5s - loss: 34380.0515 - mean_absolute_error: 83.5560 - val_loss: 4.2519 - val_mean_absolute_error: 1.9944\n",
      "\n",
      "Epoch 00005: val_loss improved from 12.72639 to 4.25194, saving model to mlp_model3.passnyc.hdf5\n",
      "Epoch 6/100\n",
      " - 5s - loss: 784552.8055 - mean_absolute_error: 189.5290 - val_loss: 1015.6027 - val_mean_absolute_error: 29.1787\n",
      "\n",
      "Epoch 00006: val_loss did not improve from 4.25194\n",
      "Epoch 7/100\n",
      " - 5s - loss: 4970.1461 - mean_absolute_error: 43.2081 - val_loss: 1.8222 - val_mean_absolute_error: 1.2574\n",
      "\n",
      "Epoch 00007: val_loss improved from 4.25194 to 1.82218, saving model to mlp_model3.passnyc.hdf5\n",
      "Epoch 8/100\n",
      " - 5s - loss: 8007.0437 - mean_absolute_error: 39.2296 - val_loss: 36.4107 - val_mean_absolute_error: 5.0017\n",
      "\n",
      "Epoch 00008: val_loss did not improve from 1.82218\n",
      "Epoch 9/100\n",
      " - 5s - loss: 140836.8760 - mean_absolute_error: 99.0096 - val_loss: 164.4820 - val_mean_absolute_error: 11.8606\n",
      "\n",
      "Epoch 00009: val_loss did not improve from 1.82218\n",
      "Epoch 10/100\n",
      " - 5s - loss: 51.6142 - mean_absolute_error: 3.6560 - val_loss: 1149.3026 - val_mean_absolute_error: 30.9213\n",
      "\n",
      "Epoch 00010: val_loss did not improve from 1.82218\n",
      "Epoch 11/100\n",
      " - 5s - loss: 219.8520 - mean_absolute_error: 7.7133 - val_loss: 32.2478 - val_mean_absolute_error: 4.7248\n",
      "\n",
      "Epoch 00011: val_loss did not improve from 1.82218\n",
      "Epoch 12/100\n",
      " - 5s - loss: 355051.1325 - mean_absolute_error: 123.2593 - val_loss: 11.5643 - val_mean_absolute_error: 3.3273\n",
      "\n",
      "Epoch 00012: val_loss did not improve from 1.82218\n",
      "\n",
      "Epoch 00012: ReduceLROnPlateau reducing learning rate to 0.00020000000949949026.\n",
      "Epoch 13/100\n",
      " - 5s - loss: 19.9754 - mean_absolute_error: 2.0133 - val_loss: 1.0745 - val_mean_absolute_error: 0.8137\n",
      "\n",
      "Epoch 00013: val_loss improved from 1.82218 to 1.07446, saving model to mlp_model3.passnyc.hdf5\n",
      "Epoch 14/100\n",
      " - 5s - loss: 48.2719 - mean_absolute_error: 4.1058 - val_loss: 4.2440 - val_mean_absolute_error: 1.3880\n",
      "\n",
      "Epoch 00014: val_loss did not improve from 1.07446\n",
      "Epoch 15/100\n",
      " - 5s - loss: 12.8470 - mean_absolute_error: 2.3842 - val_loss: 1.1748 - val_mean_absolute_error: 0.9827\n",
      "\n",
      "Epoch 00015: val_loss did not improve from 1.07446\n",
      "Epoch 16/100\n",
      " - 5s - loss: 10.5432 - mean_absolute_error: 1.8774 - val_loss: 2.2748 - val_mean_absolute_error: 0.9997\n",
      "\n",
      "Epoch 00016: val_loss did not improve from 1.07446\n",
      "Epoch 17/100\n",
      " - 5s - loss: 6.4429 - mean_absolute_error: 1.6261 - val_loss: 2.2836 - val_mean_absolute_error: 1.0360\n",
      "\n",
      "Epoch 00017: val_loss did not improve from 1.07446\n",
      "Epoch 18/100\n",
      " - 5s - loss: 33.0387 - mean_absolute_error: 1.9467 - val_loss: 0.8896 - val_mean_absolute_error: 0.6454\n",
      "\n",
      "Epoch 00018: val_loss improved from 1.07446 to 0.88958, saving model to mlp_model3.passnyc.hdf5\n",
      "Epoch 19/100\n",
      " - 5s - loss: 1.3149 - mean_absolute_error: 0.8697 - val_loss: 0.8812 - val_mean_absolute_error: 0.6364\n",
      "\n",
      "Epoch 00019: val_loss improved from 0.88958 to 0.88118, saving model to mlp_model3.passnyc.hdf5\n",
      "Epoch 20/100\n",
      " - 5s - loss: 12.9097 - mean_absolute_error: 1.7369 - val_loss: 0.4954 - val_mean_absolute_error: 0.5664\n",
      "\n",
      "Epoch 00020: val_loss improved from 0.88118 to 0.49544, saving model to mlp_model3.passnyc.hdf5\n",
      "Epoch 21/100\n",
      " - 5s - loss: 6.8797 - mean_absolute_error: 1.5303 - val_loss: 2.8011 - val_mean_absolute_error: 1.6261\n",
      "\n",
      "Epoch 00021: val_loss did not improve from 0.49544\n",
      "Epoch 22/100\n",
      " - 5s - loss: 1.4580 - mean_absolute_error: 0.9350 - val_loss: 0.8430 - val_mean_absolute_error: 0.6771\n",
      "\n",
      "Epoch 00022: val_loss did not improve from 0.49544\n",
      "Epoch 23/100\n",
      " - 5s - loss: 8.9061 - mean_absolute_error: 1.5476 - val_loss: 0.3780 - val_mean_absolute_error: 0.4957\n",
      "\n",
      "Epoch 00023: val_loss improved from 0.49544 to 0.37802, saving model to mlp_model3.passnyc.hdf5\n",
      "Epoch 24/100\n",
      " - 5s - loss: 4.4496 - mean_absolute_error: 1.0863 - val_loss: 1.0245 - val_mean_absolute_error: 0.8915\n",
      "\n",
      "Epoch 00024: val_loss did not improve from 0.37802\n",
      "Epoch 25/100\n",
      " - 5s - loss: 0.7748 - mean_absolute_error: 0.6901 - val_loss: 1.2613 - val_mean_absolute_error: 1.0520\n",
      "\n",
      "Epoch 00025: val_loss did not improve from 0.37802\n",
      "Epoch 26/100\n",
      " - 5s - loss: 10.2845 - mean_absolute_error: 1.4267 - val_loss: 0.5903 - val_mean_absolute_error: 0.6725\n",
      "\n",
      "Epoch 00026: val_loss did not improve from 0.37802\n",
      "Epoch 27/100\n",
      " - 5s - loss: 1.7031 - mean_absolute_error: 0.9599 - val_loss: 0.4403 - val_mean_absolute_error: 0.5273\n",
      "\n",
      "Epoch 00027: val_loss did not improve from 0.37802\n",
      "Epoch 28/100\n",
      " - 5s - loss: 4.3052 - mean_absolute_error: 1.0951 - val_loss: 3.9783 - val_mean_absolute_error: 1.6761\n",
      "\n",
      "Epoch 00028: val_loss did not improve from 0.37802\n",
      "\n",
      "Epoch 00028: ReduceLROnPlateau reducing learning rate to 4.0000001899898055e-05.\n",
      "Epoch 29/100\n",
      " - 5s - loss: 0.3207 - mean_absolute_error: 0.4150 - val_loss: 0.6925 - val_mean_absolute_error: 0.7632\n",
      "\n",
      "Epoch 00029: val_loss did not improve from 0.37802\n",
      "Epoch 30/100\n",
      " - 5s - loss: 0.2627 - mean_absolute_error: 0.3978 - val_loss: 0.4177 - val_mean_absolute_error: 0.5248\n",
      "\n",
      "Epoch 00030: val_loss did not improve from 0.37802\n",
      "Epoch 31/100\n",
      " - 5s - loss: 0.2079 - mean_absolute_error: 0.3568 - val_loss: 0.1455 - val_mean_absolute_error: 0.2757\n",
      "\n",
      "Epoch 00031: val_loss improved from 0.37802 to 0.14547, saving model to mlp_model3.passnyc.hdf5\n",
      "Epoch 32/100\n",
      " - 5s - loss: 0.2352 - mean_absolute_error: 0.3636 - val_loss: 0.1206 - val_mean_absolute_error: 0.2581\n",
      "\n",
      "Epoch 00032: val_loss improved from 0.14547 to 0.12058, saving model to mlp_model3.passnyc.hdf5\n",
      "Epoch 33/100\n",
      " - 5s - loss: 0.2207 - mean_absolute_error: 0.3615 - val_loss: 0.2024 - val_mean_absolute_error: 0.3427\n",
      "\n",
      "Epoch 00033: val_loss did not improve from 0.12058\n",
      "Epoch 34/100\n",
      " - 5s - loss: 0.2149 - mean_absolute_error: 0.3537 - val_loss: 0.1444 - val_mean_absolute_error: 0.2655\n",
      "\n",
      "Epoch 00034: val_loss did not improve from 0.12058\n",
      "Epoch 35/100\n",
      " - 5s - loss: 0.1892 - mean_absolute_error: 0.3345 - val_loss: 0.5899 - val_mean_absolute_error: 0.6711\n",
      "\n",
      "Epoch 00035: val_loss did not improve from 0.12058\n",
      "Epoch 36/100\n",
      " - 5s - loss: 0.2002 - mean_absolute_error: 0.3458 - val_loss: 0.2915 - val_mean_absolute_error: 0.4256\n",
      "\n",
      "Epoch 00036: val_loss did not improve from 0.12058\n",
      "Epoch 37/100\n",
      " - 5s - loss: 0.1713 - mean_absolute_error: 0.3178 - val_loss: 0.3196 - val_mean_absolute_error: 0.4795\n",
      "\n",
      "Epoch 00037: val_loss did not improve from 0.12058\n",
      "\n",
      "Epoch 00037: ReduceLROnPlateau reducing learning rate to 8.000000525498762e-06.\n",
      "Epoch 38/100\n",
      " - 5s - loss: 0.1153 - mean_absolute_error: 0.2540 - val_loss: 0.1096 - val_mean_absolute_error: 0.2414\n",
      "\n",
      "Epoch 00038: val_loss improved from 0.12058 to 0.10959, saving model to mlp_model3.passnyc.hdf5\n",
      "Epoch 39/100\n",
      " - 5s - loss: 0.0930 - mean_absolute_error: 0.2330 - val_loss: 0.0899 - val_mean_absolute_error: 0.2208\n",
      "\n",
      "Epoch 00039: val_loss improved from 0.10959 to 0.08990, saving model to mlp_model3.passnyc.hdf5\n",
      "Epoch 40/100\n",
      " - 5s - loss: 0.0895 - mean_absolute_error: 0.2304 - val_loss: 0.0905 - val_mean_absolute_error: 0.2383\n",
      "\n",
      "Epoch 00040: val_loss did not improve from 0.08990\n",
      "Epoch 41/100\n",
      " - 5s - loss: 0.0875 - mean_absolute_error: 0.2288 - val_loss: 0.0724 - val_mean_absolute_error: 0.2086\n",
      "\n",
      "Epoch 00041: val_loss improved from 0.08990 to 0.07244, saving model to mlp_model3.passnyc.hdf5\n",
      "Epoch 42/100\n",
      " - 5s - loss: 0.0841 - mean_absolute_error: 0.2226 - val_loss: 0.0875 - val_mean_absolute_error: 0.2366\n",
      "\n",
      "Epoch 00042: val_loss did not improve from 0.07244\n",
      "Epoch 43/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " - 5s - loss: 0.0827 - mean_absolute_error: 0.2203 - val_loss: 0.0907 - val_mean_absolute_error: 0.2404\n",
      "\n",
      "Epoch 00043: val_loss did not improve from 0.07244\n",
      "Epoch 44/100\n",
      " - 6s - loss: 0.0754 - mean_absolute_error: 0.2077 - val_loss: 0.0873 - val_mean_absolute_error: 0.2347\n",
      "\n",
      "Epoch 00044: val_loss did not improve from 0.07244\n",
      "Epoch 45/100\n",
      " - 6s - loss: 0.0772 - mean_absolute_error: 0.2159 - val_loss: 0.0664 - val_mean_absolute_error: 0.1900\n",
      "\n",
      "Epoch 00045: val_loss improved from 0.07244 to 0.06640, saving model to mlp_model3.passnyc.hdf5\n",
      "Epoch 46/100\n",
      " - 5s - loss: 0.0730 - mean_absolute_error: 0.2066 - val_loss: 0.0805 - val_mean_absolute_error: 0.2263\n",
      "\n",
      "Epoch 00046: val_loss did not improve from 0.06640\n",
      "Epoch 47/100\n",
      " - 7s - loss: 0.0755 - mean_absolute_error: 0.2100 - val_loss: 0.1735 - val_mean_absolute_error: 0.3415\n",
      "\n",
      "Epoch 00047: val_loss did not improve from 0.06640\n",
      "Epoch 48/100\n",
      " - 10s - loss: 0.0729 - mean_absolute_error: 0.2057 - val_loss: 0.0661 - val_mean_absolute_error: 0.1988\n",
      "\n",
      "Epoch 00048: val_loss improved from 0.06640 to 0.06610, saving model to mlp_model3.passnyc.hdf5\n",
      "Epoch 49/100\n",
      " - 5s - loss: 0.0741 - mean_absolute_error: 0.2117 - val_loss: 0.0591 - val_mean_absolute_error: 0.1821\n",
      "\n",
      "Epoch 00049: val_loss improved from 0.06610 to 0.05911, saving model to mlp_model3.passnyc.hdf5\n",
      "Epoch 50/100\n",
      " - 5s - loss: 0.0708 - mean_absolute_error: 0.2071 - val_loss: 0.0682 - val_mean_absolute_error: 0.2089\n",
      "\n",
      "Epoch 00050: val_loss did not improve from 0.05911\n",
      "Epoch 51/100\n",
      " - 5s - loss: 0.0702 - mean_absolute_error: 0.2022 - val_loss: 0.0593 - val_mean_absolute_error: 0.1855\n",
      "\n",
      "Epoch 00051: val_loss did not improve from 0.05911\n",
      "Epoch 52/100\n",
      " - 7s - loss: 0.0726 - mean_absolute_error: 0.2078 - val_loss: 0.0605 - val_mean_absolute_error: 0.1926\n",
      "\n",
      "Epoch 00052: val_loss did not improve from 0.05911\n",
      "Epoch 53/100\n",
      " - 7s - loss: 0.0669 - mean_absolute_error: 0.1992 - val_loss: 0.0564 - val_mean_absolute_error: 0.1737\n",
      "\n",
      "Epoch 00053: val_loss improved from 0.05911 to 0.05645, saving model to mlp_model3.passnyc.hdf5\n",
      "Epoch 54/100\n",
      " - 5s - loss: 0.0702 - mean_absolute_error: 0.2036 - val_loss: 0.0843 - val_mean_absolute_error: 0.2210\n",
      "\n",
      "Epoch 00054: val_loss did not improve from 0.05645\n",
      "Epoch 55/100\n",
      " - 5s - loss: 0.0668 - mean_absolute_error: 0.1988 - val_loss: 0.0693 - val_mean_absolute_error: 0.2064\n",
      "\n",
      "Epoch 00055: val_loss did not improve from 0.05645\n",
      "Epoch 56/100\n",
      " - 5s - loss: 0.0686 - mean_absolute_error: 0.2026 - val_loss: 0.0691 - val_mean_absolute_error: 0.2120\n",
      "\n",
      "Epoch 00056: val_loss did not improve from 0.05645\n",
      "Epoch 57/100\n",
      " - 5s - loss: 0.0699 - mean_absolute_error: 0.1988 - val_loss: 0.0565 - val_mean_absolute_error: 0.1815\n",
      "\n",
      "Epoch 00057: val_loss did not improve from 0.05645\n",
      "Epoch 58/100\n",
      " - 5s - loss: 0.0680 - mean_absolute_error: 0.1971 - val_loss: 0.0760 - val_mean_absolute_error: 0.2257\n",
      "\n",
      "Epoch 00058: val_loss did not improve from 0.05645\n",
      "\n",
      "Epoch 00058: ReduceLROnPlateau reducing learning rate to 1.6000001778593287e-06.\n",
      "Epoch 59/100\n",
      " - 5s - loss: 0.0507 - mean_absolute_error: 0.1702 - val_loss: 0.0494 - val_mean_absolute_error: 0.1656\n",
      "\n",
      "Epoch 00059: val_loss improved from 0.05645 to 0.04943, saving model to mlp_model3.passnyc.hdf5\n",
      "Epoch 60/100\n",
      " - 5s - loss: 0.0494 - mean_absolute_error: 0.1691 - val_loss: 0.0495 - val_mean_absolute_error: 0.1654\n",
      "\n",
      "Epoch 00060: val_loss did not improve from 0.04943\n",
      "Epoch 61/100\n",
      " - 5s - loss: 0.0493 - mean_absolute_error: 0.1677 - val_loss: 0.0480 - val_mean_absolute_error: 0.1619\n",
      "\n",
      "Epoch 00061: val_loss improved from 0.04943 to 0.04802, saving model to mlp_model3.passnyc.hdf5\n",
      "Epoch 62/100\n",
      " - 5s - loss: 0.0484 - mean_absolute_error: 0.1667 - val_loss: 0.0472 - val_mean_absolute_error: 0.1602\n",
      "\n",
      "Epoch 00062: val_loss improved from 0.04802 to 0.04719, saving model to mlp_model3.passnyc.hdf5\n",
      "Epoch 63/100\n",
      " - 5s - loss: 0.0473 - mean_absolute_error: 0.1640 - val_loss: 0.0553 - val_mean_absolute_error: 0.1771\n",
      "\n",
      "Epoch 00063: val_loss did not improve from 0.04719\n",
      "Epoch 64/100\n",
      " - 5s - loss: 0.0473 - mean_absolute_error: 0.1645 - val_loss: 0.0472 - val_mean_absolute_error: 0.1615\n",
      "\n",
      "Epoch 00064: val_loss improved from 0.04719 to 0.04716, saving model to mlp_model3.passnyc.hdf5\n",
      "Epoch 65/100\n",
      " - 5s - loss: 0.0464 - mean_absolute_error: 0.1625 - val_loss: 0.0460 - val_mean_absolute_error: 0.1575\n",
      "\n",
      "Epoch 00065: val_loss improved from 0.04716 to 0.04601, saving model to mlp_model3.passnyc.hdf5\n",
      "Epoch 66/100\n",
      " - 5s - loss: 0.0467 - mean_absolute_error: 0.1617 - val_loss: 0.0459 - val_mean_absolute_error: 0.1567\n",
      "\n",
      "Epoch 00066: val_loss improved from 0.04601 to 0.04591, saving model to mlp_model3.passnyc.hdf5\n",
      "Epoch 67/100\n",
      " - 5s - loss: 0.0458 - mean_absolute_error: 0.1627 - val_loss: 0.0457 - val_mean_absolute_error: 0.1568\n",
      "\n",
      "Epoch 00067: val_loss improved from 0.04591 to 0.04575, saving model to mlp_model3.passnyc.hdf5\n",
      "Epoch 68/100\n",
      " - 5s - loss: 0.0456 - mean_absolute_error: 0.1592 - val_loss: 0.0450 - val_mean_absolute_error: 0.1576\n",
      "\n",
      "Epoch 00068: val_loss improved from 0.04575 to 0.04497, saving model to mlp_model3.passnyc.hdf5\n",
      "Epoch 69/100\n",
      " - 5s - loss: 0.0455 - mean_absolute_error: 0.1614 - val_loss: 0.0468 - val_mean_absolute_error: 0.1573\n",
      "\n",
      "Epoch 00069: val_loss did not improve from 0.04497\n",
      "Epoch 70/100\n",
      " - 5s - loss: 0.0442 - mean_absolute_error: 0.1583 - val_loss: 0.0456 - val_mean_absolute_error: 0.1550\n",
      "\n",
      "Epoch 00070: val_loss did not improve from 0.04497\n",
      "Epoch 71/100\n",
      " - 5s - loss: 0.0447 - mean_absolute_error: 0.1603 - val_loss: 0.0468 - val_mean_absolute_error: 0.1561\n",
      "\n",
      "Epoch 00071: val_loss did not improve from 0.04497\n",
      "Epoch 72/100\n",
      " - 5s - loss: 0.0438 - mean_absolute_error: 0.1570 - val_loss: 0.0460 - val_mean_absolute_error: 0.1575\n",
      "\n",
      "Epoch 00072: val_loss did not improve from 0.04497\n",
      "Epoch 73/100\n",
      " - 5s - loss: 0.0440 - mean_absolute_error: 0.1579 - val_loss: 0.0485 - val_mean_absolute_error: 0.1600\n",
      "\n",
      "Epoch 00073: val_loss did not improve from 0.04497\n",
      "\n",
      "Epoch 00073: ReduceLROnPlateau reducing learning rate to 3.200000264769187e-07.\n",
      "Epoch 74/100\n",
      " - 5s - loss: 0.0425 - mean_absolute_error: 0.1538 - val_loss: 0.0437 - val_mean_absolute_error: 0.1528\n",
      "\n",
      "Epoch 00074: val_loss improved from 0.04497 to 0.04369, saving model to mlp_model3.passnyc.hdf5\n",
      "Epoch 75/100\n",
      " - 5s - loss: 0.0419 - mean_absolute_error: 0.1532 - val_loss: 0.0436 - val_mean_absolute_error: 0.1526\n",
      "\n",
      "Epoch 00075: val_loss improved from 0.04369 to 0.04364, saving model to mlp_model3.passnyc.hdf5\n",
      "Epoch 76/100\n",
      " - 5s - loss: 0.0418 - mean_absolute_error: 0.1533 - val_loss: 0.0436 - val_mean_absolute_error: 0.1537\n",
      "\n",
      "Epoch 00076: val_loss improved from 0.04364 to 0.04361, saving model to mlp_model3.passnyc.hdf5\n",
      "Epoch 77/100\n",
      " - 5s - loss: 0.0416 - mean_absolute_error: 0.1524 - val_loss: 0.0436 - val_mean_absolute_error: 0.1540\n",
      "\n",
      "Epoch 00077: val_loss improved from 0.04361 to 0.04359, saving model to mlp_model3.passnyc.hdf5\n",
      "Epoch 78/100\n",
      " - 5s - loss: 0.0416 - mean_absolute_error: 0.1528 - val_loss: 0.0440 - val_mean_absolute_error: 0.1560\n",
      "\n",
      "Epoch 00078: val_loss did not improve from 0.04359\n",
      "Epoch 79/100\n",
      " - 5s - loss: 0.0413 - mean_absolute_error: 0.1523 - val_loss: 0.0455 - val_mean_absolute_error: 0.1612\n",
      "\n",
      "Epoch 00079: val_loss did not improve from 0.04359\n",
      "\n",
      "Epoch 00079: ReduceLROnPlateau reducing learning rate to 6.400000529538374e-08.\n",
      "Epoch 80/100\n",
      " - 5s - loss: 0.0413 - mean_absolute_error: 0.1527 - val_loss: 0.0436 - val_mean_absolute_error: 0.1547\n",
      "\n",
      "Epoch 00080: val_loss did not improve from 0.04359\n",
      "Epoch 81/100\n",
      " - 5s - loss: 0.0410 - mean_absolute_error: 0.1514 - val_loss: 0.0435 - val_mean_absolute_error: 0.1542\n",
      "\n",
      "Epoch 00081: val_loss improved from 0.04359 to 0.04352, saving model to mlp_model3.passnyc.hdf5\n",
      "Epoch 82/100\n",
      " - 5s - loss: 0.0410 - mean_absolute_error: 0.1511 - val_loss: 0.0436 - val_mean_absolute_error: 0.1548\n",
      "\n",
      "Epoch 00082: val_loss did not improve from 0.04352\n",
      "Epoch 83/100\n",
      " - 5s - loss: 0.0410 - mean_absolute_error: 0.1515 - val_loss: 0.0434 - val_mean_absolute_error: 0.1539\n",
      "\n",
      "Epoch 00083: val_loss improved from 0.04352 to 0.04344, saving model to mlp_model3.passnyc.hdf5\n",
      "Epoch 84/100\n",
      " - 5s - loss: 0.0410 - mean_absolute_error: 0.1514 - val_loss: 0.0435 - val_mean_absolute_error: 0.1543\n",
      "\n",
      "Epoch 00084: val_loss did not improve from 0.04344\n",
      "Epoch 85/100\n",
      " - 5s - loss: 0.0410 - mean_absolute_error: 0.1513 - val_loss: 0.0435 - val_mean_absolute_error: 0.1542\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 00085: val_loss did not improve from 0.04344\n",
      "Epoch 86/100\n",
      " - 5s - loss: 0.0409 - mean_absolute_error: 0.1514 - val_loss: 0.0434 - val_mean_absolute_error: 0.1539\n",
      "\n",
      "Epoch 00086: val_loss improved from 0.04344 to 0.04341, saving model to mlp_model3.passnyc.hdf5\n",
      "Epoch 87/100\n",
      " - 5s - loss: 0.0409 - mean_absolute_error: 0.1515 - val_loss: 0.0434 - val_mean_absolute_error: 0.1538\n",
      "\n",
      "Epoch 00087: val_loss improved from 0.04341 to 0.04340, saving model to mlp_model3.passnyc.hdf5\n",
      "Epoch 88/100\n",
      " - 5s - loss: 0.0409 - mean_absolute_error: 0.1514 - val_loss: 0.0434 - val_mean_absolute_error: 0.1539\n",
      "\n",
      "Epoch 00088: val_loss did not improve from 0.04340\n",
      "Epoch 89/100\n",
      " - 5s - loss: 0.0409 - mean_absolute_error: 0.1512 - val_loss: 0.0435 - val_mean_absolute_error: 0.1543\n",
      "\n",
      "Epoch 00089: val_loss did not improve from 0.04340\n",
      "Epoch 90/100\n",
      " - 5s - loss: 0.0409 - mean_absolute_error: 0.1512 - val_loss: 0.0435 - val_mean_absolute_error: 0.1544\n",
      "\n",
      "Epoch 00090: val_loss did not improve from 0.04340\n",
      "Epoch 91/100\n",
      " - 6s - loss: 0.0408 - mean_absolute_error: 0.1512 - val_loss: 0.0433 - val_mean_absolute_error: 0.1537\n",
      "\n",
      "Epoch 00091: val_loss improved from 0.04340 to 0.04333, saving model to mlp_model3.passnyc.hdf5\n",
      "\n",
      "Epoch 00091: ReduceLROnPlateau reducing learning rate to 1.2800001059076749e-08.\n",
      "Epoch 92/100\n",
      " - 5s - loss: 0.0408 - mean_absolute_error: 0.1510 - val_loss: 0.0434 - val_mean_absolute_error: 0.1538\n",
      "\n",
      "Epoch 00092: val_loss did not improve from 0.04333\n",
      "Epoch 93/100\n",
      " - 5s - loss: 0.0408 - mean_absolute_error: 0.1509 - val_loss: 0.0433 - val_mean_absolute_error: 0.1538\n",
      "\n",
      "Epoch 00093: val_loss did not improve from 0.04333\n",
      "Epoch 94/100\n",
      " - 7s - loss: 0.0408 - mean_absolute_error: 0.1510 - val_loss: 0.0433 - val_mean_absolute_error: 0.1537\n",
      "\n",
      "Epoch 00094: val_loss did not improve from 0.04333\n",
      "Epoch 95/100\n",
      " - 7s - loss: 0.0408 - mean_absolute_error: 0.1509 - val_loss: 0.0433 - val_mean_absolute_error: 0.1538\n",
      "\n",
      "Epoch 00095: val_loss did not improve from 0.04333\n",
      "Epoch 96/100\n",
      " - 7s - loss: 0.0408 - mean_absolute_error: 0.1509 - val_loss: 0.0434 - val_mean_absolute_error: 0.1538\n",
      "\n",
      "Epoch 00096: val_loss did not improve from 0.04333\n",
      "\n",
      "Epoch 00096: ReduceLROnPlateau reducing learning rate to 2.5600002118153498e-09.\n",
      "Epoch 97/100\n",
      " - 8s - loss: 0.0407 - mean_absolute_error: 0.1509 - val_loss: 0.0434 - val_mean_absolute_error: 0.1539\n",
      "\n",
      "Epoch 00097: val_loss did not improve from 0.04333\n",
      "Epoch 98/100\n",
      " - 8s - loss: 0.0407 - mean_absolute_error: 0.1509 - val_loss: 0.0434 - val_mean_absolute_error: 0.1539\n",
      "\n",
      "Epoch 00098: val_loss did not improve from 0.04333\n",
      "Epoch 99/100\n",
      " - 8s - loss: 0.0407 - mean_absolute_error: 0.1509 - val_loss: 0.0434 - val_mean_absolute_error: 0.1539\n",
      "\n",
      "Epoch 00099: val_loss did not improve from 0.04333\n",
      "Epoch 100/100\n",
      " - 8s - loss: 0.0407 - mean_absolute_error: 0.1509 - val_loss: 0.0434 - val_mean_absolute_error: 0.1538\n",
      "\n",
      "Epoch 00100: val_loss did not improve from 0.04333\n"
     ]
    }
   ],
   "source": [
    "mlp_history3 = mlp_model3.fit(X_train3, y_train3, \n",
    "                              epochs=100, batch_size=16, verbose=2,\n",
    "                              validation_data=(X_valid3, y_valid3),\n",
    "                              callbacks=[mlp_checkpointer3,mlp_lr_reduction3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss_plot(mlp_history3,50)\n",
    "mae_plot(mlp_history3,50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_______________________________________\n",
      "MLP; \n",
      "Numeric & Encoded Categorical Features; \n",
      "Average Math Proficiency\n",
      "_______________________________________\n",
      "EV score. Train:  0.8151497194084713\n",
      "EV score. Valid:  0.794623192015042\n",
      "EV score. Test:  0.8388317087820005\n",
      "---------\n",
      "R2 score. Train:  0.8150723359501457\n",
      "R2 score. Valid:  0.794574391419199\n",
      "R2 score. Test:  0.837479637625652\n",
      "---------\n",
      "MSE score. Train:  0.04074241150843351\n",
      "MSE score. Valid:  0.04333440662071604\n",
      "MSE score. Test:  0.033786664232358474\n",
      "---------\n",
      "MAE score. Train:  0.15089797761288473\n",
      "MAE score. Valid:  0.15366046035060915\n",
      "MAE score. Test:  0.14357946925138304\n",
      "---------\n",
      "MdAE score. Train:  0.11380810737609859\n",
      "MdAE score. Valid:  0.13149845123291026\n",
      "MdAE score. Test:  0.11524397850036605\n"
     ]
    }
   ],
   "source": [
    "mlp_model3.load_weights('mlp_model3.passnyc.hdf5')\n",
    "y_train_mlp3 = mlp_model3.predict(X_train3)\n",
    "y_valid_mlp3 = mlp_model3.predict(X_valid3)\n",
    "y_test_mlp3 = mlp_model3.predict(X_test3)\n",
    "\n",
    "scores('MLP; \\nNumeric & Encoded Categorical Features; \\nAverage Math Proficiency', \n",
    "       y_train3, y_valid3, y_test3, y_train_mlp3, y_valid_mlp3, y_test_mlp3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mlp_model4():\n",
    "    model = Sequential()\n",
    "    \n",
    "    model.add(Dense(286, input_dim=286))\n",
    "    model.add(LeakyReLU(alpha=0.02))\n",
    "\n",
    "    model.add(Dense(286*4))\n",
    "    model.add(LeakyReLU(alpha=0.02))\n",
    "    \n",
    "    model.add(Dense(286*16))\n",
    "    model.add(LeakyReLU(alpha=0.02))\n",
    "#    model.add(Dense(286*16))\n",
    "#    model.add(LeakyReLU(alpha=0.02))\n",
    "    \n",
    "    model.add(Dense(1))\n",
    "    \n",
    "    model.compile(loss='mse', optimizer='rmsprop', metrics=['mae'])\n",
    "    return model\n",
    "\n",
    "mlp_model4 = mlp_model4()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "mlp_checkpointer4 = ModelCheckpoint(filepath='mlp_model4.passnyc.hdf5', \n",
    "                                    verbose=2, save_best_only=True)\n",
    "mlp_lr_reduction4 = ReduceLROnPlateau(monitor='val_loss', \n",
    "                                      patience=5, verbose=2, factor=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 890 samples, validate on 191 samples\n",
      "Epoch 1/100\n",
      " - 6s - loss: 215930694.8367 - mean_absolute_error: 2704.3470 - val_loss: 101724.0072 - val_mean_absolute_error: 287.9685\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 101724.00724, saving model to mlp_model4.passnyc.hdf5\n",
      "Epoch 2/100\n",
      " - 5s - loss: 69764.7048 - mean_absolute_error: 105.4970 - val_loss: 1.2405 - val_mean_absolute_error: 0.9907\n",
      "\n",
      "Epoch 00002: val_loss improved from 101724.00724 to 1.24050, saving model to mlp_model4.passnyc.hdf5\n",
      "Epoch 3/100\n",
      " - 5s - loss: 1079700.3141 - mean_absolute_error: 254.8439 - val_loss: 1421.7313 - val_mean_absolute_error: 34.5026\n",
      "\n",
      "Epoch 00003: val_loss did not improve from 1.24050\n",
      "Epoch 4/100\n",
      " - 5s - loss: 30706.6186 - mean_absolute_error: 58.9315 - val_loss: 1.1480 - val_mean_absolute_error: 1.0030\n",
      "\n",
      "Epoch 00004: val_loss improved from 1.24050 to 1.14804, saving model to mlp_model4.passnyc.hdf5\n",
      "Epoch 5/100\n",
      " - 5s - loss: 103089.6632 - mean_absolute_error: 132.0757 - val_loss: 23.7701 - val_mean_absolute_error: 4.6066\n",
      "\n",
      "Epoch 00005: val_loss did not improve from 1.14804\n",
      "Epoch 6/100\n",
      " - 5s - loss: 4050.1464 - mean_absolute_error: 26.4255 - val_loss: 43760.5154 - val_mean_absolute_error: 189.2604\n",
      "\n",
      "Epoch 00006: val_loss did not improve from 1.14804\n",
      "Epoch 7/100\n",
      " - 5s - loss: 166493.9082 - mean_absolute_error: 125.2021 - val_loss: 1.1517 - val_mean_absolute_error: 0.8079\n",
      "\n",
      "Epoch 00007: val_loss did not improve from 1.14804\n",
      "Epoch 8/100\n",
      " - 5s - loss: 903.4496 - mean_absolute_error: 13.5901 - val_loss: 1.2199 - val_mean_absolute_error: 0.7831\n",
      "\n",
      "Epoch 00008: val_loss did not improve from 1.14804\n",
      "Epoch 9/100\n",
      " - 5s - loss: 659874.9603 - mean_absolute_error: 145.9673 - val_loss: 32.9065 - val_mean_absolute_error: 4.6326\n",
      "\n",
      "Epoch 00009: val_loss did not improve from 1.14804\n",
      "\n",
      "Epoch 00009: ReduceLROnPlateau reducing learning rate to 0.00020000000949949026.\n",
      "Epoch 10/100\n",
      " - 5s - loss: 12.2467 - mean_absolute_error: 2.2389 - val_loss: 8.8839 - val_mean_absolute_error: 2.1215\n",
      "\n",
      "Epoch 00010: val_loss did not improve from 1.14804\n",
      "Epoch 11/100\n",
      " - 5s - loss: 176.1881 - mean_absolute_error: 7.4367 - val_loss: 1.5449 - val_mean_absolute_error: 1.1582\n",
      "\n",
      "Epoch 00011: val_loss did not improve from 1.14804\n",
      "Epoch 12/100\n",
      " - 5s - loss: 78.2585 - mean_absolute_error: 5.8195 - val_loss: 1.5811 - val_mean_absolute_error: 0.8374\n",
      "\n",
      "Epoch 00012: val_loss did not improve from 1.14804\n",
      "Epoch 13/100\n",
      " - 5s - loss: 30.2380 - mean_absolute_error: 2.4370 - val_loss: 1.1683 - val_mean_absolute_error: 0.7747\n",
      "\n",
      "Epoch 00013: val_loss did not improve from 1.14804\n",
      "Epoch 14/100\n",
      " - 5s - loss: 15.2070 - mean_absolute_error: 2.2628 - val_loss: 3.4985 - val_mean_absolute_error: 1.8463\n",
      "\n",
      "Epoch 00014: val_loss did not improve from 1.14804\n",
      "\n",
      "Epoch 00014: ReduceLROnPlateau reducing learning rate to 4.0000001899898055e-05.\n",
      "Epoch 15/100\n",
      " - 5s - loss: 0.8389 - mean_absolute_error: 0.7329 - val_loss: 0.4379 - val_mean_absolute_error: 0.5589\n",
      "\n",
      "Epoch 00015: val_loss improved from 1.14804 to 0.43788, saving model to mlp_model4.passnyc.hdf5\n",
      "Epoch 16/100\n",
      " - 5s - loss: 0.5726 - mean_absolute_error: 0.5947 - val_loss: 0.6361 - val_mean_absolute_error: 0.5357\n",
      "\n",
      "Epoch 00016: val_loss did not improve from 0.43788\n",
      "Epoch 17/100\n",
      " - 5s - loss: 0.5530 - mean_absolute_error: 0.5824 - val_loss: 0.2398 - val_mean_absolute_error: 0.3310\n",
      "\n",
      "Epoch 00017: val_loss improved from 0.43788 to 0.23977, saving model to mlp_model4.passnyc.hdf5\n",
      "Epoch 18/100\n",
      " - 5s - loss: 0.4597 - mean_absolute_error: 0.5199 - val_loss: 0.3096 - val_mean_absolute_error: 0.3680\n",
      "\n",
      "Epoch 00018: val_loss did not improve from 0.23977\n",
      "Epoch 19/100\n",
      " - 5s - loss: 0.4283 - mean_absolute_error: 0.5059 - val_loss: 0.2052 - val_mean_absolute_error: 0.3301\n",
      "\n",
      "Epoch 00019: val_loss improved from 0.23977 to 0.20518, saving model to mlp_model4.passnyc.hdf5\n",
      "Epoch 20/100\n",
      " - 5s - loss: 0.4116 - mean_absolute_error: 0.4814 - val_loss: 0.1852 - val_mean_absolute_error: 0.3141\n",
      "\n",
      "Epoch 00020: val_loss improved from 0.20518 to 0.18520, saving model to mlp_model4.passnyc.hdf5\n",
      "Epoch 21/100\n",
      " - 5s - loss: 0.3690 - mean_absolute_error: 0.4624 - val_loss: 0.2817 - val_mean_absolute_error: 0.3345\n",
      "\n",
      "Epoch 00021: val_loss did not improve from 0.18520\n",
      "Epoch 22/100\n",
      " - 5s - loss: 0.3267 - mean_absolute_error: 0.4290 - val_loss: 0.1472 - val_mean_absolute_error: 0.2765\n",
      "\n",
      "Epoch 00022: val_loss improved from 0.18520 to 0.14724, saving model to mlp_model4.passnyc.hdf5\n",
      "Epoch 23/100\n",
      " - 5s - loss: 0.3044 - mean_absolute_error: 0.4196 - val_loss: 0.5236 - val_mean_absolute_error: 0.6721\n",
      "\n",
      "Epoch 00023: val_loss did not improve from 0.14724\n",
      "Epoch 24/100\n",
      " - 5s - loss: 0.3034 - mean_absolute_error: 0.4183 - val_loss: 0.1482 - val_mean_absolute_error: 0.2574\n",
      "\n",
      "Epoch 00024: val_loss did not improve from 0.14724\n",
      "Epoch 25/100\n",
      " - 5s - loss: 0.3083 - mean_absolute_error: 0.3963 - val_loss: 0.1370 - val_mean_absolute_error: 0.2629\n",
      "\n",
      "Epoch 00025: val_loss improved from 0.14724 to 0.13698, saving model to mlp_model4.passnyc.hdf5\n",
      "Epoch 26/100\n",
      " - 5s - loss: 0.2975 - mean_absolute_error: 0.4110 - val_loss: 0.2879 - val_mean_absolute_error: 0.3717\n",
      "\n",
      "Epoch 00026: val_loss did not improve from 0.13698\n",
      "Epoch 27/100\n",
      " - 6s - loss: 0.2724 - mean_absolute_error: 0.3860 - val_loss: 0.1755 - val_mean_absolute_error: 0.2926\n",
      "\n",
      "Epoch 00027: val_loss did not improve from 0.13698\n",
      "Epoch 28/100\n",
      " - 7s - loss: 0.2409 - mean_absolute_error: 0.3820 - val_loss: 0.1250 - val_mean_absolute_error: 0.2518\n",
      "\n",
      "Epoch 00028: val_loss improved from 0.13698 to 0.12498, saving model to mlp_model4.passnyc.hdf5\n",
      "Epoch 29/100\n",
      " - 9s - loss: 0.2571 - mean_absolute_error: 0.3946 - val_loss: 0.3312 - val_mean_absolute_error: 0.4701\n",
      "\n",
      "Epoch 00029: val_loss did not improve from 0.12498\n",
      "Epoch 30/100\n",
      " - 11s - loss: 0.2435 - mean_absolute_error: 0.3845 - val_loss: 0.1371 - val_mean_absolute_error: 0.2710\n",
      "\n",
      "Epoch 00030: val_loss did not improve from 0.12498\n",
      "Epoch 31/100\n",
      " - 12s - loss: 0.2263 - mean_absolute_error: 0.3632 - val_loss: 0.1542 - val_mean_absolute_error: 0.3211\n",
      "\n",
      "Epoch 00031: val_loss did not improve from 0.12498\n",
      "Epoch 32/100\n",
      " - 11s - loss: 0.2228 - mean_absolute_error: 0.3612 - val_loss: 0.1237 - val_mean_absolute_error: 0.2577\n",
      "\n",
      "Epoch 00032: val_loss improved from 0.12498 to 0.12374, saving model to mlp_model4.passnyc.hdf5\n",
      "Epoch 33/100\n",
      " - 10s - loss: 0.2099 - mean_absolute_error: 0.3687 - val_loss: 0.7592 - val_mean_absolute_error: 0.8139\n",
      "\n",
      "Epoch 00033: val_loss did not improve from 0.12374\n",
      "Epoch 34/100\n",
      " - 10s - loss: 0.2198 - mean_absolute_error: 0.3592 - val_loss: 0.9642 - val_mean_absolute_error: 0.9298\n",
      "\n",
      "Epoch 00034: val_loss did not improve from 0.12374\n",
      "Epoch 35/100\n",
      " - 9s - loss: 0.1915 - mean_absolute_error: 0.3412 - val_loss: 0.1090 - val_mean_absolute_error: 0.2268\n",
      "\n",
      "Epoch 00035: val_loss improved from 0.12374 to 0.10904, saving model to mlp_model4.passnyc.hdf5\n",
      "Epoch 36/100\n",
      " - 6s - loss: 0.2179 - mean_absolute_error: 0.3636 - val_loss: 0.0788 - val_mean_absolute_error: 0.2027\n",
      "\n",
      "Epoch 00036: val_loss improved from 0.10904 to 0.07882, saving model to mlp_model4.passnyc.hdf5\n",
      "Epoch 37/100\n",
      " - 6s - loss: 0.1838 - mean_absolute_error: 0.3253 - val_loss: 0.7531 - val_mean_absolute_error: 0.8284\n",
      "\n",
      "Epoch 00037: val_loss did not improve from 0.07882\n",
      "Epoch 38/100\n",
      " - 7s - loss: 0.2329 - mean_absolute_error: 0.3497 - val_loss: 0.1470 - val_mean_absolute_error: 0.3039\n",
      "\n",
      "Epoch 00038: val_loss did not improve from 0.07882\n",
      "Epoch 39/100\n",
      " - 8s - loss: 0.2261 - mean_absolute_error: 0.3551 - val_loss: 0.2005 - val_mean_absolute_error: 0.3658\n",
      "\n",
      "Epoch 00039: val_loss did not improve from 0.07882\n",
      "Epoch 40/100\n",
      " - 7s - loss: 0.1705 - mean_absolute_error: 0.3066 - val_loss: 0.3119 - val_mean_absolute_error: 0.4880\n",
      "\n",
      "Epoch 00040: val_loss did not improve from 0.07882\n",
      "Epoch 41/100\n",
      " - 7s - loss: 0.1857 - mean_absolute_error: 0.3297 - val_loss: 0.0807 - val_mean_absolute_error: 0.1984\n",
      "\n",
      "Epoch 00041: val_loss did not improve from 0.07882\n",
      "\n",
      "Epoch 00041: ReduceLROnPlateau reducing learning rate to 8.000000525498762e-06.\n",
      "Epoch 42/100\n",
      " - 7s - loss: 0.0693 - mean_absolute_error: 0.1972 - val_loss: 0.0698 - val_mean_absolute_error: 0.1921\n",
      "\n",
      "Epoch 00042: val_loss improved from 0.07882 to 0.06984, saving model to mlp_model4.passnyc.hdf5\n",
      "Epoch 43/100\n",
      " - 6s - loss: 0.0645 - mean_absolute_error: 0.1923 - val_loss: 0.0669 - val_mean_absolute_error: 0.1858\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 00043: val_loss improved from 0.06984 to 0.06686, saving model to mlp_model4.passnyc.hdf5\n",
      "Epoch 44/100\n",
      " - 6s - loss: 0.0578 - mean_absolute_error: 0.1820 - val_loss: 0.0652 - val_mean_absolute_error: 0.1812\n",
      "\n",
      "Epoch 00044: val_loss improved from 0.06686 to 0.06517, saving model to mlp_model4.passnyc.hdf5\n",
      "Epoch 45/100\n",
      " - 5s - loss: 0.0604 - mean_absolute_error: 0.1872 - val_loss: 0.0500 - val_mean_absolute_error: 0.1637\n",
      "\n",
      "Epoch 00045: val_loss improved from 0.06517 to 0.05003, saving model to mlp_model4.passnyc.hdf5\n",
      "Epoch 46/100\n",
      " - 5s - loss: 0.0569 - mean_absolute_error: 0.1787 - val_loss: 0.0528 - val_mean_absolute_error: 0.1732\n",
      "\n",
      "Epoch 00046: val_loss did not improve from 0.05003\n",
      "Epoch 47/100\n",
      " - 7s - loss: 0.0556 - mean_absolute_error: 0.1786 - val_loss: 0.0564 - val_mean_absolute_error: 0.1826\n",
      "\n",
      "Epoch 00047: val_loss did not improve from 0.05003\n",
      "Epoch 48/100\n",
      " - 8s - loss: 0.0582 - mean_absolute_error: 0.1805 - val_loss: 0.0548 - val_mean_absolute_error: 0.1659\n",
      "\n",
      "Epoch 00048: val_loss did not improve from 0.05003\n",
      "Epoch 49/100\n",
      " - 7s - loss: 0.0528 - mean_absolute_error: 0.1709 - val_loss: 0.0535 - val_mean_absolute_error: 0.1668\n",
      "\n",
      "Epoch 00049: val_loss did not improve from 0.05003\n",
      "Epoch 50/100\n",
      " - 8s - loss: 0.0506 - mean_absolute_error: 0.1707 - val_loss: 0.0517 - val_mean_absolute_error: 0.1662\n",
      "\n",
      "Epoch 00050: val_loss did not improve from 0.05003\n",
      "\n",
      "Epoch 00050: ReduceLROnPlateau reducing learning rate to 1.6000001778593287e-06.\n",
      "Epoch 51/100\n",
      " - 8s - loss: 0.0401 - mean_absolute_error: 0.1487 - val_loss: 0.0433 - val_mean_absolute_error: 0.1491\n",
      "\n",
      "Epoch 00051: val_loss improved from 0.05003 to 0.04329, saving model to mlp_model4.passnyc.hdf5\n",
      "Epoch 52/100\n",
      " - 7s - loss: 0.0388 - mean_absolute_error: 0.1453 - val_loss: 0.0411 - val_mean_absolute_error: 0.1460\n",
      "\n",
      "Epoch 00052: val_loss improved from 0.04329 to 0.04108, saving model to mlp_model4.passnyc.hdf5\n",
      "Epoch 53/100\n",
      " - 6s - loss: 0.0390 - mean_absolute_error: 0.1452 - val_loss: 0.0455 - val_mean_absolute_error: 0.1590\n",
      "\n",
      "Epoch 00053: val_loss did not improve from 0.04108\n",
      "Epoch 54/100\n",
      " - 8s - loss: 0.0374 - mean_absolute_error: 0.1433 - val_loss: 0.0403 - val_mean_absolute_error: 0.1445\n",
      "\n",
      "Epoch 00054: val_loss improved from 0.04108 to 0.04025, saving model to mlp_model4.passnyc.hdf5\n",
      "Epoch 55/100\n",
      " - 7s - loss: 0.0365 - mean_absolute_error: 0.1419 - val_loss: 0.0409 - val_mean_absolute_error: 0.1452\n",
      "\n",
      "Epoch 00055: val_loss did not improve from 0.04025\n",
      "Epoch 56/100\n",
      " - 8s - loss: 0.0371 - mean_absolute_error: 0.1427 - val_loss: 0.0406 - val_mean_absolute_error: 0.1443\n",
      "\n",
      "Epoch 00056: val_loss did not improve from 0.04025\n",
      "Epoch 57/100\n",
      " - 8s - loss: 0.0353 - mean_absolute_error: 0.1397 - val_loss: 0.0390 - val_mean_absolute_error: 0.1423\n",
      "\n",
      "Epoch 00057: val_loss improved from 0.04025 to 0.03895, saving model to mlp_model4.passnyc.hdf5\n",
      "Epoch 58/100\n",
      " - 5s - loss: 0.0355 - mean_absolute_error: 0.1399 - val_loss: 0.0384 - val_mean_absolute_error: 0.1417\n",
      "\n",
      "Epoch 00058: val_loss improved from 0.03895 to 0.03838, saving model to mlp_model4.passnyc.hdf5\n",
      "Epoch 59/100\n",
      " - 5s - loss: 0.0354 - mean_absolute_error: 0.1396 - val_loss: 0.0431 - val_mean_absolute_error: 0.1525\n",
      "\n",
      "Epoch 00059: val_loss did not improve from 0.03838\n",
      "Epoch 60/100\n",
      " - 7s - loss: 0.0345 - mean_absolute_error: 0.1387 - val_loss: 0.0414 - val_mean_absolute_error: 0.1466\n",
      "\n",
      "Epoch 00060: val_loss did not improve from 0.03838\n",
      "Epoch 61/100\n",
      " - 7s - loss: 0.0347 - mean_absolute_error: 0.1383 - val_loss: 0.0386 - val_mean_absolute_error: 0.1428\n",
      "\n",
      "Epoch 00061: val_loss did not improve from 0.03838\n",
      "Epoch 62/100\n",
      " - 7s - loss: 0.0340 - mean_absolute_error: 0.1375 - val_loss: 0.0476 - val_mean_absolute_error: 0.1605\n",
      "\n",
      "Epoch 00062: val_loss did not improve from 0.03838\n",
      "Epoch 63/100\n",
      " - 6s - loss: 0.0338 - mean_absolute_error: 0.1368 - val_loss: 0.0388 - val_mean_absolute_error: 0.1428\n",
      "\n",
      "Epoch 00063: val_loss did not improve from 0.03838\n",
      "\n",
      "Epoch 00063: ReduceLROnPlateau reducing learning rate to 3.200000264769187e-07.\n",
      "Epoch 64/100\n",
      " - 6s - loss: 0.0322 - mean_absolute_error: 0.1330 - val_loss: 0.0370 - val_mean_absolute_error: 0.1392\n",
      "\n",
      "Epoch 00064: val_loss improved from 0.03838 to 0.03695, saving model to mlp_model4.passnyc.hdf5\n",
      "Epoch 65/100\n",
      " - 5s - loss: 0.0317 - mean_absolute_error: 0.1314 - val_loss: 0.0375 - val_mean_absolute_error: 0.1402\n",
      "\n",
      "Epoch 00065: val_loss did not improve from 0.03695\n",
      "Epoch 66/100\n",
      " - 6s - loss: 0.0318 - mean_absolute_error: 0.1316 - val_loss: 0.0368 - val_mean_absolute_error: 0.1387\n",
      "\n",
      "Epoch 00066: val_loss improved from 0.03695 to 0.03680, saving model to mlp_model4.passnyc.hdf5\n",
      "Epoch 67/100\n",
      " - 5s - loss: 0.0317 - mean_absolute_error: 0.1319 - val_loss: 0.0375 - val_mean_absolute_error: 0.1397\n",
      "\n",
      "Epoch 00067: val_loss did not improve from 0.03680\n",
      "Epoch 68/100\n",
      " - 5s - loss: 0.0316 - mean_absolute_error: 0.1319 - val_loss: 0.0367 - val_mean_absolute_error: 0.1383\n",
      "\n",
      "Epoch 00068: val_loss improved from 0.03680 to 0.03674, saving model to mlp_model4.passnyc.hdf5\n",
      "Epoch 69/100\n",
      " - 5s - loss: 0.0314 - mean_absolute_error: 0.1310 - val_loss: 0.0369 - val_mean_absolute_error: 0.1389\n",
      "\n",
      "Epoch 00069: val_loss did not improve from 0.03674\n",
      "Epoch 70/100\n",
      " - 6s - loss: 0.0315 - mean_absolute_error: 0.1306 - val_loss: 0.0366 - val_mean_absolute_error: 0.1381\n",
      "\n",
      "Epoch 00070: val_loss improved from 0.03674 to 0.03656, saving model to mlp_model4.passnyc.hdf5\n",
      "Epoch 71/100\n",
      " - 5s - loss: 0.0313 - mean_absolute_error: 0.1311 - val_loss: 0.0366 - val_mean_absolute_error: 0.1383\n",
      "\n",
      "Epoch 00071: val_loss did not improve from 0.03656\n",
      "Epoch 72/100\n",
      " - 6s - loss: 0.0313 - mean_absolute_error: 0.1310 - val_loss: 0.0364 - val_mean_absolute_error: 0.1380\n",
      "\n",
      "Epoch 00072: val_loss improved from 0.03656 to 0.03644, saving model to mlp_model4.passnyc.hdf5\n",
      "Epoch 73/100\n",
      " - 5s - loss: 0.0311 - mean_absolute_error: 0.1305 - val_loss: 0.0365 - val_mean_absolute_error: 0.1382\n",
      "\n",
      "Epoch 00073: val_loss did not improve from 0.03644\n",
      "Epoch 74/100\n",
      " - 6s - loss: 0.0312 - mean_absolute_error: 0.1305 - val_loss: 0.0363 - val_mean_absolute_error: 0.1378\n",
      "\n",
      "Epoch 00074: val_loss improved from 0.03644 to 0.03630, saving model to mlp_model4.passnyc.hdf5\n",
      "Epoch 75/100\n",
      " - 5s - loss: 0.0311 - mean_absolute_error: 0.1300 - val_loss: 0.0361 - val_mean_absolute_error: 0.1375\n",
      "\n",
      "Epoch 00075: val_loss improved from 0.03630 to 0.03614, saving model to mlp_model4.passnyc.hdf5\n",
      "Epoch 76/100\n",
      " - 5s - loss: 0.0309 - mean_absolute_error: 0.1298 - val_loss: 0.0366 - val_mean_absolute_error: 0.1377\n",
      "\n",
      "Epoch 00076: val_loss did not improve from 0.03614\n",
      "Epoch 77/100\n",
      " - 6s - loss: 0.0308 - mean_absolute_error: 0.1294 - val_loss: 0.0362 - val_mean_absolute_error: 0.1371\n",
      "\n",
      "Epoch 00077: val_loss did not improve from 0.03614\n",
      "Epoch 78/100\n",
      " - 6s - loss: 0.0309 - mean_absolute_error: 0.1297 - val_loss: 0.0361 - val_mean_absolute_error: 0.1374\n",
      "\n",
      "Epoch 00078: val_loss improved from 0.03614 to 0.03612, saving model to mlp_model4.passnyc.hdf5\n",
      "Epoch 79/100\n",
      " - 5s - loss: 0.0308 - mean_absolute_error: 0.1295 - val_loss: 0.0365 - val_mean_absolute_error: 0.1373\n",
      "\n",
      "Epoch 00079: val_loss did not improve from 0.03612\n",
      "Epoch 80/100\n",
      " - 6s - loss: 0.0308 - mean_absolute_error: 0.1296 - val_loss: 0.0372 - val_mean_absolute_error: 0.1388\n",
      "\n",
      "Epoch 00080: val_loss did not improve from 0.03612\n",
      "\n",
      "Epoch 00080: ReduceLROnPlateau reducing learning rate to 6.400000529538374e-08.\n",
      "Epoch 81/100\n",
      " - 6s - loss: 0.0304 - mean_absolute_error: 0.1299 - val_loss: 0.0359 - val_mean_absolute_error: 0.1369\n",
      "\n",
      "Epoch 00081: val_loss improved from 0.03612 to 0.03589, saving model to mlp_model4.passnyc.hdf5\n",
      "Epoch 82/100\n",
      " - 5s - loss: 0.0303 - mean_absolute_error: 0.1286 - val_loss: 0.0359 - val_mean_absolute_error: 0.1368\n",
      "\n",
      "Epoch 00082: val_loss did not improve from 0.03589\n",
      "Epoch 83/100\n",
      " - 6s - loss: 0.0303 - mean_absolute_error: 0.1285 - val_loss: 0.0359 - val_mean_absolute_error: 0.1368\n",
      "\n",
      "Epoch 00083: val_loss did not improve from 0.03589\n",
      "Epoch 84/100\n",
      " - 7s - loss: 0.0303 - mean_absolute_error: 0.1285 - val_loss: 0.0359 - val_mean_absolute_error: 0.1369\n",
      "\n",
      "Epoch 00084: val_loss improved from 0.03589 to 0.03586, saving model to mlp_model4.passnyc.hdf5\n",
      "Epoch 85/100\n",
      " - 5s - loss: 0.0302 - mean_absolute_error: 0.1285 - val_loss: 0.0359 - val_mean_absolute_error: 0.1369\n",
      "\n",
      "Epoch 00085: val_loss did not improve from 0.03586\n",
      "Epoch 86/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " - 6s - loss: 0.0302 - mean_absolute_error: 0.1284 - val_loss: 0.0360 - val_mean_absolute_error: 0.1366\n",
      "\n",
      "Epoch 00086: val_loss did not improve from 0.03586\n",
      "\n",
      "Epoch 00086: ReduceLROnPlateau reducing learning rate to 1.2800001059076749e-08.\n",
      "Epoch 87/100\n",
      " - 6s - loss: 0.0301 - mean_absolute_error: 0.1285 - val_loss: 0.0359 - val_mean_absolute_error: 0.1366\n",
      "\n",
      "Epoch 00087: val_loss did not improve from 0.03586\n",
      "Epoch 88/100\n",
      " - 6s - loss: 0.0301 - mean_absolute_error: 0.1284 - val_loss: 0.0359 - val_mean_absolute_error: 0.1366\n",
      "\n",
      "Epoch 00088: val_loss did not improve from 0.03586\n",
      "Epoch 89/100\n",
      " - 6s - loss: 0.0301 - mean_absolute_error: 0.1284 - val_loss: 0.0359 - val_mean_absolute_error: 0.1366\n",
      "\n",
      "Epoch 00089: val_loss did not improve from 0.03586\n",
      "Epoch 90/100\n",
      " - 6s - loss: 0.0301 - mean_absolute_error: 0.1284 - val_loss: 0.0359 - val_mean_absolute_error: 0.1367\n",
      "\n",
      "Epoch 00090: val_loss did not improve from 0.03586\n",
      "Epoch 91/100\n",
      " - 6s - loss: 0.0301 - mean_absolute_error: 0.1283 - val_loss: 0.0359 - val_mean_absolute_error: 0.1367\n",
      "\n",
      "Epoch 00091: val_loss did not improve from 0.03586\n",
      "\n",
      "Epoch 00091: ReduceLROnPlateau reducing learning rate to 2.5600002118153498e-09.\n",
      "Epoch 92/100\n",
      " - 6s - loss: 0.0301 - mean_absolute_error: 0.1283 - val_loss: 0.0359 - val_mean_absolute_error: 0.1367\n",
      "\n",
      "Epoch 00092: val_loss did not improve from 0.03586\n",
      "Epoch 93/100\n",
      " - 6s - loss: 0.0301 - mean_absolute_error: 0.1283 - val_loss: 0.0359 - val_mean_absolute_error: 0.1367\n",
      "\n",
      "Epoch 00093: val_loss did not improve from 0.03586\n",
      "Epoch 94/100\n",
      " - 6s - loss: 0.0301 - mean_absolute_error: 0.1283 - val_loss: 0.0359 - val_mean_absolute_error: 0.1367\n",
      "\n",
      "Epoch 00094: val_loss did not improve from 0.03586\n",
      "Epoch 95/100\n",
      " - 6s - loss: 0.0301 - mean_absolute_error: 0.1283 - val_loss: 0.0359 - val_mean_absolute_error: 0.1367\n",
      "\n",
      "Epoch 00095: val_loss did not improve from 0.03586\n",
      "Epoch 96/100\n",
      " - 6s - loss: 0.0301 - mean_absolute_error: 0.1282 - val_loss: 0.0359 - val_mean_absolute_error: 0.1367\n",
      "\n",
      "Epoch 00096: val_loss did not improve from 0.03586\n",
      "\n",
      "Epoch 00096: ReduceLROnPlateau reducing learning rate to 5.1200004236307e-10.\n",
      "Epoch 97/100\n",
      " - 6s - loss: 0.0301 - mean_absolute_error: 0.1283 - val_loss: 0.0359 - val_mean_absolute_error: 0.1367\n",
      "\n",
      "Epoch 00097: val_loss did not improve from 0.03586\n",
      "Epoch 98/100\n",
      " - 6s - loss: 0.0301 - mean_absolute_error: 0.1283 - val_loss: 0.0359 - val_mean_absolute_error: 0.1367\n",
      "\n",
      "Epoch 00098: val_loss did not improve from 0.03586\n",
      "Epoch 99/100\n",
      " - 6s - loss: 0.0301 - mean_absolute_error: 0.1283 - val_loss: 0.0359 - val_mean_absolute_error: 0.1367\n",
      "\n",
      "Epoch 00099: val_loss did not improve from 0.03586\n",
      "Epoch 100/100\n",
      " - 6s - loss: 0.0301 - mean_absolute_error: 0.1283 - val_loss: 0.0359 - val_mean_absolute_error: 0.1367\n",
      "\n",
      "Epoch 00100: val_loss did not improve from 0.03586\n"
     ]
    }
   ],
   "source": [
    "mlp_history4 = mlp_model4.fit(X_train4, y_train4, \n",
    "                              epochs=100, batch_size=16, verbose=2,\n",
    "                              validation_data=(X_valid4, y_valid4),\n",
    "                              callbacks=[mlp_checkpointer4,mlp_lr_reduction4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss_plot(mlp_history4,50)\n",
    "mae_plot(mlp_history4,50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_______________________________________\n",
      "MLP; \n",
      "Numeric & Encoded Categorical Features; \n",
      "Average ELA Proficiency\n",
      "_______________________________________\n",
      "EV score. Train:  0.7636185609075619\n",
      "EV score. Valid:  0.7391653489834558\n",
      "EV score. Test:  0.7787099096142305\n",
      "---------\n",
      "R2 score. Train:  0.7631497325943268\n",
      "R2 score. Valid:  0.7391086253411154\n",
      "R2 score. Test:  0.7776300900208278\n",
      "---------\n",
      "MSE score. Train:  0.030178354810995317\n",
      "MSE score. Valid:  0.035863463660018656\n",
      "MSE score. Test:  0.028930973206002047\n",
      "---------\n",
      "MAE score. Train:  0.12814060040448935\n",
      "MAE score. Valid:  0.13685672923948544\n",
      "MAE score. Test:  0.13474212300298935\n",
      "---------\n",
      "MdAE score. Train:  0.09733173847198495\n",
      "MdAE score. Valid:  0.10722801208496113\n",
      "MdAE score. Test:  0.11736362457275407\n"
     ]
    }
   ],
   "source": [
    "mlp_model4.load_weights('mlp_model4.passnyc.hdf5')\n",
    "y_train_mlp4 = mlp_model4.predict(X_train4)\n",
    "y_valid_mlp4 = mlp_model4.predict(X_valid4)\n",
    "y_test_mlp4 = mlp_model4.predict(X_test4)\n",
    "\n",
    "scores('MLP; \\nNumeric & Encoded Categorical Features; \\nAverage ELA Proficiency', \n",
    "       y_train4, y_valid4, y_test4, y_train_mlp4, y_valid_mlp4, y_test_mlp4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## &#x1F310; &nbsp; Display Predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Numeric features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (14,7))\n",
    "\n",
    "plt.plot(y_test1[1:50],'-o',color='#3636ff',label='Real Data')\n",
    "plt.plot(y_test_mlp1[1:50],'-o',color='#ff3636',label='MLP')\n",
    "\n",
    "plt.legend()\n",
    "plt.title(\"Average Math Proficiency. NN Test Predictions vs Real Data\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (14,7))\n",
    "\n",
    "plt.plot(y_test2[1:50],'-o',color='#3636ff',label='Real Data')\n",
    "plt.plot(y_test_mlp2[1:50],'-o',color='#ff3636',label='MLP')\n",
    "\n",
    "plt.legend()\n",
    "plt.title(\"Average ELA Proficiency. NN Test Predictions vs Real Data\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Numeric & encoded categorical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (14,7))\n",
    "\n",
    "plt.plot(y_test3[1:50],'-o',color='#3636ff',label='Real Data')\n",
    "plt.plot(y_test_mlp3[1:50],'-o',color='#ff3636',label='MLP')\n",
    "\n",
    "plt.legend()\n",
    "plt.title(\"Average Math Proficiency. NN Test Predictions vs Real Data\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (14,7))\n",
    "\n",
    "plt.plot(y_test4[1:50],'-o',color='#3636ff',label='Real Data')\n",
    "plt.plot(y_test_mlp4[1:50],'-o',color='#ff3636',label='MLP')\n",
    "\n",
    "plt.legend()\n",
    "plt.title(\"Average ELA Proficiency. NN Test Predictions vs Real Data\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, neural networks were used to predict the indicators of educational outcomes. \n",
    "\n",
    "It is difficult to achieve high accuracy here in general since the ultimate goal depends not only on the financing, social environment and professionalism of the staff but also on the personal qualities of the students. \n",
    "\n",
    "Adding category variables does not improve the accuracy of predictions because these indicators depend not exactly on geolocations but on the well-being of the surrounding area.\n",
    "\n",
    "I believe that it is possible to reach at least the same accuracy with neural networks as with regression algorithms like `Gradient Boosting` or `Random Forest`."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}