03 - Classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification\n",
    "\n",
    "*Supervised* machine learning techniques involve training a model to operate on a set of *features* and predict a *label* using a dataset that includes some already-known label values. You can think of this function like this, in which ***y*** represents the label we want to predict and ***X*** represents the vector of features the model uses to predict it.\n",
    "\n",
    "$$y = f([x_1, x_2, x_3, ...])$$\n",
    "\n",
    "\n",
    "*Classification* is a form of supervised machine learning in which you train a model to use the features (the ***x*** values in our function) to predict a label (***y***) that calculates the probability of the observed case belonging to each of a number of possible classes, and predicting an appropriate label. The simplest form of classification is *binary* classification, in which the label is 0 or 1, representing one of two classes; for example, \"True\" or \"False\"; \"Internal\" or \"External\"; \"Profitable\" or \"Non-Profitable\"; and so on. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Binary Classification\n",
    "\n",
    "Let's start by looking at an example of *binary classification*, where the model must predict a label that belongs to one of two classes. In this exercise, we'll train a binary classifier to predict whether or not a patient should be tested for diabetes based on some medical data.\n",
    "\n",
    "### Explore the data\n",
    "\n",
    "Run the following cell to load a CSV file of patent data into a **Pandas** dataframe:\n",
    "\n",
    "> **Citation**: The diabetes dataset used in this exercise is based on data originally collected by the National Institute of Diabetes and Digestive and Kidney Diseases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PatientID</th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>PlasmaGlucose</th>\n",
       "      <th>DiastolicBloodPressure</th>\n",
       "      <th>TricepsThickness</th>\n",
       "      <th>SerumInsulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigree</th>\n",
       "      <th>Age</th>\n",
       "      <th>Diabetic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1354778</td>\n",
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       "      <td>171</td>\n",
       "      <td>80</td>\n",
       "      <td>34</td>\n",
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       "      <td>43.509726</td>\n",
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       "      <td>0.158365</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1640031</td>\n",
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       "      <td>78</td>\n",
       "      <td>25</td>\n",
       "      <td>304</td>\n",
       "      <td>29.582192</td>\n",
       "      <td>1.282870</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1424119</td>\n",
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      ],
      "text/plain": [
       "   PatientID  Pregnancies  PlasmaGlucose  DiastolicBloodPressure  \\\n",
       "0    1354778            0            171                      80   \n",
       "1    1147438            8             92                      93   \n",
       "2    1640031            7            115                      47   \n",
       "3    1883350            9            103                      78   \n",
       "4    1424119            1             85                      59   \n",
       "\n",
       "   TricepsThickness  SerumInsulin        BMI  DiabetesPedigree  Age  Diabetic  \n",
       "0                34            23  43.509726          1.213191   21         0  \n",
       "1                47            36  21.240576          0.158365   23         0  \n",
       "2                52            35  41.511523          0.079019   23         0  \n",
       "3                25           304  29.582192          1.282870   43         1  \n",
       "4                27            35  42.604536          0.549542   22         0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# load the training dataset\n",
    "diabetes = pd.read_csv('data/diabetes.csv')\n",
    "diabetes.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This data consists of diagnostic information about some patients who have been tested for diabetes. Scroll to the right if necessary, and note that the final column in the dataset (**Diabetic**) contains the value ***0*** for patients who tested negative for diabetes, and ***1*** for patients who tested positive. This is the label that we will train our model to predict; most of the other columns (**Pregnancies**,**PlasmaGlucose**,**DiastolicBloodPressure**, and so on) are the features we will use to predict the **Diabetic** label.\n",
    "\n",
    "Let's separate the features from the labels - we'll call the features ***X*** and the label ***y***:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Patient 1 \n",
      "  Features: [0.0, 171.0, 80.0, 34.0, 23.0, 43.50972593, 1.213191354, 21.0] \n",
      "  Label: 0\n",
      "Patient 2 \n",
      "  Features: [8.0, 92.0, 93.0, 47.0, 36.0, 21.24057571, 0.158364981, 23.0] \n",
      "  Label: 0\n",
      "Patient 3 \n",
      "  Features: [7.0, 115.0, 47.0, 52.0, 35.0, 41.51152348, 0.079018568, 23.0] \n",
      "  Label: 0\n",
      "Patient 4 \n",
      "  Features: [9.0, 103.0, 78.0, 25.0, 304.0, 29.58219193, 1.282869847, 43.0] \n",
      "  Label: 1\n"
     ]
    }
   ],
   "source": [
    "# Separate features and labels\n",
    "features = ['Pregnancies','PlasmaGlucose','DiastolicBloodPressure','TricepsThickness','SerumInsulin','BMI','DiabetesPedigree','Age']\n",
    "label = 'Diabetic'\n",
    "X, y = diabetes[features].values, diabetes[label].values\n",
    "\n",
    "for n in range(0,4):\n",
    "    print(\"Patient\", str(n+1), \"\\n  Features:\",list(X[n]), \"\\n  Label:\", y[n])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's compare the feature distributions for each label value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "features = ['Pregnancies','PlasmaGlucose','DiastolicBloodPressure','TricepsThickness','SerumInsulin','BMI','DiabetesPedigree','Age']\n",
    "for col in features:\n",
    "    diabetes.boxplot(column=col, by='Diabetic', figsize=(6,6))\n",
    "    plt.title(col)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For some of the features, there's a noticeable difference in the distribution for each label value. In particular, **Pregnancies** and **Age** show markedly different distributions for diabetic patients than for non-diabetic patients. These features may help predict whether or not a patient is diabetic.\n",
    "\n",
    "### Split the data\n",
    "\n",
    "Our dataset includes known values for the label, so we can use this to train a classifier so that it finds a statistical relationship between the features and the label value; but how will we know if our model is any good? How do we know it will predict correctly when we use it with new data that it wasn't trained with? Well, we can take advantage of the fact we have a large dataset with known label values, use only some of it to train the model, and hold back some to test the trained model - enabling us to compare the predicted labels with the already known labels in the test set.\n",
    "\n",
    "In Python, the **scikit-learn** package contains a large number of functions we can use to build a machine learning model - including a **train_test_split** function that ensures we get a statistically random split of training and test data. We'll use that to split the data into 70% for training and hold back 30% for testing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training cases: 10500\n",
      "Test cases: 4500\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Split data 70%-30% into training set and test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=0)\n",
    "\n",
    "print ('Training cases: %d\\nTest cases: %d' % (X_train.shape[0], X_test.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train and Evaluate a Binary Classification Model\n",
    "OK, now we're ready to train our model by fitting the training features (**X_train**) to the training labels (**y_train**). There are various algorithms we can use to train the model. In this example, we'll use *Logistic Regression*, which (despite its name) is a well-established algorithm for classification. In addition to the training features and labels, we'll need to set a *regularization* parameter. This is used to counteract any bias in the sample, and help the model generalize well by avoiding *overfitting* the model to the training data.\n",
    "\n",
    "> **Note**: Parameters for machine learning algorithms are generally referred to as *hyperparameters* (to a data scientist, *parameters* are values in the data itself - *hyperparameters* are defined externally from the data!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression(C=100.0, solver='liblinear')\n"
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# Set regularization rate\n",
    "reg = 0.01\n",
    "\n",
    "# train a logistic regression model on the training set\n",
    "model = LogisticRegression(C=1/reg, solver=\"liblinear\").fit(X_train, y_train)\n",
    "print (model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we've trained the model using the training data, we can use the test data we held back to evaluate how well it predicts. Again, **scikit-learn** can help us do this. Let's start by using the model to predict labels for our test set, and compare the predicted labels to the known labels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted labels:  [0 0 0 ... 0 1 0]\n",
      "Actual labels:     [0 0 1 ... 1 1 1]\n"
     ]
    }
   ],
   "source": [
    "predictions = model.predict(X_test)\n",
    "print('Predicted labels: ', predictions)\n",
    "print('Actual labels:    ' ,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The arrays of labels are too long to be displayed in the notebook output, so we can only compare a few values. Even if we printed out all of the predicted and actual labels, there are too many of them to make this a sensible way to evaluate the model. Fortunately, **scikit-learn** has a few more tricks up its sleeve, and it provides some metrics that we can use to evaluate the model.\n",
    "\n",
    "The most obvious thing you might want to do is to check the *accuracy* of the predictions - in simple terms, what proportion of the labels did the model predict correctly?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.7888888888888889\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "print('Accuracy: ', accuracy_score(y_test, predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The accuracy is returned as a decimal value - a value of 1.0 would mean that the model got 100% of the predictions right; while an accuracy of 0.0 is, well, pretty useless!\n",
    "\n",
    "Accuracy seems like a sensible metric to evaluate (and to a certain extent it is), but you need to be careful about drawing too many conclusions from the accuracy of a classifier. Remember that it's simply a measure of how many cases were predicted correctly. Suppose only 3% of the population is diabetic. You could create a classifier that always just predicts 0, and it would be 97% accurate - but not terribly helpful in identifying patients with diabetes!\n",
    "\n",
    "Fortunately, there are some other metrics that reveal a little more about how our model is performing. Scikit-Learn includes the ability to create a *classification report* that provides more insight than raw accuracy alone."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.88      0.85      2986\n",
      "           1       0.72      0.60      0.66      1514\n",
      "\n",
      "    accuracy                           0.79      4500\n",
      "   macro avg       0.77      0.74      0.75      4500\n",
      "weighted avg       0.78      0.79      0.78      4500\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn. metrics import classification_report\n",
    "\n",
    "print(classification_report(y_test, predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The classification report includes the following metrics for each class  (0 and 1)\n",
    "\n",
    "> note that the header row may not line up with the values!\n",
    "\n",
    "* *Precision*: Of the predictions the model made for this class, what proportion were correct?\n",
    "* *Recall*: Out of all of the instances of this class in the test dataset, what proportion did the model identify?\n",
    "* *F1-Score*: An average metric that takes both precision and recall into account.\n",
    "* *Support*: How many instances of this class are there in the test dataset?\n",
    "\n",
    "The classification report also includes averages for these metrics, including a weighted average that allows for the imbalance in the number of cases of each class.\n",
    "\n",
    "Because this is a *binary* classification problem, the ***1*** class is considered *positive* and its precision and recall are particularly interesting - these in effect answer the questions:\n",
    "\n",
    "- Of all the patients the model predicted are diabetic, how many are actually diabetic?\n",
    "- Of all the patients that are actually diabetic, how many did the model identify?\n",
    "\n",
    "You can retrieve these values on their own by using the **precision_score** and **recall_score** metrics in scikit-learn (which by default assume a binary classification model)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overall Precision: 0.7231012658227848\n",
      "Overall Recall: 0.6036988110964333\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import precision_score, recall_score\n",
    "\n",
    "print(\"Overall Precision:\",precision_score(y_test, predictions))\n",
    "print(\"Overall Recall:\",recall_score(y_test, predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The precision and recall metrics are derived from four possible prediction outcomes:\n",
    "* *True Positives*: The predicted label and the actual label are both 1.\n",
    "* *False Positives*: The predicted label is 1, but the actual label is 0.\n",
    "* *False Negatives*: The predicted label is 0, but the actual label is 1.\n",
    "* *True Negatives*: The predicted label and the actual label are both 0.\n",
    "\n",
    "These metrics are generally tabulated for the test set and shown together as a *confusion matrix*, which takes the following form:\n",
    "\n",
    "<table style=\"border: 1px solid black;\">\n",
    "    <tr style=\"border: 1px solid black;\">\n",
    "        <td style=\"border: 1px solid black;color: black;\" bgcolor=\"lightgray\">TN</td><td style=\"border: 1px solid black;color: black;\" bgcolor=\"white\">FP</td>\n",
    "    </tr>\n",
    "    <tr style=\"border: 1px solid black;\">\n",
    "        <td style=\"border: 1px solid black;color: black;\" bgcolor=\"white\">FN</td><td style=\"border: 1px solid black;color: black;\" bgcolor=\"lightgray\">TP</td>\n",
    "    </tr>\n",
    "</table>\n",
    "\n",
    "Note that the correct (*true*) predictions form a diagonal line from top left to bottom right - these figures should be significantly higher than the *false* predictions if the model is any good.\n",
    "\n",
    "In Python, you can use the **sklearn.metrics.confusion_matrix** function to find these values for a trained classifier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2636  350]\n",
      " [ 600  914]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "# Print the confusion matrix\n",
    "cm = confusion_matrix(y_test, predictions)\n",
    "print (cm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Until now, we've considered the predictions from the model as being either 1 or 0 class labels. Actually, things are a little more complex than that. Statistical machine learning algorithms, like logistic regression, are based on *probability*; so what actually gets predicted by a binary classifier is the probability that the label is true (**P(y)**) and the probability that the label is false (1 - **P(y)**). A threshold value of 0.5 is used to decide whether the predicted label is a 1 (*P(y) > 0.5*) or a 0 (*P(y) <= 0.5*). You can use the **predict_proba** method to see the probability pairs for each case:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.81673646 0.18326354]\n",
      " [0.96318101 0.03681899]\n",
      " [0.80901144 0.19098856]\n",
      " ...\n",
      " [0.60687997 0.39312003]\n",
      " [0.1060785  0.8939215 ]\n",
      " [0.63833903 0.36166097]]\n"
     ]
    }
   ],
   "source": [
    "y_scores = model.predict_proba(X_test)\n",
    "print(y_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The decision to score a prediction as a 1 or a 0 depends on the threshold to which the predicted probabilities are compared. If we were to change the threshold, it would affect the predictions; and therefore change the metrics in the confusion matrix. A common way to evaluate a classifier is to examine the *true positive rate* (which is another name for recall) and the *false positive rate* for a range of possible thresholds. These rates are then plotted against all possible thresholds to form a chart known as a *received operator characteristic (ROC) chart*, like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# calculate ROC curve\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_scores[:,1])\n",
    "\n",
    "# plot ROC curve\n",
    "fig = plt.figure(figsize=(6, 6))\n",
    "# Plot the diagonal 50% line\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "# Plot the FPR and TPR achieved by our model\n",
    "plt.plot(fpr, tpr)\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curve')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ROC chart shows the curve of the true and false positive rates for different threshold values between 0 and 1. A perfect classifier would have a curve that goes straight up the left side and straight across the top. The diagonal line across the chart represents the probability of predicting correctly with a 50/50 random prediction; so you obviously want the curve to be higher than that (or your model is no better than simply guessing!).\n",
    "\n",
    "The area under the curve (AUC) is a value between 0 and 1 that quantifies the overall performance of the model. The closer to 1 this value is, the better the model. Once again, scikit-Learn includes a function to calculate this metric."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC: 0.8568301125198085\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "auc = roc_auc_score(y_test,y_scores[:,1])\n",
    "print('AUC: ' + str(auc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Perform preprocessing in a pipeline\n",
    "\n",
    "In this case, the ROC curve and its AUC indicate that the model performs better than a random guess which is not bad considering we performed very little preprocessing of the data.\n",
    "\n",
    "In practice, it's common to perform some preprocessing of the data to make it easier for the algorithm to fit a model to it. There's a huge range of preprocessing transformations you can perform to get your data ready for modeling, but we'll limit ourselves to a few common techniques:\n",
    "\n",
    "- Scaling numeric features so they're on the same scale. This prevents features with large values from producing coefficients that disproportionately affect the predictions.\n",
    "- Encoding categorical variables. For example, by using a *one hot encoding* technique you can create individual binary (true/false) features for each possible category value.\n",
    "\n",
    "To apply these preprocessing transformations, we'll make use of a Scikit-Learn feature named *pipelines*. These enable us to define a set of preprocessing steps that end with an algorithm. You can then fit the entire pipeline to the data, so that the model encapsulates all of the preprocessing steps as well as the regression algorithm. This is useful, because when we want to use the model to predict values from new data, we need to apply the same transformations (based on the same statistical distributions and category encodings used with the training data).\n",
    "\n",
    ">**Note**: The term *pipeline* is used extensively in machine learning, often to mean very different things! In this context, we're using it to refer to pipeline objects in Scikit-Learn, but you may see it used elsewhere to mean something else.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(steps=[('preprocessor',\n",
      "                 ColumnTransformer(transformers=[('num',\n",
      "                                                  Pipeline(steps=[('scaler',\n",
      "                                                                   StandardScaler())]),\n",
      "                                                  [0, 1, 2, 3, 4, 5, 6]),\n",
      "                                                 ('cat',\n",
      "                                                  Pipeline(steps=[('onehot',\n",
      "                                                                   OneHotEncoder(handle_unknown='ignore'))]),\n",
      "                                                  [7])])),\n",
      "                ('logregressor',\n",
      "                 LogisticRegression(C=100.0, solver='liblinear'))])\n"
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "import numpy as np\n",
    "\n",
    "# Define preprocessing for numeric columns (normalize them so they're on the same scale)\n",
    "numeric_features = [0,1,2,3,4,5,6]\n",
    "numeric_transformer = Pipeline(steps=[\n",
    "    ('scaler', StandardScaler())])\n",
    "\n",
    "# Define preprocessing for categorical features (encode the Age column)\n",
    "categorical_features = [7]\n",
    "categorical_transformer = Pipeline(steps=[\n",
    "    ('onehot', OneHotEncoder(handle_unknown='ignore'))])\n",
    "\n",
    "# Combine preprocessing steps\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('num', numeric_transformer, numeric_features),\n",
    "        ('cat', categorical_transformer, categorical_features)])\n",
    "\n",
    "# Create preprocessing and training pipeline\n",
    "pipeline = Pipeline(steps=[('preprocessor', preprocessor),\n",
    "                           ('logregressor', LogisticRegression(C=1/reg, solver=\"liblinear\"))])\n",
    "\n",
    "\n",
    "# fit the pipeline to train a logistic regression model on the training set\n",
    "model = pipeline.fit(X_train, (y_train))\n",
    "print (model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The pipeline encapsulates the preprocessing steps as well as model training.\n",
    "\n",
    "Let's use the model trained by this pipeline to predict labels for our test set, and compare the performance metrics with the basic model we created previously."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      " [[2667  319]\n",
      " [ 406 1108]] \n",
      "\n",
      "Accuracy: 0.8388888888888889\n",
      "Overall Precision: 0.7764540995094604\n",
      "Overall Recall: 0.7318361955085865\n",
      "AUC: 0.9202438327341773\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get predictions from test data\n",
    "predictions = model.predict(X_test)\n",
    "y_scores = model.predict_proba(X_test)\n",
    "\n",
    "# Get evaluation metrics\n",
    "cm = confusion_matrix(y_test, predictions)\n",
    "print ('Confusion Matrix:\\n',cm, '\\n')\n",
    "print('Accuracy:', accuracy_score(y_test, predictions))\n",
    "print(\"Overall Precision:\",precision_score(y_test, predictions))\n",
    "print(\"Overall Recall:\",recall_score(y_test, predictions))\n",
    "auc = roc_auc_score(y_test,y_scores[:,1])\n",
    "print('AUC: ' + str(auc))\n",
    "\n",
    "# calculate ROC curve\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_scores[:,1])\n",
    "\n",
    "# plot ROC curve\n",
    "fig = plt.figure(figsize=(6, 6))\n",
    "# Plot the diagonal 50% line\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "# Plot the FPR and TPR achieved by our model\n",
    "plt.plot(fpr, tpr)\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curve')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results look a little better, so clearly preprocessing the data has made a difference.\n",
    "\n",
    "### Try a different algorithm\n",
    "\n",
    "Now let's try a different algorithm. Previously we used a logistic regression algorithm, which is a *linear* algorithm. There are many kinds of classification algorithm we could try, including:\n",
    "\n",
    "- **Support Vector Machine algorithms**: Algorithms that define a *hyperplane* that separates classes.\n",
    "- **Tree-based algorithms**: Algorithms that build a decision tree to reach a prediction\n",
    "- **Ensemble algorithms**: Algorithms that combine the outputs of multiple base algorithms to improve generalizability.\n",
    "\n",
    "This time, We'll use the same preprocessing steps as before, but we'll train the model using an *ensemble* algorithm named *Random Forest* that combines the outputs of multiple random decision trees (for more details, see the [Scikit-Learn documentation](https://scikit-learn.org/stable/modules/ensemble.html#forests-of-randomized-trees))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(steps=[('preprocessor',\n",
      "                 ColumnTransformer(transformers=[('num',\n",
      "                                                  Pipeline(steps=[('scaler',\n",
      "                                                                   StandardScaler())]),\n",
      "                                                  [0, 1, 2, 3, 4, 5, 6]),\n",
      "                                                 ('cat',\n",
      "                                                  Pipeline(steps=[('onehot',\n",
      "                                                                   OneHotEncoder(handle_unknown='ignore'))]),\n",
      "                                                  [7])])),\n",
      "                ('logregressor', RandomForestClassifier())])\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Create preprocessing and training pipeline\n",
    "pipeline = Pipeline(steps=[('preprocessor', preprocessor),\n",
    "                           ('logregressor', RandomForestClassifier(n_estimators=100))])\n",
    "\n",
    "# fit the pipeline to train a random forest model on the training set\n",
    "model = pipeline.fit(X_train, (y_train))\n",
    "print (model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the performance metrics for the new model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      " [[2855  131]\n",
      " [ 182 1332]] \n",
      "\n",
      "Accuracy: 0.9304444444444444\n",
      "Overall Precision: 0.9104579630895421\n",
      "Overall Recall: 0.8797886393659181\n",
      "\n",
      "AUC: 0.9819348505265878\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictions = model.predict(X_test)\n",
    "y_scores = model.predict_proba(X_test)\n",
    "cm = confusion_matrix(y_test, predictions)\n",
    "print ('Confusion Matrix:\\n',cm, '\\n')\n",
    "print('Accuracy:', accuracy_score(y_test, predictions))\n",
    "print(\"Overall Precision:\",precision_score(y_test, predictions))\n",
    "print(\"Overall Recall:\",recall_score(y_test, predictions))\n",
    "auc = roc_auc_score(y_test,y_scores[:,1])\n",
    "print('\\nAUC: ' + str(auc))\n",
    "\n",
    "# calculate ROC curve\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_scores[:,1])\n",
    "\n",
    "# plot ROC curve\n",
    "fig = plt.figure(figsize=(6, 6))\n",
    "# Plot the diagonal 50% line\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "# Plot the FPR and TPR achieved by our model\n",
    "plt.plot(fpr, tpr)\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curve')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That looks better!\n",
    "\n",
    "### Use the Model for Inferencing\n",
    "Now that we have a reasonably useful trained model, we can save it for use later to predict labels for new data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['./models/diabetes_model.pkl']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "\n",
    "# Save the model as a pickle file\n",
    "filename = './models/diabetes_model.pkl'\n",
    "joblib.dump(model, filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we have some new observations for which the label is unknown, we can load the model and use it to predict values for the unknown label:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New sample: [2.0, 180.0, 74.0, 24.0, 21.0, 23.9091702, 1.488172308, 22.0]\n",
      "Predicted class is 1\n"
     ]
    }
   ],
   "source": [
    "# Load the model from the file\n",
    "model = joblib.load(filename)\n",
    "\n",
    "# predict on a new sample\n",
    "# The model accepts an array of feature arrays (so you can predict the classes of multiple patients in a single call)\n",
    "# We'll create an array with a single array of features, representing one patient\n",
    "X_new = np.array([[2,180,74,24,21,23.9091702,1.488172308,22]])\n",
    "print ('New sample: {}'.format(list(X_new[0])))\n",
    "\n",
    "# Get a prediction\n",
    "pred = model.predict(X_new)\n",
    "\n",
    "# The model returns an array of predictions - one for each set of features submitted\n",
    "# In our case, we only submitted one patient, so our prediction is the first one in the resulting array.\n",
    "print('Predicted class is {}'.format(pred[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multiclass Classification\n",
    "\n",
    "Binary classification techniques work well when the data observations belong to one of two classes or categories, such as \"True\" or \"False\". When the data can be categorized into more than two classes, you must use a multiclass classification algorithm.\n",
    "\n",
    "Multiclass classification can be thought of as a combination of multiple binary classifiers. There are two ways in which you approach the problem:\n",
    "\n",
    "- **One vs Rest (OVR)**, in which a classifier is created for each possible class value, with a positive outcome for cases where the prediction is *this* class, and negative predictions for cases where the prediction is any other class. A classification problem with four possible shape classes (*square*, *circle*, *triangle*, *hexagon*) would require four classifiers that predict:\n",
    "    - *square* or not\n",
    "    - *circle* or not\n",
    "    - *triangle* or not\n",
    "    - *hexagon* or not\n",
    "    \n",
    "- **One vs One (OVO)**, in which a classifier for each possible pair of classes is created. The classification problem with four shape classes would require the following binary classifiers:\n",
    "    - *square* or *circle*\n",
    "    - *square* or *triangle*\n",
    "    - *square* or *hexagon*\n",
    "    - *circle* or *triangle*\n",
    "    - *circle* or *hexagon*\n",
    "    - *triangle* or *hexagon*\n",
    "\n",
    "In both approaches, the overall model that combines the classifiers generates a vector of predictions in which the probabilities generated from the individual binary classifiers are used to determine which class to predict.\n",
    "\n",
    "Fortunately, in most machine learning frameworks, including scikit-learn, implementing a multiclass classification model is not significantly more complex than binary classification - and in most cases, the estimators used for binary classification implicitly support multiclass classification by abstracting an OVR algorithm, an OVO algorithm, or by allowing a choice of either.\n",
    "\n",
    "> **More Information**: To learn more about estimator support for multiclass classification in Scikit-Learn, see the [Scikit-Learn documentation](https://scikit-learn.org/stable/modules/multiclass.html).\n",
    "\n",
    "### Explore the data\n",
    "\n",
    "Let's start by examining a dataset that contains observations of multiple classes. We'll use a dataset that contains observations of three different species of penguin.\n",
    "\n",
    "> **Citation**: The penguins dataset used in the this exercise is a subset of data collected and made available by [Dr. Kristen\n",
    "Gorman](https://www.uaf.edu/cfos/people/faculty/detail/kristen-gorman.php)\n",
    "and the [Palmer Station, Antarctica LTER](https://pal.lternet.edu/), a\n",
    "member of the [Long Term Ecological Research\n",
    "Network](https://lternet.edu/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CulmenLength</th>\n",
       "      <th>CulmenDepth</th>\n",
       "      <th>FlipperLength</th>\n",
       "      <th>BodyMass</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>43.3</td>\n",
       "      <td>14.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>4575.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>42.4</td>\n",
       "      <td>17.3</td>\n",
       "      <td>181.0</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>45.7</td>\n",
       "      <td>17.3</td>\n",
       "      <td>193.0</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>49.8</td>\n",
       "      <td>15.9</td>\n",
       "      <td>229.0</td>\n",
       "      <td>5950.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>45.6</td>\n",
       "      <td>19.4</td>\n",
       "      <td>194.0</td>\n",
       "      <td>3525.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>44.1</td>\n",
       "      <td>19.7</td>\n",
       "      <td>196.0</td>\n",
       "      <td>4400.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>40.2</td>\n",
       "      <td>17.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>3450.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>46.9</td>\n",
       "      <td>14.6</td>\n",
       "      <td>222.0</td>\n",
       "      <td>4875.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>49.2</td>\n",
       "      <td>15.2</td>\n",
       "      <td>221.0</td>\n",
       "      <td>6300.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>54.2</td>\n",
       "      <td>20.8</td>\n",
       "      <td>201.0</td>\n",
       "      <td>4300.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     CulmenLength  CulmenDepth  FlipperLength  BodyMass  Species\n",
       "260          43.3         14.0          208.0    4575.0        1\n",
       "296          42.4         17.3          181.0    3600.0        2\n",
       "328          45.7         17.3          193.0    3600.0        2\n",
       "263          49.8         15.9          229.0    5950.0        1\n",
       "335          45.6         19.4          194.0    3525.0        2\n",
       "43           44.1         19.7          196.0    4400.0        0\n",
       "122          40.2         17.0          176.0    3450.0        0\n",
       "249          46.9         14.6          222.0    4875.0        1\n",
       "169          49.2         15.2          221.0    6300.0        1\n",
       "307          54.2         20.8          201.0    4300.0        2"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# load the training dataset\n",
    "penguins = pd.read_csv('data/penguins.csv')\n",
    "\n",
    "# Display a random sample of 10 observations\n",
    "sample = penguins.sample(10)\n",
    "sample"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset contains the following columns:\n",
    "* **CulmenLength**: The length in mm of the penguin's culmen (bill).\n",
    "* **CulmenDepth**: The depth in mm of the penguin's culmen.\n",
    "* **FlipperLength**: The length in mm of the penguin's flipper.\n",
    "* **BodyMass**: The body mass of the penguin in grams.\n",
    "* **Species**: An integer value that represents the species of the penguin.\n",
    "\n",
    "The **Species** column is the label we want to train a model to predict. The dataset includes three possible species, which are encoded as 0, 1, and 2. The actual species names are revealed by the code below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['CulmenLength' 'CulmenDepth' 'FlipperLength' 'BodyMass' 'Species'] SpeciesName\n",
      "[ 41.1 18.6 189.0 3325.0 0 ] Adelie\n",
      "[ 46.1 18.2 178.0 3250.0 2 ] Chinstrap\n",
      "[ 38.8 20.0 190.0 3950.0 0 ] Adelie\n",
      "[ 42.3 21.2 191.0 4150.0 0 ] Adelie\n",
      "[ 44.1 19.7 196.0 4400.0 0 ] Adelie\n",
      "[ 49.9 16.1 213.0 5400.0 1 ] Gentoo\n",
      "[ 52.0 18.1 201.0 4050.0 2 ] Chinstrap\n",
      "[ 49.8 15.9 229.0 5950.0 1 ] Gentoo\n",
      "[ 43.6 13.9 217.0 4900.0 1 ] Gentoo\n",
      "[ 35.0 17.9 192.0 3725.0 0 ] Adelie\n"
     ]
    }
   ],
   "source": [
    "penguin_classes = ['Adelie', 'Gentoo', 'Chinstrap']\n",
    "print(sample.columns[0:5].values, 'SpeciesName')\n",
    "for index, row in penguins.sample(10).iterrows():\n",
    "    print('[',row[0], row[1], row[2], row[3], int(row[4]),']',penguin_classes[int(row[4])])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we know what the features and labels in the data represent, let's explore the dataset. First, let's see if there are any missing (*null*) values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CulmenLength     2\n",
       "CulmenDepth      2\n",
       "FlipperLength    2\n",
       "BodyMass         2\n",
       "Species          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count the number of null values for each column\n",
    "penguins.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like there are some missing feature values, but no missing labels. Let's dig a little deeper and see the rows that contain nulls."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CulmenLength</th>\n",
       "      <th>CulmenDepth</th>\n",
       "      <th>FlipperLength</th>\n",
       "      <th>BodyMass</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     CulmenLength  CulmenDepth  FlipperLength  BodyMass  Species\n",
       "3             NaN          NaN            NaN       NaN        0\n",
       "271           NaN          NaN            NaN       NaN        1"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show rows containing nulls\n",
    "penguins[penguins.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are two rows that contain no feature values at all (*NaN* stands for \"not a number\"), so these won't be useful in training a model. Let's discard them from the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CulmenLength     0\n",
       "CulmenDepth      0\n",
       "FlipperLength    0\n",
       "BodyMass         0\n",
       "Species          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop rows containing NaN values\n",
    "penguins=penguins.dropna()\n",
    "#Confirm there are now no nulls\n",
    "penguins.isnull().sum()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we've dealt with the missing values, let's explore how the features relate to the label by creating some box charts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "penguin_features = ['CulmenLength','CulmenDepth','FlipperLength','BodyMass']\n",
    "penguin_label = 'Species'\n",
    "for col in penguin_features:\n",
    "    penguins.boxplot(column=col, by=penguin_label, figsize=(6,6))\n",
    "    plt.title(col)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the box plots, it looks like species 0 and 2 (Amelie and Chinstrap) have similar data profiles for culmen depth, flipper length, and body mass, but Chinstraps tend to have longer culmens. Species 1 (Gentoo) tends to have fairly clearly differentiated features from the others; which should help us train a good classification model.\n",
    "\n",
    "### Prepare the data\n",
    "\n",
    "Just as for binary classification, before training the model, we need to separate the features and label, and then split the data into subsets for training and validation. We'll also apply a *stratification* technique when splitting the data to maintain the proportion of each label value in the training and validation datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Set: 239, Test Set: 103 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Separate features and labels\n",
    "penguins_X, penguins_y = penguins[penguin_features].values, penguins[penguin_label].values\n",
    "\n",
    "# Split data 70%-30% into training set and test set\n",
    "x_penguin_train, x_penguin_test, y_penguin_train, y_penguin_test = train_test_split(penguins_X, penguins_y,\n",
    "                                                                                    test_size=0.30,\n",
    "                                                                                    random_state=0,\n",
    "                                                                                    stratify=penguins_y)\n",
    "\n",
    "print ('Training Set: %d, Test Set: %d \\n' % (x_penguin_train.shape[0], x_penguin_test.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train and evaluate a multiclass classifier\n",
    "\n",
    "Now that we have a set of training features and corresponding training labels, we can fit a multiclass classification algorithm to the data to create a model. Most scikit-learn classification algorithms inherently support multiclass classification. We'll try a logistic regression algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression(C=10.0, max_iter=10000)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# Set regularization rate\n",
    "reg = 0.1\n",
    "\n",
    "# train a logistic regression model on the training set\n",
    "multi_model = LogisticRegression(C=1/reg, solver='lbfgs', multi_class='auto', max_iter=10000).fit(x_penguin_train, y_penguin_train)\n",
    "print (multi_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can use the trained model to predict the labels for the test features, and compare the predicted labels to the actual labels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted labels:  [0 1 0 2 2 1 1 1 0 2 2 1 2 1 2]\n",
      "Actual labels   :  [0 1 2 2 2 1 1 1 0 2 2 1 2 1 2]\n"
     ]
    }
   ],
   "source": [
    "penguin_predictions = multi_model.predict(x_penguin_test)\n",
    "print('Predicted labels: ', penguin_predictions[:15])\n",
    "print('Actual labels   : ' ,y_penguin_test[:15])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at a classification report."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.98      0.97        45\n",
      "           1       1.00      1.00      1.00        37\n",
      "           2       0.95      0.90      0.93        21\n",
      "\n",
      "    accuracy                           0.97       103\n",
      "   macro avg       0.97      0.96      0.96       103\n",
      "weighted avg       0.97      0.97      0.97       103\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn. metrics import classification_report\n",
    "\n",
    "print(classification_report(y_penguin_test, penguin_predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As with binary classification, the report includes *precision* and *recall* metrics for each class. However, while with binary classification we could focus on the scores for the *positive* class; in this case, there are multiple classes so we need to look at an overall metric (either the macro or weighted average) to get a sense of how well the model performs across all three classes.\n",
    "\n",
    "You can get the overall metrics separately from the report using the scikit-learn metrics score classes, but with multiclass results you must specify which average metric you want to use for precision and recall."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overall Accuracy: 0.970873786407767\n",
      "Overall Precision: 0.9688405797101449\n",
      "Overall Recall: 0.9608465608465608\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, precision_score, recall_score\n",
    "\n",
    "print(\"Overall Accuracy:\",accuracy_score(y_penguin_test, penguin_predictions))\n",
    "print(\"Overall Precision:\",precision_score(y_penguin_test, penguin_predictions, average='macro'))\n",
    "print(\"Overall Recall:\",recall_score(y_penguin_test, penguin_predictions, average='macro'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's look at the confusion matrix for our model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[44  0  1]\n",
      " [ 0 37  0]\n",
      " [ 2  0 19]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "# Print the confusion matrix\n",
    "mcm = confusion_matrix(y_penguin_test, penguin_predictions)\n",
    "print(mcm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The confusion matrix shows the intersection of predicted and actual label values for each class - in simple terms, the diagonal intersections from top-left to bottom-right indicate the number of correct predictions.\n",
    "\n",
    "When dealing with multiple classes, it's generally more intuitive to visualize this as a heat map, like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "plt.imshow(mcm, interpolation=\"nearest\", cmap=plt.cm.Blues)\n",
    "plt.colorbar()\n",
    "tick_marks = np.arange(len(penguin_classes))\n",
    "plt.xticks(tick_marks, penguin_classes, rotation=45)\n",
    "plt.yticks(tick_marks, penguin_classes)\n",
    "plt.xlabel(\"Predicted Species\")\n",
    "plt.ylabel(\"Actual Species\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The darker squares in the confusion matrix plot indicate high numbers of cases, and you can hopefully see a diagonal line of darker squares indicating cases where the predicted and actual label are the same.\n",
    "\n",
    "In the case of a multiclass classification model, a single ROC curve showing true positive rate vs false positive rate is not possible. However, you can use the rates for each class in a One vs Rest (OVR) comparison to create a ROC chart for each class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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330VISAi2bdtWzZkSERFRXaHVB2cGBAQgICBA5fjVq1ejadOmWLJkCQCgdevWiI+Px5dffolhw4ZVU5aqE0Ig93Gu4rNMKlO8z5fno6i4qMJ1lWILHqKoqPBf2wVy/7dZWFtZQ0+/pC7NyspCfkF+hdtVJ9aqoRUMDEp+JbKzs5Gbl6uRWEtzS0iNpGrH5ubmIjsnu8JY8wbmMDY2Vjs2Pz8fWY+yKow1k5nB1NRU7djCgkI8zHpYYaypiSnMzMzUjpXL5ch4kKGRWGMjY8UDY4uLipGeka6RWKlUCksLS8XntHtpGok10DeAlZVVlWLT76ejuLi43Fg9PT1YN7KuUmxGRgbkRfIK87C1sa1S7MPMhygsLNRILP9GlODfCPVjNfU34t+/r1ohagkAYseOHZXG+Pr6ipCQEKW27du3CwMDA1FYWFjuOvn5+SIzM1PxunXrlgAgMjMzNZW6EEKI4uJi4R3hLRAOgXAI049NlZYPjB6oWFbe69+Gr2j8v2VhEHA6KkpKnJLX3bS7itjmXluVlv33lXj5siK2nd/GSmOPnDipiPUcuK7S2J27Dyhiew2LrDQ2atPP/zsPY7+rNHbxN1sUsa9MqTw27IsfFLET34qoNHZ2eJQidnZ4VKWxE9+KUMSGffFDpbGvTPlOEbv4my2Vxg4c+7/YqE0/Vxrba1ikInbn7gOVxnoOXKeIPXLiZKWx7fw2KmITL1+uNLa511ZF7N20u5XGNu78v5+xEBXHAULYtNmvHGuYXWGsRfMjSrES07QKY02bxCvF6lsmVRhrZHtBKdbI9kKFsfqWSUqxpk3iK4yVmKYpxVo0P1LxuTDMVoq1abO/0vP2b407V/77w78RJS/+jSh5aeNvRHXIzMwUqn5/16kJxampqbCzs1Nqs7Ozg1wuR3p6+dXlwoULYWFhoXg5OTlVS265j3Nx/NZxzW/4sSlwy0fz2yUiItJREiGE0HYSACCRSLBjxw4MGTKkwhh3d3eMHz8e8+bNU7QdO3YMPXv2REpKCuzt7cusU1BQgIKCAsXnrKwsODk5ITMzU9Gdpgk5hTkwW1jS7Xf3rbuQGcpUH5aS50C280nRNiIb+UWPFcNSOTmAnW1J9971pHuQyQS7nJ9gl7P6sRyWKsFhqarF8m9ECf6NeHpsdQxLZWVlwcLCQqXv7zpV3Dz33HPo3Lkzli5dqmjbsWMHRowYgdzcXBgaGj51P+qcHHX8u7jJnpetVNg8lTwH2FyyLkZkAwb/WzcnB3jyO4nsbECmxmaJiIh0hTrf33VqWMrLywuxsbFKbfv374enp6dKhQ0RERHpPq1eLZWdnY2///5b8TkpKQlnzpyBlZUVmjZtinnz5uGff/7BunXrAABTpkzB8uXLERoaismTJ+PEiROIiIjAxo0btXUICnoSPfRy7qV4ryn6+sDw4f97T0RERJXTanETHx8PPz8/xefQ0FAAwLhx4xAVFYWUlBTcvHlTsdzV1RW7d+/G7NmzsWLFCjg6OmLZsmW14jJwE0MTHAo+pPHtGhsDW7ZofLNEREQ6q9bMuakp1TXn5plUMueGiIiIdHjODREREdHTaHVYSpfkFObAZakLACD5jWT1rpaS6AOOA//3/t/b5dVSREREamFxo0HpuRXfD6BS+sbA87s0mwwREVE9xWEpIiIi0iksboiIiEinsLipDeQ5QIys5CXP0XY2REREdRrn3GiLPA/Ak2fZyHOAooqfpUJERESqY3GjLYcCkHf7TxQXl3aelTxYDTlQ/FR4ZRQREZH6WNxoiJ5ED56Onor3qgj4fA8OX3xeuXFiyX9MTUsuA9fXBwY+uUqcj18gIiJ6OhY3GmJiaIK4yXEVB8hzgB9dSt6/lAw8vwewkQIXK9+usTGwi1eJExERqYzFTU0q+Nd9cAxMsGcvUFysvXSIiIh0EYsbLTIx0XYGREREuoeXgmtI7uNcuCxxgcsSF+Q+rvzKp5wcwMam5JXDK7+JiIg0ij03GiKEwI3MGyXvH2cDEgAGpv8LkOcpxadX8UkNREREVDn23GiKEP97v90O2NVGefmhgJrNh4iIqJ5icaMpqt6Ez8YH0Dd9ehwRERFVCYelqsOL1wFTO+W2vr8DKC4pbHIlWkmLiIioPmBxUx0MZMrzbQDAgJdGERER1QQOSxEREZFOYc+NhkgkemhjbKR4Xxk9PcDT83/viYiISHNY3GiIqYk1LryTr1KsiQkQV8mTGoiIiKjq2G9AREREOoXFDREREekUFjcakpuXjjafGqP1h1ZIv698++Hc3JLHLJS+7t0DXFxKXrkq3h6HiIiIVMM5NxpSXFyMiysPALd84LtRjouJ/1vWtSuQmFj+ev++sTERERE9O/bcaEhuLoBbPmqt4+MDmPJmxURERBrFnptqsHfvAwA2is9xceX30JiaAhLerJiIiEijWNxUAxMT5UqGvTNEREQ1h8NSREREpFNY3BAREZFO4bCUhujp6UHfMvnJezPtJkNERFSPsbjREOtG1pA/sNZ2GkRERPUeh6WIiIhIp7C4ISIiIp3C4kZDMjIyIHNKgMwpARkZGdpOh4iIqN7inBsNkRfJkXvb48n7NC1nQ0REVH+x54aIiIh0CosbIiIi0iksboiIiEinsLghIiIincLihoiIiHQKr5bSIInpPW2nQEREVO+xuNEQWxtbFOdoOwsiIiLisBQRERHpFBY3REREpFNY3GhIRkYGLN2OwtLtKB+/QEREpEWcc6Mh8iI5Mq/1fPKej18gIiLSFvbcEBERkU5hcUNEREQ6hcUNERER6RQWN0RERKRTWNwQERGRTuHVUppkyFsUExERaRuLGw2xtbGFKCz9JNNmKkRERPUah6WIiIhIp7C4ISIiIp2i9eJm5cqVcHV1hbGxMTw8PHDkyJFK46Ojo9GxY0eYmprCwcEB48ePx/3792so24o9zHwI27axsG0bi4eZD7WdDhERUb2l1eImJiYGs2bNwnvvvYfTp0/D19cXAQEBuHnzZrnxR48exdixYzFx4kRcuHABW7ZsQVxcHCZNmlTDmZdVWFiIe4n9cC+xHwoLC5++AhEREVULrRY3ixYtwsSJEzFp0iS0bt0aS5YsgZOTE1atWlVu/B9//AEXFxeEhITA1dUVPXv2xOuvv474+PgazpyIiIhqK60VN4WFhUhISIC/v79Su7+/P44fP17uOt7e3rh9+zZ2794NIQTu3r2LrVu3YtCgQRXup6CgAFlZWUovIiIi0l1aK27S09NRVFQEOzs7pXY7OzukpqaWu463tzeio6MRGBgIqVQKe3t7WFpa4uuvv65wPwsXLoSFhYXi5eTkpNHjICIiotpF6xOKJRKJ0mchRJm2UomJiQgJCcEHH3yAhIQE7N27F0lJSZgyZUqF2583bx4yMzMVr1u3bmk0fyIiIqpdtHYTP2tra+jr65fppUlLSyvTm1Nq4cKF8PHxwZw5cwAAHTp0gEwmg6+vLz766CM4ODiUWcfIyAhGRkaaPwAiIiKqlbTWcyOVSuHh4YHY2Fil9tjYWHh7e5e7Tm5uLvT0lFPW19cHUNLjQ0RERKTVxy+Ehobi1VdfhaenJ7y8vPDtt9/i5s2bimGmefPm4Z9//sG6desAAIMHD8bkyZOxatUq9O/fHykpKZg1axa6desGR0dHbR5KyeMXFPWVrTZTISIiqte0WtwEBgbi/v37WLBgAVJSUtCuXTvs3r0bzs7OAICUlBSle94EBwfj0aNHWL58Od58801YWlqid+/e+Oyzz7R1CERERFTLSEQ9G8/JysqChYUFMjMzYW5uru10iIiISAXqfH9r/WopXfEw8yGadPkFTbr8wscvEBERaZFWh6V0SWFhIf45/cKT92lazoaIiKj+Ys8NERER6RQWN0RERKRTWNwQERGRTmFxQ0RERDqFxQ0RERHpFBY3REREpFN4KbiGWFtZ425amuI9ERERaQeLGw3R09eDrQ2fKUVERKRtHJYiIiIincLiRkOysrLg5r0Nbt7bkJWVpe10iIiI6i0OS2lIfkE+rp0Y9uR9GszBh3ISERFpA3tuiIiISKewuCEiIiKdwuKGiIiIdAqLGyIiItIpLG6IiIhIp7C4ISIiIp3CS8E1xNrKGomXrzx576blbIiIiOovFjcaoqevh9bu7tpOg4iIqN7jsBQRERHpFBY3GpKVlYX2vTehfe9NfPwCERGRFnFYSkPyC/Lx18GRT97z8QtERETawp4bIiIi0iksboiIiEinsLghIiIincLihoiIiHQKixsiIiLSKSxuiIiISKfwUnANsWpohSMn4p6876zlbIiIiOovFjcaYmBggJ49umo7DSIionqPw1JERESkU1jcaEh2dja6DlqProPWIzs7W9vpEBER1VscltKQ3LxcxO9+9cn7NJiZmWk5IyIiovqpSj03165dw/vvv49Ro0YhLS0NALB3715cuHBBo8kRERERqUvt4ubw4cNo3749/vzzT2zfvl0xBHPu3DmEhYVpPEEiIiIidahd3MydOxcfffQRYmNjIZVKFe1+fn44ceKERpMjIiIiUpfaxc358+cxdOjQMu02Nja4f/++RpIiIiIiqiq1ixtLS0ukpKSUaT99+jQaN26skaSIiIiIqkrt4mb06NF45513kJqaColEguLiYhw7dgxvvfUWxo4dWx05EhEREalM7UvBP/74YwQHB6Nx48YQQqBNmzYoKirC6NGj8f7771dHjnWCVUMr7Nz925P3z2k5GyIiovpLIoQQVVnx+vXrOHXqFIqLi9G5c2e0aNFC07lVi6ysLFhYWCAzMxPm5ubaToeIiIhUoM73t9rDUgsWLEBubi6aNWuG4cOHY8SIEWjRogXy8vKwYMGCKidNREREpAlq99zo6+sjJSUFtra2Su3379+Hra0tioqKNJqgplVXz012djZeCN4KAPglajjvUExERKRB6nx/qz3nRggBiURSpv3s2bOwsrJSd3M6IzcvF4e3BZe8X8XHLxAREWmLysVNw4YNIZFIIJFI4O7urlTgFBUVITs7G1OmTKmWJImIiIhUpXJxs2TJEgghMGHCBMyfPx8WFhaKZVKpFC4uLvDy8qqWJImIiIhUpXJxM27cOACAq6srvL29YWhoWG1JEREREVWV2nNuevXqpXifl5eHx48fKy3n5dVERESkTWpfCp6bm4sZM2bA1tYWZmZmaNiwodKLiIiISJvULm7mzJmD3377DStXroSRkRG+++47zJ8/H46Ojli3bl115EhERESkMrWHpX7++WesW7cOzz//PCZMmABfX1+4ubnB2dkZ0dHRCAoKqo48az1Lc0tEbfrlyXt/LWdDRERUf6ld3GRkZMDV1RVAyfyajIwMAEDPnj0xdepUzWZXh0iNpBgX+IK20yAiIqr31B6WatasGZKTkwEAbdq0webNmwGU9OhYWlpqMjciIiIitald3IwfPx5nz54FAMybN08x92b27NmYM2eOxhOsK7KzszFoXAQGjYtAdna2ttMhIiKqt6r8VPBSN2/eRHx8PJo3b46OHTuqvf7KlSvxxRdfICUlBW3btsWSJUvg6+tbYXxBQQEWLFiAH374AampqWjSpAnee+89TJgwQaX9VdezpdLupcHuyfO27qalwdbG9ilrEBERkaqq7dlSjx8/hr+/P7755hu4u7sDAJo2bYqmTZtWKdGYmBjMmjULK1euhI+PD7755hsEBAQgMTGxwm2OGDECd+/eRUREBNzc3JCWlga5XF6l/RMREZHuUbvnxsbGBsePH0eLFi2eeefdu3dHly5dsGrVKkVb69atMWTIECxcuLBM/N69ezFy5Ehcv369yg/pZM8NERFR3aPO97fac27Gjh2LiIiIKidXqrCwEAkJCfD3V75s2t/fH8ePHy93nZ9++gmenp74/PPP0bhxY7i7u+Ott95CXl5ehfspKChAVlaW0ouIiIh0l9qXghcWFuK7775DbGwsPD09IZPJlJYvWrRIpe2kp6ejqKgIdnZ2Su12dnZITU0td53r16/j6NGjMDY2xo4dO5Ceno5p06YhIyMDa9euLXedhQsXYv78+SrlRERERHWf2sXNX3/9hS5dugAArly5orRMIpGoncB/1xFCVLid4uJiSCQSREdHK55KvmjRIgwfPhwrVqyAiYlJmXXmzZuH0NBQxeesrCw4OTmpnScRERHVDWoXNwcPHtTIjq2traGvr1+mlyYtLa1Mb04pBwcHNG7cWFHYACVzdIQQuH37drnzgIyMjGBkZKSRnImIiKj2U3vOjaZIpVJ4eHggNjZWqT02Nhbe3t7lruPj44M7d+4o3UfmypUr0NPTQ5MmTao136exNLfE4m+2YvE3W2FpbqnVXIiIiOqzZ77PzbOIiYnBq6++itWrV8PLywvffvst1qxZgwsXLsDZ2Rnz5s3DP//8o3ggZ3Z2Nlq3bo0ePXpg/vz5SE9Px6RJk9CrVy+sWbNGpX1W19VSREREVH2q7T43mhYYGIj79+9jwYIFSElJQbt27bB79244OzsDAFJSUnDz5k1FvJmZGWJjYzFz5kx4enqiUaNGGDFiBD766CNtHQIRERHVMlrtudGG6uq5yc3NRfCbGwEAUV+Ngqmpqca2TUREVN/VmZ4bXZKdk40tqycCAJYvSGNxQ0REpCVVmlC8fv16+Pj4wNHRETdu3AAALFmyBD/++KNGkyMiIiJSl9rFzapVqxAaGoqBAwfi4cOHKCoqAgBYWlpiyZIlms6PiIiISC1qFzdff/011qxZg/feew/6+vqKdk9PT5w/f16jyRERERGpS+3iJikpCZ07dy7TbmRkhJycHI0kRURERFRVahc3rq6uOHPmTJn2PXv2oE2bNprIiYiIiKjK1L5aas6cOZg+fTry8/MhhMDJkyexceNGLFy4EN9991115EhERESkMrWLm/Hjx0Mul+Ptt99Gbm4uRo8ejcaNG2Pp0qUYOXJkdeRYJ5g3MEfYF9FP3g/TcjZERET11zPdxC89PR3FxcWwtbXVZE7Vio9fICIiqnvU+f5We87N/Pnzce3aNQAlT/auS4UNERER6T61i5tt27bB3d0dPXr0wPLly3Hv3r3qyKvOyc3NxaQ5azFpzlrk5uZqOx0iIqJ6S+3i5ty5czh37hx69+6NRYsWoXHjxhg4cCA2bNhQr7/Us3OyEfHlBER8OQHZOdnaToeIiKjeqtLjF9q2bYtPPvkE169fx8GDB+Hq6opZs2bB3t5e0/kRERERqaVKxc2/yWQymJiYQCqV4vHjx5rIiYiIiKjKqlTcJCUl4eOPP0abNm3g6emJU6dOITw8HKmpqZrOj4iIiEgtat/nxsvLCydPnkT79u0xfvx4xX1uiIiIiGoDtYsbPz8/fPfdd2jbtm115ENERET0TNQubj755JPqyIOIiIhII1QqbkJDQ/Hhhx9CJpMhNDS00thFixZpJLG6xryBOWaHf//kfaCWsyEiIqq/VCpuTp8+rbgS6vTp09WaUF1lbGyMRWHjtJ0GERFRvfdMz5aqi/hsKSIiorqnWp8tNWHCBDx69KhMe05ODiZMmKDu5nRGfn4+Qud/j9D53yM/P1/b6RAREdVbavfc6OvrIyUlpcwDM9PT02Fvbw+5XK7RBDWtunpu0u6lwe7JObmblgZbGz5QlIiISFPU+f5W+WqprKwsCCEghMCjR49gbGysWFZUVITdu3fzCeFERESkdSoXN5aWlpBIJJBIJHB3dy+zXCKRYP78+RpNjoiIiEhdKhc3Bw8ehBACvXv3xrZt22BlZaVYJpVK4ezsDEdHx2pJkoiIiEhVKhc3vXr1AlDyXKmmTZtCIpFUW1JEREREVaVScXPu3Dm0a9cOenp6yMzMxPnz5yuM7dChg8aSIyIiIlKXSsVNp06dkJqaCltbW3Tq1AkSiQTlXWQlkUhQVFSk8SSJiIiIVKVScZOUlAQbGxvFeyrLTGaGiW+tffJ+pJazISIiqr94h2IiIiKq9ar1DsXff/89du3apfj89ttvw9LSEt7e3rhx44b62RIRERFpkNrFzSeffAITExMAwIkTJ7B8+XJ8/vnnsLa2xuzZszWeYF2Rn5+P8C+jEf5lNB+/QEREpEVqD0uZmpri0qVLaNq0Kd555x2kpKRg3bp1uHDhAp5//nncu3evunLVCD5+gYiIqO6p1mEpMzMz3L9/HwCwf/9+9O3bFwBgbGyMvLy8KqRLREREpDkq38SvVL9+/TBp0iR07twZV65cwaBBgwAAFy5cgIuLi6bzIyIiIlKL2j03K1asgJeXF+7du4dt27ahUaNGAICEhASMGjVK4wkSERERqYOXgmsI59wQERFVH3W+v9UelgKAhw8fIiIiAhcvXoREIkHr1q0xceJEWFhYVClhIiIiIk1Re1gqPj4ezZs3x+LFi5GRkYH09HQsXrwYzZs3x6lTp6ojRyIiIiKVqd1zM3v2bLz44otYs2YNDAxKVpfL5Zg0aRJmzZqF33//XeNJ1gVmMjO8MiXiyXvOPSIiItIWtefcmJiY4PTp02jVqpVSe2JiIjw9PZGbm6vRBDWNj18gIiKqe6r1Pjfm5ua4efNmmfZbt26hQYMG6m6OiIiISKPULm4CAwMxceJExMTE4NatW7h9+zY2bdqESZMm1etLwQsLCrHk261Y8u1WFBYUajsdIiKiekvtOTdffvklJBIJxo4dC7lcDgAwNDTE1KlT8emnn2o8wbriYdZDzH59OABg9FBeCk5ERKQtVb7PTW5uLq5duwYhBNzc3GBqaqrp3KoF73NDRERU91TLnJvc3FxMnz4djRs3hq2tLSZNmgQHBwd06NChzhQ2REREpPtULm7CwsIQFRWFQYMGYeTIkYiNjcXUqVOrMzciIiIitak852b79u2IiIjAyJEjAQBjxoyBj48PioqKoK+vX20JEhEREalD5Z6bW7duwdfXV/G5W7duMDAwwJ07d6olMSIiIqKqULm4KSoqglQqVWozMDBQXDFFREREVBuoPCwlhEBwcDCMjIwUbfn5+ZgyZQpkMpmibfv27ZrNsI4wNTHFwLERT94HajkbIiKi+kvl4mbcuHFl2saMGaPRZOoyMzMz7Pp+orbTICIiqvdULm4iIyOrMw8iIiIijVD7DsVUvsKCQmzcuR8AMGqIP6RG0qesQURERNWBxY2GPMx6iOCRLwAAAniHYiIiIq1R+8GZmrZy5Uq4urrC2NgYHh4eOHLkiErrHTt2DAYGBujUqVP1JkhERER1ilaLm5iYGMyaNQvvvfceTp8+DV9fXwQEBODmzZuVrpeZmYmxY8eiT58+NZQpERER1RVaLW4WLVqEiRMnYtKkSWjdujWWLFkCJycnrFq1qtL1Xn/9dYwePRpeXl41lCkRERHVFVUqbtavXw8fHx84Ojrixo0bAIAlS5bgxx9/VHkbhYWFSEhIgL+/v1K7v78/jh8/XuF6kZGRuHbtGsLCwqqSOhEREek4tYubVatWITQ0FAMHDsTDhw9RVFQEALC0tMSSJUtU3k56ejqKiopgZ2en1G5nZ4fU1NRy17l69Srmzp2L6OhoGBioNhe6oKAAWVlZSi8iIiLSXWoXN19//TXWrFmD9957T+mBmZ6enjh//rzaCUgkEqXPQogybUDJ4x9Gjx6N+fPnw93dXeXtL1y4EBYWFoqXk5OT2jkSERFR3aH2peBJSUno3LlzmXYjIyPk5OSovB1ra2vo6+uX6aVJS0sr05sDAI8ePUJ8fDxOnz6NGTNmAACKi4shhICBgQH279+P3r17l1lv3rx5CA0NVXzOysqqlgLH1MQUvYZFPXk/XOPbJyIiItWoXdy4urrizJkzcHZ2Vmrfs2cP2rRpo/J2pFIpPDw8EBsbi6FDhyraY2Nj8dJLL5WJNzc3L9MztHLlSvz222/YunUrXF1dy92PkZGR0vOwqouZmRkObQ2u9v0QERFR5dQububMmYPp06cjPz8fQgicPHkSGzduxMKFC/Hdd9+pta3Q0FC8+uqr8PT0hJeXF7799lvcvHkTU6ZMAVDS6/LPP/9g3bp10NPTQ7t27ZTWt7W1hbGxcZl2IiIiqr/ULm7Gjx8PuVyOt99+G7m5uRg9ejQaN26MpUuXYuTIkWptKzAwEPfv38eCBQuQkpKCdu3aYffu3YpeoZSUlKfe86a2kMvl2BX7OwBgUL/nVJ7wTERERJolEUKIqq6cnp6O4uJi2NrWnUcNZGVlwcLCApmZmTA3N9fYdtPupcHuyXm4y8cvEBERaZQ639/P1L1gbW39LKsTERERaVyVJhSXd6l2qevXrz9TQkRERETPQu3iZtasWUqfHz9+jNOnT2Pv3r2YM2eOpvIiIiIiqhK1i5s33nij3PYVK1YgPj7+mRMiIiIiehYae3BmQEAAtm3bpqnNEREREVWJxoqbrVu3wsrKSlObIyIiIqoStYelOnfurDShWAiB1NRU3Lt3DytXrtRocnWJqYkpPAeuf/J+6FOiiYiIqLqoXdwMGTJE6bOenh5sbGzw/PPPo1WrVprKq84xMzND3K5XtZ0GERFRvadWcSOXy+Hi4oL+/fvD3t6+unIiIiIiqjK15twYGBhg6tSpKCgoqK586iy5XI6jf8Th6B9xkMvl2k6HiIio3lJ7QnH37t1x+vTp6silTst4kAFfr67w9eqKjAcZ2k6HiIio3lJ7zs20adPw5ptv4vbt2/Dw8IBMJlNa3qFDB40lR0RERKQulYubCRMmYMmSJQgMDAQAhISEKJZJJBIIISCRSFBUVKT5LImIiIhUpHJx8/333+PTTz9FUlJSdeZDRERE9ExULm6EEAAAZ2fnakuGiIiI6FmpNaG4sqeBExEREdUGak0odnd3f2qBk5HBK4WIiIhIe9QqbubPnw8LC4vqyqVOMzYyRju/TU/eD9RyNkRERPWXRJROpnkKPT09pKamwtbWtrpzqlZZWVmwsLBAZmYmzM3NtZ0OERERqUCd72+V59xwvg0RERHVBWpfLUXlKy4qxuVrfwMAWjZ3g56+2jd/JiIiIg1QubgpLi6uzjzqvPSMdLRp6Q4AuJuWBlubuj18R0REVFexe4GIiIh0CosbIiIi0iksboiIiEinsLghIiIincLihoiIiHQKixsiIiLSKWo9foEqZmxkjOZe256876flbIiIiOovFjcaYm5ujr+PD9N2GkRERPUeh6WIiIhIp7DnRkOKi4qRnpEOALC2subjF4iIiLSE38Aakp6RDjtbW9jZ2iqKHCIiIqp5LG6IiIhIp7C4ISIiIp3C4oaIiIh0CosbIiIi0iksboiIiEinsLghIiIincL73GiIVCpF486/PHnfU8vZEBER1V8sbjTE0sISt0+9oO00iIiI6j0OSxEREZFOYXFDREREOoXFjYak3UuDRAJIJCXviYiISDtY3BAREZFOYXFDREREOoXFDREREekUFjdERESkU1jcEBERkU5hcUNEREQ6hXco1hCpVAqbNrFP3nfVcjZERET1F4sbDbG0sETahX7aToOIiKje47AUERER6RQWN0RERKRTWNxoSNq9NEikOZBIc/j4BSIiIi3inBtNeix78iZHq2kQERHVZ1rvuVm5ciVcXV1hbGwMDw8PHDlypMLY7du3o1+/frCxsYG5uTm8vLywb9++GsyWiIiIajutFjcxMTGYNWsW3nvvPZw+fRq+vr4ICAjAzZs3y43//fff0a9fP+zevRsJCQnw8/PD4MGDcfr06RrOnIiIiGoriRBCaGvn3bt3R5cuXbBq1SpFW+vWrTFkyBAsXLhQpW20bdsWgYGB+OCDD1SKz8rKgoWFBTIzM2Fubl6lvMuTdi8Ndra2AIC7aWmwtbHV2LaJiIjqO3W+v7XWc1NYWIiEhAT4+/srtfv7++P48eMqbaO4uBiPHj2ClZVVdaRIREREdZDWJhSnp6ejqKgIdnZ2Su12dnZITU1VaRtfffUVcnJyMGLEiApjCgoKUFBQoPiclZVVtYSJiIioTtD6hGKJRKL0WQhRpq08GzduRHh4OGJiYmBrW/EQ0MKFC2FhYaF4OTk5PXPO5THQN4BF86OwaH4UBvq8CI2IiEhbtFbcWFtbQ19fv0wvTVpaWpnenP+KiYnBxIkTsXnzZvTt27fS2Hnz5iEzM1PxunXr1jPnXh4rKys8/LsnHv7dk8NkREREWqS14kYqlcLDwwOxsbFK7bGxsfD29q5wvY0bNyI4OBgbNmzAoEGDnrofIyMjmJubK72IiIhId2l1/CQ0NBSvvvoqPD094eXlhW+//RY3b97ElClTAJT0uvzzzz9Yt24dgJLCZuzYsVi6dCl69Oih6PUxMTGBhYWF1o6DiIiIag+tFjeBgYG4f/8+FixYgJSUFLRr1w67d++Gs7MzACAlJUXpnjfffPMN5HI5pk+fjunTpyvax40bh6ioqJpOX0navTTYu5TMFUpNFrwUnIiISEu0ep8bbeB9boiIiOqeOnGfGyIiIqLqwOKGiIiIdAqLGyIiItIpvNscEVE9UlRUhMePH2s7DaJySaVS6Ok9e78LixsionpACIHU1FQ8fPhQ26kQVUhPTw+urq6QSqXPtB0WNxpioG8A0yYJT967ajkbIiJlpYWNra0tTE1NVXrMDVFNKi4uxp07d5CSkoKmTZs+0+8oixsNsbKyQs4tPnaBiGqfoqIiRWHTqFEjbadDVCEbGxvcuXMHcrkchoaGVd4OJxQTEem40jk2pqamWs6EqHKlw1FFRUXPtB0WN0RE9QSHoqi209TvKIsbDUm/nw6DhskwaJiM9Pvp2k6HiIio3mJxoyHFxcUoeuiCoocuKC4u1nY6RET1Wnh4ODp16qRyfHJyMiQSCc6cOQMAOHToECQSCa8uq6NY3BARUa13/Phx6OvrY8CAATWyP29vb6SkpMDCwqJG9qeK4OBgSCQSSCQSGBgYoGnTppg6dSoePHigsX1IJBLs3LlTY9vTFhY3RERU661duxYzZ87E0aNHcfPmzWrfn1Qqhb29fa2bpzRgwACkpKQgOTkZ3333HX7++WdMmzZN22nVOixuiIjqM3lOxa+ifNVj5XlPj62inJwcbN68GVOnTsULL7yAqKioMjGffvop7Ozs0KBBA0ycOBH5+fllYiIjI9G6dWsYGxujVatWWLlyZYX7LG9Y6vjx43juuedgYmICJycnhISEICen/OO6fPkyJBIJLl26pNS+aNEiuLi4QAiBBw8eICgoCDY2NjAxMUGLFi0QGRlZ6bkwMjKCvb09mjRpAn9/fwQGBmL//v0qH2dhYSFmzJgBBwcHGBsbw8XFBQsXLgQAuLi4AACGDh0KiUSi+FwX8T43RET12Wazipc5DgSe3/W/z9tsgaLc8mNtewF9D/3v848uQMF/Lq4YLaqUYkxMDFq2bImWLVtizJgxmDlzJv7v//5P0auyefNmhIWFYcWKFfD19cX69euxbNkyNGvWTLGNNWvWICwsDMuXL0fnzp1x+vRpTJ48GTKZDOPGjXtqDufPn0f//v3x4YcfIiIiAvfu3cOMGTMwY8aMcguSli1bwsPDA9HR0fjwww8V7Rs2bMDo0aMhkUjwf//3f0hMTMSePXtgbW2Nv//+G3l5eWW2VZHr169j7969SveDedpxLlu2DD/99BM2b96Mpk2b4tatW7h16xYAIC4uDra2toiMjMSAAQOgr6+vci61DYsbIiKq1SIiIjBmzBgAJcMy2dnZOHDgAPr27QsAWLJkCSZMmIBJkyYBAD766CP8+uuvSr03H374Ib766iu8/PLLAABXV1ckJibim2++Uam4+eKLLzB69GjMmjULANCiRQssW7YMvXr1wqpVq2BsbFxmnaCgICxfvlxR3Fy5cgUJCQlYt24dAODmzZvo3LkzPD09AUClnpJffvkFZmZmKCoqUhzfokWLVD7OmzdvokWLFujZsyckEgmcnZ0V69rY2AAALC0tYW9v/9RcajMWNxqip6cHI9vEJ+9ttZwNEZGKRmRXvEzyn3+5D0urZEP/meXwUnJVM1Jy+fJlnDx5Etu3bwcAGBgYIDAwEGvXrlUUNxcvXsSUKVOU1vPy8sLBgwcBAPfu3cOtW7cwceJETJ48WREjl8tVnjCckJCAv//+G9HR0Yo2IQSKi4uRlJSE1q1bl1ln5MiRmDNnDv744w/06NED0dHR6NSpE9q0aQMAmDp1KoYNG4ZTp07B398fQ4YMgbe3d6V5+Pn5YdWqVcjNzcV3332HK1euYObMmSofZ3BwMPr164eWLVtiwIABeOGFF+Dv76/SOahLWNxoiHUja+TftdZ2GkRE6jGQaT+2EhEREZDL5WjcuLGiTQgBQ0NDPHjwAA0bNnzqNkpvz7FmzRp0795daZmqQy/FxcV4/fXXERISUmZZ06ZNy13HwcEBfn5+2LBhA3r06IGNGzfi9ddfVywPCAjAjRs3sGvXLvz666/o06cPpk+fji+//LLCPGQyGdzc3AAAy5Ytg5+fH+bPn48PP/xQpePs0qULkpKSsGfPHvz6668YMWIE+vbti61bt6p0HuoKFjdERFQryeVyrFu3Dl999VWZ3oVhw4YhOjoaM2bMQOvWrfHHH39g7NixiuV//PGH4r2dnR0aN26M69evIygoqEq5dOnSBRcuXFAUFqoKCgrCO++8g1GjRuHatWsYOXKk0nIbGxsEBwcjODgYvr6+mDNnTqXFzX+FhYUhICAAU6dOhaOjo0rHaW5ujsDAQAQGBmL48OEYMGAAMjIyYGVlBUNDw2d+9EFtwOKGiIhqpV9++QUPHjzAxIkTywwfDR8+HBEREZgxYwbeeOMNjBs3Dp6enujZsyeio6Nx4cIFpQnF4eHhCAkJgbm5OQICAlBQUID4+Hg8ePAAoaGhT83lnXfeQY8ePTB9+nTFBN2LFy8iNjYWX3/9dYXrvfzyy5g6dSqmTp0KPz8/pR6oDz74AB4eHmjbti0KCgrwyy+/lDu8VZnnn38ebdu2xSeffILly5c/9TgXL14MBwcHdOrUCXp6etiyZQvs7e1haWkJoGTez4EDB+Dj4wMjIyOVesZqI14KriHp99NhbJcIY7tEPn6BiEgDIiIi0Ldv33LnxQwbNgxnzpzBqVOnEBgYiA8++ADvvPMOPDw8cOPGDUydOlUpftKkSfjuu+8QFRWF9u3bo1evXoiKioKrq6tKuXTo0AGHDx/G1atX4evri86dO+P//u//4ODgUOl65ubmGDx4MM6ePVumN0UqlWLevHno0KEDnnvuOejr62PTpk0q5fNvoaGhWLNmDW7duvXU4zQzM8Nnn30GT09PdO3aFcnJydi9ezf09ErKga+++gqxsbFwcnJC586d1c6ltpAIIap2bV4dlZWVBQsLC2RmZsLc3Fxj2027lwY725KJxHfT0mBrw0nFRFQ75OfnIykpCa6uruVe1UNUW1T2u6rO9zd7boiIiEinsLghIiIincLihoiIiHQKixsiIiLSKSxuiIiISKfwPjcaoqenB33L5CfvK3kQHREREVUrFjcaYt3IGvIHfPwCERGRtnFYioiIiHQKixsiIiLSKSxuNCQjIwMypwTInBKQkZGh7XSIiHRGamoq3njjDbi5ucHY2Bh2dnbo2bMnVq9ejdzcXI3u6/nnn8esWbM0us2aIpFIFC8zMzN07NgRUVFRGtt+VFSU4hlUtR3n3GiIvEiO3NseT96naTkbIiLdcP36dfj4+MDS0hKffPIJ2rdvD7lcjitXrmDt2rVwdHTEiy++qO00a43IyEgMGDAAOTk5iImJwfjx4+Hg4ID+/ftrO7UaxZ4bIiKqtaZNmwYDAwPEx8djxIgRaN26Ndq3b49hw4Zh165dGDx4sCI2MzMTr732GmxtbWFubo7evXvj7NmziuXh4eHo1KkT1q9fDxcXF1hYWGDkyJF49OgRACA4OBiHDx/G0qVLFT0gycnJAIDDhw+jW7duMDIygoODA+bOnQu5XK7YdkFBAUJCQmBrawtjY2P07NkTcXFxFR7XvHnz0KNHjzLtHTp0QFhYGADg0KFD6NatG2QyGSwtLeHj44MbN25Uer4sLS1hb2+P5s2b491334WVlRX279+v8jk6e/Ys/Pz80KBBA5ibm8PDwwPx8fE4dOgQxo8fj8zMTMW5CQ8PrzQXbWJxQ0RUj+UU5lT4ypfnqxyb9zjvqbHqun//Pvbv34/p06dDJpOVGyORSAAAQggMGjQIqamp2L17NxISEtClSxf06dNHaarAtWvXsHPnTvzyyy/45ZdfcPjwYXz66acAgKVLl8LLywuTJ09GSkoKUlJS4OTkhH/++QcDBw5E165dcfbsWaxatQoRERH46KOPFNt9++23sW3bNnz//fc4deoU3Nzc0L9//wqnKQQFBeHPP//EtWvXFG0XLlzA+fPnERQUBLlcjiFDhqBXr144d+4cTpw4gddee01xvE9TVFSEzZs3IyMjA4aGhiqfo6CgIDRp0gRxcXFISEjA3LlzYWhoCG9vbyxZsgTm5uaKc/PWW2+plItWiHomMzNTABCZmZka3e7dtLsCEAIoeU9EVFvk5eWJxMREkZeXV2YZwlHha2D0QKVY049NK4ztFdlLKdb6c+syMer6448/BACxfft2pfZGjRoJmUwmZDKZePvtt4UQQhw4cECYm5uL/Px8pdjmzZuLb775RgghRFhYmDA1NRVZWVmK5XPmzBHdu3dXfO7Vq5d44403lLbx7rvvipYtW4ri4mJF24oVK4SZmZkoKioS2dnZwtDQUERHRyuWFxYWCkdHR/H5559XeHwdOnQQCxYsUHyeN2+e6Nq1qxBCiPv37wsA4tChQ5Weo38DIIyNjYVMJhP6+voCgLCyshJXr14VQqh2jho0aCCioqLK3X5kZKSwsLBQOZ+qqOx3VZ3vb/bcEBFRrfbf3oqTJ0/izJkzaNu2LQoKCgAACQkJyM7ORqNGjWBmZqZ4JSUlKfWOuLi4oEGDBorPDg4OSEurfJ7kxYsX4eXlpZSHj48PsrOzcfv2bVy7dg2PHz+Gj4+PYrmhoSG6deuGixcvVrjdoKAgREdHAyjpVdm4cSOCgoIAAFZWVggODkb//v0xePBgLF26FCkpKU87VVi8eDHOnDmD2NhYdOrUCYsXL4abm5vK5yg0NBSTJk1C37598emnnyqdu7qEE4qJiOqx7HnZFS7T19NX+pz2VsVFgJ5E+d/KyW8kP1NeAODm5gaJRIJLly4ptTdr1gwAYGJiomgrLi6Gg4MDDh06VGY7/77Cp3SIppREIkFxcXGleQghyhRYQgjF+v9+/7T1/m306NGYO3cuTp06hby8PNy6dQsjR45ULI+MjERISAj27t2LmJgYvP/++4iNjS13rk4pe3t7uLm5wc3NDVu2bEHnzp3h6emJNm3aqHSOwsPDMXr0aOzatQt79uxBWFgYNm3ahKFDh1Z6jmobFjcaJDG9p+0UiIjUIpOWP5elJmMr0qhRI/Tr1w/Lly/HzJkzK5x3AwBdunRBamoqDAwM4OLiUuV9SqVSFBUVKbW1adMG27ZtUypWjh8/jgYNGqBx48awsrKCVCrF0aNHMXr0aADA48ePER8fX+ll5U2aNMFzzz2H6Oho5OXloW/fvrCzs1OK6dy5Mzp37ox58+bBy8sLGzZsqLS4+Tc3NzcMGzYM8+bNw48//qjyOXJ3d4e7uztmz56NUaNGITIyEkOHDi333NRWHJbSEFsbWxTn2KA4xwa2NrbaToeISCesXLkScrkcnp6eiImJwcWLF3H58mX88MMPuHTpEvT1S3qX+vbtCy8vLwwZMgT79u1DcnIyjh8/jvfffx/x8fEq78/FxQV//vknkpOTkZ6ejuLiYkybNg23bt3CzJkzcenSJfz4448ICwtDaGgo9PT0IJPJMHXqVMyZMwd79+5FYmIiJk+ejNzcXEycOLHS/QUFBWHTpk3YsmULxowZo2hPSkrCvHnzcOLECdy4cQP79+/HlStX0Lp1a7XO35tvvomff/4Z8fHxTz1HeXl5mDFjBg4dOoQbN27g2LFjiIuLU+zTxcUF2dnZOHDgANLT0zV+jyGN0uxUoNqvuiYUExHVVpVN0qwL7ty5I2bMmCFcXV2FoaGhMDMzE926dRNffPGFyMnJUcRlZWWJmTNnCkdHR2FoaCicnJxEUFCQuHnzphCiZEJxx44dlba9ePFi4ezsrPh8+fJl0aNHD2FiYiIAiKSkJCGEEIcOHRJdu3YVUqlU2Nvbi3feeUc8fvxYsV5eXp6YOXOmsLa2FkZGRsLHx0ecPHnyqcf24MEDYWRkJExNTcWjR48U7ampqWLIkCHCwcFBSKVS4ezsLD744ANRVFRU4bYAiB07dpRp79evnwgICHjqOSooKBAjR44UTk5OQiqVCkdHRzFjxgyl35spU6aIRo0aCQAiLCzsqcenLk1NKJYI8WSwsJ7IysqChYUFMjMzYW5uru10iIiqXX5+PpKSkuDq6gpjY2Ntp0NUocp+V9X5/uawlIZkZGTA0u0oLN2O8vELREREWsQJxRoiL5Ij81rPJ+/5+AUiIiJtYc8NERER6RQWN0RERKRTWNwQERGRTmFxQ0RERDqFxQ0RERHpFF4tpUmGOdrOgIiIqN5jcaMhtja2EIWln579mSpERERUNRyWIiKiOk8ikWDnzp0VLj906BAkEgkePnxYYzmR9rC4ISKiWi01NRUzZ85Es2bNYGRkBCcnJwwePBgHDhxQeRve3t5ISUmBhYWFRnIKDw9Hp06dNLKt2iI8PBwSiQQSiQR6enpwdHREUFAQbt26pbF9uLi4YMmSJRrbXkVY3GjIw8yHsG0bC9u2sXiY+VDb6RAR6YTk5GR4eHjgt99+w+eff47z589j79698PPzw/Tp01XejlQqhb29PSQSSTVmW9bjx49rdH/Pqm3btkhJScHt27cRExOD8+fPY8SIEdpOS20sbjSksLAQ9xL74V5iPxQWFj59BSIieqpp06ZBIpHg5MmTGD58ONzd3dG2bVuEhobijz/+UIpNT0/H0KFDYWpqihYtWuCnn35SLPvvsFRUVBQsLS2xb98+tG7dGmZmZhgwYABSUlKU1unWrRtkMhksLS3h4+ODGzduICoqCvPnz8fZs2cVPR1RUVEASobHVq9ejZdeegkymQwfffQRioqKMHHiRLi6usLExAQtW7bE0qVLlXIPDg7GkCFDMH/+fNja2sLc3Byvv/56hd8nmZmZMDExwd69e5Xat2/fDplMhuzsbBQWFmLGjBlwcHCAsbExXFxcsHDhwkrPt4GBAezt7eHo6AhfX19MnjwZf/zxB7KyshQxP//8Mzw8PGBsbIxmzZph/vz5kMvliuXh4eFo2rQpjIyM4OjoiJCQEADA888/jxs3bmD27NmK81ZdOKGYiKgey6nkIk99feDfD2auLFZPDzAxqTxWpua1FhkZGdi7dy8+/vhjyMpZ2dLSUunz/Pnz8fnnn+OLL77A119/jaCgINy4cQNWVlblbj83Nxdffvkl1q9fDz09PYwZMwZvvfUWoqOjIZfLMWTIEEyePBkbN25EYWEhTp48CYlEgsDAQPz111/Yu3cvfv31VwBQGu4KCwvDwoULsXjxYujr66O4uBhNmjTB5s2bYW1tjePHj+O1116Dg4ODUq/IgQMHYGxsjIMHDyI5ORnjx4+HtbU1Pv744zK5W1hYYNCgQYiOjsaAAQMU7Rs2bMBLL70EMzMzfPnll/jpp5+wefNmNG3aFLdu3VJriCk1NRXbt2+Hvr4+9PX1AQD79u3DmDFjsGzZMvj6+uLatWt47bXXFMe9detWLF68GJs2bULbtm2RmpqKs2fPAigpvDp27IjXXnsNkydPVjmPKhH1TGZmpgAgMjMzNbrdu2l3BSAEUPKeiKi2yMvLE4mJiSIvL6/MstK/W+W9Bg5UjjU1rTi2Vy/lWGvrsjHq+vPPPwUAsX379qfGAhDvv/++4nN2draQSCRiz549QgghDh48KACIBw8eCCGEiIyMFADE33//rVhnxYoVws7OTgghxP379wUAcejQoXL3FxYWJjp27FhuHrNmzXpqvtOmTRPDhg1TfB43bpywsrISOTk5irZVq1YJMzMzUVRUVO42tm/fLszMzBTrZGZmCmNjY7Fr1y4hhBAzZ84UvXv3FsXFxU/Np/SY9PT0hEwmEyYmJgKAACBCQkIUMb6+vuKTTz5RWm/9+vXCwcFBCCHEV199Jdzd3UVhYWG5+3B2dhaLFy+uMIfKflfV+f7W+rDUypUr4erqCmNjY3h4eODIkSOVxh8+fFipO2z16tU1lCkREdUkIQQAqDx80aFDB8V7mUyGBg0aIC0trcJ4U1NTNG/eXPHZwcFBEW9lZYXg4GD0798fgwcPxtKlS5WGrCrj6elZpm316tXw9PSEjY0NzMzMsGbNGty8eVMppmPHjjA1NVV89vLyQnZ2doW9LYMGDYKBgYFi+G3btm1o0KAB/P39AZQMdZ05cwYtW7ZESEgI9u/f/9TcW7ZsiTNnziAuLg4ff/wxOnXqpNRzlJCQgAULFsDMzEzxmjx5MlJSUpCbm4tXXnkFeXl5aNasGSZPnowdO3YoDVnVFK0WNzExMZg1axbee+89nD59Gr6+vggICCjzAy+VlJSEgQMHwtfXF6dPn8a7776LkJAQbNu2rYYzJyLSDdnZFb/++6c1La3i2D17lGOTk8vGqKtFixaQSCS4ePGiSvGGhoZKnyUSCYqLi9WKLy2oACAyMhInTpyAt7c3YmJi4O7uXmaeT3n+O4S2efNmzJ49GxMmTMD+/ftx5swZjB8/XuX5mRUVd1KpFMOHD8eGDRsAlAxJBQYGwsCgZMZJly5dkJSUhA8//BB5eXkYMWIEhg8fXum+pFIp3Nzc0LZtW7z77rvo1KkTpk6dqlheXFyM+fPn48yZM4rX+fPncfXqVRgbG8PJyQmXL1/GihUrYGJigmnTpuG5556r+YnVT+3bqUbdunUTU6ZMUWpr1aqVmDt3brnxb7/9tmjVqpVS2+uvvy569Oih8j45LEVE9U1lXf213YABA0Tjxo1FdnZ2mWWlQ0xClAwH7dixQ2m5hYWFiIyMFEKUPyxlYWGhFL9jxw5R2ddijx49xMyZM4UQQnz88ceiXbt2ZWLKy2PGjBmid+/eSm19+vRRGtYqHZbKzc1VtK1evbrSYanS4zI0NBR//fWX0NfXFydOnKgwdu/evQKAuH//frnLyxtqu3btmtDX1xcJCQlCCCG8vb3FhAkTKtzHf126dEkAUKzfokUL8eWXX1YYX+eHpQoLC5GQkKDoPivl7++P48ePl7vOiRMnysT3798f8fHxFVaFBQUFyMrKUnoREVHdsHLlShQVFaFbt27Ytm0brl69iosXL2LZsmXw8vKqtv0mJSVh3rx5OHHiBG7cuIH9+/fjypUraN26NYCS+7UkJSXhzJkzSE9PR0FBQYXbcnNzQ3x8PPbt24crV67g//7v/xAXF1cmrrCwEBMnTkRiYiL27NmDsLAwzJgxA3p6FX9V9+rVC3Z2dggKCoKLiwt69OihWFY6sffSpUu4cuUKtmzZAnt7+zITsSvTrFkzvPTSS/jggw8AAB988AHWrVuH8PBwXLhwARcvXkRMTAzef/99ACVXoUVEROCvv/7C9evXsX79epiYmMDZ2Vlx3n7//Xf8888/SE9PVzkPdWmtuElPT0dRURHs7OyU2u3s7JCamlruOqmpqeXGy+XyCk/SwoULYWFhoXg5OTlp5gD+w9bGVjFtztbGtlr2QURU37i6uuLUqVPw8/PDm2++iXbt2qFfv344cOAAVq1aVW37NTU1xaVLlzBs2DC4u7vjtddew4wZM/D6668DAIYNG4YBAwbAz88PNjY22LhxY4XbmjJlCl5++WUEBgaie/fuuH//PqZNm1Ymrk+fPmjRogWee+45jBgxAoMHD0Z4eHileUokEowaNQpnz55FUFCQ0jIzMzN89tln8PT0RNeuXZGcnIzdu3dXWiyV580338SuXbvw559/on///vjll18QGxuLrl27okePHli0aJGieLG0tMSaNWvg4+ODDh064MCBA/j555/RqFEjAMCCBQuQnJyM5s2bw8bGRq081CER4l8DjDXozp07aNy4MY4fP65UfX/88cdYv349Ll26VGYdd3d3jB8/HvPmzVO0HTt2DD179kRKSgrs7e3LrFNQUKBUUWdlZcHJyQmZmZkwNzfX8FEREdU++fn5SEpKUly8QbVPcHAwHj58WOkjJOqDyn5Xs7KyYGFhodL3t9buc2NtbQ19ff0yvTRpaWllemdK2dvblxtvYGCgqAr/y8jICEZGRppJmoiIiGo9rQ1LSaVSeHh4IDY2Vqk9NjYW3t7e5a7j5eVVJn7//v3w9PQsM+udiIiI6iet3qE4NDQUr776Kjw9PeHl5YVvv/0WN2/exJQpUwAA8+bNwz///IN169YBKBm3XL58OUJDQzF58mScOHECERERlY51EhER1Xalj28gzdBqcRMYGIj79+9jwYIFSElJQbt27bB7927FxKSUlBSle964urpi9+7dmD17NlasWAFHR0csW7YMw4YN09YhEBERUS2jtQnF2qLOhCQiIl3ACcVUV2hqQrHWH79AREQ1o579W5bqIE39jrK4ISLScaUXXOTm5mo5E6LKlT6SovQp5FWl1Tk3RERU/fT19WFpaal4KKSpqanKD6MkqinFxcW4d+8eTE1NFc/HqioWN0RE9UDpTU4re0o2kbbp6emhadOmz1x8s7ghIqoHJBIJHBwcYGtrW/NPaCZSkVQqVfvxEOVhcUNEVI/o6+s/83wGotqOE4qJiIhIp7C4ISIiIp3C4oaIiIh0Sr2bc1N6g6CsrCwtZ0JERESqKv3eVuVGf/WuuHn06BEAwMnJScuZEBERkboePXoECwuLSmPq3bOliouLcefOHTRo0EDjN7HKysqCk5MTbt26xedWVSOe55rB81wzeJ5rDs91zaiu8yyEwKNHj+Do6PjUy8XrXc+Nnp4emjRpUq37MDc35/84NYDnuWbwPNcMnueaw3NdM6rjPD+tx6YUJxQTERGRTmFxQ0RERDqFxY0GGRkZISwsDEZGRtpORafxPNcMnueawfNcc3iua0ZtOM/1bkIxERER6Tb23BAREZFOYXFDREREOoXFDREREekUFjdERESkU1jcqGnlypVwdXWFsbExPDw8cOTIkUrjDx8+DA8PDxgbG6NZs2ZYvXp1DWVat6lznrdv345+/frBxsYG5ubm8PLywr59+2ow27pL3d/nUseOHYOBgQE6depUvQnqCHXPc0FBAd577z04OzvDyMgIzZs3x9q1a2so27pL3fMcHR2Njh07wtTUFA4ODhg/fjzu379fQ9nWTb///jsGDx4MR0dHSCQS7Ny586nraOV7UJDKNm3aJAwNDcWaNWtEYmKieOONN4RMJhM3btwoN/769evC1NRUvPHGGyIxMVGsWbNGGBoaiq1bt9Zw5nWLuuf5jTfeEJ999pk4efKkuHLlipg3b54wNDQUp06dquHM6xZ1z3Ophw8fimbNmgl/f3/RsWPHmkm2DqvKeX7xxRdF9+7dRWxsrEhKShJ//vmnOHbsWA1mXfeoe56PHDki9PT0xNKlS8X169fFkSNHRNu2bcWQIUNqOPO6Zffu3eK9994T27ZtEwDEjh07Ko3X1vcgixs1dOvWTUyZMkWprVWrVmLu3Lnlxr/99tuiVatWSm2vv/666NGjR7XlqAvUPc/ladOmjZg/f76mU9MpVT3PgYGB4v333xdhYWEsblSg7nnes2ePsLCwEPfv36+J9HSGuuf5iy++EM2aNVNqW7ZsmWjSpEm15ahrVClutPU9yGEpFRUWFiIhIQH+/v5K7f7+/jh+/Hi565w4caJMfP/+/REfH4/Hjx9XW651WVXO838VFxfj0aNHsLKyqo4UdUJVz3NkZCSuXbuGsLCw6k5RJ1TlPP/000/w9PTE559/jsaNG8Pd3R1vvfUW8vLyaiLlOqkq59nb2xu3b9/G7t27IYTA3bt3sXXrVgwaNKgmUq43tPU9WO8enFlV6enpKCoqgp2dnVK7nZ0dUlNTy10nNTW13Hi5XI709HQ4ODhUW751VVXO83999dVXyMnJwYgRI6ojRZ1QlfN89epVzJ07F0eOHIGBAf90qKIq5/n69es4evQojI2NsWPHDqSnp2PatGnIyMjgvJsKVOU8e3t7Izo6GoGBgcjPz4dcLseLL76Ir7/+uiZSrje09T3Inhs1SSQSpc9CiDJtT4svr52UqXueS23cuBHh4eGIiYmBra1tdaWnM1Q9z0VFRRg9ejTmz58Pd3f3mkpPZ6jz+1xcXAyJRILo6Gh069YNAwcOxKJFixAVFcXem6dQ5zwnJiYiJCQEH3zwARISErB3714kJSVhypQpNZFqvaKN70H+80tF1tbW0NfXL/OvgLS0tDJVaSl7e/ty4w0MDNCoUaNqy7Uuq8p5LhUTE4OJEydiy5Yt6Nu3b3WmWeepe54fPXqE+Ph4nD59GjNmzABQ8iUshICBgQH279+P3r1710judUlVfp8dHBzQuHFjWFhYKNpat24NIQRu376NFi1aVGvOdVFVzvPChQvh4+ODOXPmAAA6dOgAmUwGX19ffPTRR+xZ1xBtfQ+y50ZFUqkUHh4eiI2NVWqPjY2Ft7d3uet4eXmVid+/fz88PT1haGhYbbnWZVU5z0BJj01wcDA2bNjAMXMVqHuezc3Ncf78eZw5c0bxmjJlClq2bIkzZ86ge/fuNZV6nVKV32cfHx/cuXMH2dnZirYrV65AT08PTZo0qdZ866qqnOfc3Fzo6Sl/Berr6wP4X88CPTutfQ9W63RlHVN6qWFERIRITEwUs2bNEjKZTCQnJwshhJg7d6549dVXFfGll8DNnj1bJCYmioiICF4KrgJ1z/OGDRuEgYGBWLFihUhJSVG8Hj58qK1DqBPUPc//xaulVKPueX706JFo0qSJGD58uLhw4YI4fPiwaNGihZg0aZK2DqFOUPc8R0ZGCgMDA7Fy5Upx7do1cfToUeHp6Sm6deumrUOoEx49eiROnz4tTp8+LQCIRYsWidOnTysuua8t34MsbtS0YsUK4ezsLKRSqejSpYs4fPiwYtm4ceNEr169lOIPHTokOnfuLKRSqXBxcRGrVq2q4YzrJnXOc69evQSAMq9x48bVfOJ1jLq/z//G4kZ16p7nixcvir59+woTExPRpEkTERoaKnJzc2s467pH3fO8bNky0aZNG2FiYiIcHBxEUFCQuH37dg1nXbccPHiw0r+3teV7UCIE+9+IiIhId3DODREREekUFjdERESkU1jcEBERkU5hcUNEREQ6hcUNERER6RQWN0RERKRTWNwQERGRTmFxQ0RKoqKiYGlpqe00qszFxQVLliypNCY8PBydOnWqkXyIqOaxuCHSQcHBwZBIJGVef//9t7ZTQ1RUlFJODg4OGDFiBJKSkjSy/bi4OLz22muKzxKJBDt37lSKeeutt3DgwAGN7K8i/z1OOzs7DB48GBcuXFB7O3W52CTSBhY3RDpqwIABSElJUXq5urpqOy0AJQ/iTElJwZ07d7BhwwacOXMGL774IoqKip552zY2NjA1Na00xszMrFqfSFzq38e5a9cu5OTkYNCgQSgsLKz2fRPVZyxuiHSUkZER7O3tlV76+vpYtGgR2rdvD5lMBicnJ0ybNk3pCdT/dfbsWfj5+aFBgwYwNzeHh4cH4uPjFcuPHz+O5557DiYmJnByckJISAhycnIqzU0ikcDe3h4ODg7w8/NDWFgY/vrrL0XP0qpVq9C8eXNIpVK0bNkS69evV1o/PDwcTZs2hZGRERwdHRESEqJY9u9hKRcXFwDA0KFDIZFIFJ//PSy1b98+GBsb4+HDh0r7CAkJQa9evTR2nJ6enpg9ezZu3LiBy5cvK2Iq+3kcOnQI48ePR2ZmpqIHKDw8HABQWFiIt99+G40bN4ZMJkP37t1x6NChSvMhqi9Y3BDVM3p6eli2bBn++usvfP/99/jtt9/w9ttvVxgfFBSEJk2aIC4uDgkJCZg7dy4MDQ0BAOfPn0f//v3x8ssv49y5c4iJicHRo0cxY8YMtXIyMTEBADx+/Bg7duzAG2+8gTfffBN//fUXXn/9dYwfPx4HDx4EAGzduhWLFy/GN998g6tXr2Lnzp1o3759uduNi4sDAERGRiIlJUXx+d/69u0LS0tLbNu2TdFWVFSEzZs3IygoSGPH+fDhQ2zYsAEAFOcPqPzn4e3tjSVLlih6gFJSUvDWW28BAMaPH49jx45h06ZNOHfuHF555RUMGDAAV69eVTknIp1V7Y/mJKIaN27cOKGvry9kMpniNXz48HJjN2/eLBo1aqT4HBkZKSwsLBSfGzRoIKKiospd99VXXxWvvfaaUtuRI0eEnp6eyMvLK3ed/27/1q1bokePHqJJkyaioKBAeHt7i8mTJyut88orr4iBAwcKIYT46quvhLu7uygsLCx3+87OzmLx4sWKzwDEjh07lGL++0TzkJAQ0bt3b8Xnffv2CalUKjIyMp7pOAEImUwmTE1NFU9PfvHFF8uNL/W0n4cQQvz9999CIpGIf/75R6m9T58+Yt68eZVun6g+MNBuaUVE1cXPzw+rVq1SfJbJZACAgwcP4pNPPkFiYiKysrIgl8uRn5+PnJwcRcy/hYaGYtKkSVi/fj369u2LV155Bc2bNwcAJCQk4O+//0Z0dLQiXgiB4uJiJCUloXXr1uXmlpmZCTMzMwghkJubiy5dumD79u2QSqW4ePGi0oRgAPDx8cHSpUsBAK+88gqWLFmCZs2aYcCAARg4cCAGDx4MA4Oq/zkLCgqCl5cX7ty5A0dHR0RHR2PgwIFo2LDhMx1ngwYNcOrUKcjlchw+fBhffPEFVq9erRSj7s8DAE6dOgUhBNzd3ZXaCwoKamQuEVFtx+KGSEfJZDK4ubkptd24cQMDBw7ElClT8OGHH8LKygpHjx7FxIkT8fjx43K3Ex4ejtGjR2PXrl3Ys2cPwsLCsGnTJgwdOhTFxcV4/fXXlea8lGratGmFuZV+6evp6cHOzq7Ml7hEIlH6LIRQtDk5OeHy5cuIjY3Fr7/+imnTpuGLL77A4cOHlYZ71NGtWzc0b94cmzZtwtSpU7Fjxw5ERkYqllf1OPX09BQ/g1atWiE1NRWBgYH4/fffAVTt51Gaj76+PhISEqCvr6+0zMzMTK1jJ9JFLG6I6pH4+HjI5XJ89dVX0NMrmXK3efPmp67n7u4Od3d3zJ49G6NGjUJkZCSGDh2KLl264MKFC2WKqKf595f+f7Vu3RpHjx7F2LFjFW3Hjx9X6h0xMTHBiy++iBdffBHTp09Hq1atcP78eXTp0qXM9gwNDVW6Cmv06NGIjo5GkyZNoKenh0GDBimWVfU4/2v27NlYtGgRduzYgaFDh6r085BKpWXy79y5M4qKipCWlgZfX99nyolIF3FCMVE90rx5c8jlcnz99de4fv061q9fX2aY5N/y8vIwY8YMHDp0CDdu3MCxY8cQFxenKDTeeecdnDhxAtOnT8eZM2dw9epV/PTTT5g5c2aVc5wzZw6ioqKwevVqXL16FYsWLcL27dsVE2mjoqIQERGBv/76S3EMJiYmcHZ2Lnd7Li4uOHDgAFJTU/HgwYMK9xsUFIRTp07h448/xvDhw2FsbKxYpqnjNDc3x6RJkxAWFgYhhEo/DxcXF2RnZ+PAgQNIT09Hbm4u3N3dERQUhLFjx2L79u1ISkpCXFwcPvvsM+zevVutnIh0kjYn/BBR9Rg3bpx46aWXyl22aNEi4eDgIExMTET//v3FunXrBADx4MEDIYTyBNaCggIxcuRI4eTkJKRSqXB0dBQzZsxQmkR78uRJ0a9fP2FmZiZkMpno0KGD+PjjjyvMrbwJsv+1cuVK0axZM2FoaCjc3d3FunXrFMt27NghunfvLszNzYVMJhM9evQQv/76q2L5fycU//TTT8LNzU0YGBgIZ2dnIUTZCcWlunbtKgCI3377rcwyTR3njRs3hIGBgYiJiRFCPP3nIYQQU6ZMEY0aNRIARFhYmBBCiMLCQvHBBx8IFxcXYWhoKOzt7cXQoUPFuXPnKsyJqL6QCCGEdssrIiIiIs3hsBQRERHpFBY3REREpFNY3BAREZFOYXFDREREOoXFDREREekUFjdERESkU1jcEBERkU5hcUNEREQ6hcUNERER6RQWN0RERKRTWNwQERGRTmFxQ0RERDrl/wFAUHuCGA0fwwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "# Get class probability scores\n",
    "penguin_prob = multi_model.predict_proba(x_penguin_test)\n",
    "\n",
    "# Get ROC metrics for each class\n",
    "fpr = {}\n",
    "tpr = {}\n",
    "thresh ={}\n",
    "for i in range(len(penguin_classes)):    \n",
    "    fpr[i], tpr[i], thresh[i] = roc_curve(y_penguin_test, penguin_prob[:,i], pos_label=i)\n",
    "    \n",
    "# Plot the ROC chart\n",
    "plt.plot(fpr[0], tpr[0], linestyle='--',color='orange', label=penguin_classes[0] + ' vs Rest')\n",
    "plt.plot(fpr[1], tpr[1], linestyle='--',color='green', label=penguin_classes[1] + ' vs Rest')\n",
    "plt.plot(fpr[2], tpr[2], linestyle='--',color='blue', label=penguin_classes[2] + ' vs Rest')\n",
    "plt.title('Multiclass ROC curve')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive rate')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To quantify the ROC performance, you can calculate an aggregate area under the curve score that is averaged across all of the OVR curves."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average AUC: 0.9981999902100828\n"
     ]
    }
   ],
   "source": [
    "auc = roc_auc_score(y_penguin_test,penguin_prob, multi_class='ovr')\n",
    "print('Average AUC:', auc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocess data in a pipeline\n",
    "\n",
    "Again, just like with binary classification, you can use a pipeline to apply preprocessing steps to the data before fitting it to an algorithm to train a model. Let's see if we can improve the penguin predictor by scaling the numeric features in a transformation steps before training. We'll also try a different algorithm (a support vector machine), just to show that we can!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(steps=[('preprocessor',\n",
      "                 ColumnTransformer(transformers=[('preprocess',\n",
      "                                                  Pipeline(steps=[('scaler',\n",
      "                                                                   StandardScaler())]),\n",
      "                                                  [0, 1, 2, 3])])),\n",
      "                ('regressor', SVC(probability=True))])\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "# Define preprocessing for numeric columns (scale them)\n",
    "feature_columns = [0,1,2,3]\n",
    "feature_transformer = Pipeline(steps=[\n",
    "    ('scaler', StandardScaler())\n",
    "    ])\n",
    "\n",
    "# Create preprocessing steps\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('preprocess', feature_transformer, feature_columns)])\n",
    "\n",
    "# Create training pipeline\n",
    "pipeline = Pipeline(steps=[('preprocessor', preprocessor),\n",
    "                           ('regressor', SVC(probability=True))])\n",
    "\n",
    "\n",
    "# fit the pipeline to train a linear regression model on the training set\n",
    "multi_model = pipeline.fit(x_penguin_train, y_penguin_train)\n",
    "print (multi_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can evaluate the new model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overall Accuracy: 0.9805825242718447\n",
      "Overall Precision: 0.9767195767195768\n",
      "Overall Recall: 0.9767195767195768\n",
      "Average AUC: 0.9990361381446328\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get predictions from test data\n",
    "penguin_predictions = multi_model.predict(x_penguin_test)\n",
    "penguin_prob = multi_model.predict_proba(x_penguin_test)\n",
    "\n",
    "# Overall metrics\n",
    "print(\"Overall Accuracy:\",accuracy_score(y_penguin_test, penguin_predictions))\n",
    "print(\"Overall Precision:\",precision_score(y_penguin_test, penguin_predictions, average='macro'))\n",
    "print(\"Overall Recall:\",recall_score(y_penguin_test, penguin_predictions, average='macro'))\n",
    "print('Average AUC:', roc_auc_score(y_penguin_test,penguin_prob, multi_class='ovr'))\n",
    "\n",
    "# Confusion matrix\n",
    "plt.imshow(mcm, interpolation=\"nearest\", cmap=plt.cm.Blues)\n",
    "plt.colorbar()\n",
    "tick_marks = np.arange(len(penguin_classes))\n",
    "plt.xticks(tick_marks, penguin_classes, rotation=45)\n",
    "plt.yticks(tick_marks, penguin_classes)\n",
    "plt.xlabel(\"Predicted Species\")\n",
    "plt.ylabel(\"Actual Species\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use the model with new data observations\n",
    "\n",
    "Now let's save our trained model so we can use it again later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['./models/penguin_model.pkl']"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "\n",
    "# Save the model as a pickle file\n",
    "filename = './models/penguin_model.pkl'\n",
    "joblib.dump(multi_model, filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OK, so now we have a trained model. Let's use it to predict the class of a new penguin observation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New sample: [  50.4   15.3  224.  5550. ]\n",
      "Predicted class is Gentoo\n"
     ]
    }
   ],
   "source": [
    "# Load the model from the file\n",
    "multi_model = joblib.load(filename)\n",
    "\n",
    "# The model accepts an array of feature arrays (so you can predict the classes of multiple penguin observations in a single call)\n",
    "# We'll create an array with a single array of features, representing one penguin\n",
    "x_new = np.array([[50.4,15.3,224,5550]])\n",
    "print ('New sample: {}'.format(x_new[0]))\n",
    "\n",
    "# The model returns an array of predictions - one for each set of features submitted\n",
    "# In our case, we only submitted one penguin, so our prediction is the first one in the resulting array.\n",
    "penguin_pred = multi_model.predict(x_new)[0]\n",
    "print('Predicted class is', penguin_classes[penguin_pred])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also submit a batch of penguin observations to the model, and get back a prediction for each one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New samples:\n",
      "[[  49.5   18.4  195.  3600. ]\n",
      " [  38.2   20.1  190.  3900. ]]\n",
      "2 (Chinstrap)\n",
      "0 (Adelie)\n"
     ]
    }
   ],
   "source": [
    "# This time our input is an array of two feature arrays\n",
    "x_new = np.array([[49.5,18.4,195, 3600],\n",
    "         [38.2,20.1,190,3900]])\n",
    "print ('New samples:\\n{}'.format(x_new))\n",
    "\n",
    "# Call the web service, passing the input data\n",
    "predictions = multi_model.predict(x_new)\n",
    "\n",
    "# Get the predicted classes.\n",
    "for prediction in predictions:\n",
    "    print(prediction, '(' + penguin_classes[prediction] +')')"
   ]
  },
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    "## Further Reading\n",
    "\n",
    "Classification is one of the most common forms of machine learning, and by following the basic principles we've discussed in this notebook you should be able to train and evaluate classification models with scikit-learn. It's worth spending some time investigating classification algorithms in more depth, and a good starting point is the [Scikit-Learn documentation](https://scikit-learn.org/stable/user_guide.html).\n",
    "\n",
    "## Challenge: Classify Wines\n",
    "\n",
    "Feel like challenging yourself to train a classification model? Try the challenge in the [/challenges/03 - Wine Classification Challenge.ipynb](./challenges/03%20-%20Wine%20Classification%20Challenge.ipynb) notebook to see if you can classify wines into their grape varietals!\n",
    "\n",
    "> **Note**: The time to complete this optional challenge is not included in the estimated time for this exercise - you can spend as little or as much time on it as you like!"
   ]
  }
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