ipython_no_submission.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%qtconsole"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from __future__ import print_function\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import random\n",
    "\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import Imputer\n",
    "from sklearn.preprocessing import normalize\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "from sklearn import ensemble\n",
    "from sklearn import linear_model\n",
    "from sklearn import svm\n",
    "\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.cross_validation import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "microclimate_train = pd.read_csv('dataset/Training set Microclimate (2 hour intervals).csv',\n",
    "                                 index_col=0, parse_dates=[0])\n",
    "\n",
    "microclimate_test = pd.read_csv('dataset/Test set Microclimate (2 hour intervals).csv',\n",
    "                                index_col=0, parse_dates=[0])\n",
    "\n",
    "labels = pd.read_csv('dataset/Target Variable Water Yield.csv',\n",
    "                     index_col=0, parse_dates=[0])\n",
    "\n",
    "submission_format = pd.read_csv('dataset/Submission format.csv',\n",
    "                                index_col=0, parse_dates=[0])\n",
    "\n",
    "microclimate_5min = pd.read_csv('dataset/Training set Microclimate (5 minute intervals).csv',\n",
    "                                 index_col=0, parse_dates=[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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OAQAA6J3iFAAAgN4pTgEAAOid4hQAAIDeKU4BAADoneIUAACA3ilOAQAA6J3iFAAAgN4p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\nAACA3ilOAQAA6J3iFAAAgN79f/BicKk8gtnOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1043bfbd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_time = microclimate_train.copy()\n",
    "y_time = labels.copy()\n",
    "\n",
    "X_test_time = microclimate_test.copy()\n",
    "y_test_time = submission_format.copy()\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(16, 4))\n",
    "\n",
    "ax.plot_date(X_time.index, [1 for i in xrange(y_time.shape[0])], c='r', ms=3)\n",
    "ax.plot_date(X_test_time.index, [2 for i in xrange(X_test_time.shape[0])], c='r', ms=3)\n",
    "ax.plot_date(y_test_time.index, [3 for i in xrange(y_test_time.shape[0])], c='r', ms=3)\n",
    "ax.set_ylim([0, 5])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_time = microclimate_train.copy()\n",
    "y_time = labels.copy()\n",
    "X_joint = microclimate_train.copy()\n",
    "\n",
    "for i in [2]:\n",
    "    offset = i\n",
    "    y_pretime = labels.copy()\n",
    "    y_pretime.index = y_pretime.index + pd.DateOffset(hours=offset)\n",
    "    y_pretime = y_pretime.rename(columns={'yield': 'pre_yield_' + str(offset) + '_hours'})\n",
    "    y_joint = pd.concat([y_time, y_pretime], axis=1, join='inner')\n",
    "    X_joint = pd.concat([X_joint, y_joint], axis=1, join='outer')\n",
    "    X_joint = X_joint.drop(labels='yield', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X_train_pre_yield, X_test_pre_yield, y_train_pre_yield, y_test_pre_yield = train_test_split(X_joint,\n",
    "                                                    y_time,\n",
    "                                                    test_size=0.3, random_state=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "steps_random_forest = [('imputer', Imputer()),\n",
    "         ('pca', PCA()),\n",
    "         ('rf', ensemble.RandomForestRegressor())]\n",
    "pipe_random_forest = Pipeline(steps_random_forest)\n",
    "\n",
    "# create the grid search\n",
    "params = {\n",
    "    'pca__n_components': range(2, X_train_pre_yield.shape[1]),\n",
    "    'imputer__strategy': ['mean', 'median', 'most_frequent'],\n",
    "    'rf__n_estimators': [5, 10, 20],\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 72 candidates, totalling 216 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done 136 tasks      | elapsed:    8.6s\n",
      "[Parallel(n_jobs=-1)]: Done 216 out of 216 | elapsed:   21.8s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1.7266732423220803"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimator_random_forest = GridSearchCV(pipe_random_forest, param_grid=params, n_jobs=-1, verbose=1)\n",
    "estimator_random_forest.fit(X_train_pre_yield, y_train_pre_yield.values.ravel())\n",
    "\n",
    "y_hat_pre_yield_random_forest = estimator_random_forest.predict(X_test_pre_yield)\n",
    "rmse_pre_yield_random_forest = np.sqrt(mean_squared_error(y_test_pre_yield, y_hat_pre_yield_random_forest))\n",
    "rmse_pre_yield_random_forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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j84tqvBe/ANPV+Lx6HD4f/tQ594sigz0Vb+B8GTjZzG7Bz3N+BX6U9XB8mX4C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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e3e2c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16, 4))\n",
    "err = y_test_pre_yield.values.ravel() - y_hat_pre_yield_random_forest\n",
    "\n",
    "ax.plot_date(y_test_pre_yield.index, err, c='r', ms=3)\n",
    "ax.set_title('9 Features + Previous Yield + Random Forest Regression: residuals on test data (each)', fontsize=20)\n",
    "ax.set_ylabel('error')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-danger\">\n",
    "Classifier.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.        ,  0.00091682,  0.01788412, ...,  0.00739163,\n",
       "         0.27372533,  0.00662224],\n",
       "       [ 0.        ,  0.00092281,  0.01775545, ...,  0.00761486,\n",
       "         0.27220775,  0.0069001 ],\n",
       "       [ 0.        ,  0.00081023,  0.01875063, ...,  0.00745248,\n",
       "         0.27227188,  0.00675496],\n",
       "       ..., \n",
       "       [ 0.        ,  0.00078708,  0.03060067, ...,  0.00748404,\n",
       "         0.15971649,  0.00572088],\n",
       "       [ 0.        ,  0.00081195,  0.02876198, ...,  0.00978416,\n",
       "         0.1629566 ,  0.00888447],\n",
       "       [ 0.        ,  0.00084619,  0.02856601, ...,  0.01075866,\n",
       "         0.14647318,  0.00986602]])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_time = microclimate_train.copy()\n",
    "y_time = labels.copy()\n",
    "\n",
    "X_time = X_time.drop(labels=X_time.index[0:4], axis=0).copy()\n",
    "y_time = y_time.drop(labels=y_time.index[0:4], axis=0).copy()\n",
    "\n",
    "for timestamp in y_time.index:\n",
    "    if y_time.loc[timestamp]['yield'] > 0:\n",
    "        y_time.loc[timestamp]['yield'] = 1\n",
    "\n",
    "X_time.fillna(method='pad')\n",
    "        \n",
    "for timestamp in X_time.index:\n",
    "    if np.isnan(X_time.loc[timestamp]['percip_mm']):\n",
    "        X_time = X_time.drop(labels=timestamp, axis=0).copy()\n",
    "        y_time = y_time.drop(labels=timestamp, axis=0).copy()\n",
    "    elif np.isnan(X_time.loc[timestamp]['gusts_ms']):\n",
    "        X_time = X_time.drop(labels=timestamp, axis=0).copy()\n",
    "        y_time = y_time.drop(labels=timestamp, axis=0).copy()\n",
    "    elif np.isnan(X_time.loc[timestamp]['leafwet460_min']):\n",
    "        X_time.loc[timestamp]['leafwet460_min'] = 0      \n",
    "\n",
    "normalize(X_time, copy=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_time,\n",
    "                                                    y_time,\n",
    "                                                    test_size=0.3, random_state=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Shauro/anaconda/lib/python2.7/site-packages/sklearn/utils/validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test size = 1734\n",
      "right_predict_count = 1638\n",
      "in right_predict_count: zero_to_zero = 1318\n",
      "in right_predict_count: one_to_one = 320\n",
      "wrong_predict_count = 96\n",
      "in wrong_predict_count: zero_to_one = 43\n",
      "0.0315944158707\n",
      "in wrong_predict_count: one_to_zero = 53\n",
      "0.142091152815\n",
      "0.14745308311\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.94463667820069208"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimator = ensemble.AdaBoostClassifier(n_estimators=40,learning_rate=1)\n",
    "#estimator = svm.SVC(C=1.0, kernel='poly', degree=30, gamma='auto', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape=None, random_state=None)\n",
    "estimator.fit(X_train, y_train)\n",
    "y_hat = estimator.predict(X_test)\n",
    "\n",
    "wrong_predict_count = 0\n",
    "zero_to_one = 0\n",
    "one_to_zero = 0\n",
    "one_to_one = 0\n",
    "zero_to_zero = 0\n",
    "for i in xrange(y_test.shape[0]):\n",
    "    if y_test.values[i] != y_hat[i]:\n",
    "        wrong_predict_count += 1\n",
    "        if y_test.values[i] == 0:\n",
    "            zero_to_one += 1\n",
    "        else:\n",
    "            one_to_zero += 1\n",
    "    else:\n",
    "        if y_test.values[i] == 0:\n",
    "            zero_to_zero += 1\n",
    "        else:\n",
    "            one_to_one += 1\n",
    "            \n",
    "print('test size =', y_test.shape[0])\n",
    "# print('non-zeron in y_hat =', np.count_nonzero(y_hat))\n",
    "print('right_predict_count =', y_test.shape[0] - wrong_predict_count)\n",
    "print('in right_predict_count: zero_to_zero =', zero_to_zero)\n",
    "print('in right_predict_count: one_to_one =', one_to_one)\n",
    "print('wrong_predict_count =', wrong_predict_count)\n",
    "print('in wrong_predict_count: zero_to_one =', zero_to_one)\n",
    "print(1.0*zero_to_one/(zero_to_one+zero_to_zero))\n",
    "print('in wrong_predict_count: one_to_zero =', one_to_zero)\n",
    "print(1.0*one_to_zero/(one_to_one+one_to_zero))\n",
    "\n",
    "print(1.0*55/373)\n",
    "\n",
    "estimator.score(X_test, y_test)\n",
    "# estimator.staged_score(X_test, y_test)\n",
    "# rmse = np.sqrt(mean_squared_error(y_test, y_hat))\n",
    "# rmse\n",
    "\n",
    "# X_joint = pd.concat([X_train, y_train], axis=1, join='outer')\n",
    "# X_joint.head(n=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-danger\">\n",
    "Regression.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.        ,  0.00091682,  0.01788412, ...,  0.00739163,\n",
       "         0.27372533,  0.00662224],\n",
       "       [ 0.        ,  0.00092281,  0.01775545, ...,  0.00761486,\n",
       "         0.27220775,  0.0069001 ],\n",
       "       [ 0.        ,  0.00081023,  0.01875063, ...,  0.00745248,\n",
       "         0.27227188,  0.00675496],\n",
       "       ..., \n",
       "       [ 0.        ,  0.00078708,  0.03060067, ...,  0.00748404,\n",
       "         0.15971649,  0.00572088],\n",
       "       [ 0.        ,  0.00081195,  0.02876198, ...,  0.00978416,\n",
       "         0.1629566 ,  0.00888447],\n",
       "       [ 0.        ,  0.00084619,  0.02856601, ...,  0.01075866,\n",
       "         0.14647318,  0.00986602]])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_time = microclimate_train.copy()\n",
    "y_time = labels.copy()\n",
    "\n",
    "X_time = X_time.drop(labels=X_time.index[0:4], axis=0).copy()\n",
    "y_time = y_time.drop(labels=y_time.index[0:4], axis=0).copy()\n",
    "        \n",
    "X_time.fillna(method='pad')\n",
    "        \n",
    "for timestamp in X_time.index:\n",
    "    if np.isnan(X_time.loc[timestamp]['percip_mm']):\n",
    "        X_time = X_time.drop(labels=timestamp, axis=0).copy()\n",
    "        y_time = y_time.drop(labels=timestamp, axis=0).copy()\n",
    "    elif np.isnan(X_time.loc[timestamp]['gusts_ms']):\n",
    "        X_time = X_time.drop(labels=timestamp, axis=0).copy()\n",
    "        y_time = y_time.drop(labels=timestamp, axis=0).copy()\n",
    "    elif np.isnan(X_time.loc[timestamp]['leafwet460_min']):\n",
    "        X_time.loc[timestamp]['leafwet460_min'] = 0      \n",
    "\n",
    "normalize(X_time, copy=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_time,\n",
    "                                                    y_time,\n",
    "                                                    test_size=0.3, random_state=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Shauro/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:2: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    }
   ],
   "source": [
    "estimator = ensemble.RandomForestRegressor()\n",
    "estimator.fit(X_train, y_train)\n",
    "y_hat_regression = estimator.predict(X_test)\n",
    "\n",
    "\n",
    "# steps_random_forest = [('imputer', Imputer()),\n",
    "#          ('pca', PCA()),\n",
    "#          ('rf', ensemble.RandomForestRegressor())]\n",
    "# pipe_random_forest = Pipeline(steps_random_forest)\n",
    "\n",
    "# # create the grid search\n",
    "# params = {\n",
    "#     'pca__n_components': range(2, X_train.shape[1]),\n",
    "#     'imputer__strategy': ['mean', 'median', 'most_frequent'],\n",
    "#     'rf__n_estimators': [5, 10, 20]\n",
    "# }\n",
    "# estimator = GridSearchCV(pipe_random_forest, param_grid=params, n_jobs=-1, verbose=1)\n",
    "# estimator.fit(X_train, y_train.values.ravel())\n",
    "\n",
    "# y_hat_regression = estimator.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0701903248284124"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for i in xrange(y_hat.shape[0]):\n",
    "#     if y_hat[i] == 1:\n",
    "#         y_hat[i] = y_hat_regression[i]\n",
    "        \n",
    "# rmse = np.sqrt(mean_squared_error(y_test, y_hat))\n",
    "# rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Unknown label type: array([[ 0.],\n       [ 0.],\n       [ 0.],\n       ..., \n       [ 0.],\n       [ 0.],\n       [ 0.]])",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-80-14807bee2564>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mestimator\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mensemble\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAdaBoostClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_estimators\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m40\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlearning_rate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m#estimator = svm.SVC(C=1.0, kernel='poly', degree=30, gamma='auto', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape=None, random_state=None)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0my_hat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/Shauro/anaconda/lib/python2.7/site-packages/sklearn/ensemble/weight_boosting.pyc\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    403\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    404\u001b[0m         \u001b[0;31m# Fit\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 405\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mAdaBoostClassifier\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    406\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    407\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_validate_estimator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/Shauro/anaconda/lib/python2.7/site-packages/sklearn/ensemble/weight_boosting.pyc\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    138\u001b[0m                 \u001b[0miboost\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    139\u001b[0m                 \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 140\u001b[0;31m                 sample_weight)\n\u001b[0m\u001b[1;32m    141\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    142\u001b[0m             \u001b[0;31m# Early termination\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/Shauro/anaconda/lib/python2.7/site-packages/sklearn/ensemble/weight_boosting.pyc\u001b[0m in \u001b[0;36m_boost\u001b[0;34m(self, iboost, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    460\u001b[0m         \"\"\"\n\u001b[1;32m    461\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0malgorithm\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'SAMME.R'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 462\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_boost_real\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miboost\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    463\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    464\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# elif self.algorithm == \"SAMME\":\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/Shauro/anaconda/lib/python2.7/site-packages/sklearn/ensemble/weight_boosting.pyc\u001b[0m in \u001b[0;36m_boost_real\u001b[0;34m(self, iboost, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    474\u001b[0m             \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    475\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 476\u001b[0;31m         \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    477\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    478\u001b[0m         \u001b[0my_predict_proba\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict_proba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/Shauro/anaconda/lib/python2.7/site-packages/sklearn/tree/tree.pyc\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[1;32m    175\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    176\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_classification\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 177\u001b[0;31m             \u001b[0mcheck_classification_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    178\u001b[0m             \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    179\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/Shauro/anaconda/lib/python2.7/site-packages/sklearn/utils/multiclass.pyc\u001b[0m in \u001b[0;36mcheck_classification_targets\u001b[0;34m(y)\u001b[0m\n\u001b[1;32m    171\u001b[0m     if y_type not in ['binary', 'multiclass', 'multiclass-multioutput', \n\u001b[1;32m    172\u001b[0m             'multilabel-indicator', 'multilabel-sequences']:\n\u001b[0;32m--> 173\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Unknown label type: %r\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    174\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    175\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Unknown label type: array([[ 0.],\n       [ 0.],\n       [ 0.],\n       ..., \n       [ 0.],\n       [ 0.],\n       [ 0.]])"
     ]
    }
   ],
   "source": [
    "estimator = ensemble.AdaBoostClassifier(n_estimators=40,learning_rate=1)\n",
    "#estimator = svm.SVC(C=1.0, kernel='poly', degree=30, gamma='auto', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape=None, random_state=None)\n",
    "estimator.fit(X_train, y_train.values.ravel())\n",
    "y_hat = estimator.predict(X_test)\n",
    "\n",
    "wrong_predict_count = 0\n",
    "zero_to_one = 0\n",
    "one_to_zero = 0\n",
    "one_to_one = 0\n",
    "zero_to_zero = 0\n",
    "for i in xrange(y_test.shape[0]):\n",
    "    if y_test.values[i] != y_hat[i]:\n",
    "        wrong_predict_count += 1\n",
    "        if y_test.values[i] == 0:\n",
    "            zero_to_one += 1\n",
    "        else:\n",
    "            one_to_zero += 1\n",
    "    else:\n",
    "        if y_test.values[i] == 0:\n",
    "            zero_to_zero += 1\n",
    "        else:\n",
    "            one_to_one += 1\n",
    "            \n",
    "print('test size =', y_test.shape[0])\n",
    "# print('non-zeron in y_hat =', np.count_nonzero(y_hat))\n",
    "print('right_predict_count =', y_test.shape[0] - wrong_predict_count)\n",
    "print('in right_predict_count: zero_to_zero =', zero_to_zero)\n",
    "print('in right_predict_count: one_to_one =', one_to_one)\n",
    "print('wrong_predict_count =', wrong_predict_count)\n",
    "print('in wrong_predict_count: zero_to_one =', zero_to_one)\n",
    "print(1.0*zero_to_one/(zero_to_one+zero_to_zero))\n",
    "print('in wrong_predict_count: one_to_zero =', one_to_zero)\n",
    "print(1.0*one_to_zero/(one_to_one+one_to_zero))\n",
    "\n",
    "print(1.0*55/373)\n",
    "\n",
    "estimator.score(X_test, y_test)\n",
    "# estimator.staged_score(X_test, y_test)\n",
    "# rmse = np.sqrt(mean_squared_error(y_test, y_hat))\n",
    "# rmse\n",
    "\n",
    "# X_joint = pd.concat([X_train, y_train], axis=1, join='outer')\n",
    "# X_joint.head(n=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test size = 1734\n",
      "right_predict_count = 1571\n",
      "in right_predict_count: zero_to_zero = 1234\n",
      "in right_predict_count: one_to_one = 337\n",
      "wrong_predict_count = 163\n",
      "in wrong_predict_count: zero_to_one = 127\n",
      "0.0933137398971\n",
      "in wrong_predict_count: one_to_zero = 36\n",
      "0.0965147453083\n",
      "loss = 2.22166143322\n"
     ]
    }
   ],
   "source": [
    "y_classifier = np.copy(y_hat)\n",
    "y_regression = np.copy(y_hat_regression)\n",
    "\n",
    "predict_total = y_test.copy()\n",
    "\n",
    "for i in xrange(y_regression.shape[0]):\n",
    "    if abs(y_regression[i]) < 0.01 :\n",
    "        y_regression[i] = 0\n",
    "# print(y_regression)\n",
    "\n",
    "predict_total['y_classider'] = y_classifier\n",
    "predict_total['y_regression'] = y_regression\n",
    "\n",
    "# for i in xrange(y_regression.shape[0]):\n",
    "#     predict_total.append((y_classifier[i], y_regression[i], y_test.values[i]))\n",
    "# print(predict_total)\n",
    "\n",
    "wrong_predict_count = 0\n",
    "zero_to_one = 0\n",
    "one_to_zero = 0\n",
    "one_to_one = 0\n",
    "zero_to_zero = 0\n",
    "\n",
    "for i in xrange(y_test.shape[0]):\n",
    "    if y_test.values[i] == 0:\n",
    "        if y_regression[i] == 0:\n",
    "            zero_to_zero += 1\n",
    "        else:\n",
    "            wrong_predict_count += 1\n",
    "            zero_to_one += 1\n",
    "    else:\n",
    "        if y_regression[i] == 0:\n",
    "            wrong_predict_count += 1\n",
    "            one_to_zero += 1\n",
    "        else:\n",
    "            one_to_one += 1\n",
    "            \n",
    "print('test size =', y_test.shape[0])\n",
    "# print('non-zeron in y_hat =', np.count_nonzero(y_hat))\n",
    "print('right_predict_count =', y_test.shape[0] - wrong_predict_count)\n",
    "print('in right_predict_count: zero_to_zero =', zero_to_zero)\n",
    "print('in right_predict_count: one_to_one =', one_to_one)\n",
    "print('wrong_predict_count =', wrong_predict_count)\n",
    "print('in wrong_predict_count: zero_to_one =', zero_to_one)\n",
    "print(1.0*zero_to_one/(zero_to_one+zero_to_zero))\n",
    "print('in wrong_predict_count: one_to_zero =', one_to_zero)\n",
    "print(1.0*one_to_zero/(one_to_one+one_to_zero))\n",
    "\n",
    "rmse = np.sqrt(mean_squared_error(y_test, y_regression))\n",
    "print('loss =', rmse)\n",
    "        \n",
    "predict_total.head(n=1734)\n",
    "\n",
    "predict_total.to_csv(\"./three_combine.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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Jewx4S67tmJ/LbQ6+69J+DO6uuTnHfu7N/sA5t83MesM2Iq8GmvEFpaRtLQf+Keuz6J26\nZTn2fTu+u9xU4A9DhQvfEgS++1e2DP5cvyRhWT7fdc51DLFOdB52Ow7n3Atm9n/4tPhKfOtx3Irs\n75jZXsBr8Nfjpwq8Hn8EvBu418x+GsJyZ5FpqlCHAdnTej0PzHTO3Z/1eSl5y+vD7zuzV8Y/4MiX\n/+0Wn8Ex4fe0HHlB9M7sUfjK+jJ8l+/Pm3/PsDuE548ulLxifoQfvOTPZnYt/nWGu93gbuWJQjfB\nl+HfiXssYZXbw+/sax18N9gkT7IrDgfk2H4h8sWpA16eI05fxa789Vb8A5rV+IEMX47vonkHvjW/\nkNdmovzi9zmWL8d3wx+2Mt3bSk4XQZSn/sE5tyFh+e34rrlJaaPQfHs4jsFXQGbnuK5HA4eY2YRw\nzD/Bj/nxKzP7b3yauMs5V+irRoWYiq/g3Z2wbHmuL5nZKfjXMabhH7DFy+8O/1Dy+UIDYX6KvM/g\nXx2YhG8RjW+v0LLR/uH32oRlb8Q/LLQc119z+J1dbismbNE1ntjNOYfnc5Q3o89ypb+1+O78kjKq\nzEpRnHM7zezd+CfaH8RXPLbiC1ans+sdm2eL3PRQo+9FGear2L2QGrfnwAbNXoEv5EzA36Rvwncj\n6ce/y3UmgzPJSno6x+fRcZ1M7tF9HbHjwrc2nI/v5hq929tnZtcBn3bOPVdAeFbhu11m+xS+C2z2\n+zKOXe8uFmqgkmVmB+LfCboI/77c8c657IFvluC7LGXw779k8GkL/BPrQ3LsJ9fUGDsYXBneJ/xO\neu8Wks/RPvhjfyphGeFzw1eGsq3PESaXZxn4roLlFh17vuOA3Y+j3zmXVECaGH4fRP7rceDdaOfc\ndWa2BZ93fBhfMDMzWwHMd87dnmMbpbjNhRG5wzt9Z+DfTfulmc3IukaKzlvIk5acczvMLKlgB/7c\n58sLDN/Smc9eYT/rw7tiC/EPCU4O3+81syvwrZHRO3zzgEfwA7XMxw9es8PMbgT+fYhKZKlpBwq/\nNodrqDjdbaCZGMeuOO03s1Z8OngPvtXJgA1m9n18r40tebZVSh5TtDLe24aTLiD9aSPKp/Jd19H5\n3+ic+4OZvQUfD2fg49HM7EF8D4hryxCmvYGnEh44QY70EQYmugxfWb0VP0ZBXwh7NL5AwWUZ8/Nt\n34JP27fhe81sxD+Em4bPTwrdXl/43ZywLMpb3xR+kgwq35QQtihtZQoML+RPe5A7/Y1j1/FKiqgy\nK0ULBaTLws8AM9sDPyjBc865v5d5t1Hh/zrn3PsL/M55+JvtB51zP44vMD967ZwiwxC1tuS6bvKN\nRJt044JdxzXXOfetQgIRWmAXAgvN7CX4bo3/jD+ef8B34xpqG/eR0GpiZmeTMK3JcDnnnsVPOr4/\ncC6+En5+bL8H49+Xug84PruVObTYDlcU1wflWH5wwmfr8TfVpGXgK9i5KqdpEoXvYHzhNdshWesV\nur0Vzrlj8q4Z45y7EbjR/OTzx+ALJh/FVzJf75xLCtuwOD/qepeZNeO7EX4DPwhTpJS8JXqwcxC+\ny+wA89OU7UdySwXkzwsc/p3phwoJhHNuDf7dZczs1fhWuo/j8weHf3iEc24n/ti/bmYH4AeumY0v\nCB9pZlNiFd+kcEH+ayC+Xi0MFadtzrnfFLQhn14+CXwyVBpb8Q9ePoGvPGYPBJa9PygujynlvlKW\ne9sw0wWkP21sAF50zuUK326cc3cD7w6Dwk3HD9b2ceAaM3vaOfe7MoTpADOzhArtbuEM+cmF+Mra\n1OyH1Wb21hLCcAG+QvjmcLzx7X0eny8XKmq42D9hWXTeL3POnZ+wvBxhiyqmFe1lF87DBKCgvFmq\nS1PzSDl9AP/OyzUV2Paf8U/njjWzQtPtK8Lv/0lY1kpyAaifHK3E4cazAV9hHMTMmsg9EmE+UdfU\nt5TwXZxza5xz1+BHyn0MaLWE6QZSpBP/dPkToSIeeQU+3m9OqMi+DDi0DPt+AN/SO83M9kxYPpPd\n00RU4W/Nsc1Z4XdSt+E0uQ8fv63ZC0LL5evw774VWolaH9Z9rSVMc1PA9/ucc8ucc/+Ob/1qxo9u\nGYkK0OVsvbsCPxL1GWYWnxqqlLwlShdvTlh2PKXdW/+AP0elFE5xzj3gnPsGu+LxtBzrPeecu945\ndwa+Ve+V+O6iuba7Dt8S9FLzI69ni66BpG71tTbcOP2rc+67+OumjxxxGhPlA0npAnwek72PUu4r\npdzb8io2XQR/wXfjnRpePchW6fxxqHziD0CLmR1R7Iadc9udc3c75y7E9yYZxeCR5kvNo1bhH1wk\ndTdPmvblIHwl6o6EiuwEkrtwDxW2VwDPZlcWg9Yc38nlT/hr7MiEZf+LT4vFlG+KDVt0jZdluq88\nouP7Y961pCZUmZWiJVWWwpD2UTeYS8u9z/C+0jfwN/z/CK3A2WE4xMziGerj4Xdr1npt+G5VSZ4H\nRplZrqd89+Dfv2rN+nwhxb/riHPuf/HvzrwvV+ujmb0utGhiZi1m9pqE1Sbgu+psJ2vakTQJ7yVd\nhq+8xN+heTz8fku8QhHSWhdlyKucc9vwI+/uS1a3s9Bd8/8lfO13+PcTW83s1KzvvB/fuviAcy77\nfdm0WYpPF/+WUCG5GN/N7gdDtMJk+xq+29X3kiq0ZrZfyBei/9+ao7IYtUbEu29GXZtfSpmEY1uI\nL/hcHPu8lLzl6rCdz8fzw/Dd7GkgCvVdfKVmsZm9ISEMo8zsbbH/X2N+qqlsUXxuDus1m59eKnt7\nY9jVDTNf11nwo4A34aeuGHjYF14f+By+wHrVENsYkpm93MxeVcRDhaFcj89bPmE55qg0s2PNTz2E\n+TlxX5aw2kR89/9c7+0D4Jx7Av/KzRFm9rGs/byH3O/LFntfeTz8HrT+EPe2QcqRLsKrItfg89RB\nvXlCBfLj+MruDwsJUwmi6bpy5RNfw1+n3wm9fwYxsz0tNq9tSAtJ3Wtz5VGWZ9+5XBW+d3GU7sK+\nD8B3b85+EPEUPg5nmJ+OK1p/DD7fSnq/c6h4eRxfyc9+V/Ucdj2AKNTvQph3S0vOuafx7yEfY2bn\nJ13X5udrjoez2LDdgH/X9XQze2/C9svVYhsdXzlfh5EyUTdjKcUtZtYH3I9v0TgKaMff6N8dMrBK\n6MTPVfYx4FQzux0/oM5B+O7Nx+EHDXgwrH8FvrvVDebfJ30a/27JScC1DO5qGLkNP4XCz83sJvzT\n+MdC6yfAV/DdeLvN7Cf4roTHs2uO2FJaWN+Pfw/mKjM7F1+wWYcvxLweH78z8DfPlwIrzA/Ysxrf\nxXEf/Jy0LcBXnXNpf6fjG/gn3R8ys0udc4865zLmB9x4D7DKzG7FH9dJ+DS2mqxBIkp0Pv6G+OlQ\ngb0TH8/vw88JOajC6pxzZvYh/HyTPzOzG/Atkkfh571bR/Hd1Yd6P7zsnHN/M//e1deBP5of7OU5\nfEvAm/DXcuLk8nm2+W0zm4Z/x/NtZnYzfgC4ifj39t6CfxDxifCVK4CDzOwOfIFlB74bXyt+PtX4\n+2i34bt6XmVmP8OP1vmCc+6/ijvy3cJ8rZl9DphlZjOdc9GAWEXlLc65283se/ju/X8OYdyBTz+9\n+HcmkwaBynnunXPPmdkZ+Hk67wnXwAP4guJLQxj2wr9zB74F9hIzuws/Im0vPh86Fd8yE70Gsid+\nPtVH8K2nT+AfJp2Ef0/4Z27oOT0vDfs7Hfg/84PQRfPMHoB/P/eeIbZRiN8R5pll12BpJQsDCp2O\nfwf/JjO7E9+y0oeP0xn4Xh8t+ArANOBaM7sH3+r4FH76oVPxlflCHtR+DD8I2DfM7J34lqsj8K26\nv8DnG9mKva+Ucm/LVo50Ab7L85vxD8rehJ9y5kB82hiPn6JoTZ7vl8w5t8HMVuLntF6Kvw6iaZ/+\n7Jy7JVzvFwGPhHT7GP46OhR4G75yEp2TBfgHqr8P623Gx+s78fllfNDE2/HX5pfDQ7t1wE6XPEdp\nPMxLzex9+CmqVpvZL/Dx/h58S+ahWevvND+exKdi6++Bv4/tjY/vt2Z9J1e8XO/8fLSX49Pb3eFe\nsAE/WNMx+Pxnt0phnuNZa2bL8D1booG04j6Kb239InBWyP+juWNfDbwBn1aiuXqLClu4xs/AX+PX\nhrDcg3/Q+mp82hzP8J2Mbyz4ZRm2JeXmUjCksn7q6wefqa7A3/z78C1X/0mR02iEbT2GLwS+tIjv\nnImv/D2H7zb6JD5D/0x2GPAFwNvwFcF1Yb12fGbZjx94Jr5+E75l5a/4p6FJc76dGo6/D58pL8W/\nrxG1fmVPzdMPXDnEMe2Fv5GuxGfem0K8/gI/ZUtzWG9f/Dslt4Xj7sO/S3MbCUPjl3A+ngB+M8xt\nfHioY8a/N9sP/Dj22Tj8De9h/BPwx4H/wFdqf49/9ym+jcRzGFv+JAlTZOArKN/FVzg24wtys/Nt\nD1+4Wxri+sXw+/tkzdka1s05ZUMsbmYnLMt7PKXEc8J3TsJXzKNr96EQ5xMS1t0tznNs8134G/wz\nIW568L0NFhKbEgPf8n1N2OcG/PX4p7DebnNa4vOZB0I4+8maLiZHWKI4zJmG8ZWKfhKm/6CIvCWs\n/0l8pacvrPt1fC+JzcA9hZ77rPUOxT/wia6DtSEergLaY+sdhZ8X954Q91vw81T+GHhjbL0x+ApH\nN/6a2oKv/NwZwtSUtf9c180e+DxqddjGevzoq0lzO+bN93KlLUqbZ3bIawZfWb0khH1TSH8PAT/F\nV/wsrPcS/PVwR0jHffgu1r8gTN9TyDHiR56+Dn+dbQzbOylfGqCI+0pYv9h726A4LzZdDBG/++Ar\n+g+H8D8ftjuz2HOWK/3l2ffh4fw8F+Jpt/jFV2iuxT/83Yq/Xu7FT9v1+th6J+F7IfwZf91twF97\nXwNekiO/uA9/vfcnpekcYR6D7x301xBff8U/UGsmIf/C90z6VAjXZvz956oh0kfeeMHn23fjr+Po\nfB2XL43mOZ7T8ZXlD+c53o+HtLU2pLXH8PPMfpzd5/AtOmz4Bz/fxOeBffh3ee8Czis0fZHj3o0v\nd/UBPy00TvRT3Z8oA68ZM/suPuE+45x7XfisEz/yafRi+QLnXDHDbouIiFRd6B73Z2Cpc+5DtQ6P\niEglmX+3+8/ABufcG4dav96Y2SfxvSeOdeXpgSJlloZ3Zq8ieUqSrznnpoUfVWRFRCQ1zGy3EWvN\nDyx2Ob77YdLgPCIiDcX58QjOA95gfurGhmF+5P3PAD9RRTa9av7OrHPujhwDLlT9vTIREZECfToM\nOPJb/LuVBwMn4t/5/KVz7ue1DJyISLU4534ZWjDL8X5qmhwG/Be++7mkVM27GcPA1Bu/zOpmfBa+\nv/xK4FPOTwUhIiJSc2b2dvw7s0fjB73agX8P84fAElfcyNAiIiJSgrRWZluA55xzzsy+ABzinMs3\nUbmIiIiIiIiMIDXvZpzEOdcb+/fb5BgK28xqXxMXERERERGRinHOJb6CmoYBoMC/HxufiD0+ufXp\n+DkQE9V6OOh6++ns7Kx5GPSjc9RoPzpn9fejc1Z/Pzpn6f/ROaq/H52z+vsZiecsn5q3zJrZNUAr\nsL+ZPYGfa2tmmIR6J37+s3NK2XZPJsOSjg4A5nV1MWny5HIEWURERERERGqs5pVZ59zshI+vKse2\nl3R0cGF3NwCLOzq45MYby7FZERERERERqbG0dDOWKmltba11EGQIOkf1R+es/uic1R+ds/TTOao/\nOmf1R+dssFSMZlwqM3P5wq9uxiIiIiIiIvXLzHA5BoBq6MqsiIiIiIiI1K98lVl1MxYREREREZG6\no8qsiIiIiIiI1B1VZkVERERERKTuqDIrIiIiIiIidUeVWREREREREak7qsyKiIjIiNGTyTC/vZ35\n7e30ZDK1Do6IiAyDpuYRERGREWN+ezsXdncDsLitjUtuvLHGIRIRkXw0NY+IiIiIiIg0FLXMioiI\nyIjRk8mwpKMDgHldXUyaPLnGIRIRkXzytcyqMisiIiIiIiKppG7GIiIiIiIi0lBUmRUREREREZG6\no8qsiIiIiIiI1B1VZkVERERERKTuqDIrIiIiIiIidUeVWREREREREak7Na/Mmtl3zewZM/tT7LP9\nzOxmM3s5fnqUAAAgAElEQVTIzH5jZvvUMowiIiIiIiKSLjWvzAJXASdnfXY+cKtz7lXA7cD8qodK\nREREREREUqvmlVnn3B3A2qyPTwV+EP7+AXBaVQMlIiIiIiIiqVbzymwOBzrnngFwzj0NHFjj8IiI\niIiIiEiKpLUym83VOgAiIiIiIiKSHqNrHYAcnjGzg5xzz5jZwcCzuVZcuHDhwN+tra20trZWPnRV\n1pPJsKSjA4AzFi3ius5OAOZ1dTFp8uRaBk1ERERERKRsli9fzvLlywta15yrfaOnmR0K/NI599rw\n/6XAC865S83ss8B+zrnzE77n0hD+Spvf3s6F3d0AzG5p4ZreXgAWt7VxyY031jJoIiIiIiIiFWNm\nOOcsaVnNuxmb2TXAXcArzewJM/tn4EvA283sIeCE8L+IiIiIiIgIkJKW2VKNlJZZdTMWEREREZGR\nKF/LrCqzIiIiIiIikkqp7mYsIiIiIiIiUixVZmVE6clkmN/ezvz2dnoymYbbn4iIiIjISKHKrKRe\nOSuESzo6uLC7mwu7uwfeQ66kau9PRETqmx6CiogUTpVZKVm1briqEIqIyEihe56ISOFG1zoAUr+i\nGy7A4o6Oupjzdl5XF4tD4WBeV9fA5/ERo8s5SnSu/YmIiIiIyPBoNGMp2fz29l2V2ba2ilVmK1XR\njKvWsYiIiORTjXueiEg9yTeasVpmpWTVanWcNHmyKpciIjIi6J4nIlI4tcyKoCfhIiIiIiJplK9l\nVpVZERERERERSaV8lVmNZiwiIiIiIiJ1R5VZERERERERqTuqzI5AmpBdRERERETqnSqzI9BQE7Kr\nsisiIvnoPiEiImmgyuwIUGyh4+Izz6S/u5v+7m4uPvPMKoSweqpZAFNhT0SqoRZ5zVAPRUVERKpB\n88yOAFGhA2BxR8eQ88Nm7r+fa8Lfs++/v1rBrLieTIa5U6dyRG8v5+DjpZJz+cXjffbUqVxx332a\n8kdEyi47j9ccpfVP08WJSC7KHwZTZXYEGmpC9kNf+1q4/fZdfzeIJR0dXNPbC0An0FTFfR/R21vx\nyrOISLUM9VBUhkcPKEQkLl6B3djXx2XLlgHKH0CV2RGh2ELHeVdfPbD+eQ1aSHmkpYUrKnxs87q6\nmB1rCf5ORfcmIiNVLSqWQz0UFRGR8hnU26+lpcahSRdzztU6DCUzM1fP4ZfqqkW3DHUFERGRYune\nISJx89vbByqzC2bNorm5GRg5+YOZ4ZyzxGVprgya2ePAemAnsN0598as5TWtzOpmozgQEREREamk\nkV7erufK7N+ANzjn1uZYXtPKbPwpyeK2thHZ5aoacTDSL2ARERERkZEqX2U27VPzGOkPo1SYpoAo\nL00ZJCIiIiKNIO0VRQfcYmYrzOzsWgcm27yuLha3tbG4rW3EjuaYKw7SXGGqRtjSfPx6OCCSXmnO\nO8qpXMc5UuJLRESSpX004+Odc0+ZWQu+UvsX59wd8RUWLlw48Hdrayutra1VC5xGc8wdB+WcVqDc\nI3VWY8oDTasgIqWI8o4eYO7UqRw5Y0ZDvl5RrjxSea2ISONZvnw5y5cvL2jdVFdmnXNPhd+9ZnY9\n8EYgZ2W2kfVkMlw2Zw6Pr17N5ClTWLB0acMVbnLRQ4Py0vyQIul3Jfh5sbu7VUkbgTRWhIiMZNkN\nlIsWLcq5bmoHgDKz8cAo59wmM9sTuBlY5Jy7ObbOiJmaJz7QUifQlPIBp9J8I65G2NJ8/CKSXlHe\n8eCKFb4yS2MOMFiuPLJR81oNMClp1ajXnAyWtvOcbwCoNLfMHgRcb2YOH84fxSuyUrhaJMg0t6ZW\nI2xpPn4RSa8o7+jJZBq6B0W58kjltSLVpa79I0M9nefUVmadc48Br691ONJiXlcXC+LdjIso3NRT\nghQREVXSRjq9DiLSuNLW6lnvUtvNuBCV7mbcKIlN3ZVEREREZLgapWxcS/VQLk/bea7XbsY1F2/R\nnD11Klfcd1/NT2axejIZNvb1MbulhUOOOIJRfX3Mb2/njEWLuK6zE0hHIs2WtotIqkPnXUTKSXmK\nSHmp10jh6jn/qafzrJbZPOpt0KUk8WOY3dIyMKBI/O80PhWq9lOres5wGkk9PK0UkfqhPEVEaiVX\n/qMyZ/HytcyOqnZg6sm8ri5mt7TQCZxT68BIRUWt8Bd2dw9kMCNNTybD/PZ25re305PJ1Do4IiIV\nE8/vVq1cqbxPRKomavW85MYbi6rIqpyWTC2zQ6j3pyfx8Me7Fqub8WB6ep+OOKj36008nUdJi1xp\nMVevpZGa/4tI6XLlM+W+F6ahnFYremd2GOqpz3iS7PBPy/F3NRVyccenp6hGofiMRYuYvWIFABfk\nmZhZdlfOc1Tv15t4GkFd0iIpT+nJZHgw5PciIsOV656nMk11qDJbBxqtlaOYgm45C8X54vG6zs5d\nT+Y7O2tW0a+lUqeCUMWlsTVa/iMjV5SWH1yxgq/09tIJPNLSwgXd3SyO9VQSERmufPfOUu+rmrIr\nmSqzdeCyOXO4+PbbAVgwZw6X33ZbjUNUHeV+eq5KV356gihJSrludMMtjR4cVFaUljuBScAiYPGM\nGUybPj1VDzCVDkTqS9I9L9+9s9TyqMppyVSZrQOPr16d+He9KrSgu6SjY9DT8ysqWChW4bt0ijvJ\nphtuaWrxwK1eK07DCfc5+Pdkj5wxI5V5ViNMCygyklTqnpeUz9Vrnl1Rzrm6/fHBr3+ZNWvc+W1t\n7vy2NpdZs2a35Wcde6w7Fdyp4M469tiK7G+oMNTC+W1tbgu4LeDOb2sb9vbSeIwiaafrpnrKneel\ndZ/lUEq4k9LyvStWuNNaWtxpLS3u3hUrKhnkgsWP7bw6Oy8i4mXnN/H/712xoqD7alI+V8s8u5bl\ngVDnS6wPqmU2BYZ6Gr/vuHF8M/y9YNy4orad9AQnaX9p7IJb7hY/tRaJFE/XTfWol0NholdQip02\nLyktX9TWtmv+9bY2rn/22fIFNKaY1pR5XV3MnjqVI3p7OQf4TkVCJCKVlJ3fxEciXrB1K83NzbUK\nWsnSWFeABupm3MjN7s3NzfQAV+K7GfdkMgUfX1oT3lCi87mxr48xZizp6CjbeW3ktCIi9asWDw7q\nsQK9pKNjVwU0zysoPZkMl82Zw+OrVzN5yhQWLF1as/y+mHvxpMmTueK++1jS0cF3qJ/zIlIujV5O\nu/+OO5i6bRvn4POGXPlBUv5cj3l2xeVqsq2HH2LdjOu1q5RzQzfbZ9ascae1tJR0fEnxUg/djKNw\nnxfCXs7zWs9pRURkpCs0Dy+ku261uhnrviNSuFpdL5UsC0fbPq2lxT1ap68RqJtxg6nm3JqTJk/m\nyBkzIDzVLUbSE5xqPP0fKn4a/albWqU53tMcNhFJj3K2TEybPr1iXYvj1JoiI0G938cr2ZsxKnvP\nb29nUthHfHDTVStXclFbGwAXdHczbfr0Qd9PQ9ym9rWjXLXcevgh1jJb7acF1X5qlP30uNzHW+7j\nGWp7Qy2Pjm/uzJnu3Fmzynpe0zjgR7WkuXUgzWEbydLWa0OkUJk1a9y5s2a501pa3NyZMwel37Sm\n60re+0TKIde1E/UiPA/co8O8j9fq+hxOOSTKb941caI7eb/93LmzZuXsbZl0bPEemKe1tOQN29yZ\nM1OZf1USI6FlNrVPC8rkus7OgXeEosndP9LdzZXA3CGG7s9+mgPU/OnOUCp5PrPj8uCuroH4OGPR\nIq4L8ZvWuGkEaXjCKENbtXIlnz3+ePbcto3PkP/dHpFyivKIrVu3st05JowbV3ReMWny5Jzzsqd1\nPIn4XLiLwmdpCp9Irmsn/i57J9A0jH3Uqkw/nB4USzo6uPj22wF//Ituv31Q/OSaZueTJ5zA46tX\n8/zatQXvK3P//bvKsRXIH+qujJarllsPP9Rwap5qPTWK97GPP7E5d9asgt8nzX7SVOh7tOUId773\ngIfaX6XiOF98nDh2bFmeKkbS9vQ/LeHJlQZztaLUg0Zs8Y/nO6eqxbwi0nJNVsJwji37fddy99io\nZk+QYuKhkuNFiJRDrmsn/vlpLS0Nl58NZag8a6hpds4JZdBcZYh4PnLurFkVzR/S2FOOPC2zNa+Q\nDuenlpXZaokS1KNZFa25M2e6UyZOHEhs586aNeQ24pW3R8PFFs9w0laoys4YK1XRTsqAyvVSfhoz\nhDSIZ8TxtFvP8TVUF6F6FD+mE8eOTUW+UA5pyuvieXw587k0yHWvKfS7lazMVjMNFJOvReH6yHHH\nuXdNnNhQD8ekMeS6dm668UZ34tix7sSxY91NN96YuG45rrs05d/x8MydOdN9NFy3Sd2Mh6rMnhrK\n9vn2Uc54zCeNDyZUmU25fIkyO0HFL4S5M2e688KNPt8FkN3alWt05LRVJJIKM/kq7aVKGmGu1Is3\n+32nUyZOHNhmJcKeFsVmrHNnznQfDZn3uyZOHPhO2tJgMRqxMltKa3PaChpJ0pTOklri2vfd153W\n0uI+ctxxA+9G5YvLtMb5+W1tJbUwxu9bhcZBru2kIV6y01sh4UpTGhUpRNI9sJCegbnkuk7Sdm0U\nGp7oeM6dNWsgT7t3xQp3ysSJAw1VucqJ1T7mcr7/XC51W5kF3gE8CDwMfDZhebnjqibyJdL4xXzv\nihU5WxRzJbRCuoNE2ysk4RZaOMi3XrHbOHHs2IEKYSVbh8pR8EkqmJ46xAOHRlBsRptdyD0ltECc\ndeyxJQ96UqmCa6HbbcRuxqUo9023Euc1TYWheD6X1EOkkFbJNB1PXGbNmoJ7EMUNdTyFDJJUyD2t\n2q8LJd27cz04beQWe2lM5a7MFlp+rbV4eKI8KV95Jvv6L6TLcC2OOW3xXJeVWWAU8CjwMmAM8Efg\nyKx1djvYWjyJHe4+i00wSU93in26W8rN1bnc3UOLOaZij3fuzJkDFcJKPCEq57uaSZXZ88DdSf53\nISqtml1SCjk/965YkbPwXur5rVTGW4sMPS0tSqUod3xVIv7TGL9RT5tTQz6X5spsMfFXSA+ibEMd\nT7T8o7E4yt52fBu58pVaFdYKCVu8x1CaCpQiuSQ90B1O99hCy6+1Fn+4Fn94d2qsHBgva2fPM1vI\nyMT5GrYqfVxpied8lVnzy9PHzI4BOp1z7wz/n48/kEtj67hpwF6AAWuB8cA4YALQC7wYW74R2AHs\ng68pPwkcBOwBvGL6dBbfcMPAiF2rVq5k0ckns2nDBjbs2MHm8D3Dj9A2CtjvFa/ghWeewW3axB5A\nP7A+rDMhhHE98KUbb+TkMHfUqpUrWXDCCWzZsIEtIWzbgW34GnuTGWObmujr72eLcwNhfekb3sAl\nP/85kyZP5uzjj2e/u+4CYO1xx/HtO+8ciLdVK1dy3qxZ7Ny4EQfsO2UKhx14IM3NzYNGJLvm6qu5\n5EMf4oDwvR3jxvH9vj6uBFYA/Ycdxpgnn8T193PwtGl8Jez7Hw88cGAEtdktLVzQ3c2Fb387m9et\nowc4JBz/BuBa4KvAA+EY+4HmcE46gJ8DG8z47K9+xcltbaxauZJ/e9vb6Nuyhb2AcRMm8MUwMtxn\njz+eg7dt41FgbNjGGBiIw+j8WkgDa4H9gaamJj7zi18MxH88nhacdBLbN25k+44djAMOA/4c0siY\nsI/ovPaHY4jO68bw/8smTmTRb37DtOnTWbVyJZ8/4QT6NmxgE3D4a1/LlkyGjdu3s3PjRl4OPBvS\n5dgQ1j5gdNifA3rNONg5DHgOmP+DHzB7zhx6Mhk+feqpPHnvvTQBm2JhcyFeozBuCcdOiIf9wjo3\nhc/eNXo0R73lLSxYurSgEepWrVzJ/BNOYP2GDYzFp8engRb8tdAX4syAsWYc/trXsvGpp5g8ZUrO\nfcxvb+fd3d1cBGwNcf854Erg6de9jhdHj+bJVavYIxzruLD9J4BDw/G8vbOTzy1cyG+6u/nyKaew\ns7+fZ4ADwz76gOeBfwBGNTXxXFMT+23bxs5wbl0I/9iw/rawr4nhGM8OcT+/vX1g5MaTwnZ3hvjf\nA9gM7NnUxF4TJrBhv/1wjz1GEz6NjAvnty+EfxTwT5dfztxzzx0Uv5+dOZNNmzbhongM8dIcfkdp\nZGvYtwP2ZFea34td538c8N7YPr64cCG/WbSIUfj0EKXhaN3NYTvj8XmcAzL4a3kUsM2MA17zGlxP\nD6OAfzj66EHnddXKlcxtbYXNm2kO8bgZaBk3ji1bt2LODaTZcSGcY8LvneFY14bfEwAbP56v/Pa3\nTJs+nZ5MhrlTp7JHby9/j8XLHuzKM0eFbY8PcbE9rLejuZmtW7eyN7uul9EhbONCHPQDc8N5rrVo\n9Mj77r6bQ9eu5WF8uEfjj6d57725+LbbBuYe7MlkuPjMM8ncfz+HHHEEm198kQdXrWIP59iOj5cm\n/LXp2BVHO/Dx3I+PywnA9qYmDn/jG/niddcNul5XrVzJ/BNPZNP69Yw2w4VtjwW+h79e7x01ii07\nd7IZf+1sDfu1sI89gFOAjwILZs0aGF04ytP+fu+9jMbnW9F5eQ5/b2bUKDbt3MlewIQpU3jFgQey\naetW/nbPPbxhxw4eAK4L33n/xIn8/PnnB7Y9d+pUruntpQf4SFMTO/r72RHCthPYd9992XHggax7\n+GHGh21sPeQQRj31FHvgr9koz+8H3h+uqZ5Mhk+98538ffVqxoZwj2HXPWMTsG/Yz4ZwTHs0NbFt\n5076nGPPsP/t+Ov2E8DyWLxkm9/ezjHd3XwjbH8dPk8nFtcvO+oovnrLLekfcVRSqyeT4axZs1j/\n8MMD98XDY+XiVStX8um3vY1tW7bQH5Y/jb9PvMiu9NzPrry5n3APwd+v1gN7h882NDfz7d//fiCf\n/9gJJ7D+oYfYxq77a1TOGDtqFM/t3Ml4/D1xBzBqr7045uyzuefyy9kR9rFHCMs4YOu4cXzzd78b\nKJt99K1vZUdf38D1vAl/v4vKTHuH7Ub3pBfw5agXw7Zh171zM7D/fvtx8c03M236dH7T3c0XTzmF\npv5+HPDqEBd3ANPDdu9hV3nf8Pn6xhAvBrwwdiz7jxnDqC1bsKYmPvPznw+UW6+5+mq6PvQhthHy\n1lGjuGrnTp4BPmNGv3OMCWFbH45hdDg+8PfXqGy8BzBu3335wi23DMRNVBYeNWYMa/v62As44Mgj\n2XPPPcn86U/09/fTt3Mno9hVPo7uHY5d+VxUFt1nyhS+edNNA+nms9E9JIR/I7Df+PG82NfHFueY\nEOJ4e/j+hrDt0WE/L4Zj+S3gnDOS5Krl1voHeA/QFfv/g8B/Zq0z6MlH1HoXf+KZvTz+fyu5RwzM\nHsVzZtZT8qTtn5ewj1NDi1yu7ca/O9S2oxbTeItWfNvR9rO7uCY9zZ1p5k6MrffWcIzxuEnaRvZT\n9vjxxL/z7oT4jbe+xdeNjuG0lpbd4i/qXpUdR+exexzGv3tiwvbzxVN8e9n7yJWWon1E3WnicXEe\nuJlmux130nbj/8fPwYlhG875J5TZ+8+Vbk5MWC8pngptJYmOK1c44+k+Oyz5usxkr9sK7iRwx4Vj\nSIqv1qy/nXODrofscMXXz142VNxFcR89nYzCl32sucKXdH1HYcoVv0lpPCm9JKXX+LrxfeQL00Ba\nZfe8cUvW8lzn9bSWlkHnK/797Lwo6XiSrq/omop6ggwVL7mOK2mf2etG5zktovQ202y3NBp/Fzv7\nGjpx7Ni8cXwiu8dzdlxlX69RPpkvDWZfO7nSY3braZSnFXo+k9LU22J/v2vixEHbjgaeytUDJNpm\nvjJB9rJ4vOe6PpPy36Q0Hr9X5cuLM2vWDJmvbEk4dyLFiO5x2dd2lK6yyzfxe2pSfpD0O552ZzK4\nK3LSNX8ig8sw2ddm9n0q+7vxstmJJF9/ufKb7Os4qa4RbT8anDV7G/MSrv15sfXa8+Q98XJrdlly\nqPtb0rFmx188bnLdp7P399HYdtpzfCeKu3i6yd7WUHGfK9/0VdbkOuOoxBpuHXkQ+EL4ea7GYam0\nI3p7mTt1alSRB6B5r73yfmcz8OCKFfRkMnnXi57iD2XB0qU0tbXR1NbGgqVL826vHDatX8/WrVvL\ntLWh7SzjtvZOUa+HSfjWkQ/gW8t/jJ+nrBZ6Mhk29vVx39ixgz434JvAHpb84K1WovnuzGyg5Ujy\nK9cE5o+vXl2mLdWH+Nx+O5qKm6Vxx7Zt7KhEoBKcA9xX5HcWARPGlXYF9eCf3H8Af8+PbMfP59gJ\nvOzoowd9Z1LY51D3yFrbk/zxMmnyZCxleaKIDK054bMnYn+/GPu72HvmfWPHsjmrDFVpPfiy4yIG\nh71Sfgfcya56Xj5p72a80Dn3jvB/wd2Moybw0Qyvm/FnjjsOtm/nRXzX0P1I7mbMpk00Af3NzWza\nunVY3YxHjxrFmFGjBnUzNuDl7OqGeQ7wr2PHstc++3BBd/dAt7No+/Fuxt/F39QXZHVjyu5mPOaV\nr2RjJkPz5s2YGTsOPTSxm3EkKnQ9vXYtzz7wAFvWrx/UzXg9vin9JnZ1H+gndP8A3tXZyfJLLgHg\n09dfn9jNeF/gM8A1M2cyYdw4ep55hodXrWKsc2XtZrxjxw5eE7azjF1dOuPdjHewq3se5O5mfMHb\n386Wdes4DPgIcFlLCwcccQQ9DzzAc+vWDXQbrXY34+1mbAtd3H4dlmWniVyK6WbcBOy79940jx49\nqDtqvJC+sa+Py5YtowfoGD+enVu2sDVsp2XsWE679FJ+0dnJ1g0b2M6u7rD5uhl/qb0dgKeywlWO\nbsaRa66+mss/9CEcu3czbgbGjh7N5oMPZsyaNWXpZrwlfP9F6qub8XbgPOA2YCW7HpSV0s3447Nm\nMX7ZMjYBq0I899F43Yyj6+PBFSsGXuOYN306D65axc6dO3frGhZ95+Izz+SB5cuZ7hznAO/HH1ul\nuxk3Rd9tamKP5mbWb96ct5vx2Fe+kjccfvigV13ydTNeDxwwahQ0N9O3ZQvjgWn4QlQPcM6YMUzY\nd1/+9fvf56avfpXHV68e9FpDPL95bt06xt11F3/D5wdHh7h5aPRoTr/sMn7wyU9WvZsx+HQ4YdQo\nDnvTm3aL92zXXH013znrLHY4p27GUhFp7Gb8PD7P33/PPXl+82ZGk/5uxgfiyyH74q/TJ4D9jzyS\nvz/4IHuG7b0lbOd3IcwTgLOBL4UH5kN1Mx6/995cEspun5k5k82bNlWlm/Ee7HpdrZVdXYvVzTh/\nN+Mmdg0ANRY/ANRRWesU35eiCGkZySvppfFCwlPJqULi3biSBowa7pxY1Yz7YkaWLCQ8aXtpPq6S\nYSt00K+hBjQp5dzXMs6HuhZGmnKei2IGu6tnUZqPd0ErdOTfRpwSKi4+KnFSvlDoYFGF5D0iUro0\nl32qIenVj+wBP6s9X2w51Tqs5OlmXPNKa74f/NQ8DwGPAOcnLC93XA1S6xM33PCUMopkobIv2kqM\nMFytuK/0nH9pS0eVUmhlNhqqPld8lBrXtYrnSl8L0vgKGZ03l2gE0VMmTnRnHXtsQ+YzmTVrBsZo\nOHfWrKLmnKzFKKAiI1FaGoBqJXrw1gjl4jSq28rsUD+VrszWu0om/EpftJVWbNwMJy5HSgZfjrmF\ni103rlbxXO/XgtReOaZhaaR8phr5hYiUVyPlQaWqVe+3kSBfZbZc43RIFcTfA4q/e5RLNHBNJUya\nPJkr7ruPxbHw1JMlHR0D060s7ugYMp4qGZeNIl8cFRN/aYvroa67er8WpPaiNN+TySgdUVz+nLb8\nQmSkmtfVNeLzL+VHtaHKbIl6MhkumzNnt4EnKqmYG3xUAH9u3TqefvBBRjc17TZYVCmyC/a6aIem\nDL46KhXPhVx3uoEVp9gHcyPFcNKR8pn8lOZEKkv3wcpSHp9Hribbevihht2M48392fMuVmOfQ+0v\nWjfXHIXVCEMuaegWloYwSH0Y6V17KkFxKvlUIn9OW5rTPUikMenargzUzbgxNMpTmWK7+FaCniDu\nrpItF/XcKtIo151IvShn/hyf9ijJqpUruShMgVGO3kuFSsN9UETKT9d29akyW6J5XV0siHczrkIh\nt5gbfFQAX7tuHe+LdTOG4VUszli0iNmhUHDBokVFHoG3devWxL+z1XMFKA2Kjb9KZsD1nLnrwUf5\n6QGBVEuU9/QAs1taOHLGjEFp7qK2toG5fWe3tXH9s8/WKKQi0mgeXLGCnkxGjQMVpspsiSZNnszl\nYdLiNJo0eTLzurpY0tHBAcccMyjBD6dicV1n56Abf1QwKOZi2u4cnbG/c6nnClAaKP4krfSAQKpt\nEnDkjBmpSXf5HuiosCpSv85YtIhTbr2VPbdt4zO9vSxR40DFqTLbwJISfE8mk7O7VbGO6O3lwu7u\noi+mCePGcWH4e/G4cWUJS7FUWNhdJVvL1BI3cujaknIbTpoaKu+5oLub2bFuxtWS74GOCqsi9eu6\nzk5+sW0bAJ1AUxm3HZXhO4FzyrjdupfrZdp6+GEEzTNbygvlSQNenN/W5h4NA0Od1tJS9Mvp8fkQ\nHy1xMI1Cj0XzdQ2PBiGQWhgJ15ZU10hLUyPteEUaSfz6LaWcXattpx0aAKr+lfKkNtcT6UnAImDx\njBklt5pMnjKFb5jR3NxcdEtboV0Mq90VsdFalNSVU0QaQaHjLDQK9WQRqV/x6/eKCpYljxxGGb7R\nmMvzzmLamZmr5/AXIj4S43m9vdwAPNLSwhX33VdSIs5XYSukMje/vX1XpbqtrW4rS0nH2ijHJlJL\njfZQSGrv47NmMX7ZMgC2zJzJN26/vcYhEhGpvpF8fzUznHOWtEwtsyWqVoKKt8ieMnas74ff21vy\nezR6T8dTq6VIZejaEhERKT/dX5ONqnUA6lVU8buwu3ugUltpe+2zz8DfW7duZX57O/Pb2+nJZIb8\nbk8mU9T6uZyxaBGzW1qY3dLCGSVOzZNW87q6WNzWxuK2NnXtEhFJiTFmLMK/HjPGEh/MN6xy3btF\npJQ2uVIAABrGSURBVLrq8dqtxzCDWmbzSkNzfrzv/QWLFrG4009qs72vj4uLaEUtpNW1kPd04lPz\nLO7sZFoDPSHSEy8RkfRpbm5O/HskGEk9pkQaST1eu/UYZiigMmtmTcAnnHOXVyE8qZLvpJ6xaBGz\nwxQ3F1SwhTJewar0U5KRUplLw0MKKe950DkVaVwaEElERHIpaAAoM7vHOffGKoSnKJUeACrfgEDx\nZbOHMSBTqeFZMGvWwBPqQgrvPZkMl82Zw+OrVzN5yhQWLF1a9gGk6kFPJsPcqVM5oreXc4DvaKCn\nminngFsavEvqWb3nq1I5Shsi9aker900h7kcA0DdaWbfAH4KbI4+dM6tKkP4UqvQp8GH9PYyd+pU\njpwxo2onv7m5uagC+6TJk2lubuYrvb1cuWwZc6dOHaiAF5N4y9F6W8uLZUlHx0A36XJPZi0iu6T5\nppg29dq1S0REktVjb8d6DDNA4uSz2T/AsoSf2wv5biV/fPBrI7NmjTutpcWdB25emMC40hOcZ9as\ncee3tbnz29pKmij5/LY2d15CWKs9QXstJ4QfyRNOp0E8Dd90443utJYWd1pLi7t3xYqybVfnNB1q\neZ3XG8VV7aU1D1HaEBFxLtT5EuuDBbXMOudmVqYqXb8mTZ7MFffdx5KODp5csQJCa1+l91noE5NV\nK1dyUVsbABd0dzNt+nTmdXUxd+rUqoQ1rao1mbUkGzTV1K238q1t25jE8AcTq9uniQ1sY18fneHv\nLX19NQ1L2umd0MJVqsVfreMiIvWpoMqsme2D75X51vDRb4HFzrn1lQpYPYgK0D2ZTOoKIhe1tQ10\np53d1sb1zz47UAHPDmuxBanhFiZqWXBTpSc9pm7bxpX46Tak8UTTqQAsGGHTqRRL+VLhRlqlUw86\nRETyK/Sd2e8B9wPvC/+fCVwFnF6JQJlZJ3A28Gz4aIFz7qZK7Ksc6qUgkqsSWmz4h1uYqJf4kvKJ\n0t7Gvj7eP3Eir3rhBc4BPt3SwuLwrrk0lpE8nYqUV/zetXXr1orsI62VRt0vRUTyK3Q04z86514/\n1GdlC5SvzG50zn1tiPVcIeEfiZK6GZdrxNf57e18pLubK4FHqjSSs9S3eNo7b+ZMJowbB2hgoEam\nAaCkXJR/iIiMbOUYzbjPzN7snLsjbPB4oNIvQalf2jBMmz6d6599dugVSzCvq4uPvu51/OSFF6C3\nlwVz5nD5bbdVZF/SeCaMG6eWhhFALUpSCco/REQkrtCW2aOBq4F9wkdrgQ855/5UkUD5ltmzgPXA\nSuBTSe/nqmW2OOVsKfnHAw/c9U5uS0vFKs71RC1RuSluRKRUyj9EREa2fC2zQ1ZmzWwU8F7n3LVm\ntjeAc25DGQJ1C3BQ/CPAAZ8D/gA855xzZvYF4BDn3IcTtuE6OzsH/m9tbaW1tXW4QZMCfHzWLMYv\nWwbAlpkz+cbtt9c4RLVXrm7cIiJSfqoUi4jUh+XLl7N8+fKB/xctWlR6ZRbAzFY656aXLYRFMLOX\nAb90zr0uYZlaZmtEhYLdqTIrIpJeyqNFROpTOd6ZvdXMPg38FNgcfeice6EM4duNmR3snHs6/Hs6\nfiRlSRG9D7e7tI6GKSIiIiJSiHprsCq0ZfaxhI+dc+7l5Q8SmNnVwOuBncDjwDnOuWcS1htRLbP1\nlrhERETSQvdQEZGhpbEXy7BaZsM7sx90zt1Z9pDl4JybU6191ZORNlm8iIhIuahHkYhI4xk11ArO\nuZ3AN6oQFhGpUz2ZDPPb25nf3k5PJlPr4IiIFEV5mIiIN6+ri8VtbSxua6uL1+YK7Wb8FeBu4H/S\n1K93JHYzvmzOHB5fvZrJU6awYOnSinSTUlcsKVYau6SIiBRKeZiISHrl62Y8ZMtscA5wLfCimW0w\ns41mNuzpeaQ4kyZPprm5mWt6e7ls2bKBCme5Rd2ZL+zurtg+RBqJWnVEREREqq/Q0Yz3Af4JOMw5\nt9jMXgocUrlgiUg9KWYk50Zs+df77CL1TaPRi4jUp0K7Gf8XfmThWc65o8xsP+Bm59yMSgdwiHCN\nqG7GUJ2KQCNWNiQ9Gq07X08mw9ypU7mmtxdojGMSERERSYtyzDP7JufcNDO7D8A5t9bMxpYthFKw\naozGqBEfRQq3pKODr/T20gk80tLCFWrVERERkTJTY1OyQiuz282sCXAAZtaCb6kVSRVd6Omn7nwi\nIiIixdErTckKHQDqP4HrgQPN7IvAHcDFFQuVSIk0eFX6RS3/l9x4Y0M8bJjX1cWnW1pYBFzT26t0\nJyXRIGIiIiLFK6hl1jn3IzO7FzgBMOA059xfKhoyEZE6MGnyZI6cMQPC01KRUuiJu4iI5KOebckK\n7WaMc+5B4MEKhkVk2HSh185I7uKtdCciIiKVpDFtkhU0mnFajcTRjEvVk8lw2Zw5PL56NZOnTGHB\n0qUjqrIhlddooxSLVNNIfhgkIiKSTzlGM5Y6d/GZZzJ+2TKOADYtW8YSdWMTEUkNPXEXEREpniqz\nI0Tm/vu5Jvz9AWCfWgZGGpK62oqIiIhINamb8QjxyRNO4OLbbwfgfRMncuWf/qRubA2uXrst1mu4\nRURERKT88nUzVmV2hFAFYeRJ2zushabBtIVbRERERGpH78yK3seSmit06pGtW7cm/i0iIiIilVOP\njV+qzIo0qHp9h3W7c3TG/hYRERGRyqvHOc9VmRVpUGlrjT9j0SJmr1gBwAWLFuVcb8K4cVwY/l48\nblwVQiYiIiIi9UjvzIpIVRT6Lmw9dnERERERqXdpLYOl8p1ZM3svsBA4CpjhnFsVWzYf+BdgB/Bv\nzrmbaxJIEam6tLUoi0jtpLVgJSLSiOqxDFazllkzexWwE7gS+HRUmTWzo4BrgBnAS4BbgSOSmmDV\nMlseKixINSidiUixNLq5iIiksmXWOfcQgJllB+xU4CfOuR3A42b2CPBG4H+rHMQRox5f9pb6U49P\n+wqlirqIiIhI9aVxAKjJwN2x/zPhMxGRVNIDIZHKqNdR2UVEpDoqWpk1s1uAg+IfAQ74nHPul+XY\nx8KFCwf+bm1tpbW1tRybHVFUWBARkbRRjwcRkZFp+fLlLF++vKB1az6asZktAz4Ve2f2fMA55y4N\n/98EdDrndutmrHdmJW1U+BqZdN5Fyk/vy4qICKT0ndks8cD9AviRmV2O7158OHBPTUIlUqSR3t10\npFbqGvl9YBEREZG0quXUPKcBS4ADgF+Z2R+dc+90zj1gZtcCDwDbgY+p+VWkPoz0yryIlI9egRER\nkaHUcjTjG4Abciy7BLikuiESGT4VvkREykM9HkREZCg1f2d2OPTOrEi6jNRuxiIiIiJSGfnemVVl\nVkRERERERFIpX2V2VLUDIyIiIiIiIjJcqsyKiIiIiIhI3VFlVorWk8kwv72d+e3t9GQytQ6OiIiI\niIiMQHpnVoqmiexFRERERKQa9M6sSBWp5Xrk0TkXERERqT61zErRNP1KfvXYcq1zOjz1eM5FRERE\n6kG+ltnR1Q6M1D9NZN94lnR07KqMdXRU9Pyq4iwiIiIi5aDKrEiZzevqYnGssiaDVbPiXC065yIi\nIiLVp8qsSJnVY8u1KmPDU4/nXERERKTe6Z1ZEakqdTMWERERkULle2dWlVkRERERERFJJU3NIyIi\nIiIiIg1FlVkRERERERGpO6rMioiIiIiISN1RZVZERERERETqjiqzIiIiIiIiUndUmZWi9WQyzG9v\nZ357Oz2ZTK2DIyIiIiIiI1DNKrNm9l4zu9/M+s1sWuzzl5nZFjNbFX6+WaswSrIlHR1c2N3Nhd3d\nA/OFioiIiIiIVNPoGu57NfCPwJUJyx51zk1L+FxERERERESkdpVZ59xDAGaWNAFu4qS4kg7zurpY\nHFpk53V11Tg0IiIiIiIyEplzrrYBMFsGfMo5tyr8/zLgfuARYD1wgXPujhzfdbUOv4iIiIiIiFSG\nmeGcS2zsrGjLrJndAhwU/whwwOecc7/M8bUe4KXOubXhXdobzOzVzrlNSSsvXLhw4O/W1lZaW1vL\nEXQRERERERGpsuXLl7N8+fKC1k1dy2wxy9UyKyIiIiIi0rjytcymZWqegcCZ2QFmNir8/XLgcOBv\ntQqYiIiIiIiIpE8tp+Y5zcyeBI4BfmVmvw6L3gr8ycxWAdcC5zjn1tUqnCIiIiIiIpI+Ne9mPBzq\nZiwiIiIiItK46qGbsdRQTybD/PZ25re305PJ1Do4IiIiIiIiQ1JlVljS0cGF3d1c2N3NkjB/rJRG\nDwZERERERKpDlVmRMtKDARERERGR6qjoPLNSH+Z1dbE4VLzmdXXVODQiIiIiIiJD0wBQImXUk8kM\ntMjO6+pi0uTJNQ6RiIiIiEj9yjcAlCqzItIQ9CBBREREpPGoMisiDW9+ezsXdncDsLitjUtuvLHG\nIRIRERGR4dLUPCIiIiIiItJQ1DIrIg1B3YxFREREGo+6GYuIiIiIiEjdUTdjKaueTIb57e3Mb2+n\nJ5OpdXBERERERGQEUsusFE0D7YiIiIiISDWoZVZEREREREQailpmpWgaaEdERERERKpBA0CJiIiI\niIhI3VE3YxEREREREWkoqsyKiIiIiIhI3VFlVkQ03ZKIiIiI1J2aVWbN7Mtm9hcz+6OZ/czM9o4t\nm29mj4TlJ9UqjCIjxZKODi7s7ubC7u6Bwb1ERERERNKsli2zNwOvcc69HngEmA9gZq8G3gccBbwT\n+KaZJb7wKyIiIiIi8v/bu/fgvIo6jOPfB1AU5SJTgaGBQqEMoqK00HqpUtECNg4wRUeRW6GCM9WO\no+g4pVqw6uAVgUrRCqKgFB1wsNByCZegTC0ihZZLlSL31AtyBxUq/PzjbMhp8iZNm6Tn7JvnM5PJ\nObsn++55f8nZd8/u2djwtEVVLxwR15d2lwFHpu3DgEsj4n/AQ5JWA+OBWzdxFc2GjZkLFjC39O+W\nzMzMzMzqrrLObDcnAgvT9kjgD6W8jpRmZkNk55EjOWPx4qqrYWZmZmbWb0PamZXUBuxYTgICmB0R\nV6ZjZgNrI2JhgyLMzMzMzMzMehjSzmxETO4rX9I0YApwUCm5A9iltN+S0ho6/fTTX92eNGkSkyZN\n2vCKmpmZmZmZWeXa29tpb2/v17GKiKGtTW8vLB0KfB94f0Q8UUrfB/glMIFienEbMCYaVFRSo2Qz\nMzMzMzNrApKIiIYLAle5mvE84I1Am6TlkuYDRMS9wK+Be4ElwAz3WM3y5/9la2ZmZmaDqbKR2cHg\nkVmzfMxqbWXOkiUAzJ0yxQtOmZmZmdl61XVk1szMzMzMzGyjeGTWzDaJNR0dzCv9L9udR/o/bpmZ\nmZlZ3/oamXVn1szMzMzMzGrJ04zNzMzMzMysqbgza2ZmZmZmZtlxZ9bMzMzMzMyy486smZmZmZmZ\nZcedWTMzMzMzM8uOO7NmZmZmZmaWHXdmzczMzMzMLDvuzJqZDcCajg5mtbYyq7WVNR0dVVfHzMzM\nbNhQRFRdh40mKXKuv5nlb1ZrK3OWLAFg7pQpnLF4ccU1MjMzM2sekogINcrzyKyZmZmZmZllxyOz\nZmYDsKajg3knnwzAzAUL2HnkyIprZGZmZtY8+hqZdWfWzMzMzMzMasnTjM3MzMzMzKypuDNrZmZm\nZmZm2XFn1szMzMzMzLLjzqyZmZmZmZllp7LOrKTvSFol6U5Jl0vaJqWPkvRvScvT1/yq6mhmZmZm\nZmb1VOXI7HXAWyPincBqYFYp7/6IGJu+ZlRTvebU3t5edRVsPRyj/Dhm+XHM8uOY1Z9jlB/HLD+O\n2boq68xGxPUR8UraXQa0lLIbLr1sA+c/gPpzjPLjmOXHMcuPY1Z/jlF+HLP8OGbrqsszsycCV5f2\nd0tTjG+SNLGqSpmZmZmZmVk9bTGUhUtqA3YsJwEBzI6IK9Mxs4G1EXFJOmYNsGtEPCVpLHCFpH0i\n4vmhrKuZmZmZmZnlQxFR3YtL04CTgIMi4sVejrkJOCUiljfIq67yZmZmZmZmNuQiouFjqEM6MtsX\nSYcCXwLeX+7IShoBPBkRr0gaDewJPNCojN5OyszMzMzMzJpbZSOzklYDrwWeSEnLImKGpKnAXOAl\n4BVgTkQsqaSSZmZmZmZmVkuVTjM2MzMzMzMz2xh1Wc3YSiS9nFZzviN937WPYw+UdGU/yvyFpD9L\nWinpfEmbl/LOkbRa0p2S9iulXyDpH5JW9lLmKZJekbT9hp5j7tJ5X1Ta31zS45IWDULZH5L0J0kr\nJN0m6QOlvLEphvdJOquU/j5Jt0tam2Y3dC9za0mPSjpnoPXLmaQjUuz2GsQyHa8hkmL13dL+KZLm\nDEK5n5d0T7rmtUnapZR3fIrXXyQdV0r/TLpOvtzomifpgN7iOZzUvf2SdJqkx1LdlqdHnoaVnNqv\nbr9PVwy0fjnLof1yvLrk0H6la/DTpevhVwZavyq4M1tPL0TE2IjYL31/ZD3H92d4/RcRsXdE7Ats\nBXwKQNKHgT0iYgzwaeC80s9cCBzSqDBJLcBk4OF+vHYzegF4m6Qt0/5k4NFBKvtx4CMR8Q5gGnBx\nKe88YHpE7AXsJakzPg8DxwO/7KXMrwM3D1L9cvYJ4PfAUYNYpuM1dF4EpjbqPA7QcmBcRLwTuBz4\nLoCkNwFzgAOACcBpkrZNP3ML8EEaXPMkbQZ8C7h2kOuZo9q3X8CZqW5jI+Kafrx+s8mp/Sr/Ph0x\nSHXMVQ7tl+PVJYv2C/hd6Xr4jUGu6ybhzmw99VjYStJmkr4j6dZ0N+akUva2kq5Kd67nNyqwW4P9\nR6AlbR8OXJSOuTWVtWPavwV4qpc6/oBiAa/hbAnQmraPAhZ2ZqRRmqXp7uUtksak9Jsl7Vs67veS\n3l4uNCJWRMTf0/Y9wOskvUbSTsDWEXFbOvQi4Ih03CMRcTcNPhhKGgfsAFw3OKedJ0lvAN4LTKf0\nYaD76JCkeZ13NCVNkbQq3bE+Ww1GkRyvIfU/YAHwhe4ZkkZJuqF0d7pF0jaSHiods5WkR1QayQOI\niJsj4r9pdxkwMm0fAlwXEc9ExNMUMTg0/cyK1DFrtPDgTOAy4J8DOtvmkEP75cUjM2m/cKyAfNov\nHK+yXNqv7GPmzmw9vV5d0zQuT2nTgacjYgIwHjhZ0qiUdwDwGeAtwJ7qY5qbpC2AY4GrU9JI1r0j\n20HXH0ZvZRwGPBoRd23geTWTAC4Fjkp3t/cFbi3lrwImRsQ44DTgjJR+PnACQPqAsGVf76OkjwLL\nI2ItRVweK2U/xvpjJeB7wBdpggvWAB0OXBMR9wP/UmlKIo07lVsCPwIOiYgDgDc3Oq7bzzhegyuA\nc4GjJW3dLW8ecGG6O30JMC8ingXukHRgOuYjFDF/uY/XmM7Aroc7A0dExHk4ZlDz9iv5bPoQeX5p\n5GI4yaL9SrZUMQ12qaTD+3V2zan27VfieHWpffuVvDtdDxdL2qcfx9eOO7P19O/SNI0jU9rBwHGS\n7qBodLYHxqS8P0bEw1Gs5rUQmNhH2fOBmyNi6cZUTNLrgVMpGrhXkzemrNylO5O7UdwlXcy678N2\nwGWS7qIYxe68QFwGtKY7bScCP+utfElvpfgQcfIAqjkDWBwRazqLHUBZuTuK4gMcwK+AT67n+L2B\nv5amSS7s62DHa2hExPPAz4HPdct6N10xuZhi1ALg18DH0/YnKGLdkKRjgHGkaVob6Szgy+ViB1BW\nM6ht+1UqY3T6EPl34MwBlJWtTNovgFERsT9wNHCWpN0HWF6ucmi/wPFaRwbt1+3Arul6+EMgy+ec\nK/s/s7bBBMyMiLZ1Eos7ON3vtjW8+6biwfMREVG+WHUAu5T2W1Jab/agaABXpFGkFuB2SeMjYjhO\nsVtEcSGZBIwopX8duDEipqYRiJsAIuI/ktooput8jOJC1IOKZ5J/AxwbEQ+l5A2NFRQXzImSZgBb\nA6+R9FxEnNrvM2wCKp4lOYjiObEANqf4O/kSxVSg8o2915V/tJ/lO15D62yK54QuLKX1NsqwCPhm\nivlY4MZGB0n6EDCL4n+dr03JHRR/y51aSH+7fbzu/sCl6Xo4AviwpLURMeDFdJpIXdovIuLx0u5P\ngPUuQNXE6t5+ERF/S98flNQO7Ac82L/Taw4ZtV+OV2O1bb9SZ7tz+2pJ8yVtHxFP9nlGNeOR2Xpq\ndAG6FpiRplkhaUwaJQWYkObfb0ZxR+eWHgVKn6KYT9994YBFQOfzFe+imAr2j251ebU+EXF3ROwU\nEaMjYneKqSf7DcOObOd78lPga+lZk7Jt6brwn9At7wLgHIoRiWd6FFxMe7sK+HJELOtMT8+1PCNp\nfPrgfBzw2z7qRkQcExG7RcRoiqmrFw3TjtHHKM599/S7Owp4UNJEigUR9knPCW1HsUgCwF+A3dW1\nGuvHexbreA0xAUTEUxR3rKeX8pbSdT07hmJhFCLiBeBPFB8grkojfusWWkzR+xFwWEQ8Ucq6Fpgs\nadv0YWIyPRd16n5NHF26Hl4GzBjmHdnatl/puJ1Ku1OBu/t7Yk0ki/ZL0naSXpu2RwDvAe7t3yk2\nlSzaL8erh9q3X0prDKTt8YBy68gCEBH+qtkX8GyDNAHfBFYCdwE3UIzcHAi0U9xdXgWc20uZa4HV\nwB0Ud4i+Usr7IXA/sAIYW0q/BFhDsSLbI8AJDcp9ANi+6vesJjE6EFiUtt9F0ZjcDswFHuh27Cpg\nci9lzwaeS3HqjNeIlDcuxX81cHbpZ/aneFbiOYrVCe9qUO7xwDlVv3cVxesG4OBuaTM7/16Ab6d4\nXUPRITkupbemWN1GMT3xYsdrk8bt2dL2DsDzwFfT/q4prncCbUBL6dgjgZcpnvtrVG4b8LdSzK4o\n5U1L8bqv8/eg9PvyKPASxU28BQ3K/Skwter3rS4xK6XVpv2iWMhmZfq9uQLYser3rCYxql37RTFT\nZWUqZwUwrer3rqJ4ZdF+OV493tvat18U6xXcncpZCkyo+n3bmC+lkzGzTUTFgjE3RsTeVdfF+ibp\nDVHcKUXSucB9EXF2xdUyM6uE2698uP2y4cLTjM02IUnHAn+gWETL6u8kFauy3gNsA/y46gqZmVXB\n7Vd23H7ZsOCRWTMzMzMzM8uOR2bNzMzMzMwsO+7MmpmZmZmZWXbcmTUzMzMzM7PsuDNrZmZmZmZm\n2XFn1szMzMzMzLLjzqyZmZmZmZll5/9EuFoKsCYc2AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e3ef050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16, 4))\n",
    "err = y_regression - y_test.values.ravel()\n",
    "\n",
    "ax.plot_date(y_test.index, err, c='r', ms=3)\n",
    "ax.set_title('9 Features + Random Forest Regression: residuals on test data (each)', fontsize=20)\n",
    "ax.set_ylabel('error')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "# cc = 0\n",
    "# for i in xrange(err.shape[0]):\n",
    "#     if y_regression[i] - y_test.values[0][i] < 0:\n",
    "#         cc += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-danger\">\n",
    "Add previous yield.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.        ,  0.00091682,  0.01788412, ...,  0.00739163,\n",
       "         0.27372533,  0.00662224],\n",
       "       [ 0.        ,  0.00092281,  0.01775545, ...,  0.00761486,\n",
       "         0.27220775,  0.0069001 ],\n",
       "       [ 0.        ,  0.00081023,  0.01875063, ...,  0.00745248,\n",
       "         0.27227188,  0.00675496],\n",
       "       ..., \n",
       "       [ 0.        ,  0.00078708,  0.03060067, ...,  0.00748404,\n",
       "         0.15971649,  0.00572088],\n",
       "       [ 0.        ,  0.00081195,  0.02876198, ...,  0.00978416,\n",
       "         0.1629566 ,  0.00888447],\n",
       "       [ 0.        ,  0.00084619,  0.02856601, ...,  0.01075866,\n",
       "         0.14647318,  0.00986602]])"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_time = microclimate_train.copy()\n",
    "y_time = labels.copy()\n",
    "\n",
    "X_time = X_time.drop(labels=X_time.index[0:4], axis=0).copy()\n",
    "y_time = y_time.drop(labels=y_time.index[0:4], axis=0).copy()\n",
    "        \n",
    "X_time.fillna(method='pad')\n",
    "        \n",
    "for timestamp in X_time.index:\n",
    "    if np.isnan(X_time.loc[timestamp]['percip_mm']):\n",
    "        X_time = X_time.drop(labels=timestamp, axis=0).copy()\n",
    "        y_time = y_time.drop(labels=timestamp, axis=0).copy()\n",
    "    elif np.isnan(X_time.loc[timestamp]['gusts_ms']):\n",
    "        X_time = X_time.drop(labels=timestamp, axis=0).copy()\n",
    "        y_time = y_time.drop(labels=timestamp, axis=0).copy()\n",
    "    elif np.isnan(X_time.loc[timestamp]['leafwet460_min']):\n",
    "        X_time.loc[timestamp]['leafwet460_min'] = 0      \n",
    "\n",
    "normalize(X_time, copy=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X_joint = X_time.copy()\n",
    "y_new = y_time.copy()\n",
    "for i in [2]:\n",
    "    offset = i\n",
    "    y_pretime = y_new.copy()\n",
    "    y_pretime.index = y_pretime.index + pd.DateOffset(hours=offset)\n",
    "    y_pretime = y_pretime.rename(columns={'yield': 'pre_yield_' + str(offset) + '_hours'})\n",
    "    y_joint = pd.concat([y_new, y_pretime], axis=1, join='inner')\n",
    "    X_joint = pd.concat([X_joint, y_joint], axis=1, join='outer')\n",
    "    X_joint = X_joint.drop(labels='yield', axis=1)\n",
    "    \n",
    "for timestamp in X_time.index:\n",
    "    for i in [2]:\n",
    "        if np.isnan(X_joint.loc[timestamp]['pre_yield_' + str(i) + '_hours']):\n",
    "            X_joint = X_joint.drop(labels=timestamp, axis=0).copy()\n",
    "            y_new = y_new.drop(labels=timestamp, axis=0).copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_joint,\n",
    "                                                    y_new,\n",
    "                                                    test_size=0.3, random_state=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Shauro/anaconda/lib/python2.7/site-packages/sklearn/utils/validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test size = 1722\n",
      "right_predict_count = 1628\n",
      "in right_predict_count: zero_to_zero = 1312\n",
      "in right_predict_count: one_to_one = 316\n",
      "wrong_predict_count = 94\n",
      "in wrong_predict_count: zero_to_one = 36\n",
      "0.026706231454\n",
      "in wrong_predict_count: one_to_zero = 58\n",
      "0.155080213904\n",
      "0.14745308311\n"
     ]
    }
   ],
   "source": [
    "y_for_ada = y_train.copy()\n",
    "\n",
    "for timestamp in y_for_ada.index:\n",
    "    if y_for_ada.loc[timestamp]['yield'] > 0:\n",
    "        y_for_ada.loc[timestamp]['yield'] = 1\n",
    "\n",
    "estimator = ensemble.AdaBoostClassifier(n_estimators=40,learning_rate=1)\n",
    "#estimator = svm.SVC(C=1.0, kernel='poly', degree=30, gamma='auto', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape=None, random_state=None)\n",
    "estimator.fit(X_train, y_for_ada)\n",
    "y_hat = estimator.predict(X_test)\n",
    "\n",
    "wrong_predict_count = 0\n",
    "zero_to_one = 0\n",
    "one_to_zero = 0\n",
    "one_to_one = 0\n",
    "zero_to_zero = 0\n",
    "for i in xrange(y_test.shape[0]):\n",
    "    if y_test.values[i] == 0:\n",
    "        if y_hat[i] == 0:\n",
    "            zero_to_zero += 1\n",
    "        else:\n",
    "            wrong_predict_count += 1\n",
    "            zero_to_one += 1\n",
    "    else:\n",
    "        if y_hat[i] == 0:\n",
    "            wrong_predict_count += 1\n",
    "            one_to_zero += 1\n",
    "        else:\n",
    "            one_to_one += 1\n",
    "            \n",
    "print('test size =', y_test.shape[0])\n",
    "# print('non-zeron in y_hat =', np.count_nonzero(y_hat))\n",
    "print('right_predict_count =', y_test.shape[0] - wrong_predict_count)\n",
    "print('in right_predict_count: zero_to_zero =', zero_to_zero)\n",
    "print('in right_predict_count: one_to_one =', one_to_one)\n",
    "print('wrong_predict_count =', wrong_predict_count)\n",
    "print('in wrong_predict_count: zero_to_one =', zero_to_one)\n",
    "print(1.0*zero_to_one/(zero_to_one+zero_to_zero))\n",
    "print('in wrong_predict_count: one_to_zero =', one_to_zero)\n",
    "print(1.0*one_to_zero/(one_to_one+one_to_zero))\n",
    "\n",
    "print(1.0*55/373)\n",
    "\n",
    "# estimator.score(X_test, y_test)\n",
    "# estimator.staged_score(X_test, y_test)\n",
    "# rmse = np.sqrt(mean_squared_error(y_test, y_hat))\n",
    "# rmse\n",
    "\n",
    "# X_joint = pd.concat([X_train, y_train], axis=1, join='outer')\n",
    "# X_joint.head(n=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Shauro/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:2: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    }
   ],
   "source": [
    "estimator = ensemble.RandomForestRegressor()\n",
    "estimator.fit(X_train, y_train)\n",
    "y_hat_regression = estimator.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test size = 1722\n",
      "right_predict_count = 1582\n",
      "in right_predict_count: zero_to_zero = 1239\n",
      "in right_predict_count: one_to_one = 343\n",
      "wrong_predict_count = 140\n",
      "in wrong_predict_count: zero_to_one = 109\n",
      "0.0808605341246\n",
      "in wrong_predict_count: one_to_zero = 31\n",
      "0.0828877005348\n",
      "loss = 1.77872383975\n"
     ]
    }
   ],
   "source": [
    "y_classifier = np.copy(y_hat)\n",
    "y_regression = np.copy(y_hat_regression)\n",
    "\n",
    "predict_total = y_test.copy()\n",
    "\n",
    "for i in xrange(y_regression.shape[0]):\n",
    "    if abs(y_regression[i]) < 0.01 :\n",
    "        y_regression[i] = 0\n",
    "# print(y_regression)\n",
    "\n",
    "predict_total['y_classider'] = y_classifier\n",
    "predict_total['y_regression'] = y_regression\n",
    "\n",
    "# for i in xrange(y_regression.shape[0]):\n",
    "#     predict_total.append((y_classifier[i], y_regression[i], y_test.values[i]))\n",
    "# print(predict_total)\n",
    "\n",
    "wrong_predict_count = 0\n",
    "zero_to_one = 0\n",
    "one_to_zero = 0\n",
    "one_to_one = 0\n",
    "zero_to_zero = 0\n",
    "\n",
    "for i in xrange(y_test.shape[0]):\n",
    "    if y_test.values[i] == 0:\n",
    "        if y_regression[i] == 0:\n",
    "            zero_to_zero += 1\n",
    "        else:\n",
    "            wrong_predict_count += 1\n",
    "            zero_to_one += 1\n",
    "    else:\n",
    "        if y_regression[i] == 0:\n",
    "            wrong_predict_count += 1\n",
    "            one_to_zero += 1\n",
    "        else:\n",
    "            one_to_one += 1\n",
    "            \n",
    "print('test size =', y_test.shape[0])\n",
    "# print('non-zeron in y_hat =', np.count_nonzero(y_hat))\n",
    "print('right_predict_count =', y_test.shape[0] - wrong_predict_count)\n",
    "print('in right_predict_count: zero_to_zero =', zero_to_zero)\n",
    "print('in right_predict_count: one_to_one =', one_to_one)\n",
    "print('wrong_predict_count =', wrong_predict_count)\n",
    "print('in wrong_predict_count: zero_to_one =', zero_to_one)\n",
    "print(1.0*zero_to_one/(zero_to_one+zero_to_zero))\n",
    "print('in wrong_predict_count: one_to_zero =', one_to_zero)\n",
    "print(1.0*one_to_zero/(one_to_one+one_to_zero))\n",
    "\n",
    "rmse = np.sqrt(mean_squared_error(y_test, y_regression))\n",
    "print('loss =', rmse)\n",
    "        \n",
    "predict_total.head(n=1734)\n",
    "\n",
    "predict_total.to_csv(\"./three_combine_pre_yield.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2013-12-26 22:00:00 y_regression = 26.822742476 ; y_test = 1.114827202\n",
      "2014-08-03 02:00:00 y_regression = 14.9324136001 ; y_test = 2.773132664\n",
      "2014-04-02 12:00:00 y_regression = 11.6241638796 ; y_test = 0.15328874\n",
      "2013-12-26 04:00:00 y_regression = 27.208751395 ; y_test = 11.23188406\n",
      "2014-02-17 04:00:00 y_regression = 14.6760033446 ; y_test = 26.06605351\n",
      "2014-04-19 04:00:00 y_regression = 22.479793758 ; y_test = 0.10451505\n",
      "2015-10-31 18:00:00 y_regression = 2.9967948717 ; y_test = 16.10925307\n",
      "2015-01-31 16:00:00 y_regression = 18.6858974343 ; y_test = 30.2745262\n"
     ]
    },
    {
     "data": {
      "image/png": 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w7L8Ffhr2qT2UvMd3lHS/GfhNSM/9RK9UOjik8fcJ0y8M+9GTRAH418N6lds/Km4XooKS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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114c6ce90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16, 4))\n",
    "err = y_regression - y_test.values.ravel()\n",
    "\n",
    "for i in xrange(len(err)):\n",
    "    if abs(err[i]) > 10:\n",
    "        print(y_test.index[i], \n",
    "              'y_regression =', y_regression[i], \n",
    "              '; y_test =', y_test.values[i][0])\n",
    "\n",
    "ax.plot_date(y_test.index, err, c='r', ms=3)\n",
    "ax.set_title('residuals on test data (each)', fontsize=20)\n",
    "ax.set_ylabel('error')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "# cc = 0\n",
    "# for i in xrange(err.shape[0]):\n",
    "#     if y_regression[i] - y_test.values[0][i] < 0:\n",
    "#         cc += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<div class=\"alert alert-danger\">\n",
    "Read training set for 5 minites interval.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.        ,  0.00091682,  0.01788412, ...,  0.00739163,\n",
       "         0.27372533,  0.00662224],\n",
       "       [ 0.        ,  0.00092281,  0.01775545, ...,  0.00761486,\n",
       "         0.27220775,  0.0069001 ],\n",
       "       [ 0.        ,  0.00081023,  0.01875063, ...,  0.00745248,\n",
       "         0.27227188,  0.00675496],\n",
       "       ..., \n",
       "       [ 0.        ,  0.00078708,  0.03060067, ...,  0.00748404,\n",
       "         0.15971649,  0.00572088],\n",
       "       [ 0.        ,  0.00081195,  0.02876198, ...,  0.00978416,\n",
       "         0.1629566 ,  0.00888447],\n",
       "       [ 0.        ,  0.00084619,  0.02856601, ...,  0.01075866,\n",
       "         0.14647318,  0.00986602]])"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_5min = microclimate_5min.copy()\n",
    "\n",
    "X_time = microclimate_train.copy()\n",
    "y_time = labels.copy()\n",
    "\n",
    "X_time = X_time.drop(labels=X_time.index[0:4], axis=0).copy()\n",
    "y_time = y_time.drop(labels=y_time.index[0:4], axis=0).copy()\n",
    "        \n",
    "X_time.fillna(method='pad')\n",
    "        \n",
    "for timestamp in X_time.index:\n",
    "    if np.isnan(X_time.loc[timestamp]['percip_mm']):\n",
    "        X_time = X_time.drop(labels=timestamp, axis=0).copy()\n",
    "        y_time = y_time.drop(labels=timestamp, axis=0).copy()\n",
    "    elif np.isnan(X_time.loc[timestamp]['gusts_ms']):\n",
    "        X_time = X_time.drop(labels=timestamp, axis=0).copy()\n",
    "        y_time = y_time.drop(labels=timestamp, axis=0).copy()\n",
    "    elif np.isnan(X_time.loc[timestamp]['leafwet460_min']):\n",
    "        X_time.loc[timestamp]['leafwet460_min'] = 0      \n",
    "\n",
    "normalize(X_time, copy=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X_joint = X_time.copy()\n",
    "y_new = y_time.copy()\n",
    "for i in [2]:\n",
    "    offset = i\n",
    "    y_pretime = y_new.copy()\n",
    "    y_pretime.index = y_pretime.index + pd.DateOffset(hours=offset)\n",
    "    y_pretime = y_pretime.rename(columns={'yield': 'pre_yield_' + str(offset) + '_hours'})\n",
    "    y_joint = pd.concat([y_new, y_pretime], axis=1, join='inner')\n",
    "    X_joint = pd.concat([X_joint, y_joint], axis=1, join='outer')\n",
    "    X_joint = X_joint.drop(labels='yield', axis=1)\n",
    "    \n",
    "for i in [60]:\n",
    "    X_joint['pre_' + str(i) + '_min_wind_dir'] = np.nan\n",
    "    \n",
    "for timestamp in X_time.index:\n",
    "    for i in [2]:\n",
    "        if np.isnan(X_joint.loc[timestamp]['pre_yield_' + str(i) + '_hours']):\n",
    "            X_joint = X_joint.drop(labels=timestamp, axis=0).copy()\n",
    "            y_new = y_new.drop(labels=timestamp, axis=0).copy()\n",
    "            \n",
    "    # Add pre_x_min_wind_dir features.\n",
    "    for i in [60]:\n",
    "        pre_time = timestamp - pd.DateOffset(minutes=i)\n",
    "        if pre_time in X_5min.index:\n",
    "            pre_wind_dir = X_5min.loc[pre_time]['wind_dir']\n",
    "            if isinstance(pre_wind_dir, pd.Series):\n",
    "                pre_wind_dir = pre_wind_dir.mean()\n",
    "            if timestamp in X_joint.index:\n",
    "                if not np.isnan(pre_wind_dir):\n",
    "                    X_joint.loc[timestamp]['pre_' + str(i) + '_min_wind_dir'] = pre_wind_dir\n",
    "                else:\n",
    "                    X_joint = X_joint.drop(labels=timestamp, axis=0).copy()\n",
    "                    y_new = y_new.drop(labels=timestamp, axis=0).copy()\n",
    "        elif timestamp in X_joint.index:\n",
    "            X_joint = X_joint.drop(labels=timestamp, axis=0).copy()\n",
    "            y_new = y_new.drop(labels=timestamp, axis=0).copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_joint,\n",
    "                                                    y_new,\n",
    "                                                    test_size=0.3, random_state=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Shauro/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:2: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    }
   ],
   "source": [
    "estimator = ensemble.RandomForestRegressor()\n",
    "estimator.fit(X_train, y_train)\n",
    "y_hat_regression = estimator.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Shauro/anaconda/lib/python2.7/site-packages/sklearn/utils/validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test size = 1722\n",
      "right_predict_count = 1628\n",
      "in right_predict_count: zero_to_zero = 1312\n",
      "in right_predict_count: one_to_one = 316\n",
      "wrong_predict_count = 94\n",
      "in wrong_predict_count: zero_to_one = 36\n",
      "0.026706231454\n",
      "in wrong_predict_count: one_to_zero = 58\n",
      "0.155080213904\n",
      "0.14745308311\n"
     ]
    }
   ],
   "source": [
    "y_for_ada = y_train.copy()\n",
    "\n",
    "for timestamp in y_for_ada.index:\n",
    "    if y_for_ada.loc[timestamp]['yield'] > 0:\n",
    "        y_for_ada.loc[timestamp]['yield'] = 1\n",
    "\n",
    "estimator = ensemble.AdaBoostClassifier(n_estimators=40,learning_rate=1)\n",
    "#estimator = svm.SVC(C=1.0, kernel='poly', degree=30, gamma='auto', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape=None, random_state=None)\n",
    "estimator.fit(X_train, y_for_ada)\n",
    "y_hat = estimator.predict(X_test)\n",
    "\n",
    "wrong_predict_count = 0\n",
    "zero_to_one = 0\n",
    "one_to_zero = 0\n",
    "one_to_one = 0\n",
    "zero_to_zero = 0\n",
    "for i in xrange(y_test.shape[0]):\n",
    "    if y_test.values[i] == 0:\n",
    "        if y_hat[i] == 0:\n",
    "            zero_to_zero += 1\n",
    "        else:\n",
    "            wrong_predict_count += 1\n",
    "            zero_to_one += 1\n",
    "    else:\n",
    "        if y_hat[i] == 0:\n",
    "            wrong_predict_count += 1\n",
    "            one_to_zero += 1\n",
    "        else:\n",
    "            one_to_one += 1\n",
    "            \n",
    "print('test size =', y_test.shape[0])\n",
    "# print('non-zeron in y_hat =', np.count_nonzero(y_hat))\n",
    "print('right_predict_count =', y_test.shape[0] - wrong_predict_count)\n",
    "print('in right_predict_count: zero_to_zero =', zero_to_zero)\n",
    "print('in right_predict_count: one_to_one =', one_to_one)\n",
    "print('wrong_predict_count =', wrong_predict_count)\n",
    "print('in wrong_predict_count: zero_to_one =', zero_to_one)\n",
    "print(1.0*zero_to_one/(zero_to_one+zero_to_zero))\n",
    "print('in wrong_predict_count: one_to_zero =', one_to_zero)\n",
    "print(1.0*one_to_zero/(one_to_one+one_to_zero))\n",
    "\n",
    "print(1.0*55/373)\n",
    "\n",
    "# estimator.score(X_test, y_test)\n",
    "# estimator.staged_score(X_test, y_test)\n",
    "# rmse = np.sqrt(mean_squared_error(y_test, y_hat))\n",
    "# rmse\n",
    "\n",
    "# X_joint = pd.concat([X_train, y_train], axis=1, join='outer')\n",
    "# X_joint.head(n=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test size = 1722\n",
      "right_predict_count = 1587\n",
      "in right_predict_count: zero_to_zero = 1246\n",
      "in right_predict_count: one_to_one = 341\n",
      "wrong_predict_count = 135\n",
      "in wrong_predict_count: zero_to_one = 102\n",
      "0.0756676557864\n",
      "in wrong_predict_count: one_to_zero = 33\n",
      "0.0882352941176\n",
      "loss = 1.79147608374\n"
     ]
    }
   ],
   "source": [
    "y_classifier = np.copy(y_hat)\n",
    "y_regression = np.copy(y_hat_regression)\n",
    "\n",
    "predict_total = y_test.copy()\n",
    "\n",
    "for i in xrange(y_regression.shape[0]):\n",
    "    if abs(y_regression[i]) < 0.01 :\n",
    "        y_regression[i] = 0\n",
    "# print(y_regression)\n",
    "\n",
    "predict_total['y_classider'] = y_classifier\n",
    "predict_total['y_regression'] = y_regression\n",
    "\n",
    "# for i in xrange(y_regression.shape[0]):\n",
    "#     predict_total.append((y_classifier[i], y_regression[i], y_test.values[i]))\n",
    "# print(predict_total)\n",
    "\n",
    "wrong_predict_count = 0\n",
    "zero_to_one = 0\n",
    "one_to_zero = 0\n",
    "one_to_one = 0\n",
    "zero_to_zero = 0\n",
    "\n",
    "for i in xrange(y_test.shape[0]):\n",
    "    if y_test.values[i] == 0:\n",
    "        if y_regression[i] == 0:\n",
    "            zero_to_zero += 1\n",
    "        else:\n",
    "            wrong_predict_count += 1\n",
    "            zero_to_one += 1\n",
    "    else:\n",
    "        if y_regression[i] == 0:\n",
    "            wrong_predict_count += 1\n",
    "            one_to_zero += 1\n",
    "        else:\n",
    "            one_to_one += 1\n",
    "            \n",
    "print('test size =', y_test.shape[0])\n",
    "# print('non-zeron in y_hat =', np.count_nonzero(y_hat))\n",
    "print('right_predict_count =', y_test.shape[0] - wrong_predict_count)\n",
    "print('in right_predict_count: zero_to_zero =', zero_to_zero)\n",
    "print('in right_predict_count: one_to_one =', one_to_one)\n",
    "print('wrong_predict_count =', wrong_predict_count)\n",
    "print('in wrong_predict_count: zero_to_one =', zero_to_one)\n",
    "print(1.0*zero_to_one/(zero_to_one+zero_to_zero))\n",
    "print('in wrong_predict_count: one_to_zero =', one_to_zero)\n",
    "print(1.0*one_to_zero/(one_to_one+one_to_zero))\n",
    "\n",
    "rmse = np.sqrt(mean_squared_error(y_test, y_regression))\n",
    "print('loss =', rmse)\n",
    "        \n",
    "predict_total.head(n=1734)\n",
    "\n",
    "predict_total.to_csv(\"./three_combine_pre_yield_pre_wind_dir_2.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2014-06-26 00:00:00 y_regression = 13.7834448143 ; y_test = 3.393255295\n",
      "2013-12-26 22:00:00 y_regression = 21.913322184 ; y_test = 1.114827202\n",
      "2015-11-01 04:00:00 y_regression = 10.7357859531 ; y_test = 21.39074693\n",
      "2014-02-02 02:00:00 y_regression = 6.5774804909 ; y_test = 17.9138796\n",
      "2014-08-03 02:00:00 y_regression = 17.5013935327 ; y_test = 2.773132664\n",
      "2013-12-26 04:00:00 y_regression = 24.747770346 ; y_test = 11.23188406\n",
      "2014-04-19 04:00:00 y_regression = 24.165273132 ; y_test = 0.10451505\n",
      "2015-10-31 18:00:00 y_regression = 2.6518952063 ; y_test = 16.10925307\n",
      "2014-02-17 22:00:00 y_regression = 11.3566053519 ; y_test = 22.43589744\n",
      "2014-02-17 08:00:00 y_regression = 13.2044314373 ; y_test = 23.48104794\n"
     ]
    },
    {
     "data": {
      "image/png": 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B/xKx7FcBv/L71AB57/EdIt3v8tfsXn+d/izB+2T/Cvw5YvoL/X60jSAA/55fr0L7R9Ht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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11cb6d910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16, 4))\n",
    "err = y_regression - y_test.values.ravel()\n",
    "\n",
    "for i in xrange(len(err)):\n",
    "    if abs(err[i]) > 10:\n",
    "        print(y_test.index[i], \n",
    "              'y_regression =', y_regression[i], \n",
    "              '; y_test =', y_test.values[i][0])\n",
    "\n",
    "ax.plot_date(y_test.index, err, c='r', ms=3)\n",
    "ax.set_title('residuals on test data (each)', fontsize=20)\n",
    "ax.set_ylabel('error')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "# cc = 0\n",
    "# for i in xrange(err.shape[0]):\n",
    "#     if y_regression[i] - y_test.values[0][i] < 0:\n",
    "#         cc += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for i in [2,4,6,8,18,20,22]:\n",
    "    offset = i\n",
    "    y_pretime = labels.copy()\n",
    "    y_pretime.index = y_pretime.index + pd.DateOffset(hours=offset)\n",
    "    y_pretime = y_pretime.rename(columns={'yield': 'pre_yield_' + str(offset) + '_hours'})\n",
    "    y_joint = pd.concat([y_time, y_pretime], axis=1, join='inner')\n",
    "    X_joint = pd.concat([X_joint, y_joint], axis=1, join='outer')\n",
    "    X_joint = X_joint.drop(labels='yield', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_time = microclimate_train.copy()\n",
    "y_time = labels.copy()\n",
    "X_joint = microclimate_train.copy()\n",
    "\n",
    "for i in [2,4,6,8,18,20,22]:\n",
    "    offset = i\n",
    "    y_pretime = labels.copy()\n",
    "    y_pretime.index = y_pretime.index + pd.DateOffset(hours=offset)\n",
    "    y_pretime = y_pretime.rename(columns={'yield': 'pre_yield_' + str(offset) + '_hours'})\n",
    "    y_joint = pd.concat([y_time, y_pretime], axis=1, join='inner')\n",
    "    X_joint = pd.concat([X_joint, y_joint], axis=1, join='outer')\n",
    "    X_joint = X_joint.drop(labels='yield', axis=1)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}