diff --git a/EuroScipy.ipynb b/EuroScipy.ipynb index bfc2cb0..caa1c94 100644 --- a/EuroScipy.ipynb +++ b/EuroScipy.ipynb @@ -43,7 +43,6 @@ "from sklearn.svm import SVC\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.ensemble import RandomForestClassifier\n", - "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.grid_search import GridSearchCV, RandomizedSearchCV\n", "from scipy.stats.distributions import randint\n", "from multilayer_perceptron import multilayer_perceptron\n", @@ -102,7 +101,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In order to feed the data into our classification models, the imported wine DataFrame needs to be converted into a `numpy` array (http://scipy-lectures.github.io/intro/numpy/array_object.html). Subsequently, we need to split our initial dataset into the data matrix X (independent variable) and the associated class vector y (dependent or target variable). " + "In order to feed the data into our classification models, the imported wine DataFrame needs to be converted into a `numpy` array. For more information on numpy arrays, see http://scipy-lectures.github.io/intro/numpy/array_object.html. Subsequently, we need to split our initial dataset into the data matrix X (independent variable) and the associated class vector y (dependent or target variable). " ] }, { @@ -124,7 +123,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "It is always a good practice to check the dimensionality of the imported data prior to constructing any Machine Learning models to check that you really have imported all the data and imported it in the correct way (e.g. one common mistake is to get the separator wrong and end up with only one column).
Try printing the size of the input matrix X and class vector y using the \"`shape`\" command: " + "It is always a good practice to check the dimensionality of the imported data prior to constructing any classification model to check that you really have imported all the data and imported it in the correct way (e.g. one common mistake is to get the separator wrong and end up with only one column).
Try printing the size of the input matrix X and class vector y using the \"`shape`\" command: " ] }, { @@ -147,7 +146,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Based on the class vector y, the wine samples are classified into two distinct categories of high quality (class 1) and low quality (class 0).\n", + "Based on the class vector y, the wine samples are classified into two distinct categories: high quality (class 1) and low quality (class 0).\n", "

An important thing to understand before applying any classification algorithms is how the output labels are distributed. Are they evenly distributed? Imbalances in distribution of labels can often lead to poor classification results for the minority class even if the classification results for the majority class are very good. For the purposes of this workshop, the ratio between the two classes has been kept constant." ] }, @@ -169,7 +168,7 @@ "source": [ "It is usually advisable to scale your data prior to fitting a classification model. The main advantage of scaling is to avoid attributes of greater numeric ranges dominating those in smaller numeric ranges. For the purposes of this case study, we are applying auto-scaling on the whole X dataset. (Auto-scaling: mean-centering is initially applied per column, followed by scaling where the centered columns are divided by their standard deviation). \n", "\n", - "Use as a reference the `sklearn` preprocessing documentation page in order to scale your data (http://scikit-learn.org/stable/modules/preprocessing.html) " + "Use as a reference the sklearn preprocessing documentation page in order to scale your data (http://scikit-learn.org/stable/modules/preprocessing.html) " ] }, { @@ -183,14 +182,15 @@ "### Write your code here ###\n", "\n", "# Solution #\n", - "X = preprocessing.StandardScaler().fit_transform(X) " + "# X = preprocessing.StandardScaler().fit_transform(X) \n", + "X = preprocessing.scale(X)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "You can visualise the relationship between two variables (features) using a simple scatter plot. This step can give you a good first indication of the model and the complexity (linear vs. non-linear) of the algorithm you may need to investigate. At this stage, let’s plot the first two variables against each other:" + "You can visualise the relationship between two variables (features) using a simple scatter plot. This step can give you a good first indication of the ML model model to apply and its complexity (linear vs. non-linear). At this stage, let’s plot the first two variables against each other:" ] }, { @@ -204,6 +204,7 @@ "f0 = 0 \n", "f1 = 1\n", "\n", + "plt.figure(figsize=(8, 5))\n", "plt.scatter(X[y==0, f0], X[y==0, f1], color = 'b', edgecolors='black', label='Low Quality')\n", "plt.scatter(X[y==1, f0], X[y==1, f1], color = 'r', edgecolors='black', label='High Quality')\n", "plt.xlabel(header[f0])\n", @@ -283,11 +284,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "To build KNN models using scikit-learn, you will be using the `KNeighborsClassifier` object, which allows you to set the value of K using the `n_neighbors` parameter (http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html). The optimal choice of the value K is highly data-dependent: in general a larger K suppresses the effects of noise, but makes the classification boundaries less distinct.
\n", + "To build KNN models using scikit-learn, you will be using the `KNeighborsClassifier` object, which allows you to set the value of K using the `n_neighbors` parameter (http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html). The optimal choice for the value K is highly data-dependent: in general a larger K suppresses the effects of noise, but makes the classification boundaries less distinct.
\n", "\n", "### 4.1 Uniform weights\n", "\n", - "We are going to start by trying two predefined random values of K and compare their performance. For every classification model built with sklearn, we will follow four main steps: 1) Building the classification model using default, pre-defined or optimised parameters, 2) Training the model with data, 3) Testing the model, and
4) Evaluating and reporting on the model with performance metrics." + "For every classification model built with scikit-learn, we will follow four main steps: 1) Building the classification model (using either default, pre-defined or optimised parameters), 2) Training the model with data, 3) Testing the model, and 4) Evaluating and reporting on the model with performance metrics.

\n", + "\n", + "We are going to start by trying two predefined random values of K and compare their performance. Let us start with a small number of K such as K=3." ] }, { @@ -327,6 +330,10 @@ }, "outputs": [], "source": [ + "# Check the arguments of the function\n", + "help(visplots.knnDecisionPlot)\n", + "\n", + "# Visualise the boundary\n", "visplots.knnDecisionPlot(XTrain, yTrain, XTest, yTest, n_neighbors= 3, weights=\"uniform\")" ] }, @@ -334,7 +341,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Let us try a larger number of K, for instance K = 99 (or an *odd* number of your own choice). Can you generate the KNN model and print the metrics for a larger K using as guidance the previous example? " + "Let us try a larger value of K, for instance K = 99 or another number of your own choice; remember, it is good practice to select an *odd* number for K in a binary classification problem to avoid ties. Can you generate the KNN model and print the metrics for a larger K using as guidance the previous example? " ] }, { @@ -367,7 +374,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Try to visualise the boundaries as before using the K neighbours of your choice and the `knnDecisionPlot` command from `visplots`. What do you observe? " + "Visualise the boundaries as before using the K neighbors of your choice and the `knnDecisionPlot` command from `visplots`. What do you observe? " ] }, { @@ -397,7 +404,7 @@ "source": [ "### 4.2 Distance weights\n", "\n", - "Under some circumstances, it is better to give more importance (\"weight\" in computing terms) to nearer neighbours. When weights = \"distance\", weights are assigned to the training data points in a way that is proportional to the inverse of the distance from the query point. In other words, nearer neighbours contribute more to the fit.
\n", + "Under some circumstances, it is better to give more importance (\"weight\" in computing terms) to nearer neighbors. This can be accomplished through the `weights` parameter. When `weights = 'distance'`, weights are assigned to the training data points in a way that is proportional to the inverse of the distance from the query point. In other words, nearer neighbors contribute more to the fit.
\n", "\n", "What if we use weights based on distance? Does it improve the overall performance?" ] @@ -435,10 +442,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The sklearn library provides the grid search function `GridSearchCV` (http://scikit-learn.org/stable/modules/generated/sklearn.grid_search.GridSearchCV.html), which allows us to exhaustively search for the optimum\n", - "combination of parameters by evaluating models trained with a particular algorithm with all provided parameter combinations. Further details and examples on grid search with sklearn can be found at http://scikit-learn.org/stable/modules/grid_search.html
\n", + "Rather than trying one-by-one predefined values of K, we can automate this process. The scikit-learn library provides the grid search function `GridSearchCV` (http://scikit-learn.org/stable/modules/generated/sklearn.grid_search.GridSearchCV.html), which allows us to exhaustively search for the optimum combination of parameters by evaluating models trained with a particular algorithm with all provided parameter combinations. Further details and examples on grid search with scikit-learn can be found at http://scikit-learn.org/stable/modules/grid_search.html
\n", "\n", - "You can use the `GridSearchCV` function to search for a parametisation of the KNN algorithm that gives a more optimal model:" + "You can use the `GridSearchCV` function with the validation technique of your choice (in this example, 10-fold cross-validation has been applied) to search for a parametisation of the KNN algorithm that gives a more optimal model:" ] }, { @@ -450,25 +456,28 @@ "outputs": [], "source": [ "# Define the parameters to be optimised and their values/ranges\n", - "n_neighbors = np.arange(1, 51, 2) \n", + "n_neighbors = np.arange(1, 51, 2) # odd numbers of neighbors used\n", "weights = ['uniform','distance']\n", "\n", "# Construct a dictionary of hyperparameters\n", "parameters = [{'n_neighbors': n_neighbors, 'weights': weights}]\n", "\n", - "# Apply a grid search with 10-fold cross-validation using the dictionary of parameters\n", + "# Conduct a grid search with 10-fold cross-validation using the dictionary of parameters\n", "grid = GridSearchCV(KNeighborsClassifier(), parameters, cv=10)\n", "grid.fit(XTrain, yTrain)\n", "\n", "# Print the optimal parameters\n", - "print \"Best parameters: n_neighbors=\", grid.best_params_['n_neighbors'], \"and weight=\", grid.best_params_['weights']" + "bestNeighbors = grid.best_params_['n_neighbors'] \n", + "bestWeight = grid.best_params_['weights']\n", + "\n", + "print \"Best parameters found: n_neighbors=\", bestNeighbors, \"and weight=\", bestWeight" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "
Let us graphically represent the results using a heatmap:" + "
Let us graphically represent the results of the grid search using a heatmap:" ] }, { @@ -516,7 +525,7 @@ "outputs": [], "source": [ "# Build the classifier using the optimal parameters detected by grid search \n", - "knn = KNeighborsClassifier(n_neighbors=grid.best_params_['n_neighbors'], weights = grid.best_params_['weights'])\n", + "knn = KNeighborsClassifier(n_neighbors = bestNeighbors, weights = bestWeight)\n", "\n", "# Train (fit) the model\n", "knn.fit(XTrain, yTrain)\n", @@ -533,7 +542,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Randomized search on hyperparameters. \n", + "#### Randomized search on hyperparameters\n", "Unlike `GridSearchCV`, `RandomizedSearchCV` does not exhaustively try all the parameter settings. Instead, it samples a fixed number of parameter settings based on the distributions you specify (e.g. you might specify that one parameter should be sampled uniformly while another is sampled following a Gaussian distribution). The number of parameter settings that are tried is given by `n_iter`. If all parameters are presented as a list, sampling without replacement is performed. If at least one parameter is given as a distribution, sampling with replacement is used. You should use continuous distributions for continuous parameters. Further details can be found at http://scikit-learn.org/stable/modules/grid_search.html" ] }, @@ -570,9 +579,9 @@ "\n", "### 5.1 Random Forests\n", "\n", - "The random forests model is an `ensemble method` since it aggregates a group of decision trees into an ensemble (http://scikit-learn.org/stable/modules/ensemble.html).\n", + "The random forests model is an `ensemble method` since it aggregates a group of decision trees into an ensemble (http://scikit-learn.org/stable/modules/ensemble.html). Ensemble learning involves the combination of several models to solve a single prediction problem. It works by generating multiple classifiers/models which learn and make predictions independently. Those predictions are then combined into a single (mega) prediction that should be as good or better than the prediction made by any one classifer. Unlike single decision trees which are likely to suffer from high Variance or high Bias (depending on how they are tuned) Random Forests use averaging to find a natural balance between the two extremes.
\n", "\n", - "One of the most important tuning parameters in building a random forest is the number of trees to construct." + "Let us start by building a simple Random Forest model using the default parameters. For further details and examples on how to construct a Random Forest, see http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html" ] }, { @@ -600,6 +609,39 @@ "print \"Overall Accuracy:\", round(metrics.accuracy_score(yTest, predRF),2)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can visualise the classification boundary created by the linear SVM using the `visplots.rfDecisionPlot` function. You can check the arguments passed in this function by using the `help` command. For easier visualisation, only the test samples have been included in the plot. And remember that the decision boundary has been built using the _training_ data!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Check the arguments of the function\n", + "help(visplots.rfDecisionPlot)\n", + "\n", + "### Write your code here ### \n", + "\n", + "### Solution ### \n", + "visplots.rfDecisionPlot(XTrain, yTrain, XTest, yTest)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Tuning for Random Forests\n", + "\n", + "Random forests offer several parameters that can be tuned. In this case, parameters such as `n_estimators`, `max_features`, `max_depth` and `min_samples_leaf` can be some of the parameters to be optimised. " + ] + }, { "cell_type": "code", "execution_count": null, @@ -608,12 +650,43 @@ }, "outputs": [], "source": [ + "##### TO BE FIXED!!! ##### \n", + "\n", "# Define the parameters to be optimised and their values/ranges\n", "\n", "parameters = [{\"n_estimators\": [250, 500, 1000]}]\n", "sample_leaf_options = [1,5,10,50,100,200,500]\n", "\n", - "###### WHAT ELSE DO WE ADD HERE?!?!?!!? ###### \n" + "###### WHAT ELSE DO WE ADD HERE?!?!?!!? ###### \n", + "\n", + "##### DO WE TUNE WITH A GRID SEARCH??? ##### \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, testing our independent XTest dataset using the optimised model: " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "#################################################################################### \n", + "# Write your code here \n", + "# 1. Build the classifier using the optimal parameters detected by grid search \n", + "# 2. Train (fit) the model\n", + "# 3. Test (predict)\n", + "# 4. Report the performance metrics\n", + "#################################################################################### \n", + "\n", + "\n", + "## WE HAVE NO SOLUTION" ] }, { @@ -626,9 +699,9 @@ "\n", "#### Linear SVMs\n", "\n", - "The parameter C, common to all SVM kernels, trades off misclassification of training examples against simplicity of the decision surface. A low C tolerates training misclassifications and allows softer margins, while for high C the misclassifications become more significant leading to hard-margin SVMs and potentially cases of overfitting. \n", + "The hyperparameter `C`, common to all SVM kernels, trades off misclassification of training examples against simplicity of the decision surface. A low `C` tolerates training misclassifications and allows softer margins, while for high `C` the misclassifications become more significant leading to hard-margin SVMs and potentially cases of overfitting. \n", "\n", - "In this example, we will use linear SVMs with the default value for C" + "At first, let us build a linear SVM model using the default value for the hypeparameter `C` (`C=1.0`). Thorough documentation on how to implement SVMs with scikit-learn can be found at http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html" ] }, { @@ -648,7 +721,7 @@ "##################################################################\n", "\n", "## Solution ## \n", - "linearSVM = SVC(kernel='linear')\n", + "linearSVM = SVC(kernel='linear', C=1.0)\n", "linearSVM.fit(XTrain, yTrain)\n", "yPredLinear = linearSVM.predict(XTest)\n", "\n", @@ -660,7 +733,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can visualise the classification boundary created by the linear SVM using the following function. For easier visualisation, only the test samples have been included in the plot. And remember that the decision boundary has been built using the _training_ data!" + "We can visualise the classification boundary created by the linear SVM using the `visplots.svmDecisionPlot` function. You can check the arguments passed in this function by using the `help` command. For easier visualisation, only the test samples have been included in the plot. And remember that the decision boundary has been built using the _training_ data!" ] }, { @@ -671,8 +744,12 @@ }, "outputs": [], "source": [ + "# Check the arguments of the function\n", + "help(visplots.svmDecisionPlot)\n", + "\n", "### Write your code here ### \n", "\n", + "\n", "### Solution ### \n", "visplots.svmDecisionPlot(XTrain, yTrain, XTest, yTest, 'linear')" ] @@ -681,9 +758,18 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Non-linear SVMs\n", + "**Tuning:** For more details and examples on how to tune linear SVM models using grid search and cross-validation you can use as a reference the following link: http://scikit-learn.org/stable/modules/grid_search.html" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Non-linear (RBF) SVMs\n", + "\n", + "In addition to C, which is common for all types of SVM, the gamma hyperparameter in the RBF kernel controls the nonlinearity of the SVM bounaries. The larger the gamma, the more nonlinear the boundaries surrounding individual samples. Lower values of gamma lead to broader, more linear boundaries.

\n", "\n", - "In addition to C, which is common for all types of SVM, the gamma parameter in the RBF kernel controls the nonlinearity of the SVM bounaries. The larger the gamma, the more nonlinear the boundaries surrounding individual samples. Lower values of gamma lead to broader, more linear boundaries.

In this example, we will use non-linear SVMs with the default values for C and gamma" + "At first, let us build an RBF SVM model using the default values for the hypeparameters `C` (`C=1.0`) and `gamma` (`gamma=0.0`). Thorough documentation on how to implement SVMs with scikit-learn can be found at http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html" ] }, { @@ -715,7 +801,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can visualise the classification boundary created by the RBF SVM using the following function. Once more, for easier visualisation, only the test samples have been included in the plot. And remember that the decision boundary has been built using the _training_ data!" + "We can visualise the classification boundary created by the RBF SVM using the `visplots.svmDecisionPlot` function. You can check the arguments passed in this function by using the `help` command. For easier visualisation, only the test samples have been included in the plot. And remember that the decision boundary has been built using the _training_ data!" ] }, { @@ -726,6 +812,9 @@ }, "outputs": [], "source": [ + "# Check the arguments of the function\n", + "help(visplots.svmDecisionPlot)\n", + "\n", "### Write your code here ### \n", "\n", "### Solution ### \n", @@ -738,7 +827,7 @@ "source": [ "#### Hyperparameter Tuning for non-linear SVMs\n", "\n", - "Proper choice of C and gamma is critical for the performance of SVMs. Optimisation (tuning) of the hyperparameters can be achieved by applying a coarse tuning (often followed by a finer-tuning in the \"neighborhood\" of good parameters)" + "Proper choice of `C` and `gamma` is critical for the performance of SVMs. Optimisation (tuning) of the hyperparameters can be achieved by applying a coarse tuning (often followed by a finer-tuning in the \"neighborhood\" of good parameters)" ] }, { @@ -754,25 +843,30 @@ "g_range = 2. ** np.arange(-15, 5, step=2)\n", "C_range = 2. ** np.arange(-5, 15, step=2)\n", "\n", - "# Construct a dictionary of hyperparameters as in task 4.3\n", - "parameters = [{'gamma': g_range, 'C': C_range}] # Solution \n", + "############################################################################################## \n", + "# Write your code here \n", + "# 1. Construct a dictionary of hyperparameters (see task 4.3)\n", + "# 2. Conduct a grid search with 10-fold cross-validation using the dictionary of parameters\n", + "# 3. Print the optimal parameters (don't forget to use np.log2() this time)\n", + "############################################################################################## \n", "\n", - "# Apply a grid search with 10-fold cross-validation using the dictionary of parameters\n", - "grid = GridSearchCV(SVC(), parameters, cv= 10) # Solution \n", - "grid.fit(XTrain, yTrain) # Solution \n", "\n", - "# Print the optimal parameters\n", - "bestG = np.log2(grid.best_params_['gamma']);\n", - "bestC = np.log2(grid.best_params_['C']);\n", + "# Solution \n", + "parameters = [{'gamma': g_range, 'C': C_range}] \n", "\n", - "print \"The best parameters are: gamma=\", bestG, \" and Cost=\", bestC" + "grid = GridSearchCV(SVC(), parameters, cv= 10) \n", + "grid.fit(XTrain, yTrain)\n", + "\n", + "bestG = grid.best_params_['gamma']\n", + "bestC = grid.best_params_['C']\n", + "print \"The best parameters are: gamma=\", np.log2(bestG), \" and Cost=\", np.log2(bestC)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Plot the results of the grid search using a heatmap" + "Plot the results of the grid search using a heatmap (see task 4.3)." ] }, { @@ -783,15 +877,19 @@ }, "outputs": [], "source": [ - "### Write your code here ### \n", + "##########################################\n", + "# Write your code here \n", + "# 1. Fix the scores \n", + "# 2. Make a heatmap with the performance\n", + "# 3. Add the colorbar\n", + "##########################################\n", + "\n", "\n", "\n", "### Solution ### \n", - "# grid_scores_ contains parameter settings and scores\n", "scores = [x[1] for x in grid.grid_scores_]\n", "scores = np.array(scores).reshape(len(C_range), len(g_range))\n", "\n", - "# Make a heatmap with the performance\n", "plt.figure(figsize=(10, 6))\n", "plt.imshow(scores, interpolation='nearest', origin='higher', cmap=plt.cm.get_cmap('jet_r'))\n", "plt.xticks(np.arange(len(g_range)), np.log2(g_range))\n", @@ -799,19 +897,16 @@ "plt.xlabel('gamma (log2)')\n", "plt.ylabel('Cost (log2)')\n", "\n", - "# Add the colorbar\n", - "\n", "cbar = plt.colorbar()\n", "cbar.set_label('Classification Accuracy', rotation=270, labelpad=20)\n", - "\n", - "plt.show()" + "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Finally, testing with the optimised model (best hyperparameters) for C and gamma:" + "Finally, testing our independent XTest dataset using the optimised model: " ] }, { @@ -832,7 +927,7 @@ "\n", "\n", "## Solution ## \n", - "rbfSVM = SVC(kernel='rbf', C=grid.best_params_['C'], gamma=grid.best_params_['gamma'])\n", + "rbfSVM = SVC(kernel='rbf', C = bestC, gamma = bestG)\n", "rbfSVM.fit(XTrain, yTrain)\n", "predictions = rbfSVM.predict(XTest) \n", "\n", @@ -846,9 +941,9 @@ "source": [ "### 5.3 Logistic Regression\n", "\n", - "Logistic regression is based on linear regression, but rather than the predicted output being a continuous value, it predicts the probability that a sample belongs to a class based on the values of the input variables (for more details, see: http://www.omidrouhani.com/research/logisticregression/html/logisticregression.htm). In the case of classification, we can use this to then assign the sample to the most likely class.\n", + "Logistic regression is based on linear regression, but rather than the predicted output being a continuous value, it predicts the probability that a sample belongs to a class based on the values of the input variables. In the case of classification, we can use this to then assign the sample to the most likely class. For more details, see: http://www.omidrouhani.com/research/logisticregression/html/logisticregression.htm \n", "\n", - "In sklearn, you can learn a logistic regression model using the LogisticRegression object (http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html). As with linear regression, there are certain assumpttions that you might make or constraints that you wish your model to fulfil, e.g. whether or not you want a constant to be included in the function. You can also specify the way you wish learning to take place by using different solvers or how you wish errors to be penalised." + "In scikit-learn, you can learn a logistic regression model using the LogisticRegression object (http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html). As with linear regression, there are certain assumpttions that you might make or constraints that you wish your model to fulfil, e.g. whether or not you want a constant to be included in the function. You can also specify the way you wish learning to take place by using different solvers or how you wish errors to be penalised." ] }, { @@ -891,6 +986,9 @@ }, "outputs": [], "source": [ + "# Check the arguments of the function\n", + "help(visplots.logregDecisionPlot)\n", + "\n", "### Write your code here ### \n", "\n", "## Solution ## \n", @@ -924,24 +1022,31 @@ "pen = ['l1','l2']\n", "C_range = 2. ** np.arange(-5, 15, step=2)\n", "\n", - "# Construct a dictionary of hyperparameters as in task 4.3\n", - "parameters = [{'C': C_range, 'penalty': pen}]\n", + "\n", + "############################################################################################## \n", + "# Write your code here \n", + "# 1. Construct a dictionary of hyperparameters (see task 4.3)\n", + "# 2. Conduct a grid search with 10-fold cross-validation using the dictionary of parameters\n", + "# 3. Print the optimal parameters\n", + "############################################################################################## \n", "\n", "\n", - "# Apply a grid search with 10-fold cross-validation using the dictionary of parameters\n", + "# Solution\n", + "parameters = [{'C': C_range, 'penalty': pen}]\n", + "\n", "grid = GridSearchCV(LogisticRegression(), parameters, cv= 10)\n", "grid.fit(XTrain, yTrain)\n", "\n", - "\n", - "# Print the optimal parameters\n", - "print \"The best parameters are: cost=\", grid.best_params_['C'], \" and penalty=\", grid.best_params_['penalty']" + "bestC = grid.best_params_['C']\n", + "bestP = grid.best_params_['penalty']\n", + "print \"The best parameters are: cost=\", bestC , \" and penalty=\", bestP" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Plot the results of the grid search with a heatmap." + "Plot the results of the grid search with a heatmap (see task 4.3)" ] }, { @@ -952,12 +1057,19 @@ }, "outputs": [], "source": [ - "# grid_scores_ contains parameter settings and scores\n", + "##########################################\n", + "# Write your code here \n", + "# 1. Fix the scores \n", + "# 2. Make a heatmap with the performance\n", + "# 3. Add the colorbar\n", + "##########################################\n", + "\n", + "\n", + "# Solution\n", "scores = [x[1] for x in grid.grid_scores_]\n", "scores = np.array(scores).reshape(len(pen), len(C_range))\n", "scores = np.transpose(scores)\n", "\n", - "# Make a heatmap with the performance\n", "plt.figure(figsize=(12, 6))\n", "plt.imshow(scores, interpolation='nearest', origin='higher', cmap=plt.cm.get_cmap('jet_r'))\n", "plt.xticks(np.arange(len(pen)), pen)\n", @@ -975,7 +1087,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now try these out to see how the performance metrics are affected." + "Finally, testing our independent XTest dataset using the optimised model: " ] }, { @@ -986,7 +1098,17 @@ }, "outputs": [], "source": [ - "l_regression = LogisticRegression(C=grid.best_params_['C'], penalty=grid.best_params_['penalty'])\n", + "#################################################################################### \n", + "# Write your code here \n", + "# 1. Build the classifier using the optimal parameters detected by grid search \n", + "# 2. Train (fit) the model\n", + "# 3. Test (predict)\n", + "# 4. Report the performance metrics\n", + "#################################################################################### \n", + "\n", + "\n", + "## Solution ## \n", + "l_regression = LogisticRegression(C=bestC, penalty=bestP)\n", "l_regression.fit(XTrain, yTrain)\n", "l_prediction = l_regression.predict(XTest)\n", "\n", @@ -1010,7 +1132,21 @@ "source": [ "### 5.4 Neural Networks\n", "\n", - "A neural network is a set of connected input-output units. During training, the connections are assigned different weights. This allows the classification function to take on highly complex \"shapes\" (equivalent to complicated mathematical expressions that go beyond the linear or polynomial models of logistic regression). This might also mean that the resulting model is difficult to interpret and map to domain knowledge. (NB. even though you might think of the second layer of a neural network as just a logistic regression model, the non-linear transformation in the hidden units gives the input to output mapping a non-linear decision boundary.)\n" + "A neural network is a set of connected input-output units. During training, the connections are assigned different weights. This allows the classification function to take on highly complex \"shapes\" (equivalent to complicated mathematical expressions that go beyond the linear or polynomial models of logistic regression). This might also mean that the resulting model is difficult to interpret and map to domain knowledge. (NB. even though you might think of the second layer of a neural network as just a logistic regression model, the non-linear transformation in the hidden units gives the input to output mapping a non-linear decision boundary.)\n", + "
\n", + "\n", + "We will build our Neural Network classifier using the `multilayer_perceptron.MultilayerPerceptronClassifier` function. Further details on the `multilayer_perceptron` library can be found at https://github.com/IssamLaradji/NeuralNetworks. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "help(multilayer_perceptron.MultilayerPerceptronClassifier)" ] }, { @@ -1022,17 +1158,18 @@ }, "outputs": [], "source": [ - "############################################################################### \n", + "##################################################################################### \n", "# Write your code here \n", - "# 1. Build the Neural Net classifier classifier using the default parameters\n", + "# 1. Build the Neural Net classifier classifier ... you can use parameters such as \n", + "# activation='logistic', hidden_layer_sizes=2, learning_rate_init=.5\n", "# 2. Train (fit) the model\n", "# 3. Test (predict)\n", "# 4. Report the performance metrics\n", - "###############################################################################\n", + "#####################################################################################\n", "\n", "\n", "# Solution #\n", - "nnet = multilayer_perceptron.MultilayerPerceptronClassifier(activation='logistic',\n", + "nnet = multilayer_perceptron.MultilayerPerceptronClassifier(activation='logistic', \n", " hidden_layer_sizes=2, learning_rate_init=.5)\n", "nnet.fit(XTrain, yTrain)\n", "net_prediction = nnet.predict(XTest)\n", @@ -1045,18 +1182,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can visualise the classification boundary created by the neural network using the built in visualisation function `nnDecisionPlot`. As with the above examples, only the test samples have been included in the plot. And remember that the decision boundary has been built using the _training_ data!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "visplots.nnDecisionPlot(XTrain, yTrain, XTest, yTest, 2, .5)" + "We can visualise the classification boundary of the neural network using the built-in visualisation function `visplots.nnDecisionPlot`. As with the above examples, only the test samples have been included in the plot. And remember that the decision boundary has been built using the _training_ data!" ] }, { @@ -1067,6 +1193,15 @@ }, "outputs": [], "source": [ + "# Check the arguments of the function\n", + "help(visplots.nnDecisionPlot)\n", + "\n", + "### Write your code here ###\n", + "### Try arguments such as hidden_layer = 2 or (2,3,6) and learning_rate = .5\n", + "\n", + "\n", + "# Solution #\n", + "visplots.nnDecisionPlot(XTrain, yTrain, XTest, yTest, 2, .5)\n", "visplots.nnDecisionPlot(XTrain, yTrain, XTest, yTest, (2,3,6), .5)" ] }, @@ -1103,6 +1238,16 @@ "layer_size_range = [(3,2),(10,10),(2,2,2),10,5] # different networks shapes\n", "learning_rate_range = np.linspace(.1,1,3)\n", "\n", + "\n", + "############################################################################################## \n", + "# Write your code here \n", + "# 1. Construct a dictionary of hyperparameters (see task 4.3)\n", + "# 2. Conduct a grid search with 10-fold cross-validation using the dictionary of parameters\n", + "# 3. Print the optimal parameters\n", + "############################################################################################## \n", + "\n", + "\n", + "# Solution\n", "parameters = [{'hidden_layer_sizes': layer_size_range, 'learning_rate_init': learning_rate_range}]\n", "\n", "grid = GridSearchCV(multilayer_perceptron.MultilayerPerceptronClassifier(), parameters, cv= 10)\n", @@ -1110,7 +1255,6 @@ "\n", "best_size = grid.best_params_['hidden_layer_sizes']\n", "best_best_lr = grid.best_params_['learning_rate_init']\n", - "\n", "print \"The best parameters are: hidden_layer_sizes=\", best_size, \" and learning_rate_init=\", best_best_lr" ] }, @@ -1118,7 +1262,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now try these out to see how the performance metrics are affected." + "Plot the results of the grid search using a heatmap (see task 4.3)." ] }, { @@ -1129,19 +1273,37 @@ }, "outputs": [], "source": [ - "nnet = multilayer_perceptron.MultilayerPerceptronClassifier(hidden_layer_sizes=best_size, learning_rate_init=best_best_lr)\n", - "nnet.fit(XTrain, yTrain)\n", - "net_prediction = nnet.predict(XTest)\n", + "##########################################\n", + "# Write your code here \n", + "# 1. Fix the scores \n", + "# 2. Make a heatmap with the performance\n", + "# 3. Add the colorbar\n", + "##########################################\n", "\n", - "print metrics.classification_report(yTest, net_prediction)\n", - "print \"Overall Accuracy:\", round(metrics.accuracy_score(yTest, net_prediction),2)" + "\n", + "# Solution\n", + "scores = [x[1] for x in grid.grid_scores_]\n", + "scores = np.array(scores).reshape(len(layer_size_range), len(learning_rate_range))\n", + "scores = np.transpose(scores)\n", + "\n", + "plt.figure(figsize=(12, 6))\n", + "plt.imshow(scores, interpolation='nearest', origin='higher', cmap=plt.cm.get_cmap('jet_r'))\n", + "plt.xticks(np.arange(len(layer_size_range)), layer_size_range)\n", + "plt.yticks(np.arange(len(learning_rate_range)), learning_rate_range)\n", + "plt.xlabel('hidden layer topology')\n", + "plt.ylabel('learning rate')\n", + "\n", + "cbar = plt.colorbar()\n", + "cbar.set_label('Classification Accuracy', rotation=270, labelpad=20)\n", + "\n", + "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Plot the results of the grid search using a heatmap." + "Finally, testing our independent XTest dataset using the optimised model: " ] }, { @@ -1152,23 +1314,22 @@ }, "outputs": [], "source": [ - "# grid_scores_ contains parameter settings and scores\n", - "scores = [x[1] for x in grid.grid_scores_]\n", - "scores = np.array(scores).reshape(len(layer_size_range), len(learning_rate_range))\n", - "scores = np.transpose(scores)\n", + "#################################################################################### \n", + "# Write your code here \n", + "# 1. Build the classifier using the optimal parameters detected by grid search \n", + "# 2. Train (fit) the model\n", + "# 3. Test (predict)\n", + "# 4. Report the performance metrics\n", + "#################################################################################### \n", "\n", - "# Make a heatmap with the performance\n", - "plt.figure(figsize=(12, 6))\n", - "plt.imshow(scores, interpolation='nearest', origin='higher', cmap=plt.cm.get_cmap('jet_r'))\n", - "plt.xticks(np.arange(len(layer_size_range)), layer_size_range)\n", - "plt.yticks(np.arange(len(learning_rate_range)), learning_rate_range)\n", - "plt.xlabel('hidden layer topology')\n", - "plt.ylabel('learning rate')\n", "\n", - "cbar = plt.colorbar()\n", - "cbar.set_label('Classification Accuracy', rotation=270, labelpad=20)\n", + "## Solution ## \n", + "nnet = multilayer_perceptron.MultilayerPerceptronClassifier(hidden_layer_sizes=best_size, learning_rate_init=best_best_lr)\n", + "nnet.fit(XTrain, yTrain)\n", + "net_prediction = nnet.predict(XTest)\n", "\n", - "plt.show()" + "print metrics.classification_report(yTest, net_prediction)\n", + "print \"Overall Accuracy:\", round(metrics.accuracy_score(yTest, net_prediction),2)" ] }, { diff --git a/EuroScipyExecuted.ipynb b/EuroScipyExecuted.ipynb index 9e694f4..41f9174 100644 --- a/EuroScipyExecuted.ipynb +++ b/EuroScipyExecuted.ipynb @@ -18,7 +18,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Load the required libraries" + "## 1. Load the required libraries" ] }, { @@ -42,16 +42,11 @@ "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.svm import SVC\n", "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.grid_search import GridSearchCV, RandomizedSearchCV\n", "from scipy.stats.distributions import randint\n", - "\n", "from multilayer_perceptron import multilayer_perceptron\n", "\n", - "\n", - "from sklearn.ensemble import RandomForestClassifier\n", - "from sklearn.tree import DecisionTreeClassifier\n", - "\n", - "\n", "%matplotlib inline" ] }, @@ -59,14 +54,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Exploring and pre-processing data" + "## 2. Exploring and pre-processing data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The first thing you will need to do in order to work with the wine dataset (adapted dataset based on the wine quality case study at https://archive.ics.uci.edu/ml/datasets/Wine+Quality) is to read the contents from the provided wine.csv data file using the `read_csv` command:" + "The dataset we will be using throughout this workshop is an adapted version of the wine quality case study, available from the UCI Machine Learning repository at https://archive.ics.uci.edu/ml/datasets/Wine+Quality. The first thing you will need to do in order to work with the wine dataset is to read the contents from the provided wine.csv data file using the `read_csv` command:" ] }, { @@ -77,7 +72,7 @@ }, "outputs": [], "source": [ - "wine = pd.read_csv(\"data/wine_test.csv\")\n", + "wine = pd.read_csv(\"data/wine.csv\", sep=\",\")\n", "header = wine.columns.values" ] }, @@ -85,7 +80,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Explore the first few rows from the wine data frame using the \"`head`\" command from the `pandas` package:" + "At this point, you should try to explore the first few rows of the imported wine DataFrame using the \"`head`\" function from the `pandas` package (http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.head.html):" ] }, { @@ -222,14 +217,15 @@ "source": [ "### Write your code here ###\n", "\n", - "wine.head() # Solution" + "# Solution #\n", + "wine.head() " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Once the data have been converted into a numpy array, we need to construct the data matrix X and the associated class vector y (target variable). In this case, the target variable is the last column, whereas the input data all the remaining columns. " + "In order to feed the data into our classification models, the imported wine DataFrame needs to be converted into a `numpy` array. For more information on numpy arrays, see http://scipy-lectures.github.io/intro/numpy/array_object.html. Subsequently, we need to split our initial dataset into the data matrix X (independent variable) and the associated class vector y (dependent or target variable). " ] }, { @@ -243,8 +239,7 @@ "# Convert to numpy array\n", "npArray = np.array(wine)\n", "\n", - "# Cast the associated class vector into type int\n", - "X = npArray[:,:10]\n", + "X = npArray[:,:-1]\n", "y = npArray[:,-1].astype(int)" ] }, @@ -252,12 +247,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "It is always a good practice to check the dimensionality of the imported data using the \"`shape`\" command: " + "It is always a good practice to check the dimensionality of the imported data prior to constructing any classification model to check that you really have imported all the data and imported it in the correct way (e.g. one common mistake is to get the separator wrong and end up with only one column).
Try printing the size of the input matrix X and class vector y using the \"`shape`\" command: " ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": { "collapsed": false }, @@ -266,8 +261,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "X dimensions: y dimensions: X dimensions: (1000, 10)\n", - "y dimensions: (1000,)\n" + "X dimensions: y dimensions:\n" ] } ], @@ -275,20 +269,22 @@ "print \"X dimensions:\", ### Write your code here ###\n", "print \"y dimensions:\", ### Write your code here ###\n", "\n", - "print \"X dimensions:\", X.shape # Solution\n", - "print \"y dimensions:\", y.shape # Solution" + "# Solution #\n", + "# print \"X dimensions:\", X.shape \n", + "# print \"y dimensions:\", y.shape " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "An important thing to understand before applying any classification algorithms is how the output labels are distributed. Are they evenly distributed? Imbalances in distribution of labels can often lead to poor classification results for the minority class even if the classification results for the majority class are very good. For the purposes of this excercise, the ratio in-between the classes has been kept constant." + "Based on the class vector y, the wine samples are classified into two distinct categories: high quality (class 1) and low quality (class 0).\n", + "

An important thing to understand before applying any classification algorithms is how the output labels are distributed. Are they evenly distributed? Imbalances in distribution of labels can often lead to poor classification results for the minority class even if the classification results for the majority class are very good. For the purposes of this workshop, the ratio between the two classes has been kept constant." ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": { "collapsed": false }, @@ -313,12 +309,12 @@ "source": [ "It is usually advisable to scale your data prior to fitting a classification model. The main advantage of scaling is to avoid attributes of greater numeric ranges dominating those in smaller numeric ranges. For the purposes of this case study, we are applying auto-scaling on the whole X dataset. (Auto-scaling: mean-centering is initially applied per column, followed by scaling where the centered columns are divided by their standard deviation). \n", "\n", - "With `sklearn`, you can use `preprocessing.StandardScaler()` to scale the data. " + "Use as a reference the sklearn preprocessing documentation page in order to scale your data (http://scikit-learn.org/stable/modules/preprocessing.html) " ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": { "collapsed": false }, @@ -326,28 +322,30 @@ "source": [ "### Write your code here ###\n", "\n", - "X = preprocessing.StandardScaler().fit_transform(X) #Solution" + "# Solution #\n", + "# X = preprocessing.StandardScaler().fit_transform(X) \n", + "X = preprocessing.scale(X)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "You can visualise the relationship between two variables (features) using a simple scatter plot. To illustrate, let’s plot the first two variables against each other:" + "You can visualise the relationship between two variables (features) using a simple scatter plot. This step can give you a good first indication of the ML model model to apply and its complexity (linear vs. non-linear). At this stage, let’s plot the first two variables against each other:" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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Ou/FQK8fuJRFKuY1DXM8drjWupQC4DVubcYHlPhyrfSu9/BVwSKbhku1MAXcz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\nLpUCEBHRpX4ApIP0f4hfBKIAAAAASUVORK5CYII=\n", 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0uqrYrwEvFpHfAlYAGRH5sKq+KtpJRG6OvB1T1bHFm6JhGIZh1A8R2QhsrGgf1ebwEROR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\nNlhbnJZG7pnDmlFEF0w+G+dENEu+kthWXFKU0Fl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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -355,10 +353,14 @@ } ], "source": [ - "plt.scatter(X[y==0, 0], X[y==0, 1], color = 'b', edgecolors='black', label='Low Quality')\n", - "plt.scatter(X[y==1, 0], X[y==1, 1], color = 'r', edgecolors='black', label='High Quality')\n", - "plt.xlabel(header[0])\n", - "plt.ylabel(header[1])\n", + "f0 = 0 \n", + "f1 = 1\n", + "\n", + "plt.figure(figsize=(8, 5))\n", + "plt.scatter(X[y==0, f0], X[y==0, f1], color = 'b', edgecolors='black', label='Low Quality')\n", + "plt.scatter(X[y==1, f0], X[y==1, f1], color = 'r', edgecolors='black', label='High Quality')\n", + "plt.xlabel(header[f0])\n", + "plt.ylabel(header[f1])\n", "plt.legend()\n", "plt.show()" ] @@ -367,21 +369,26 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Training and testing a classifier" + "You can change the values of *f0* and *f1* to values of your own choice in order to investigate the relationship between different features. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Training and testing a classification model on the same dataset is a methodological mistake: a model that would just repeat the labels of the samples that it has just seen would have a perfect score but would fail to predict anything useful on yet-unseen data (poor generalisation).
\n", - "\n", - "To use different datasets for training and testing, we need to split the breast cancer dataset into two disjoint sets: train and test (**Holdout method**).
" + "## 3. Training and testing a classifier" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Training and testing a classification model on the same dataset is a methodological mistake: a model that would just repeat the labels of the samples that it has just seen would have a perfect score but would fail to predict anything useful on yet-unseen data (poor generalisation). To use different datasets for training and testing, we need to split the wine dataset into two disjoint sets: train and test (**Holdout method**).
" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": { "collapsed": false }, @@ -406,7 +413,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": { "collapsed": false }, @@ -433,23 +440,25 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## KNN" + "## 4. KNN" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "To build KNN models using scikit-learn, you will be using the `KNeighborsClassifier` function, which allows you to set the value of K using the `n_neighbors` parameter. The optimal choice of the value K is highly data-dependent: in general a larger K suppresses the effects of noise, but makes the classification boundaries less distinct.
\n", + "To build KNN models using scikit-learn, you will be using the `KNeighborsClassifier` object, which allows you to set the value of K using the `n_neighbors` parameter (http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html). The optimal choice for the value K is highly data-dependent: in general a larger K suppresses the effects of noise, but makes the classification boundaries less distinct.
\n", "\n", - "### Uniform weights\n", + "### 4.1 Uniform weights\n", "\n", - "We are going to start by trying two predefined random values of K and compare their performance:" + "For every classification model built with scikit-learn, we will follow four main steps: 1) Building the classification model (using either default, pre-defined or optimised parameters), 2) Training the model with data, 3) Testing the model, and 4) Evaluating and reporting on the model with performance metrics.

\n", + "\n", + "We are going to start by trying two predefined random values of K and compare their performance. Let us start with a small number of K such as K=3." ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": { "collapsed": false }, @@ -470,10 +479,16 @@ } ], "source": [ + "# Build the classifier \n", "knn3 = KNeighborsClassifier(n_neighbors=3)\n", + "\n", + "# Train (fit) the model\n", "knn3.fit(XTrain, yTrain)\n", + "\n", + "# Test (predict)\n", "yPredK3 = knn3.predict(XTest)\n", "\n", + "# Report the performance metrics\n", "print metrics.classification_report(yTest, yPredK3)\n", "print \"Overall Accuracy:\", round(metrics.accuracy_score(yTest, yPredK3), 2)" ] @@ -482,21 +497,31 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can visualise the classification boundary created by the KNN classifier using the following function. For easier visualisation, only the test samples have been included in the plot. Remember that the decision boundary has been built using the _training_ data!
" + "We can visualise the classification boundary created by the KNN classifier using the built-in function `visplots.knnDecisionPlot`. For easier visualisation, only the test samples are depicted in the plot. Remember though that the decision boundary has been built using the _training_ data!
" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on function knnDecisionPlot in module visplots:\n", + "\n", + "knnDecisionPlot(XTrain, yTrain, XTest, yTest, n_neighbors, weights)\n", + "\n" + ] + }, { "data": { - "image/png": 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Wg8FxzYLZs+enU6evHFaelrYppdi1azE5cgTRfvIsbtx9G4u1DL8eHMv5f28w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\nRU5ErkJggg==\n", 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RAyKy1tWxaJqmaZmXyxMaMBA4BuhbU2uapmkPzaUJTUQCgVbAPCDB7bQ1TdM0LbVcfYQ2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nMfbyGKR8iXrxy+jXKnun3VQoWoWkgSW0jQVufUvXsIgsD5hw8ZNaocBoMeURSoWbM3NjaOmJvb\nULZsC2PLmlQwGrRviOVHj3Lx4k6GDNmVru1fx8VRcehQrCMjsQb6K5Uc9fZOMpbtKlbkl127yBQX\nRzZRZIpKxeCmTXG2s2P/hAnc8ventJcXvZAU8acBrjY2KNIZOxJFEYVcTmNBoAlSIjZyOW8eGa+i\no4mIiaEzsAywxIw+ZESOO3pOoQ0JYc+VKzT/czEK2a+I3KdCvqPsHtqXZZ6eLPP0JEeXLhyKjubN\no2CyAc7fu/dOooBMLkeN1MS0O1JDGT+ge44cgFRA7eJSgIwZs6aIrYSGPmX0oCKMTYjGWRTpK5Nj\nY+OEXWKSg0EQaDZ5Mo/u3ycv0FcUWT94cNIDKC0sTE1ZOSBlKvqwNWsw37mTsomfvQwGKjx4kPR9\nw5IlGb5uBxp9H/SGopirZ9KvVl0yWlmxefhwAJ6FhvLL8OGUq+LJH01HvXNcuVxB3br9qV27L9Om\nNcAy/gknxs1671w/xLFbj4jTtENy/Y4kASVyEbaOHIlHtmyfNfbHcv7BA5ro9eRI/DxQEPB5+jTV\nbWUyGW0qVGDe/v0EBd1HFMX/xKA1bjyQdeuaoNXmA46hUqnY2/P95SNvMFWpqOYhlfDUKFKEMc2a\nAZI3of/BZwQG3iMg4DYbN47GxkYqhzA1taRBgyFkzpwLlco06ec/I0aD9g3x9z0NDRoMJmfO9Llw\npm/bRvaXL3E0GNABdYDBS5eybZT0oHPJmJGzU6cyc/t2zsTEML1sWRonc+0UzJqVvWPH0tbHh20J\nCThkzkw5V1e6zZlDl5o1Ke3mRnR8PNO2buVZUBDF8+WjT506Sa64LHZ2aOVyCgoCR4C8wEm5HOfE\nPlo2FhbogNtIavN+OCByD2mNdhH4lU4LVhGn2YSUmqLj8M3iVBoxgrLu7rx+/RrBYOAMb5rdwzmV\niqr/V+j95549LNuzB5ASX44hJbxYqVTkd3bm6tOnLNi9m9AXjwEZ+/fPo3LlzqjV5jg45MBz4FZ8\nl3pi0MSSM3NO7t8/w+/z5zO4aVOuP39O0L17XEksuj4EdJ8zh8dLlnzMrZXuh709e1QqBK0WOVKC\njrPtW3UUGwsLrk0by8QtuwgMv02dYhXpVLliijGev3qFlX0eWracmCIWc+7cZs6d242lpTUNGw7A\nwSE7AwduxdMzO6M3bGB8ixa8j4uPHjF333EEUcSzxq+UzZsXgyCw9tQpHoU8RyqEXgS0AypjbjGJ\nS48fs+/KFW49foybqysDGzXC7AOtd9LDg6AgZu/YQVx8PE0qVqRu8eJvr2HGjGxVKtEZDJggvbg4\nv6d0YOT69dx/Fcv8cZv/s8SLOnV68+jROa5fP0TDomXxbtUk6W/iU8nj5MTuDtKLqij+ytWnT0lI\nLPp/FBLCyJX9SEiIITY2kuLF6+HsnB8TEzWVKnVMau3zM2DMcvxGmL13L3+sXMmUKVdwdMzNy5fP\n2LJlIs7O+djcIvXVQIOJEzl54wZDkLLzxgNWtrY8XLToo49/+t49Gk2cyPBEd+FklYoNQ4cydMUK\n3EJCqKrTsUqtJkfx4iz/44+k/WZu347P5s2Ul8s5K4p4NmjA0GSqGL4nT9J/8WKyCgJX9PWBLYnf\niIAJMkAkBsnIgZyutGAZz5ESPGoCfwKVTUx4qVCgdnLi4IQJSXHHiVu2MH3DBryRkibGA6VNTPCX\ny6lcqhTdatWi1tixDNZo0ALeCiWuuUsREvIQa+tM6PU6QkMe0UwUqAyMQnJV9gFWmZnRtWZNonft\nYo5eD4nHyCiX47doES+josjv7JzuB6VGp6Pm6NHEBAaSVSbjjCiyZ8yYD7p2DYLAcF9flAoFg+rX\np/SUJYSFBWBmZoWNjRO5cpVi3771aDRDkcmeYm6+Ah+fS9jZORMREUzv3jnYP2xIqoowAOcePOC3\n8T7EaUcCCsxV49k7vC+LDx/mn1fg5laGixf3ExlpBdgjl1+iefMh+P7tRQmZjG56PbtMTIjKkYP9\n48d/VuzxyYsXlBk8mF4JCTiJIt4qFRO7dqVdpUpJ16KJtzdPHz7EDTguimwcMoTKaZybeYcudOjw\nJ1WqdP7kOX0McXGvWbt2KJcv72TAgE2MzJt6z8N/i6i4OHoeDCEu7jWRkcFcvrwTW1snFAoTatTw\nJHfuX5DLFTg75/+uMyuNafvfON0OhrFtmzeRkS9QKlU4OGQnISGG1q2n8Ge51F2AVUeOpNKDB7xx\nPO0AvCwtmdO7N519fIjS6cigUrF60CCqJUv9DwwPp8usWVx+9ozsdnYs6tePqevXU+3aNbolbrME\nWJ8rF68DA7mUkIAM6WHupFTybPHiJJccwNWnT7kbGEjeLFmSkkGSczcggPFbtrDhzA1EzgP5keGD\nyFiKZs/BDb8mGIQxwBPMKckRIiiM1IbzLlLG4jAbG+b+/jvVPDxSBNVd27dnRkICzRM/TwcWWFmx\nysuLCvnz03X2bNzPnuWN8289sDpvXuZ6ehKv1TJv3z5Cjh7llihSGuiGtFYMAnxkMkLKl+fIhQuc\n1GrJAYyTyzmaLRuPEkwIDn7AyXHjqJA//bWCOr2ewzdvEh0fT/l8+ciSRrFwcrZcuEBTHx9kMgWu\nGV1Z2rMtWWxtEQSBvVevMmzdRgThBCTKQSsUv1O/vj0KhQKdTsM//+zG3/82af0t1Z86n11XWiAV\nBQCsoFqhZcRnyM6rV88ZOnQPJiZqbtw4hFYbT4ECFTl3bhMrlvfmFFKphAHIp1azacIEiqQzUzE1\nRvr6otmxg+mJz6UTwB8ODlyd91as+E1MMyw6mjJubu+VZjt++zatFq0le/aidO485191xz16dIkZ\nMxpRtGhtNrctm6Su8jXxf/WK13FxvIyKot+2s7x+/YK4uNfY27uSN285ZDI5FSq0IWvW1Ivbv1WM\nafvfOEuqZ2RJ9Znc8vNDJpPhnjXrB/fJ6eCAdbIYjCWQwcKCFlOnMkUUaQqs12ppMmkSfsuWYWNp\niSAI1Bs3jnovXrBKEDgSHEydceMoli1bCm1BS6SHr4VMRgiS7FR2pF8YXeJq5Q1Fc+SgaGKs6nFI\nCJFxceTOnJlnL1+iUirJ5+xM7aJFeXThEjf1RRCQ4YySQFk8m716UGfyXB6GTEMQdEzHQGmkB6QK\n0CGVHFiamFA7lWw6QRBSzNsKUMpk5MycGZlMhlane+e8tDpdkoK+rbk5mUWRdUBWSJGSbimKOFhZ\nMbJtWwqtXo1MFHG1syNKqyY4+B6l8+Sh8EfEkDbRjE7d7FChJXzFinTv571lHwCiuJTnrxxoMK0t\n16aNJY+LC25ZsjBy/WYEapFYRYfBEMO2bRH0rlkTZzs7SlcoTCbrtxJekbGxPAwOxtnOTnIb6w2J\nV+btVdLo9RzvXY+cg70JCLiDm1vppDYuAQF32blzGvZKJWUTfxfkgFmibuTnoNXpsEz2km0JaA0p\n9RvlcnlSnOlDVHJ358mMsUzYvJmBAz1o3XoylSt3/uKxtMjIEP7+eyBVqnRlU/NvxzhktbfnzZPk\nRuIq1iAI+J4+TWC4P9Hx8YwdWxGVyhyZTEa5cq2S7nOOHMVS7Xn3LWM0aN8YBV1dP7xRIm1/+40W\nFy+SRaslA9BPraakmxtRL17gmbhNP+BPUeTAjRu0KFuWkMhIAl69YqwgZfW1BlYBRfPlY+iTJ1gl\nuhyHqlRMqlcPr6VLyQvkAJ4Cbo6OqRZOi6KI58KFbDlzBgeFAj+tloxKJQaZDI/cuVnYuzdD1CZ4\n62PJD/xlIlKuaElyZs7M3T8nEhQeTqVhwwh+/ZrTgshfQGbgPuClVtOuWrVUr0HlEiXocvYsJZGM\n31FAH60lu2c/SuTMTiZrSwYqFGQyGDAD/lCrGV2zZtL+zcuXp/qBA+TRapmIVMJgALbyVsuxRK5c\ndK1WjYlbtjBl1z4cHKSOBNPatk2qrUsPzdhE89gIVB/x5q43GLj+/AGSA1ad+NPanLhzhzxOTqiU\nSvrVrse8Aw9J0A4D/FErx1KlWl/mdnpXIeXorVs0mDYXucwZrd6fiS0b06tGWU7dHUKcNgOSy3EA\nnjWaoZDLUavNiYgIerv/0WWsXTsU76b1WHPoEJ4hIbTW69mhUECGDB9l4FOjZYUK1Dx8mNwaDU7A\nQLWa9r/99sH93oeZSsWk1q1pWa4cjf9axKlTa+jefTFOTu/vq5ceRFHk2LHl+PoOw7NKOUY1/Pwx\n/20Ucjntfv016fPQhg2JiI0lTqOh544brF07FJ0ugZiYcIoUqQnIKFasNsWK1fl6k04nX8XlKJPJ\nmgFjgXxASVEU/0lju5/G5fip7Lt6FZ+NG9HqdLStXh3XTJloNXkyAYAFUjzIGdg3bhzl8+cnOj4e\np86deWwwkBnJCHio1SwdMYKAsDD+2rEDEfi9fn2K5sxJhUGDuKjTkR3J/dPU1JTA5ctTFE4DbDp3\njonz59NOqyUBiEBK+zgKlFcqsc6XjyqFCnHh1i1ehIdTwcOD8W3aJMXCQHKFDl66lMdBQThmykRU\ndDQ6nY5mlSvTp06dVN+qu82Zw6HTp3kOZACUyAinJiLlUSimUa5cA86d20h5FycMgkCnWrXoWCVl\n4erJO3eY5OtLTHw89hkzEhgSgp2VFcNat6aSuztxGg2rTpzg+O3bbLl4CXfnLOR2dGRy69YfbAD6\nuYiiiHnbziTo/gHcABFL03Ks6FWGZjNnIm7ciEEQ8N66i41nr2NjYYZP+4aUyvPug1Wn15OxiyfR\n8VuQiij8MFMV5/KU4dz2D2DytiOIwKD6FZPkwrZdvEi7hcvw9j5HTEwEM2c2ZUOvTvzm4UF4TAyD\nly3j1tOn5M2alWldupD5A93O08OJO3eYtHYtsQkJNK1YkX716n2xFZVBEJi7bx+jt+6mbl2vzy68\n3r59CrfP/MUqT890F4V/L5y4c4dbfn7oDAam7D9NdPSrpPvg7l6ZKlW6IEts/5Qx47sC2P8m31QM\nTSaT5UNKWlsEeBkN2pdDEASK9OmD/uVLGiAVS5s7OvLPnDlJ24xbtw7fvXtpqtVyUqUiUzItx+Ts\nunyZv+bNY0/c247ZWVQqLvz5Z1I/sTeMWLeOldu2UQbJbbcSiANGIOkstgLOqdVY5c7N9lGjPitx\nIDnlvbxo4e/PJqRY2xqgJ3WIYTempi1o2bIsu3fP5OWCGZ80frxWy69DhuD08iUFdDoWiyIlZDJy\nKBTsVKk4PWXKexuAfgkWHDjMoL93k6Brj6nJZfI6B3PeewTq1q3TjIulRnBEBLn6DCde+yrpZ9Zm\ntVnRq8B79RLzjZqNlVVGHj26yMIOLWhRtux/kv7+b/IsNJRGS3cTERFEjx5LkqSp0oter2P37pns\n2jWdXV59qPgf1uJ9DfQGA9Hx8YD0UtD7UDD3759BFAWePLmCh0d15HIFOXIUo1atPu9t4Pol+KZi\naKIo3gO++z+Kz0UURebu2cOmY8cwNzVlSKtWaWaipRe5XM4hb28qjx7NgvBwstrbs3/sWERR5K/9\n+1l35AimKhVtGzRAFEW6OTjQpkKFVDOe3LJk4bJez3OkONZJQCeXk8namqlbtrDr7FkUSiWB4eFE\nREVhi6SH4YBUQtAQKevwEVKCh16jodjjx5y8cyfNrLTU2EQzmrEp1e/csmZlXkAA+UQRPbAWFfEU\nB4IRhDNkztweQTB8cg3SxrNnyfjqFTu0WmRAC6COKHJQr8fZYGDqpk0s7tPno8f9GHrV+I0CLk6c\nvHsXR5uctP+1Iyql8qOMGYC9lRVKhYBU2FAZ8EdvuEw+59TduW/ImNGFs2c3cGHSJH7JnXpx8PfG\nm8LrdWfO4DmtAWXLtqBly4mYmn5YZNnf/zZz57Ylj7XAtUljP7op7veIUqFIIUC9vok1UqEOPAqp\ny7kHDxBFkTWnVtL6by9AhqtrQerW9cLERI2jY550lyN91jz/9SMYSZNZO3eyavNmfDQaXgItp0xh\n99ixn/XQ0Or11Bw9mpovX9LYYGDjixfUGTeOtlWrsmTDBmZpNEQAvZ8/Z/WgQWO9jMYAACAASURB\nVFT38EgzfTdvliyMbtWKor6+ZFMqCRAEfAcOZPLmzRzYt4+JGg29gCpAe2AjUAO4gFQ3loCUnP9G\nE0UJZJPJiEy24vt/RFEkTqPBXK1OlwGqX64cay9eRqswIZtgIEpvwES9HblhHo0aDaFQoSqYmVnT\navZs1icrNwDQ6/X4h4WRLVOmNK9BZFwc2RN1IgEckdypsUBWUeRWVFSq+31pKrm7p6qn+TGYKJVs\nH9SHBtOaIJdlQasPYHyLxhRwcUGr1yMIQgoXsE6v52VUFKVtYxg0cCAF05Go9D0hk8loXb48NQoX\npvGqE3h5FaRr14UULVorzX00mjiWL+9DqyLZ8W7V6qd/KQdJsu9Nz7v2FStiEAREUWTLhQvMviDV\nh967dxoXlwJJtZ8NGw7F1NQKExP1F13N/WsuR5lMdgjp7///GS6K4q7EbY7xE7scC3t6sujlS96U\nOk8CXtWowcwuXT55zH+ePKHt2LHcTky1FwE3U1PMrayY+/IlvyJpFtYAXstkmKlUrOzXj3olSqQ5\nZnBEBAFhYeR2dMTW0pLsXbqwLzoaA9Iq7CEkHSsfMAUYgKR6LyIJH29FEv/tY2rKtdmzcUpWUJyc\noWvXMnXHDnr1WsH8Sh9Onrjx/Dnlxk2mRYsJuLtXwsbGiRcvHmNr64SpqRXr1g3nwIH5ODm5ETR7\nYtJ+07ZvZ4yvLyC1yZnbs2cKgWFBENh+6RLHb99m3v79qBPPUZv4vQrJYJfImZPu1apRp1ixNM/p\nY3gQFMT9oCASSkxNc1X6uUTGxvIoJIQstrZktrFh/v79DNu4Da02nk4VK1AyVy7CY2Lw3nUQnS6B\n8uVbc/fuKV68eEz9YkWoVbQotYoWTbM/2ffKoRs3aLfEl9y5S9Gx459kyJBS4uzGjcMsWdKDKrmd\nWNit2weFr428JTI2lpN37yKKIodu3GDZCcldqVZbUK/eQCwsbLGxyUzx4umLl/7nLkdRFN/vx0gn\nY5MZtC/xlvotoVIqSaHcJ5OhMvk8fTYTpZIEUcSAdHP1QIIgkEGhIAYpg68BUvaja+JqqPPs2VyZ\nNStNEWInW9sUD2sThYKnSBmIkUgPdrPEsSORVmtVEr+PA35FypLMZmfHTi+v9z74b/v7Y2Vlz9xf\n0yfE6pEtG0u6tOOP9T6ULNkAS0tbBCEb9++fZfnyPhQuXJ2w5ctT1M3d9vdnnK8v+4GKSMa2/cKF\nNChZEp1ez4k7d5i5Zw8vDRnInbskXbou4NDWSUTFReJs40jE65coFQoa1uqHqFCw8tZthq8bxMSW\nLalfogSZM2T4pDd3vcFAkcGDiddqETem/YLxudhYWFAiVy5u+fnRxMeH10pHvL3PkyFDZnbtmsH6\nR6HI5VaMGHEghZtIq01g794/WXX7JsN8B1HQ1ZVmpUvTuFQpHKytv+tCXYBqHh48mZGXVptuMHBg\nIdq0mUbFiu2JiQln9Wov7tw5zsourVItHzHyfmwsLKif+NLcoGRJ5iW+tF969IjhR58gCAaePLnM\nunUjsLa2x9IyI40bj0hq1vr48WUeP778weN81cLqxBXaQFEUr6Tx/Q+9Qlt3+jSD//qLEVotoTIZ\n89Rqzkyd+t6W9h9CEATqjB2L6ePHNNDp2KJSgZsbHapVo9/8+XhqtUxF0uWoCFxGMoIzBgx47yot\nOUPXrGHuzp1URuqrFgmMQ0pAuWFmhk6rZbvBkFjmC0uBqYCoVlO7XDnm/P77J59fahgEgfGbNzPr\nwHFUKlPi4l7j4JCTFR0apBqrm7lrF2v+/pvkbgFnYFjnzoxcvwNBLEV0/CGyZCnE1Kmn0lWL4+d3\nk2XLeuPnd5OiWTPT6JdfqFmkCAVc0p/9teTwYbovXoy7e2Vujen54R0+E7sef9Co0TCqV+/10cYo\nNPQpgYH32LlzOs+fX8fdKSPNy5ShmocHhT6i9ORb5Z8nT2i6aAOWlhnx979F3UJ5WNitG5ampl97\naj8seoOBcw8eoDcYuPbsGd57jqHXSz4RrTaOSpU6kSlTNvLkKc2oUeW+qSzHRkj91+2Rek9eFUXx\nHcf1j27QQGoZsvnECcxMTenboAF5v0AaeIJWi8+OHdx79owCOXMyoH591CYmHLh2jdWHDrH50iVu\nIIV0XyPFu/7s04c2FSq8M1ZqWo5lvLzoHxhIK6RU1aoyGXdNTXFzcSF/lixsO32agQYDg5Fcjp0A\nF2AIUFitxnfUKEq7uX32ef4/t/39Wf0iN+M8olPEgv6fA9eu0XzSJB4j/QI+Q3KVumXNx03//khO\n0ooolZVp0eJXGjQYlOZY/48gGDh2bAXPnl3jytnV9KxenRGNG6N+z8r7TbflJUeOkKtADUxNLTnW\nK+04zqdw6MYNlh09h5mJCV71fqOgqysWHbsxf/4zLCw+L9VeEAROnlzNkydXuHJ2NVkzZqSahwed\nKlcmm739e+/Ft4zeYOD3w+G8ePGYBbVzv5PZ+18giiIrjx3j2D//4GBnx+AmTdLVQPdHIzgiAs/9\nz9Fq4ylcuDqTJtX6dgxaevkZDNp/TUBYGMV69yY0mfpCFaWS/qms0LR6PRWGDEnSclypUpGzRAn2\nX7vG+bg43ryHjwMiatZk8+nTtIuLw0oQmISk4B+FZPROINWJNTQzo13PnjT5iP5X/wZVR4zg5sOH\nlEbK3qz1yy+ceBhCcMQ+JAXJMUAk1ar5063bn590jPDwQJYu9SQm+DI7Bw9Oc+U9bccOll58zMCB\nW7l6dR///LOH814tvljCwfaLF2kzZzVx2jHIiMBcPYMLk0bxy6jxX8SgJSc6Oozg4IccODCfBw/O\nYi7G0LZCBSrkz59udQ8jbxnj68uOffvoq9FwTaFgr7U1l2fO/CZktb4msubNUzVo37fT28hH42Rr\ni4WlJX8nfr4I3FQoUi0KPX3vHoaXL1mt09ER2KPVsvniRYpmz85UmQwBSfNwlVJJtEZDzYQEJgsC\nw4G9wDWlkudqNT2QjNk/wOH4eNrMnEnOzp15GBz8wfkmaLX0nD8fxw4dyNWtG2tPnvwSl4Ej3t5M\n7dmTrDVqsLR/f9YNHEj5vLlRK6cjRQXLoFavokCBTze8dnbODB68HZf8dRj099/EJPZmS862ixeZ\nsvcY7dv7YGvrRKFCVQkOfkDLLfc+/eT+jzGbDhCnXQL0QqQTsZoseAwaQVxcFM+eXftixwGwssqI\nm1tp+vT5m7lzH9Om+xruUIC2i9fi2GcoFebs4PyDB4R9ZJfrnxFRFPHZvZu9Gg2dgTkGAwXj4th5\n+cOxpJ8Vo0H7joiOj+fy48f4v3r14Y3TQCGXs2PkSMba2GClUFBTrWZ5v36pJoS80XJ88xqkAmSi\niF4U2SGKWAI5gQhBwESpTKHB5wKYq1ScnDSJWXZ2WMrllEVKRnkG1IiJodKQIR+c7+Dlywk4d45L\n8fGsff2aQYsX89fBg4S+fv3J1+ANnSpXZn6XLjQtI0X7lvzenpK5bwIvkcmKUqOGVJ/0ubRpM5VH\n8ZZM27Ejxc+3X7xIn+XLadlyInnzSl3S9HotWm08JiZfLlajS9JqFIH6QDUEQQkMZNq0FkRGvvhi\nx/p/PDyq0aLFeGbOvMOwYXuxt89K62Vbydl/KH2WL2fFsWMIwqd3Zf+REUURvSCQfC1mKYqfrZf5\nI2N0OX4nXHj4kIbe3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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -504,6 +529,10 @@ } ], "source": [ + "# Check the arguments of the function\n", + "help(visplots.knnDecisionPlot)\n", + "\n", + "# Visualise the boundary\n", "visplots.knnDecisionPlot(XTrain, yTrain, XTest, yTest, n_neighbors= 3, weights=\"uniform\")" ] }, @@ -511,12 +540,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Let us try a larger number of k. For instance k = 99 (or a number of your own choice).
Can you generate the same code for a larger k? " + "Let us try a larger value of K, for instance K = 99 or another number of your own choice; remember, it is good practice to select an *odd* number for K in a binary classification problem to avoid ties. Can you generate the KNN model and print the metrics for a larger K using as guidance the previous example? " ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": { "collapsed": false }, @@ -537,7 +566,13 @@ } ], "source": [ - "### Write your code here ### \n", + "############################################ \n", + "# Write your code here \n", + "# 1. Build the KNN classifier for larger K\n", + "# 2. Train (fit) the model\n", + "# 3. Test (predict)\n", + "# 4. Report the performance metrics\n", + "############################################\n", "\n", "\n", "### Solution ### \n", @@ -553,21 +588,21 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Try to visualise the boundaries as before using the `knnDecisionPlot` command from `visplots`. What do you notice? " + "Visualise the boundaries as before using the K neighbors of your choice and the `knnDecisionPlot` command from `visplots`. What do you observe? " ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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8zZ13vpQlZSml0ic9vRxvDT4dYoyZizXekI63qFIVFubE798GXB2cspn8+c9/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38QXbqMplWrp7WKTR63Z8/aq8qhLVgw9tYmtBsRkZ5AJNDCGJNuRVKdoSmlsiLB6eTgyZN0m22d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\n", 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KqTxBE5pSSqk8QROaUkqpPEETmlJKqTxBE5pSSqk8QROaUkqpPEETmlJKqTxBE5pSSqk8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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -585,23 +620,23 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "** Answer: For smaller values of K the decision boundaries present many \"creases\". In this case the models may suffer from instances of overfitting. For larger values of K, we can see that the decision boundaries are less distinct and tend towards linearity. In these cases the boundaries may be too simple and unable to learn thus leading to underfitting. **" + "** Answer:
For smaller values of K the decision boundaries present many \"creases\". In this case the models may suffer from instances of overfitting. For larger values of K, we can see that the decision boundaries are less distinct and tend towards linearity. In these cases the boundaries may be too simple and unable to learn thus leading to cases of underfitting. **" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Distance weights\n", + "### 4.2 Distance weights\n", "\n", - "Under some circumstances, it is better to give more importance (\"weight\" in computing terms) to nearer neighbours. When weights = \"distance\", weights are assigned to the training data points in a way that is proportional to the inverse of the distance from the query point. In other words, nearer neighbours contribute more to the fit.
\n", + "Under some circumstances, it is better to give more importance (\"weight\" in computing terms) to nearer neighbors. This can be accomplished through the `weights` parameter. When `weights = 'distance'`, weights are assigned to the training data points in a way that is proportional to the inverse of the distance from the query point. In other words, nearer neighbors contribute more to the fit.
\n", "\n", "What if we use weights based on distance? Does it improve the overall performance?" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": { "collapsed": false }, @@ -622,10 +657,16 @@ } ], "source": [ + "# Build the classifier with two parameters\n", "knnW3 = KNeighborsClassifier(n_neighbors=3, weights='distance')\n", + "\n", + "# Train (fit) the model\n", "knnW3.fit(XTrain, yTrain)\n", + "\n", + "# Test (predict)\n", "predictedW3 = knnW3.predict(XTest)\n", "\n", + "# Report the performance metrics\n", "print metrics.classification_report(yTest, predictedW3)\n", "print \"Overall Accuracy:\", round(metrics.accuracy_score(yTest, predictedW3), 2)" ] @@ -634,21 +675,21 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Tuning KNN parameters" + "### 4.3 Tuning KNN " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The sklearn library has a grid search function, `GridSearchCV`, that allows us to search for the optimum\n", - "combination of parameters by evaluating models trained with a particular algorithm with all provided parameter combinations. You can use this function to search for a parametisation of the KNN algorithm\n", - "that gives a more optimal model." + "Rather than trying one-by-one predefined values of K, we can automate this process. The scikit-learn library provides the grid search function `GridSearchCV` (http://scikit-learn.org/stable/modules/generated/sklearn.grid_search.GridSearchCV.html), which allows us to exhaustively search for the optimum combination of parameters by evaluating models trained with a particular algorithm with all provided parameter combinations. Further details and examples on grid search with scikit-learn can be found at http://scikit-learn.org/stable/modules/grid_search.html
\n", + "\n", + "You can use the `GridSearchCV` function with the validation technique of your choice (in this example, 10-fold cross-validation has been applied) to search for a parametisation of the KNN algorithm that gives a more optimal model:" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": { "collapsed": false }, @@ -657,34 +698,39 @@ "name": "stdout", "output_type": "stream", "text": [ - "Best parameters: n_neighbors= 15 and weight= distance\n" + "Best parameters found: n_neighbors= 15 and weight= distance\n" ] } ], "source": [ - "# We want to use odd numbers of k to avoid ties\n", - "n_neighbors = np.arange(1, 51, 2)\n", + "# Define the parameters to be optimised and their values/ranges\n", + "n_neighbors = np.arange(1, 51, 2) # odd numbers of neighbors used\n", "weights = ['uniform','distance']\n", "\n", - "# GridSearchCV accepts parameter values only as a dictionary\n", - "\n", + "# Construct a dictionary of hyperparameters\n", "parameters = [{'n_neighbors': n_neighbors, 'weights': weights}]\n", + "\n", + "# Conduct a grid search with 10-fold cross-validation using the dictionary of parameters\n", "grid = GridSearchCV(KNeighborsClassifier(), parameters, cv=10)\n", "grid.fit(XTrain, yTrain)\n", "\n", - "print \"Best parameters: n_neighbors=\", grid.best_params_['n_neighbors'], \"and weight=\",grid.best_params_['weights']" + "# Print the optimal parameters\n", + "bestNeighbors = grid.best_params_['n_neighbors'] \n", + "bestWeight = grid.best_params_['weights']\n", + "\n", + "print \"Best parameters found: n_neighbors=\", bestNeighbors, \"and weight=\", bestWeight" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "
Let us graphically represent these results using a heatmap:" + "
Let us graphically represent the results of the grid search using a heatmap:" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": { "collapsed": false }, @@ -693,7 +739,7 @@ "data": { "image/png": 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HWHrpPMS8ch5i3jT+mADL5yHmfFx/gKvmIea8vK/m6VrNx9BQ8zXc1Ly8r+br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\naNtK/B2a/xh2bNevAj7XPn8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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -701,10 +747,8 @@ } ], "source": [ - "# plot the scores of the grid\n", "# grid_scores_ contains parameter settings and scores\n", - "score_dict = grid.grid_scores_\n", - "scores = [x[1] for x in score_dict]\n", + "scores = [x[1] for x in grid.grid_scores_]\n", "scores = np.array(scores).reshape(len(n_neighbors), len(weights))\n", "scores = np.transpose(scores)\n", "\n", @@ -716,6 +760,7 @@ "plt.xlabel('Number of K nearest neighbors')\n", "plt.ylabel('Weights')\n", "\n", + "# Add the colorbar\n", "cbar = plt.colorbar()\n", "cbar.set_label('Classification Accuracy', rotation=270, labelpad=20)\n", "\n", @@ -726,12 +771,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "When evaluating the resulting model it is important to do it on held-out samples that were not seen during the grid search process (XTest). So, we are testing our independent XTest dataset using the optimised model:" + "When evaluating the resulting model it is important to do it on held-out samples that were not seen during the grid search process (XTest).
\n", + "So, we are testing our independent XTest dataset using the optimised model:" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "metadata": { "collapsed": false }, @@ -752,11 +798,16 @@ } ], "source": [ - "knn = KNeighborsClassifier(n_neighbors=grid.best_params_['n_neighbors'], \n", - " weights = grid.best_params_['weights'])\n", + "# Build the classifier using the optimal parameters detected by grid search \n", + "knn = KNeighborsClassifier(n_neighbors = bestNeighbors, weights = bestWeight)\n", + "\n", + "# Train (fit) the model\n", "knn.fit(XTrain, yTrain)\n", + "\n", + "# Test (predict)\n", "yPredKnn = knn.predict(XTest)\n", "\n", + "# Report the performance metrics\n", "print metrics.classification_report(yTest, yPredKnn)\n", "print \"Overall Accuracy:\", round(metrics.accuracy_score(yTest, yPredKnn), 2)" ] @@ -765,12 +816,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Randomized search on hyperparameters. Unlike `GridSearchCV`, `RandomizedSearchCV` does not exhaustively try all the parameter settings. Instead, it samples a fixed number of parameter settings from the specified distributions. The number of parameter settings that are tried is given by `n_iter`. If all parameters are presented as a list, sampling without replacement is performed. If at least one parameter is given as a distribution, sampling with replacement is used. You should use continuous distributions for continuous parameters." + "#### Randomized search on hyperparameters\n", + "Unlike `GridSearchCV`, `RandomizedSearchCV` does not exhaustively try all the parameter settings. Instead, it samples a fixed number of parameter settings based on the distributions you specify (e.g. you might specify that one parameter should be sampled uniformly while another is sampled following a Gaussian distribution). The number of parameter settings that are tried is given by `n_iter`. If all parameters are presented as a list, sampling without replacement is performed. If at least one parameter is given as a distribution, sampling with replacement is used. You should use continuous distributions for continuous parameters. Further details can be found at http://scikit-learn.org/stable/modules/grid_search.html" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "metadata": { "collapsed": false }, @@ -779,14 +831,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "The best parameters are: n_neighbors= 11\n" + "Best parameters: n_neighbors= 3\n" ] }, { "data": { - "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -798,7 +850,7 @@ "random_search = RandomizedSearchCV(KNeighborsClassifier(), param_distributions=param_dist, n_iter=20)\n", "random_search.fit(XTrain, yTrain)\n", "\n", - "print \"The best parameters are: n_neighbors=\", random_search.best_params_['n_neighbors']\n", + "print \"Best parameters: n_neighbors=\", random_search.best_params_['n_neighbors']\n", "\n", "neig = [score_tuple[0]['n_neighbors'] for score_tuple in random_search.grid_scores_]\n", "res = [score_tuple[1] for score_tuple in random_search.grid_scores_]\n", @@ -813,16 +865,20 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Random Forest\n", + "## 5. Get your hands dirty\n", "\n", - "Random forests aggregates a group of decision trees into an ensembles. It adds randomness in 2 ways, one is by sampling with replacement(bootstrap sampling) from the training data and then fitting a tree for each of these samples. Then splitting on a feature in the decision tree, random forest considers random subset of variables to split on.
\n", + "Choose a machine learning algorithm that you are familiar with or interested to learn about, and apply the four-step method introduced above to train, test, evaluate and tune a model. Towards the end of the session you get a chance to share your results.\n", "\n", - "One of the most important tuning parameters in building a random forest is the number of trees to construct." + "### 5.1 Random Forests\n", + "\n", + "The random forests model is an `ensemble method` since it aggregates a group of decision trees into an ensemble (http://scikit-learn.org/stable/modules/ensemble.html). Ensemble learning involves the combination of several models to solve a single prediction problem. It works by generating multiple classifiers/models which learn and make predictions independently. Those predictions are then combined into a single (mega) prediction that should be as good or better than the prediction made by any one classifer. Unlike single decision trees which are likely to suffer from high Variance or high Bias (depending on how they are tuned) Random Forests use averaging to find a natural balance between the two extremes.
\n", + "\n", + "Let us start by building a simple Random Forest model using the default parameters. For further details and examples on how to construct a Random Forest, see http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "metadata": { "collapsed": false }, @@ -833,21 +889,26 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.86 0.93 0.89 149\n", - " 1 0.92 0.85 0.88 151\n", + " 0 0.83 0.94 0.88 149\n", + " 1 0.93 0.81 0.87 151\n", "\n", - "avg / total 0.89 0.89 0.89 300\n", + "avg / total 0.88 0.88 0.88 300\n", "\n", - "Overall Accuracy: 0.89\n" + "Overall Accuracy: 0.88\n" ] } ], "source": [ - "### Write your code here ### \n", + "############################################################# \n", + "# Write your code here \n", + "# 1. Build the RF classifier using the default parameters\n", + "# 2. Train (fit) the model\n", + "# 3. Test (predict)\n", + "# 4. Report the performance metrics\n", + "#############################################################\n", "\n", "## Solution ## \n", - "## To be hidden ## \n", - "clf = RandomForestClassifier(n_jobs=2)\n", + "clf = RandomForestClassifier()\n", "clf.fit(XTrain, yTrain)\n", "predRF = clf.predict(XTest)\n", "\n", @@ -855,6 +916,60 @@ "print \"Overall Accuracy:\", round(metrics.accuracy_score(yTest, predRF),2)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can visualise the classification boundary created by the linear SVM using the `visplots.rfDecisionPlot` function. You can check the arguments passed in this function by using the `help` command. For easier visualisation, only the test samples have been included in the plot. And remember that the decision boundary has been built using the _training_ data!" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on function rfDecisionPlot in module visplots:\n", + "\n", + "rfDecisionPlot(XTrain, yTrain, XTest, yTest)\n", + "\n" + ] + }, + { + "data": { + "image/png": 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mTbtRtGgZ1q5dSGhoGN27r6VYsfJZVu6ddz7D0qXNiI9/GKezFBbLl/Tte+Vd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\neu3TT5kZH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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Check the arguments of the function\n", + "help(visplots.rfDecisionPlot)\n", + "\n", + "### Write your code here ### \n", + "\n", + "### Solution ### \n", + "visplots.rfDecisionPlot(XTrain, yTrain, XTest, yTest)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Tuning for Random Forests\n", + "\n", + "Random forests offer several parameters that can be tuned. In this case, parameters such as `n_estimators`, `max_features`, `max_depth` and `min_samples_leaf` can be some of the parameters to be optimised. " + ] + }, { "cell_type": "code", "execution_count": null, @@ -863,31 +978,63 @@ }, "outputs": [], "source": [ - "# Example code for a model and a set of grid-search parameters\n", - "# To be fixed\n", - "# model = RandomForestClassifier()\n", - "# parameters = [{\"n_estimators\": [250, 500, 1000]}]\n", - "# sample_leaf_options = [1,5,10,50,100,200,500]\n" + "##### TO BE FIXED!!! ##### \n", + "\n", + "# Define the parameters to be optimised and their values/ranges\n", + "\n", + "parameters = [{\"n_estimators\": [250, 500, 1000]}]\n", + "sample_leaf_options = [1,5,10,50,100,200,500]\n", + "\n", + "###### WHAT ELSE DO WE ADD HERE?!?!?!!? ###### \n", + "\n", + "##### DO WE TUNE WITH A GRID SEARCH??? ##### \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, testing our independent XTest dataset using the optimised model: " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "#################################################################################### \n", + "# Write your code here \n", + "# 1. Build the classifier using the optimal parameters detected by grid search \n", + "# 2. Train (fit) the model\n", + "# 3. Test (predict)\n", + "# 4. Report the performance metrics\n", + "#################################################################################### \n", + "\n", + "\n", + "## WE HAVE NO SOLUTION" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Support Vector Machines (SVMs)\n", + "### 5.2 Support Vector Machines (SVMs)\n", "\n", "SVMs attempt to build a decision boundary that accurately separates the samples of different classes by *maximizing* the margin between them.\n", "\n", - "### Linear SVMs\n", + "#### Linear SVMs\n", "\n", - "The parameter C, common to all SVM kernels, trades off misclassification of training examples against simplicity of the decision surface. A low C tolerates training misclassifications and allows softer margins, while for high C the misclassifications become more significant leading to hard-margin SVMs and potentially cases of overfitting. \n", + "The hyperparameter `C`, common to all SVM kernels, trades off misclassification of training examples against simplicity of the decision surface. A low `C` tolerates training misclassifications and allows softer margins, while for high `C` the misclassifications become more significant leading to hard-margin SVMs and potentially cases of overfitting. \n", "\n", - "In this example, we will use linear SVMs with the default value for C" + "At first, let us build a linear SVM model using the default value for the hypeparameter `C` (`C=1.0`). Thorough documentation on how to implement SVMs with scikit-learn can be found at http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 23, "metadata": { "collapsed": false }, @@ -908,10 +1055,16 @@ } ], "source": [ - "### Write your code here ### \n", + "################################################################### \n", + "# Write your code here \n", + "# 1. Build a linear SVM classifier using the default parameters\n", + "# 2. Train (fit) the model\n", + "# 3. Test (predict)\n", + "# 4. Report the performance metrics\n", + "##################################################################\n", "\n", "## Solution ## \n", - "linearSVM = SVC(kernel='linear')\n", + "linearSVM = SVC(kernel='linear', C=1.0)\n", "linearSVM.fit(XTrain, yTrain)\n", "yPredLinear = linearSVM.predict(XTest)\n", "\n", @@ -923,21 +1076,31 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can visualise the classification boundary created by the linear SVM using the following function. For easier visualisation, only the test samples have been included in the plot. And remember that the decision boundary has been built using the _training_ data!" + "We can visualise the classification boundary created by the linear SVM using the `visplots.svmDecisionPlot` function. You can check the arguments passed in this function by using the `help` command. For easier visualisation, only the test samples have been included in the plot. And remember that the decision boundary has been built using the _training_ data!" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on function svmDecisionPlot in module visplots:\n", + "\n", + "svmDecisionPlot(XTrain, yTrain, XTest, yTest, kernel)\n", + "\n" + ] + }, { "data": { - "image/png": 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nsViswKPuLXXx8WnkXolBiKu73sQLueG666F5ktxDK9hSUhKJidlGoUJFCQur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zhrtDE7lwrf/RA13/dsmPCyulKgELgDI4+2TP1VpPz49ruVOJEuUoUaLc9QuKArFhwxJm\nz+6H2ZxISEgLhg37hpIly19URmvNggUjWLHiQ7R20LLlE/TrNwsvL5Obohb5xWAw8uqrC5g2rTNG\nY0Nstn20b9+H6tWbujs0kQtuWz5GKVUWKKu13qGUCsB5f66b1np/tjK3bC9HUfjExu5mxIh7sVh+\nBephMIwjJGQt77677qJyK1fO5Ysv5mI2/wz4YTI9wv33N+GJJ8a7JW6R/xISThAbu5tSpSpSuXJd\nd4cjriM3vRxTcdacrkRrrW+q/7TW+jRw2vU4VSm1HygPyN1YkS8OHtyAs8HBOQrF4RhDTIwvdrvt\noubHyMi1mM0DgGAALJah7NgxmieeKPiYRcEoVaoipUpVdHcY4iZdq1NIAIBS6m2cPR6zJtB7HGfi\nyTNKqVCgIbA5L88rRHbFigVjMOwCbDj/9Hfi61vysntpQUHBGI2R2O1PAqDUdkqUCC7weIUQNyYn\ns+0/cEkHkI+UUruA0XkRgKu58VtgoNY6NS/OKcSVNGnSherVP+Pw4RY4HPWAZbz44qzLyvXoMZR/\n/mlFevoRwB+jcR19+64u8HiFEDcmJzOFbAT+ByxybXoU6K+1vumZ9pVS3sBy4Fet9bQr7NcPPTTm\nwvOIiDZERLS52cuK25jDYWfbtuUkJ8cTHt6CypXrXLFcenoyW7cuw+Gw0aDB/dJDVQg32rt3DXv3\nrrnw/Ntvx13xHlpOEloV4EMgK4Gtx1mbirmZAF2Lhc4HErTWg65SRjqFCCGEuMgNdwrJorU+CjyQ\nDzG1Ap4AdimlIl3bhmutV+TDtcQtym638fXXb7Fp088UKVKcp54aR82ard0dlhCiELpWL8ehWut3\nlVIzrrBba60H3MyFtdZ/k7PZ/sVtbN68oaxevR2LZRZwhHfe6c6ECWuoVCnC3aEJIQqZa9XQ9rn+\n3cbF3fcVV+/OL0Se+uuvRVgs64EqQDOs1m1s2fKjJDQhxGWu1W1/mevfeQUWjRCX8PLyBRJxJjQw\nGBLx9s7TUSNCCA9x3SY/pdTvSqni2Z6XVEr9lr9hiYKktWbFitmMG9eNqVP7curUIXeHdEHPnsMw\nmXoAMzEYBuHn9wd33dXb3WEJIQqhnIxDK621Tsp6orU+p5SSUaYe5JtvxvPzz0sxm0eiVBQ7d97F\nlClb830NsqiojSxbNhuHw06HDk9Tp849l5W5777nKVmyLJs2/ULRosXo0mUTxYqVyde4hBC3ppwk\nNLtSKkRrfQwuzOrhyM+gRMH69ddZmM1/A9XRGiyWaDZsWEznzlccTZEnDh7cwPjxXbFYxgAmdu7s\nxeuvz6NBg/svK9ukyQM0aZIfHW2FEJ4kJwltJPCXUiprBte7gOfzLySRX5KT4/npp6kkJp6lSZP2\ntGzZ07VHc3Hrs5H87vezbNlsLJaxQH8ALJYAvv9+5hUTmhBC5EROxqGtUEo1Bprj/JR7VWt9Nt8j\nE3kqNTWRN95oSUrK/djtjdiy5U3i42Pp1u117rvvBVaseASzeTRKReHt/QPNm2/J13jsdjvgk22L\nDw6HPV+vKYTwbDmpoYFzNtd4wBeorZRCa73uOseIQmTTpm9JS2uI3T4TALP5Xr77rgXdur3OY4+N\npVixIDZtmk1gYAl69VpDUFDlfI2nQ4en2L37SSyWAMCEyTSYjh0n5+s1hRCe7boJTSn1HDAAqAjs\nwFlT2whcfgdfFFpWayZaF8+2pQR2uxkAg8FA584D6Nz5psbK35B69dozePAnLF36PxwOO506vU/L\nlg8X2PWFEJ4nJzW0gcAdwEatdVulVE1gYv6GJfJao0adWLRoPFZrCyACk2kszZv3cntMjRp1cmsM\nQgjPkZOElqm1zlBKoZTy1VofUErVyPfIRJ4KDq7K2LG/8tlnI0hOPkvjxu154om3bugcDoeDJUvG\ncuTIFkJCGvDYY+9gMOTP7GXbt/9CbOxuypULo2nT7jjnshZCiKvLyWz7S4GncdbU2uGctsFLa90x\n34OT2fYLlSFDWnLs2CmgG7CcsmWLMn369jy/zoIFI/j996XYbJ3w8lpF06ZNefnl2ZLUhBDAzc22\n3931cKxSag0QCMiM+LeZw4e3cOzYLuA4UAJ4i9OnKxMZ+SsNG3bIs+skJZ1mxYqPsNmOACWx29PY\nvLkG3brtu2z+RofDztatP5GcHE+NGq2uurZZfjp9+gh79vyJv38gTZp0xWTyLfAYhBBOOe3lCIDW\nek0+xSEKuYSEWKAkzmQGUBQoS0LC8Ty9TmpqIl5eQdhsJV1bimA0ViI19dxF5RwOO+PHd+XIkTM4\nHHWB0bz88kc0b94jT+O5ln371jFxYg+07oRSsZQpM4UJE1bj4+NfYDEIIf4jy7eIHImIaIdSSTgX\nL08CPgeO06hR5xs6T0LCCdav/5odO3674rizsmWr4eurca5alAR8gcEQS0hIvYvKbdnyI0eOnCUz\ncz0WyydYLD8ze3b/XL663JkzZxBm88dYLPMwm1dx+nR5/vzz0wKNQQjxH0loIkcCAoozdOjXeHmN\nA4IxGt9g0KB5lCyZ85nvDxxYz6BBjZgzZwlTpgxn7NhO2GzWi8p4eZkYO/YXKlVahJdXJcqWncqY\nMT/j71/sonLJyXE4HPX4r5GhARkZZ687OFtrzcGDG/jnn6WcPXtztcuUlDigoeuZwmptQGJi3E2d\nUwiRezlqcnTN31hda/2HUsofZ6eQlPwMTBQ+jRp1ZOHC+AvPU1LOEhn5K0WKlCAsrNl1O23MnNmP\nzMw5QHfAztGj7Vm37gvuuefpi8qVLx/O5MkbrnmuGjVaAWOBF4F6GAxjCA29E4PBeNVjtNZMm/Y0\n27evx2CoicPxPK+//hX16993zWtdTe3abYiMHIfNNgs4jsn0OXXqfJyrc4n/xMTs5Ny5E1SuXI+g\noEruDkfcQnKyfMzzwBJgjmtTRWBpfgYlCr/o6G288kodpk2bwvjxT/Luu49et3aUnHwCaOV6ZsRi\nac65c//m6vohIfXo128mvr7/h1K+hIZuYOjQhdc8JjLyVyIjt2E2v0VGxp2YzZP58MNncnV9gP79\n/0etWucwGALx9m5Cr15DqFevfa7PJ2DRvFeZMqol/0x/nJGDarJt23J3hyRuITmpofUHmgKbALTW\nUUopWb/jNjdt2nNkZEwFHgPM7N3blvXrv+bOOx+/6jHVqrXg4MH3sdvfBf7FZPqa8PA5Vy1/PS1b\nPkzLlg/jcNivWTPLEh8fQ2bmOZx/0qHAAVJTM7DbbRiNN9Q/CgB//2KMHv0DDocdpQwyrOAmHTq0\nmS2rPmGfJZ0SFtgM3DftEebOT8nR71eInNxDM2utzVlPlFJe5PdU7KLQO3fuKJBVG/HBYrmL+Pij\n1zzm1Vc/pVKljRiNRTEaw3nooZfzpEaT0w+7Eyf24fyTPwpsAxYD/rlKZpdeX5LZzYuPP0pjZbjQ\nj7YZ4LDbSE9PdmdY4haSk//Ja5VSIwF/pVR7oB+wLH/DEoVd5cqNiI6ejcMxEjiDybSUqlWnXfOY\n4sWDee+9v0lPT8Fk8sPLy7tggnVJSzsHtME5lBLgfiAdiyVTxo8VAiEh9fjSYSMKCAcWAUWLFKdI\nkRLXOVIIp5zU0IYBZ4DdwAvAL8Co/AxKFH6DB39OUNA3eHuXw2isSqdOj+d4gLW/f+B1k5nZnM7J\nk1FkZqbmRbgA1K59N845AU66tszDYCh2Syazc+dOEh8fg8ORu7V209KSOHXqEFar+fqFC0jFirV5\npO90Gnn7UM7Hn0FFg3h1xAqp/Yocy8lMIXZgrutHCMA5sNlms+JsgTZgseTdB+OuXb8zeXIvIBCH\n4xwvv/wpzZs/eN3jbDYLiYmnKFYs+IpJqn37F9i8+Rd27aoKFEepNF577cs8i7sg2O02pkzpw44d\nK1DKhwoVqvPmmz9RpEjx6x/s8vPP/+Orr0bg5VUKLy8ro0f/RJUqDa9/YAFo0+5Zmrd6jPPnz1Ky\nZIWbbg4Wt5erzuWolNp9jeO01rreNfbnCZnLsfAaNqwNR492ROs3gAR8fFrz6qvv07jxjQ20vlRm\nZirPP1+FzMzvcC6OHonJ1J4ZM3ZTokS5qx63b99a3n23Jw6HN1qnM2DA5zRt2vWKZU+fPkJcXDQ1\narTA1zfgpuItaD/9NJXFi3/GYlkG+ODl9RItWmheeSVn3zePHo1k9OhOWCwbgRBgEcWLj2LOnMM3\nVBNyOBykpMQTEFCqwJuOhcjNXI5d8jEecYs7cWInWn/relYKi6UrMTE7bjqhnTlzDCiFM5kBNMTL\nqyanTkVdNaGZzem8+25PMjK+xNlRZQvTp3fg/fdrU7p06GUfuGXLVqNs2Wo3Fae7HDq0A4ulF+AH\ngM32FIcPD8zx8bGxuzEY2uJMZgCPkpLyNGZzWo6Te3T0diZM6E5GRhpK2Vw16IKbckyIq7nqPTSt\ndcy1fgowRlEIBQVVA351PcvEZFpN2bLVb/q8JUtWwG6PA/a5thzDaj1A6dKhVz3mzJljaF2M/3pd\n3oHVWplBg+rSu3cxvv/+/ZuOq7CoVKk63t4rAOe9M4PhF8qXz3lyDg6uhtYbcS6aAbAWX99i+PgU\nydHxDoedd97pTkrKe1itZ7FYVjNz5ovX7eEqREG4akJTSq13/ZuqlDp/yY/MEnKbe/XVT/D3H4q/\n/134+NSiQYNwWrToedPnLVKkOC+8MBOT6S78/dtiMjWhV69xlC4dctVjSpQoh90eDxx0bTmJ1tE4\nHHuw26NYunQOO3Z4xgIRXbu+RqVK8fj61sXPryklSizhuecm5/j4mjVb0a7dI5hMEfj7t8HXtyev\nvfZVjpsbExNPYTZbgEdcWxri5dXMtRKDEFd3vYkX8sJ110NzJ7mHVrilpiYSE7ODIkWKExraIE97\no509e5yTJw8SHFyV4OCq1y3/55/z+eyz1zEaG5ORsRHn8n1ZC5i+SY8eikceGZdn8bmT3W7jyJGt\n2GwWqlVrkqvZ/U+c2E9i4kkqVapD8eLBOT7OYsmkb99grNb1QB0gEZOpHuPHF56OJaJw2b//L6ZM\neZLk5FiCg+swdOgiKlasfVPnvNo9tJxMffVFTraJW0ts7B4GDGjIo4+aePnlusTE7LjhcwQElKBO\nnbZUqdLwhpOZ1prFXw3j6Sf8eaqXL198/NJF3+CCgipRr969OUpmAPfc04fJkzcxYMDLBAVVAOq6\n9tgxmTZRqlTFG4qvMDMavQgPb07t2nfleqmaihVrUbduuxtKZgAmky8vvjgbk+ke/Py64uPTgHvv\nfUKSmbii5OR4Jk7sQXLyLMBCXNzLvPVWl8smJc8rOekTe9Gqia6ZQhrnSzSiQJjN6Ywb15Hz58cC\njxEf/z3jxnVi1qwD+PkVLZAYVq38iKgVM9hnycAEdF+7gGUlytP1odG5PmdWZ4+iRYN4++0HUOpr\ntD5G5colaNOmzzWPzcxMY+bMF4mMXIaPTyB9+kzg7rufyHUsnuzOOx+jevUmxMbupnTpN6laVT4O\nxJUdO7YTg6EOkDVG9TkyMt7m7NnYfOmYddWEppQaAQwH/JRS57PtspJHY9KUUp8BnYB4rXXd65UX\neePUqSis1gAga5b7x7HbP+DEiX2EhTW7qOyOHSuYM2cwaWlnqV69KefPJxMXd4Dg4BoMHPgxFSvW\nyvF1MzPT+Oijl9mx4xdsFhtN7WYaUgQHmvssFvZt+fGmElqW8PDmTJsWyYEDf+PvX4y6de+97nim\n2bNfITLSitV6CKs1ho8/7kaZMiHUqnXnReXOnDnG1KnPcOLEToKCqjFw4NzL1mrLrX371jJr1iuk\npJwmPLw1AwbMJTAwKE/OndfKlQujXLkwd4chCrnAwDLYbIeA8zgXBT6B3Z5I0aKl8uV61+rlOEFr\nXRSYrLUumu2npNZ6WB5d/3Oc8w+JAuTjE0BmZiyQ4NqSTGbmEUwmv4vKHT++l8mTe5OQMI3MzM3s\n2bOZY8ceIDNzN8eOPc6YMfff0Ewes2b1Z+vWNDIytmO1P896KpHAJhKJ5HvCSDLf2ODsffvWMXBg\nY/r2rcTkyb1JT/+vr1LJkhVo2fIRGjS4/6Jktn//Xwwc2IS+fSvx/vtPXJgncMeO37Ba3wVKA3dg\nsTzDrl2zXTiZAAAgAElEQVR/XHQ9u93G2LEdiY6+h8zM3Zw48QJjx3YgNTWRmxUff5SJEx8iPv5t\nMjN3sndvBd57r9dNn7egrV49nxdfrMUzz1RhwYIRBdIRQBReoaH1adWqKz4+zTCZnsfHpyU9e469\noYkAbkROZgoZppQqAYQBvtm2r7vZi2ut/3KttSYKkNmchtEYiN3eErgPWIXRWBSrNROAFStm89NP\n/yM9PQWbrSbO7vB7cI4PG+I6y0vYbJ8SG7uH8PDmObrujh0/Y7XuBMoDR4C3yWrRtvIBNuM7OX4N\np08fZuLEHpjNHwMN2L59DC+/3ACDQVGuXBj9+//vsiaN06ePMGHCg5jNc4GGREa+xQcf9GH06B/w\n8ytGevoQYAdQDKW8WbEihtWrl3DffU/RvfsQzpw5RkrKeRyO4YAC+qL158TERFKnzj05jv1K9u1b\ni/N38QAAdvtUDh8ugtVqxtvb56bOnZ/sdhvffjWMLesXYtOa+FQjNtsSoAS///4cJtM7PProm+4O\nU7jRiy/OoGXL34mLO0JIyFPUqNEy36513YSmlHoOGABUAiKB5sBG4Ob+B+fQrl1/EBJSj2LFZMWa\nvFKkSHEMBqtrGZejQDsMhpfw9y/GunUL+eqrKZjN8wEjzuVhPsFZkT4DpOCc3DcNu/3UZStJX4uv\nb3EyM6NxJrTiwOFsew9TsmTZHJ9r9+5VaN0F6AaAzTab1NRAYBfnz//C6NHtmTFj10WDhffs+RNn\nC3d31zGz2Ls3ELvdBtiB0ziX+otB60dJS5tEWlpzli59FpPJl7vu6oXdnoxzDFdJIBO7/cQNvQdX\n4+9fHKWO4hxfZgBiMRi8MRoL9ywc3341jFMrP2K5JZ3B+HKS94AWAJjN77N+fX9JaLc5pVSuF9G9\nUTnpFDIQuAPYqLVuq5SqCUzM37D+s3z5Bxw6tIkiRUoQHt6CsLDmhIe3oFq1JgUVgscpXTqEZs0e\nYMOG/jgc3hgMVho1ake5cuF89tlIzOZxZH0owRSUehU4ilJgMLTEZuuGj89v3HFHBypUqJnj6z79\n9CRmznwIq/UpvLxisdkWYjCcBHzw9v6axx//Pcfn8vMLRKlYnCsZKeA4EADUROtaWCxLOHo08qJ7\nYP7+gUD2Y05gNPphMBhJSDiJc6B4VSAC55ppZ4HGmM3vsXbtO3TuPID77nuJVavuxGLpjsn0J3Xr\nNqdKlUY5jvtqGjXqRIUKH3L8eAes1iZ4e39Fr17vYjDkZP5w99m64Wt+sqRTF6iDjVVEZ1tbKtb1\nngtRMHKS0DK11hlKKZRSvlrrA0qpGvkemUv16s2oVq0p58+fxcenCLGxu4mM/IURI369/sHiipzj\nmCKBnkAvtP6Wo0d/wWaz4OdXBDiVrfS/VKhQhpYt/YiI+IHk5HhiY/dQocJrtGz5yA1112/evAdB\nQZXYsWMlAQGdqVNnKtu3L8fhsNO8+cYb6vXUtGk3vvtuCvHxPbBa6wOzgDdxJioLDseZy2a/aNKk\nK2XKTOH06QexWhtiMn3OY4+943oNBpyz8GcNEzgBNHA9Pul6X6BPn4lERLTg6NEdlC3bj9ate+XJ\n+DsvL2/eemsFa9cuIDHxFLVqfXbTzZgFwcfHn5NAPWAwNj5iDhaVBgTh7f0xvXsvdnOEwhPs3buG\nvXvXXLfcdQdWK6WW4uwONxBoh7O9xUtr3fHmwwTXPbRlV+rleKMDq2NidrJ06YQLtbgqVRpeuP9w\n7NguvvzyLVJSEmnevANduw4u9N9+80ts7B5GjepOZmYUzgSg8fOrx5gx8/DyMjFy5D1YLM+gtRc+\nPnMYO/bXfK8RnzwZxYIFb5KYGE/Dhm15+OHhV+yZGBn5K0uXzsRut3Pffb1IS0skMTGew4e3sn//\nGRyOxzAYfqJmzUDefHPZZb9jszmdVas+ITExjjp17r7QFNKrVzFstiLAK0A0ztW4ngRKYzLNYtSo\npdSs2Tpf34Nb0datP/HZtEcZYMngpMGLJb5Fad3+BYxGE82bP0hoaH13h3hF5879y+efjyAuLpba\ntZvRq9fYW3IZodtVbiYnBkBr3d31cKxSag3OGyh5Mo+QUmoRcDdQSil1HHhTa/15bs9XqlRFGjXq\nRFTURtaunc/p04cICalPo0ad+eGHaWRmvgmEcfLkGFJTE+ndO+edEDyJt7cPDkc6YAF8ABsOx3m8\nvHyoXLkO7777N6tXL0BrB3ffvYZKlSLyNZ7ExFOMGNGGjIzBaF2Pf/+dRFJSHC++OPOicrt3r+KD\nD57GYpkK+HDs2Kv06/c+99//CiNHtgP8gX/Q2puEhBPY7VYMhos7VPj4+NOx44DLYggIKENS0mCc\nyawySrWjVq0oqlUrxp13/k5oaIPLjhHQpMkDBIz6nW2bv8PkV5QJ7V+85qoIhUF6egrDht1NSspj\nOBy9OXlyFidPPsGIEd9e/2BRqF1r+ZiS1zpQa30uXyK6OIabmvoqMzOVI0e2snHjYlatUtjt/3Pt\nicbPryUzZuzB17fIZd3VPZ3WmokTH2LfvlQslgcxmZYRFgajR/90WY3mzJlj/LBoJGlJp6jVpCvt\n7385z2q2+/atY9my2cTHx3DqlA8222rXngSMxkosXJh2UXPe++/3ZsuWO4HnXVuWEhY2mwEDPuK1\n1+7EYonF2ZFF4+vbkJEjZ+W4R9Uff3zCvHlvY7EMwWCIwd9/ER98sKXQfzgnJp5i0aLxnDlzivr1\nW/HAA4MwGIzuDguApKQ4vv56PHFxJ6hTpwXdur1WKNY327p1GTNmTCMjY5VrixmjsRSffvpvnnTw\nEfkvNzW07cDVsonmv5sNhZavbwAREW04enQ7Su3LtseMwWBk1aqP+f77t6lYMcLVTOlsqixdOhSl\nFPHxMSz/9i0yUuKJaNaDu9s85RGr5yqleOONr/nllxlER/9DSMjddOky8LJElZwcz9ihjXghLYl6\n2sHEQ5tIPBvLo0/mfDLcq9m//y8mTHgIi+VtwBvncICfcfZCtKDU5UnTaDQC2ceqOX+PBoMRrbN6\nKjoTGlhv6IP93nufpXjxYDZuXEZAQDEeeGBToU9m6enJDB3amvPne2C3P87hwzM4eTKafv1muTs0\nMjLOM2zYXSQnd8Zu783hw7P4999DDBjwibtDc/1dWPivc5ANcFzxb07cWm6LyYnPnTvJa681IT39\nebQOw8dnIt279+bBB4diNqdz9Oh2oqI2EhW1iUOHNtK373Rq1mzNiEG1eT49mZrawTs+/jTpNpyu\nPUblwStzj8TEUyxdOpnExLM0adKeu+56/JoJ+vff55A0fxCLLRkA/AvU8Pbhsy8zcp3YN25YzM5N\nS4g6spMTZ/oAI117vgbGAyMxmT6gatUSBAaWp3LlMLp1ex2TyY+oqE289VYXLJY3AV9MpjcZPPhT\nGjbswNtvd+PgQbBYHsPb+2cqVDjKxIlrCkWNIL9s2PANs2cvIDPzZ9eWZAyGMnz5ZarbF93855+l\nzJw5i8zMrJ6rqRgMQSxYkOj2FhGzOZ3XX29OQkJrbLY78fH5hMaNQ3j11c/cGpfIuVzfQwNQSnXF\nueKiBtZqrZflcXz5qmTJ8kya9DfffvseKSkHaNbsddq2dc7t5+PjT82arS/c8Ndao7Xm11+n09Gc\nzjvaue7UHeZ0mn//DiVKhxAW1pyyZavfUrW18+cTeOONlqSmdsNuv5MdOyZy5sxxHnpo+FWP0Vpf\n9AdiBG7m+8+Kn6ex5uuRDDOncxCYxUQyeAYoC3gRGGinSpUfSEgwEh3thcXSlsjI5ezc2Znx41cS\nHt6cN9/8iWXLZmO327n//i+oV+9eAIYNW8z337/H4cPfUblyGA8//JFHJzNw/n6cv5UsWY/d/yX1\n8tgKT+3Hx8efiRPX8M0373D69PdERHSgS5dX3R2WyAM5GVg9Cec4tK9w1s8HKKVaaq2v/klYCAUH\nV6V//9nXLecanoDDYccv2weDL+BAs3XrMhYtGkl6ehIlipSgVHA1+r28oNDP5r5p07dkZDTFbp8K\ngNnclh9/bHrNhHbHHV0ZuXA4k6xm6moH4338ademb64T+c/fjWelOZ2smQ9PkcYi2mCkCnht5/nn\nZxMa2pBBg5phtf4F+GC1Psnx47WJidlB1aqNKVKkBKVLl8Vut180H5y3tw+PPHLz80DeSurXvw+T\naRgWy1gcjqaYTNO4444n8PIyuTs06tZth6/vECyWUTgcLTGZZtKw4cNur51lCQgoyTPPfODuMEQe\ny8nXpk7AfVrrz7TWn+KcMqJz/oblfs2aPcgSLxMzgd+BR3z8adf+RQYPXkzXzoMpbbfx2tlYGuxb\ny4RRLS+aRzBLYWrOtdutOKfmzBKI3W655jElSpRj9MR/+K3JA4wNa05495E83vfDXMdgs9vIHkFx\n4G4OMooVFCGZYsWCsdut2O0K5301AAMWi3MdrtjYPQwbdifLl3vz66/FGD36Pg4cWJ/reG51AQEl\nmTRpHU2bxlC9+od07nxnjr60FQR//2JMmrSO5s1PUr36h3Ts2JSBAz91d1jCw+WkTUbj/OzJmsm2\nOIWhTSOflSlTheFv/c2iL4eQnnKWiGYP0qW7szbz/Tej2WjJoCaAdtAlNYFNm5Zwzz3PXDjeed+u\nDmFhzS7McBIW1qzAe1HFxUWz/u+FpKYmYjD8gLOyHYHJNI5WrZ687vHlyoXRb8jSPInlzjZP8fiq\nT5hkSecwzir/P0A4UNFm5tNv3uTxFz/B4cjAOVNHb+BHHI44jEYvli6dhtk8BHgDAIulIt988x5j\nxvyI1pqNG5dw7NguypWrzl139S40vf201mze/B1Hj+4gOLgqbdr0ybPYgoIqM3jwvDw5V14rWbKC\n3JcSBSonCW0isN01Bg2c48byarb9Qi00tD6vjlp52fZMq5nsM0sGOxyYzekXlSlZsjxTp+4jKmoT\nUVEbWbp0AtHR26hf//947bWCGe9y4sQ+3h7RnF6WdMoAJi8TFap+jdmcSePG9xb4HHuP9pnCj/7F\n6Lf5O84lnGBgRgrhrn3BwPnkOGw2MyZTMSwWKzAICMPXNxSHw05mZjpc/M67tsHHHw/ir7/WYjZ3\nw8fnE/755zeGDFlYKO5zzps3lD//XIHZ3AMfn/ls2vQLw4cvKRSxCeFJrrUe2ixgodZ6kVJqLc6v\n9hoYprU+dbXjbgetmnanz9ZlvGPNYC/wvcHI2AaXr4JTvHhZmjbtRtOmWRPoWklOjrviOU+fPsyp\nU4cIC2tGQMA1hwDm2PJvxjA0M5U3XBXqKpYMlpYI4OWhq69zZM7ExOxg9+5VFClSnFatHrvq6smR\nkb8SG7ub8uVr0K3nOLo/8hZvjm7H7IN/0RYrJqA/JrTDi+DgapQqVYr4+CDs9k8xGJbh57eZ0NAG\ntGnzMHv2DMZsDgV88PEZQtu2A0lKOs2aNfOx2WKAYpjNQ9m9uxbHju10+4DolJSz/P77HFdsJTCb\nh7N/fwRHjmyhevWmbo1NCE9zrRpaFPC+Uqo88A2wSGsdWTBhFW59+8/j688H0jXyF4oWLc3gZ/+X\no8UOvby8r9p5JCHhBD/99D7R0VspUaL8hem76tVrT3Bw7ob8Zaado0q21uFQIDMP1u4C55RHn0x7\nlF52Gwe9vFm1bDJvTtp2WVKbP384f/zxAzZbR7y8vqJFi9/o128Wvn5BxPMUXfkTcJDKPQRZN7Fy\n5Uc8++z7LF8+l5iYhyhfPpzu3T9i9erP8PcvzpNPjmTZstew22106PAC7ds/x6lThzAaS2CzZU2E\n64vBUPaK9zULWmbmeQyGojhb6gFMGI3lC0VsQniaqyY0rfU0YJprrsVHgc+UUv7AQpzJLaogAlz6\nYWtahIeTFt6PkJD6bh9fA2Ay+fHkC3myaPcFERFtiIhog8Nh5/jxva5xcRvx8fHPdUKr1+oxRh/a\nRLg5HQMw0sef1q3zZtHIhXNf5HtLBncD2m6l45ljrFv3Be3bv3ChzLlzJ1m5cg5W62GgJHZ7Khs2\nhNO16wBaterCvn3jSLH0wVnxn8G5c9X48st9KPU2/frNZPjwxezcuZL33nsMeBCloilTJoXJk//G\nZPLD4bCzZcuPJCWdwt9fYbW+g8PRB1iOl9e/bq+dgfMeV8mSQcTHj8XheBZYgVJHbovVIhwOBzt2\n/EpCwgmqV29KlSoN3R2S8HA5mcsxBpgETFJKNcS5yvSbXDzIJN+0r1ePjVFRbFrVg6i4BEJDG9Al\nvCSOsD6Eh7egZMnyBRFGgTEYjISE1CMkpN5FyeFSCxcOJyHhxIWaXEhIvcvGXbW95xnSzyfQafkU\nNJq2HQbQ7r6X8iTO8xnJ1HI9VkCEzcK/5xMuKpOWlojRWAarNasJNQAvr0qcP59AnTptMRheB9YB\nJsCMzfYFznVkn2HOnM60bPkwc+YMwmL5Evg/QBMX15U1a+Zx773PM358N44cicPhqIPWKQQHf0dy\n8iyCg8N45ZXfCsXSJQaDkTFjljN9+gscO/YxpUtX45VXVuTbir2Fhdaajz7owdndf9DY4WAy8PAz\nM2nTtq+7QxMeLCfj0LyAjjhrae2A1cCYfI7rgr5t29K3bVsAUtLT2XLkiDPBrZnA/I8P4W8y0SI8\nnOZhYbQIDye6yuuFeoXfvNKmTV8OHPiLqKiNrFw5izNnjlG1amNeeGEu5cs7V/dRStG521A6dxua\n59evX6cdr+1cyXSbmUPAAi8fXq3b7qIyZctWx8fHQmbmLOAJ4EeUOkHlynVZsGAEFktv4H1X6Qk4\n/6wWAnXJzEzA4XCQmnoGyFqIQWG11iU5+QxbtvzIkSPxZGZuwPln/A/JyQ8wb97pPHl9ZnM6u3ev\nwuGwUbt2GwICSuT6XKVKVWTcuJ+vW05rzf7960hKOk3Vqk1uaDmdvJKUFMeBA3/h41OEunXvzXWL\nyN69qzm1+w92ZabiAxwAGs59AS9vH8qUqUJ4eIvrnUKIG3atTiH34UxinXD2rl4EPK+1Ti2g2C4T\n6O9Pu7p1aVfX+QGntebw6dNsOnSIjVFRLFi3jv2nJlC5cr0LNZfw8OaUKlXJ43qUlS8fTvny4ReG\nCqSlJXH48D8UL37l+QePH99LuXJheTbo9ulXvuTT6b2otPsPAnwCePyZmYSFNbuojLe3D+PG/crk\nyU9y6tQQSpcOZ/DgX/D3DyQu7jgOx2PZSjcBvgMyMBpHUa1aWwwGAxER7di5czQ223QgGm/v+dSp\ns4jjx/fgcNTnvz/hhmRknMXhsN90l/jU1HOu2dhLopQ/3t6DmDBhDWXKhN7Uea9Fa83cDx8jZtty\nIgwG5tvtPPfqIpo0eSDfrnmpmJidjB17P1o3RetTlCs3kfHjf8vVYOikpDhqK0XWV8stgJfdyrG5\nL7BUaxre3Ycnnv3ftU4hxA271mz7f+JMYt8VxMz6V4lB68U3tkBgamYmW121uPUHD7L+4EGMSnFn\nrVq0qlGD5uHhHKv6eqGZsSC/paYmsXr1Z/zyy1RSUhKoWrXRRcm+ZMkKgLM3XnT0NgICSlCt2h03\n9QVAa83Ro9tJSTlDlSqNyMxM5eTJgwQHV6N8eWdH/VGj7iEqKgn4DWeTYxcgEqUyqFKlNcOHL6ZY\nsTKkpyczZUpf9uz5GZOpGE899S733NOXmJidjBp1HxbLCqAuBsMYQkPXM2nSmuvGFhOzg+TkOEJD\nG1C8eNnLysybN5SVK5Ow2WYDCoPhHWrU+Ivu3V+96jE3a9eu31ky+UEiM1PxAzYD9/sWZc785Hz5\nMnbs2C4SE09SuXK9C832Q4e24ejRJ3Euf+jA27s7vXq1pVOnG58W6vTpI4x5vR7LLOk0BErjTGq1\ngRQgwqcI/ceuuS3uJYq8d8NzOWqtC/9yuVcQ4OtLm4gIalesyNw/NmB31MDqsLL58CnKBAbyzcaN\n7D4xiYoVa1/4YA8La06ZMlU8rhZ3+vQRXhvUBLs9GAjAYDjPPfc8S2LiSdasmcc334xmypS9REdv\n4623OqNUBHb7MerWbcrrr3+Zq2VitNbMmPEcW7aswmishtW6Da01JlNTbLadPPLISLp0GYCXVxFg\nL1ABZ6eQAMAfP79mnDy5i5MnD1KsWBn8/YsxatT3aK0v+v2EhtbnpZemM3v2vVgsyYSEtOSNN76+\nbmyff/Q0uzcuprrBm4+0nQFDlxER0eaicnFxx7HZOpC1+KnDsZ0DByKZOvUDHI4dDB26mDp12t7w\ne3MtZ88ep7HWZH3NagqkmtOwWs15vvDkl5/0Z8uaeYQbvfnIYeOl176jQYP/IyHhOJC1iKkBq7Ul\n8fEncnWNsmWr8fygb3hgxhMkpScTgKK2q8dtIBBhMJKQcEISmshTHjt766D5izlxrjs2+1RAY7U/\ni8n7LFsmTiTDYmFbdLTzXtymD1nyxcvYHY4L9+Gah4VxvNoQfH2LcPLkQVJTz1G5cl18fQPc/bJu\nyOR3H8Ju744D15RDjn789P0Ups7YdVG5adOeJSNjGs4WZjO7dt3JpEmdaNPmKcLDW9xQk+22bcvZ\nunULZvNenAtuLgNexmYbA5hYtOh+ypatTHr6GeA+YB7OZWNeByJJTy8C/MzUqU8xd+6RC+e90vVb\ntXqEli17YrfbcnSvZ+fOlRzZuIQD5nQCcNYN+0x5mBmfnrmoXEREc/bsmYvZ3BX4C9iO1kfIyAgA\nfmfKlCf57LPjOXo/cqpatSa8qzX7gVrAdKUIDa52U8nMbE4nNnY3vr4BVKxYG6UU+/f/xc6189lv\nSacYzlfXdWpPZs9LokaN5kRGTsVmmwkk4OPzBbVqjc319Rs37kzjeUlYLJkM6R/K/OQ4+gDbgH/s\nNroU0tWsxa3LYxNa1MkEbPaswc4Ki+1+9p+YBoCfyUTrmjVpXbMm4PzmfjwhgU1RUWyMimLYwoVE\nHnuPYiZf7OnnKeflTZyXD0PHrSMkpO5Vrlj4JCYm4KALzpoGOOhMUvLlCyWcO3cUZ3IB54TAd6P1\nbv7+exGffz4Ag8FIWFgLWrR4mFatHr3mNePjj2K3t8aZzHCd9wQwGDiIzWZn+vSxWCwngKI4pxON\nBdoARS4ck5wcg8PhuG4tUSmV444L8fFHaaUdZH0tuRc4cz4Bu912UQ/Rjh37Ext7gHXrgtHaDvRE\n66yj2pGWdgqbzZqnQ0hCQurx6LOzaDL3BRSaoBLlGDz8+h1JriYuLppJo1tTwpzGOYeNanXa0X/I\nUuLjj9JMKbImYGsNpJvTMZvTeOmlmUyc+DDR0YGAg/vvH0KzZj1u+rWZTL4MHrWSEe/8H/1Tz2Ew\nePHCgC8pU6bKTZ9biOw8NqG1qFGZPcc/JtN6D2DHz/QprWpWvmJZpRSVg4KoHBREz5bOFY6/Xr+e\nCbNmscFhI8BiY64lgzfeqE/rhg0v1OSaVq9OUT9nI9ESHr7iudPTkzl79jhBQZULvBt5hQqhRB36\nCAcdAYWBWZQr9997kJ6ewtmzsVSoUJ9jx+ag9TAgHpPpRzp1mk6DBvejtebMmRiiojZd9b6jxZKJ\nt7cPSimqVGmIwTAFGIGzOfEjnHdONgONgWcxm18C0oBmwJc4ezG+jXM0SEVgLmXLNsizlbGzhIY2\n4H8oYoHKwMcoqgRXvWy4g8FgpF+/WTz77AccOrSZiROfwGI5BoQAn1K6dO18GQ95V5s+tLrzcTIy\nzlOkSPGbagKfN/NJBiTH8YZ2kAm027OK1as/p1q1O1jssBONc4XeL4DSxYMvtD68884fpKen4O3t\nk6e9hUNC6jF1zknS0pLw9w8sNPNsCs/isQltUq8e7Dr2IZsPBaO1g7YRdRj5YJ8cHx8dF0cHu/3C\nt/kewOtGI33btGHToUOMWbyYyJgYqgUH0yI8HK/wNMLCmlOuXPiFD+LNm5cyY8YzGAzBaB3PgAGf\nc8cdBddrbdjI5Qx8pS7J50sCiiJFijNitLO5cevWZXz44VMoVQa7/SRFix4nM3MmDkcKnToNoYFr\nKi+lFGXKVLnmt+kff3yXlStnuSZgbk6rVh1Zt64mShXFak0DtrpKxuB8J8FZG+sEPAP4YDR6A7Xw\n8iqGv78fQ4fmvnZyNeHhzbn/kbeotWgExYzeGPwCeX3Y8quWN5n8iIhow6OPDmXhwjoYjcXx8zMx\nbFj+LQdoNHrd1BCBLCdPHuRB11p+vkAXczobju+hXbtn6d77A+rNH0Sg0Yj2KcJrw3+56Nj8+uKl\nlMqT1ybE1RT6FatvtJdjdlprTiclYVCK4OI3NpD1xy1bGDl9On+bzRQHZirF/7N33gFVlW8c/5x7\nL5chAgoigiAbB+BWMHP/1NQ0G+bKnLlKzZGmWe5tajlylTkrK01z5x7gxoEiQxRQEdnz3nPvPef3\nxwWSQAXS1OLzlxfO+573HK7nOc/7PM/32ezszKkFC/KPEfV6Lt26ZYzF5ZYOpGVnU9/dHa8qVfj2\nUBBa/RGgHnAWtbo933wT8dS0GouDJEnExl5FliVcXPxRKBRkZaUyeLAHorgHY/rBRUxMWjN27Cac\nnX2xs3N+5Hx6vUhq6n2sre0LvMEnJsbmq5tERp4mOvoinTuPZceOpeh0+3LP0xDogXH7MS33Z2OB\n9iiVn9G0qTndun1KxYpOT705Z0ZGEpJkwMqqEhpNJpmZySU6T05OBhkZSdjaVn0pGocumNKCDmEn\n+EIykAW0MC1HowFLadGiLwAaTSbp6YlUrOj0QqjvlFFGSXhUluO/2qD9HWRZZuzataw7fJjKKhUa\ntZq9U6fi7fh4ZZKJGzawZNculLJMpuyBTET+70xMajJ8+BcEBLzz1LfTSkJ09EWmTHmfnJw/k0ME\noSZKZTyyLNK166d06zap0LirVw8zf/67GAwqBEHL6NEbqVv3tULH7dixmC1bPkOhsMTMzAytNhWl\n0hm9PgYTE0t0Ogv0+vvIcjOMSSMCcAA3t9nMnXvoqV6rwaBn8eL+nD//G6DE2zuQCRN+wsys3BPH\nvosi8DMAACAASURBVMwkJsYy9/NXUWYmkWrQU69RVwZ+VLrM1TLKeNEoM2ilJDYxkZSsLLyrVMFM\n/fii5AOXLzNk/nxOaLUAuGCOnouAD3AdpaIBLnaWJGVk0MjTMz+jsrGXF7blja0vHxWLe5qkpycy\ndKgXOt1JjPGtcIze03VAganpq4wdu4zatdvmj8nJyWDwYA80mh+AVsApTE27sGzZdays7PKPCws7\nwcyZvdBqTwDOCMIiqlTZxKhRa7Czc0GttmDTpvGcOvUraWkBGHWvBRSK4QQGiowcueapXuu2bfP5\n5Zf9iOJvgAkmJn1o0aIyvXtPx9y8/BPHv8zo9SJ374ZjZmaJvb0rer2IJBn+MzWYZfx7KXEdWhlG\nnO3scLaze/KBwPmbN3lTpyNPq+NLNIygHtYWvoj6cJYP7E/fFs1ITE8nOCKC4IgIFv7+O2ejoqhi\nY0OAlxdq7yS8vQNxdq71zLa2rKzsGDx4KatWNUOl8iE7OwSjXKfR+xTFLkRFnStg0O7fv4ks22I0\nZgBNABfu3r1RwKDdvHkBSeoEGLctZXkY9+6No1q12vlJDv36LaFHj5lMntyOe/eqI8sKlMo0evU6\n89SvNSzsPKLYl7ysS72uKkcPLOT4wWVUrezOqEn7nqkCyPNEpVLj4uKLLMt8++1Y9u9fCoCfX0fG\njt3wyHY/ZZTxsvLCGzSDJKF8QbdJ/ppW7lqpEttMTNBqtZgCVZDxqWjGyhEd8HToj1NFY+zMzsqK\nTvXr06l+fcB4jaGxsQSFhxMU/gNrd0/nbkoKDdzdCfDyIsDbm0BvbypZGYP1T8OLa9asF76+LYiP\nj2TmjK7o9JVzfyOCvB9R7FLg+HLlbNBqbwFRgAcQi1Z7o5CXY2/vilL5LTpdDmAOHMTa2rVQxp6Z\nmSVz5hwlOvoikmTA3b1ekbJcmZkpfP55Uzw9G+eqmwRStWqNYmfJOTq6cuXKIfT6nkAI5VlMMDLV\nJT1z4iNZNvd1pi68Uqy5XlYOHlzL4cNHkaS7gCXXrr3HunWfMnjwkue9tJcCWZaRZblsu/Yl4IU3\naA6DBuHj6JifKh/o7Y1jxX8uqaIoHqSn02vePA5FRFDB1JQlAwfSs1kzugUGsv3ECfyuXsVNoSBE\nltkxZgyNvR7fK02pUOBfrRr+1aox+H//AyA5M5PTuV7csn376LN0KbblyxPo5YWp9328vQNxcfH/\nWwH9ihWdjEkBcjam9EXBlxiIxZ5UTEwKFvRmZ6dhr1KQqa+DCn90XKGCiYBGU1Das37916lb91cu\nXPBDofBEli8walTRHbqVShWeng0fu0YLCys++mgjERHBhIWdYOfO+aSm3qdx4zcZNuy7J17jO+98\nSkhIa5KSGmEwpNBRZ8jvEjBOlpgcF1qoDu3fxpUrp9BqPwCM/290uo8JDf3o+S7qJWH79oVs3ToV\ng0FLvXpvMXLkmjLP9gXmhf9fHPX115zLVfX47sgRBq9ejVPFilyaP//Jg58RfRYsoFZUFDtlmWsa\nDa+tWoW3kxMNPDzY8sknBEdEkJyZSUMPD+ytrZ88YRFUtLTktbp1ea2usYeUJEmE3b2b68VtY8OB\nedxKSKCeuzuBD3lxDrnZnCXx4uxt7JmRFEsFTmMFjFdbFMp0tLaujEYwsJdsUjlFBaAzZlSoUFAM\nWRAERo36jqios/lajn89piQoFErc3Ori5laXtrmtb9LTE3NlmgqTmhpPamo8zs6+KJUqLCysmTfv\nJGFhJwgLO8mVHfPQarMwxai4bWNu/a82ZgD29k6oVEHo9QMBAUEIws7O6Xkv64XnzJlt/PLLSnS6\nS0AlLl3qx9q1Yxk2bPnzXloZj+ClSwqRZZl7KSlFemnxqakcu3aNAG9vnG1tn5k2o1n37iRKfypO\nfKRS4d6zJx936lSq+bafOcOY1atJzsmhra8vq0aMwNriyW+BadnZnImMzC8bCA4Px9rCggAvL8y8\nu+HtHYira538rbxz53ayadUHpGWn4V+zOYNGbqFcORuuXTvKktkdaa5QcFOWUbvVY8znBws96Hdt\nn8u+n6fxikJBkCzRvNMYur47rVTX/FcuXtzDhm8GkpqZgl+Npgwa9UOpyhsuXz7Ad9+NyNcJ9PIy\nijB7ezfB0rIiy+a/wYPQw9RE4LBk4IOPf6R+/dL93V4WsrJSmTChOWlpFQBrlMpzzJx5OF8o+t+K\nRpPJuHGvcv9+GKAiMLALH3+8sdjjV60awR9/uGIsMwG4QsWK3fjmm+vPYLVllIT/RJbjtbg4Jm7e\nTFBEBEqFIn+b8n/+/tRxdX1q63IZMIDNGRk0BSSgpakpH3zwAb1efbXEc12Mjqb95Mn8LIrUAMap\nVGT6+bH1009LPJckSYTfu5dfExccEUFkfDx1XF3xqFyZXadO8ateTy1ggkrFtZqt+PizfQA8eHCb\nGzdOUq5cBWrXbvvIGNXNmxeIi7uGo6M3np6NSrzGooiLu8aMCQ3ZKmbjD0xSqbnoHci4KUdKPWdm\nZgqRkWcIDw8iIiKI2rXb0anTaGRZ5sqVg6SnJ+Dp2QgHB8+ncg0vOlptNpcu7UevF/H1bYmVVaXn\nvaRnztixgcTEmGNUo3kAtOPtt4fQrduUYo3funUG27aFo9evz/3JBlxd1zBv3tFns+Ayis1/wqDl\nIcsytx48yNdmdLO3L7X3VBS/nT3LoCVL6AJcVygwc3Zmz9SpmKhKvnW1YMcO4rZsYbHBAEAyUE2l\nImPz5qey1oycHM5GRbFw506cQkJYlfv3TgfsBAWfTzuOm1u9EovgimIOq1eP5sKFPVhYVGDAgNn5\n6iIlZd++5cjrx7JWlwOABigvKNi4RffMA/G7di0mJeVufteForZHw8JO8s03H5Oefp+aNZsxbNhS\nLCxKt5X8XyIq6hzLl48gJSUOb+9Ahg9fTvnytsUen57+gK+/HkpU1BlsbV0YPnwprq51ij2+e3c7\nJOkAUDf3J4twdNzA4sUXijU+OzuNTz5pSlpaVWS5MgrFbj7//Pen9iJXRun5T6XtC4KAm709bvb2\n9Gja9JHHLdy5k+PXr+fHnxq4u1PO7MkP9i4NG+IxaxbHw8L4n6UlXRs1KpUxA6hgaclBlQrZYEDA\nWBFWoRhrKC7lzc1p5etLbGIiG69dQ9Zq889TTqlg27e9Cbt7F19n5wKJNy52dgiC8MhY3DfffMTp\n00nodH+QkRHBggV9mDHjAK6lUFAvV64CF5RKZB35a7M0tfhHssq8vQO5dGk/Bw+uZsWKAVhYWOHl\nFcC7706nShUvEhKimTnzDbTa5UB9LlyYwYIFffj889+e+dpeZpKT7zJ1akc0moXAK1y6tIDZs7sx\na9bBYo2XZZkZM7oSG9sQg2E+mZlHmTLlNZYsuYS1tX2x5lAqTZCkcP40aNewtCx+7aGFhTULFgRx\n5sx2RDEbf/8p/9oSj38Lz9WgCYLQHlgMKIE1sizP/SfP3+OVV3C2tSU4IoIJmzZxOSYGH0dHFr//\nPs1q1ixw7J3kZEauWEFYXBy1qlVjyZAhDG3b9hEzl2wNK3//nU4JCdTQ69moUvHVwIF/e96/8m6T\nJqzYuZMOCQnU0unYpFKxfMgQejRtSrZWy7moKIIjIvjx1ClGrVuHInfL1sIrGi+vADw8GhTI7jp7\ndjs63RWgCuCJXv8eISF7SmXQGjd+k4M759Pm7g3q6EQ2qkzo1f/rp3fxj8HLq3F+p21Zlrl3L5zw\n8OB8D+zq1UPAa5Br2PX6FYSGln9qavunTv3E1q1foteLtG3bh06dRj4x9hsctJU9P32OXi9So0EX\nroeHkpJyl1q1mjJgwPwi2xwFB//Cjz/OR68XadOmN507f/xM+/+FhR1HEF4BegNgMHxFVFQ5hgyp\nQeXKHgwe/OVjY3gZGYnExYViMBzD2JHBDVneSnh4EA0bdnnkuId5771JfPttf+AYEI8gHGDo0NMl\nug4zM0uaNetdojFlPD+em0ETBEEJLMXYxeMOcFYQhB2yLP9jEVfHihXp1qRJvsK+VqfjYnQ0rvYF\n3wA1osj/PvuMt5KSmCzLbElNpd3kyZxbtKjUnlkeFqamHJ0zh43HjpGcmclOX18aej79uI6ZWs3h\n2bPZdPw4iRkZbK9VK7+cwMLUlGY1a+YbcVmWuf3gQW4c7gi7NqwiNC6OGk5O+V5cORMVWm0s5JaR\nq1SxmJmVrh2IiYkpE2cEcfz4JuLTExhRszne3oFP5bpLgiAIODr64Ojok/8zM7PyCEIsxiakAnAH\nWRZYsqRH/jalu3v9UvUtCwnZy/LloxHFtYAlP/00FIVCRceOHz5yzKVL+9m07H2+F3PQAl13fYPE\nl0AAp07NIzW1D5Mm/VpgzOXLB1i6dASiuAaw4eefh6JQKHj99ZJ3oi4uZmblkeU4jFFmBZCALMsk\nJ28gJeUEn33Wmq++uvxIsWK12gJZ1gKJgD1gQJbvlKgnYfv2H1Kpkiv796/A1LQcPXtexMHB4+9f\nXBkvLM/TQ2sERMqyfAtAEIQfwBiWel4LMjUxIcC78Fvj1dhYVFlZTM+NP/kbDGxJTGTO9u28HRCA\nj6Pj39oeM1erGdSmTanHl+Q8A1u3fuJxgiDgam+Pq709fi4uhMam4FrJjBpODrjY2bH97FkkKQto\nD4xAobiBmdlZGjVaVKL1nD37G7/+uhRJMhAY2J6rV4NJSUkgMzMLd/cGL4RoboMGnalUaSH377+F\nKNZDrf6WypVrExFxnVu3wjl6dD1pafGsXHmvxN+Bw4d/QhQnAe0A0GoXcejQlMcatDNH1jFZzKEd\nsAkwpRk5DAFAp/uOK1esEEVNAQN79OhWRHECRk8TtNolHDo04ZkaNH///+HoOI+4uM6IYgCwCvgM\naIAsN8Bg2M2NGycLZZgmJNzix28/IiUhGjdnf2LvtUSr7YlafRwXF3tq1mxeonXUr9/pX5/FWsaf\nPE+D5gQ8XEwUh7FB1guHmYkJ6ZKECKgBLZANnImM5PujR7k4b15+X7R/E7GJiTSZPIvMnC+Q8edW\nwlTUqmS2jjamMW8NCmLT8b0kZ6ah0SkZPdIDLweHfI3KQG9vvKpUKTIWFxKylyVLhiGKXwNqoqMH\nYnywj+D+/Vmkpn7Ihx+u/Mev+a+o1WbMmnWYgwdXk5R0j7NnLbl3zxe9vi9K5W4qVNjBkiXhRRqz\n1NR4jh5dX+SWLYCZmTmCkMSfeVmJmJo+/ntkYmZJoiCALGMGyCTyp/eYiiAoCpVbGOdMeugnic9c\nz1GlMmHatL0cOrSGe/dusm/ffSQpr5hbQpaTCq0hMzOF6Z82YnhWMs0kA4tUpiir1sDbLwN7+860\nbj3wX18zWMbf43l+O17I9MrdFy6wZtcuBGBoly608fenlrMzdX18eD0sjNdFkW1qNa39/NjyySeP\njENk5OQwat26/Id7japVX1gJr7/yx+XLzPvtMDFJiWh1tZEZAUC2WIuNx6uxZkhfBEHgncBA3gn8\nc2swb8v2dGQke0JC+Pynn8jUaAjw8sLSKwxv70A8PRthYWHFvn3rEcVpwJu5o1dgfItvgSjW5uRJ\nR4YNW1HIUFy9epjt25chSQY6dOhHgwbPvr+cqakFHTqM5MGD2+zbtw69fjWgxGBoRlbWIW7dukTN\nms0KjdPrdaSk3GXjxnHExl7F0bE63t6B1KvXkbp1X6Nz548ICmqGRqMByqNWL6R798dnt7Z9fQxT\nT25B1GZhLsuI3ECp7I3BEICp6Wratx9X6KHfqdNwTpxoilYrIss2qNUL6dFj/SPO8PRQq81o397o\nbWq1IkePtsBgGIAg/IG9vZoaNQres9DQw/jqNUyWjBm/AXotNrFXmTTteLG3GvV6ka1bZxMaehoH\nB2d6956GjU3lJw8s41/B8zRod8hTsDXijNFLK8CUh9L2W9SqRYtatZ7ZgnZduMCgL79knihiAHrf\nuMGm8eNp7efHTxMmsHzvXkJv36armxtD2rV7YlC9vrs7x65fZ+5vv5GQlkYjT0861qvHqI4dn9k1\n/F0OXrlC53nfkCMuwJirMxLYhbEZZzbKx2go5m3ZBnh7M7JDBwDuJifnCjGf48jPm1kUHY2bvT2Z\nGiXwcJwsiz+/jtkIgrLQ/b127Rhz5nRHFOcApoSHD2PECAONGnV9Slf/eJRKE2RZB+gw3hsJWc55\npNdgZ+dM376LAWNX7+joC4SHB5GUZPyaOzlVZ86c4+zfvwadLoNmzX7Dx+fxsUNHRx++mHOeQ/uX\nY9BpmNj4bW7cCOLBg+v4+0/glVd6FDHGm7lzT7Jv3yp0ujs0a7YNH58mf+dWlJiUlASM77D7gVSy\nslLQ68UC28pKpQnZ8p/+pgaQZLlE3a0XLerHpUupiOJQoqKOcfVqMxYvPl+i2FsZLx6hoUcIDT3y\nxOOeWx2aIAgq4AbQGriLUYmox8NJIf90+5jOU6bQ49o18h4Ja4ED9erxw4QJf3vuPIX9jJycIksJ\nNKKISqlEpXy+rek7zVnKrgt9MHaSBtgMzADGY2E6j9Ed/Zje/a1HT/AX0rOz6TxvMdfiUvCoXJ5e\nTRuxNfgKSZnJXI+7jyRPxbiROxGjkn9XVKr5dOzYlV69pheYa8GCPpw5EwgMzf3Jz3h5rWHmzL3F\nXk9qajw7fp5GRmIsPnVfo3XbocXO9pNlmblzu3P1ahqi2BMTk304Od1m1qzDTyXet337HE6e3PKQ\nukkgDg5eL70obmZmCoMGuWAwPMDYPxvMzV9l1KiJBfrpabXZfD7Wj5ZJcTTXiyw3taBC4Lv0H/Zt\nsc6TnZ3OgAFVMBgSMQpjg7l5Sz76aDQNGrz+tC/rH0GSJPbsWcaVK0HY2zvy9tsTCnS3+K/ywtWh\nybKsFwThQ2Afxtfdtf9khmNRCBTcB817U3wa5CnsP4qfg4MZumYNDTw88rUZA7y8Sq0F+fco+JLj\nWCGHhh7reL1Bc/q3bFHsWSRJwnX4BFKy/IExPEj/meCIbcBCFEIkkrwQYz80BWoy8GEPKv7grj6b\n4D9uUTHtRH6NYM2qVflBuMOZv6ytJKnnWVmpTPmkLu9kJFLfoOfL0MMkxkfR/f2FxRovCAJjx25k\n585FhIfvxdnZkzffXPXUklc6dvwYX99WhIcHERKyj61bp5Cdnc6QIWto3PjNJ0/wkvHXv52pqQWT\nZ59jxy/T+C7+JrV8W9L2tRFP/TwvE6tXj+TEiQtotUNQKoM5d64ZX355pszjfATPNcIqy/IeYM/z\nXMPDDO7cmYGRkehztxw/U6vZ/BQVRh5FQloa12NieKdePZwcHFAqFPkK+10aNMBCEKhobc2ozp3z\nW8g8K0Z3asHhq5+SLSoBJRbqcawe3J8O9eqVeK5j16+TkqUBfgdMgB5AdaA2ktwPuAfUAirSkovs\nRQuI3AW8NBoaenhwMiyMBTt3cj81Fc8qVVAqJ2EwqAEz1OrxdO68rNjrOX9+J3VzMlhs0APQWptF\ntd1LeJCegbOzFx07jsDExPSxc6hUJnTt+kmRv7tzJ4y9e1eh1+to2bIX3t4BxV4bGMsXPD0b4enZ\niA4dRgKQknKvyLY6YNyGsbKqhJNTjRfai7O0rECdOh25cuVtRHEgSuURLC2TC8XQ8o7t+X7JsmXz\nsLCwon79roSEvIkoDkapPI6FxT1q1WrxN6/g+aDTaTl8eDWSFA/YYDC8R2ZmGy5d2v+vfMF5GpSl\nDD1Eh3r1WDtmDGt37UIQBDZ17kwrX99nes7kzEwCx42jXUYGdQ0GlpiaMuLdd9k3aRJLdu7k6x9/\nZKQocl2pJODYMc5++SUVLS3Zce4cBkkiwMuLKhWKruUpDa18fdkxfijzd3yLJMt83HFAvuJ/SdHq\ndBi/YnnbqArAFNDnfrbI/bcei4c8LzOM1UtD2rZlaDtjSntSRganIyLYcvIkey5+Tmp2DrblBBLO\nLCIxdReB3t74OjsX2rJ9OLvSYNDzcF7dh5ihl2tx/Hgd1Oq9nD//B1On7i5RzCaP2NhQJk5sgVY7\nDCjH8eOd+eSTzfj7/71yjMd1Krh4cQ+nT/9CRkYinp6N8vvF1azZolR1cc+SMWPWs3XrbK5fX0uV\nKi706nXkmbRhGTXqW375ZS6hoWtxcHCmV69jL603I8tS7r8efskyx2DQF3V4GfxLtRxfJlbs38+R\n9ev5URQBuAa0Mjcn/vvvcXj/fQ7l5JCnWfKuWk2LPn0Y2rYt644cYWtQEMEREZQ3N8/fpuzZtOkz\n9+KKIjI+nu+PHENGplfTV6hRtSqiXo/1+x+h0b0OvA/8jDEmtxG4BYzD2CnbEnMGMBOZ2sBstRrP\nwEBWDB9e6Dw3799n3ZGj6AwSjTzdSUxPN7bUiYggLimJ+u7uBcoGjlr/qbqSmhrPxFE1GJ+TRjVZ\npjvlkEggz7Camfny+efrS6TVFxZ2gnPndnH58lFu3eoITMr9zRa8vNYxc+a+kt/MEpKWlkBExGki\nIoIJDw9izJifS9WpACA8PJgzZ3Zgbl6O1q0HlmUIPmfmzevBpUvZ6HSjEIRgLC1XsHjxxRJpYv4b\neeFiaGUY0YgitpKU/9kW0OiNb2AavZ6Hv7a2koQm1/D1bdGCvi1aIMsyEffu5fZJCydLo/nHDdq1\nuDgaT5xOtrYvMmoW75rO0anjqe/uTtjiabSe9iVxSXuobGNO14avcvTaRCqWt+D1em/zQ9D3GAwS\n7zbpzckrl9memkrLunWZ1K1bofOE3blDo4nTydb2QZLNsFCv5NAX4/KL0lMyMzkdGUlweDgr9u+n\n7/LlmFrOxssrAC8vY5LFp9NP8OuGsSQm3EK4nwyGPJ9NhSBYI4qaYl93UNDPLFv2EaI4lFy554d+\na1uiuf4O1tb2NGjw+mMTH0Qxh7lzO+d7cl5eAYWSC86d28nixYMQxaEolTHs2dOIBQvOlBm158io\nUd+xefMUrl79Ajs7R/r3P/qfN2aPo8xDe85E3LtHk08+YZFWS3VgslpNtcBAvhk+nCHLlnE7KIjp\nokgY8LGpKafmzcOrSskaZkqSRO1x46ju5JTvvdRzc8NMXXRs5nHzTNqyhXM3b+JfrRrze/dGoVDQ\nffFKfjr1P2TG5x65nHa1N7N30sgSzf8k3vt6DZtONEOW87yg1bTy/Y6Dn3/8yPXeuHuXvSEh/BQU\nxK2EBNJycqjv7k5jT0+2Bl/mXsrr6AwDUCh2YW39PUuWhGBmVq5Y6xk6tBZJScuB5hhDwe8D64Fy\nmJoOpVev4bRvb8zIvHBhF7duhWBv706TJu+WOOZ169YlQkL25msLllTtX68XuXLlIOHhQYSHBxEZ\neQZra3vq1etE377GmNWIEfWJj59FnnKJUjmEN9904p13JpfoXH8Hg0HP8eObSE6Ow8srAD+/Jyvb\nPI64uGucO7cTU1MLmjbtWWYM/iWUeWgvKF5VqrBz8mQmffcdSRkZtK1fnxnvvQfAV4MH85mFBQPP\nn8e2fHl29utXYmMGxiyv7ePG5fdJ23T8OGF379LQw4PDX3xR7CywwLFjSYqL401g55Ur/HHmDJeW\nLiU1S0T+S0lhWra2xOt8EqlZWmT54fNUJf0x51EoFJir1Uz/ZTfZYkckyRy16kd6v/oqCWlpeFex\n5l7KBhSKzZQvX5FWrXoRE3MFN7e6T0wOAdBqM4GquZ9eA16lfPkPKVeuAu3aDaZdO6Mk1dZN47mw\ndxlvijkcVJtzKegnho39tdj3PSRkH98seJM+BpEYpQmTf5vLtAWXKVfOpljjAVQqNXXrvpafJi9J\nBuLirpOcfOcR1wMGgxM5OZnFPsffRZIMTJ/+BlFRGeh0gZiYDOSdd0bQuXPRLyxP4vr148ya9SZ6\nfS8Uigf8+usiFiwILrZafxkvH2Ue2r+EQ1evEhobSw0nJ9r4+z/x+Gytlht371LXrbCgcGpWFpdv\n36aBhwcWpsYH++nwcFp/9hl3AGuMZdDOwMYJE0jKzGLI6l1ka7cAJliY9mJer6YMb/+/Yq9fbzDw\n6+nTJKSn82r16tQuoiHrDydPMeCb7WRrfwBMsTDtzczujRnV0diH7ei1a1y+fRuvKlVoV7s2giAw\nYMV3rDtSF0memTvLMlr7beKPycaHZN6WbZ6xDwoPJyI+Hn8XFwK8vGji40OAlxfBdkMLrWflypEc\nPx6OKC4GbqNWv8eUKTsLxOAyM5P56IMqROtFKmEsFvYxLcfQKUfw8GhQrHszaYQXi+Mj6ZD7uZdK\njdBtGl3eGP/YcSVl3brxHDhwBp1uOcbS0LewsbGgVq0W+duUrq61H5l1+Xe5fPkA8+ePRav9HGMG\nbDWUynfZsCGtVKUR48Y14/btDwHj9rVSOYzOne3o0ePpdFkv4/lR5qH9ixm7/ke+OXAeg/Q/lIpN\nDGwdyuK+hRUjHsbC1LRIYwYQm5TEuI0buRobS3VHRwK8vFApFFTAaMwAymHUQP8xKIguDRow893m\nLPi9G5IsM+K15gxrV/zsPr3BQIsp87l0W4Ve8kNgLt8Pf593AgumvXd/pQnJmdnM3tYdvWRgeLtm\njOxg3B6b+dNPrN25k3aSxAqFgjZNm/LV4MEkpGcjyT4PzeJDckZO/idBEPB2dMTb0ZE+zY3Ct5ka\nDeeioggKD2fDsWMMX7sWg2pmgVicu3s9BgyYj0IxntOnO2BubsX7768plFCSlZWKlVJFJb0x9mkG\nOCtVZGWlFvv+ZGal4vXwFehFQjISiz2+uDRs2IG9e1di3EIVsLa2Yfz4n4mJuUJ4eBAHD66mXr2O\n9Ow5+6mfGyAjIxmdLhOYhVG7fDaSBKKYjUpV8nrMrKwUeOjOGQxepKdHP63llvECUmbQXnJiExNZ\ntu8gGl0kxpSSFFYe8GBkh9a42Zdua8XPxYXTs2ahEUUuREcTHBHBjjNnSAGWAL2AXzEqS0efsOHX\n08fxrpLF/N5dUCoU/M/fv0TFrNvOnCHktooszSmMKf6DGbSyfSGDBjCsXZtCxvJBejrztm8nA5XX\n1wAAIABJREFUXK+nMpABVD9+nCEdO/J2Yz8OX51JlrYhYIaF6UTebPx4+TRLM7MCMmuyLHPz/v1c\nCa+D/HZqBdfv3KFW1aoEeHnRr28nAry9OVupcM2inZ0LaqtKzE6MZZAssRcIAwa4F7+ur079Tow5\n+QMrdRpigWUm5rQwL8/Jkz/g59caK6tKT5xDq80mJGQvOp0WP7/WRW67ffvtp0jSKowejUxWVi+u\nXDnEG298QsuW/fLvRVGcOLGZ1NT7uLrWJT09AUky4OvbqkQJJYIgIEkGIAijeswYZLkWpqbFi2n+\nlYYNX+PgwU9z2+Y8QK3+ioYNi1+3WMbLR5lBe8l5kJ6OWuWARpcX7K6AWuVEYnp6qQ1aHmZqNU18\nfGji44OLrS3vh4XxBTAeY2WMllcwGLYjGu4RFu3KymVhWKhUfGJqyrE5c3CxK55ET0JaGgbJjz/r\n1fzJyElHkqRiJU8kZWRQSaWicm52aHnATaXiQXo6fZq/yt2UNObtaI4kGRjYqgWfdi2ZDJIgCHg4\nOODh4ECvV18FjFu2F6KjCQoPZ2twMKPXrydHnp7vwRm9uPqYmZVj3BdHWL2oG7Njr1LF1oVxIzeX\nKK2+18DlfK8XqXluB6YmpmgES3bsOAJcQKkcw8yZhx/bLDMrK5UJE5qRlmYHWKFUjmXmzEMF+r4B\npKcnAHnb1QJ6vT+pqQ8K3YuiKF/ejitXDrJ58wz0ejcEwQ6lchiffPIDdeq0K+aVyqjVdRDFvC1N\nD5RKNRpNZonihXm8994MNJrRBAXVw8TEnHffnUS9eh2ePLCMl5Yyg/aS4+PoiEqZhLG2qxvwC0rF\nfao7OT3V8zhUqIAMhGLs+/MAcOM8WURjyiz6o+drgwQGA59ptbT89FM2jB1LEx+fx08MvFqjBoI8\nG2gCWKNU7KOhh1+xMwHd7O0xmJqyRqOhD0Yp5UhZxs/FBUEQ+LTr68UyYg/S0zkdEYGVuTlNq1d/\n7PktTE1pWr06TatXB4yeS0xiYm4s7hi7N64psGU75rXGBHq/x4XKHz7SKMiyTFTUWVJT43F1rYud\nnTEBRq02Z9CITQwCNmyYyJ49D9DrVwECgvAla9eOZ/LkbY9c6/btC0hKaohevyZ3zGK++moIb7/9\nMa6udbCzcwHA378VwcFT0OnWAPcwNV2Nv/+fncOjos6RknK3wJg8atduy/Xrp4BOwDpkWUCv/5of\nf5xXpEEzGPSFRJ09PRtjVMM7DLyCICykUiXPEmd05qFSqRk6dClDhy4t1fgyXj7KDNpLTjkzMw59\nPo435k8mJrEvzrbObBs39qn3Z4tPSaESRmMGUAmoioEbxKImlFf5s5auKbDX3PyROpT3U1OpZGWV\nbzD8XFxo4mbHtfABuAGXZAXjSpDZZmpiwq4vvqDn3LkMSUjAo0IFfhszhoqWxVeIuBgdTYepU6kt\ny8TKMu4eHmz77LNii0ULgkC1SpWoVqkS7+Z2QNeIIhdv3SIoPJwd584xccsW0sRpuV6c0ZPz8GiI\nuXl5ZFnmuxX9CQ3aSnWFktWSnqFjfqFOnfYFzvPgwV30+lfJUxmV5QCSkn587NoSEu6i1wcWGHP7\n5lQufv0eqyU9gz/+iXr1OjJo0CKyswdw8aIdKpU53btPo169DsiyzOrVH3P8+HYUilpI0hlGjfqu\nUONM49oC+FMBNZCMjKKFhUeO9KZ8edsCHm2lSq6MG7eZJUv6kpl5B2fnxowfv/2l1mIs45+lLMvx\nX4Qsy8/sP39CWhpugwaxEeiKUVG6K6BBiVqhoL5CZo9ejxJ4W63GvWlTujVrhr+LCxX+Yljaz5zJ\n6chIGnl6EujlBYLAth07CNZqMc+de4iVFdFr1pR4naW9BwGjRzMsLo4+GMW42pqa0rNv32J1+M5D\nbzBwMToancHwyDq/uKQkYywuV90k5NYtPB0ccLa1JezyZUL0eiyB48AbFtas+C6lwPUcOLCa9etX\no9XuAcphYtKbli1dGDjwy0eu648/1vL99yvQavcBlgi8TV8O8C1agoDXzCxZ9X16/nn+eg+N6e/9\n0GovAFbAaUxNO7B+fWKB444c+Z61a5fknscaE5P3adrUtkgPSRRzuHnzPOHhRnWTiIggFAolS5dG\no1Sqnvh3lCSJW7cuIoo5uLnVeyYyWmW8uJRlOf4HeJZvsvbW1nw1ZAjvrVyJVpZRA/P692dwmzYI\ngsBHK1dS6dgxZFmmuo0Nl0+cICQ4mChZZsfkyTTy9Myfa++kSSSkpXE6N1V+a3Awr+QaM4CWQGxG\nRrFjaA9T2ntwOymJVrn/VgHNtFpuJyQUe3y2VkvzL+YRdjcbQTDD1jKL4JkTqWxTMPZT1daWt21t\neTvAmPAi6vWE3LrFol27qC9J5Jn+pkB6djoXLuyiRo1X87fd2rQZSGzsDfbvdwQEfH1fp0+fmTyO\n1q37ExMTxv79jsgyVBZMWSoZ6/cCAI2oQavNytc8/Os9fPDgNoLQEKMxA2iETpeDRpOJuXn5/OOa\nN+/D7dth7Nlj3CqtUeM1+vWbW+Sa1GpzqldvSvXqxlZKsiyTlnY/fxvy4TVkZ6dx7twOvLwCcHDw\nxGDQMX36G9y8GYFCYYOZWSozZx4stA1axn+PMg/tCcQkJpKcmUl1R8cSK2s8bfKUL8AYOyvOw/7h\nMd6Ojn+7a7YkScSnpuJgY1Po/Fqdjr0hIUz86iuCtVrKY1Rv/NTGhh8nTKBapUrYli9faM5Fv//O\nzPXrOQ9UAxYB0xUKkn/4AVGvZ/rPP9PYy4sALy9sLCw4cOUKol7Pa3Xr8iA9PbdIugrlzEouyJua\nlcXN+/f59LvvqBcRwSxJIhFobmrK7BEj6NKwYbHmmbRlK1/+LqHRGdvhmCjH0aXBRbaOGVKs8Rej\no+kweTInRBEPYCUwzdwcD1dXLty8iau9fb4+ZYCXFyGVhwMSanXBreXs7DTi46Owta1aKJNRr9dx\n+3YIC79ozkkxB2+MPf+m2jqzcEXMI9cWE3OViRPbIIpHAR9gHRUrzmLFihtFvkDo9TokSV9obaXl\nwYPbbNgwjoiIYEQxGyurysTHO2Ew7AZUKBQzqFXr/GPjiGX8uyjz0EqILMuMXrOGDUeO4KBSkaVW\ns3fqVHwcHZ/LejI1GjpPm8bN2FgA3KpWZecXX2D5mId4lkZD5+nTiYqJQQCqOTmx84sv/lZ8TaFQ\n4Fix6Aw9UxMTbj94QAtJ4mGzFZeaSr+pU4k1GFj6wQf0bFawbYiJSkVNhYJakkQ5oCKQKklIudqV\nMrB4925Oh4ejFEVUsowK0AgCglJJVRMTUpRKdk6e/MjauqLYfeECfRYtwkmhIEavJ8rKijVZWWRL\nEmPaty+2MQO4GvMAja4veZmaOkMXrt0pfuPRum5uTOvThzrr1mGhUGBVrhx/TJ5MjapV0en1XImJ\nISg8nCOhoczeto17GdMK6DJ6eQVw8+Y5li54kyqCgjt6kR7vL6J12z8NqkplgodHQ97pu4R6332E\nuaBAbWHNmImP7+Dk4uJL//5zWLu2IYJgjrl5OSZO3PFIb9hYBP10esQBVKpUjdGjjS+2ycl3+Oqr\ngdy505G8x5ckvc7duz88tfOV8fJS5qE9gt/OnuWzr77ihFaLNbBMENjk7MypBQv+0XVoRJEzkZFs\nPHyYrFOnWK/TAdDXxASbZs0Y3bUrzra2RSYvTFi3jpgDB9iQO6a/iQl2LVuycODAQsc+LQ5fvcrA\nuXMJ0moxBVyAP4CGGDMkm6nVXPnqKxwrVkSSJC7HxBAcHs6c77/nN52ObCAE+KZyZS59/XWBuVtO\nmkS5iAi2A8eAPsBFjAkqm4CZdnaELltWrG3HLI0Gl0GD+F2rJRBj6/RXHnppKanRn/Xrb8z4NYEc\ncSdgglo1iO5N7vD9h8bO3xpR5E5yMg42Npip1cQmJmJpZobdX4Skc0SR5MxMHGxsHutN52VkBoWH\nc/z6dc7fvIms07FHlmkORAGN1OZMWXAZBwfPQuNFMYfMzGRsbBwwGPQkJcVhY1P5sa1WHh5TmhY7\nOp22WOd5Env3rmDTph9y44jmKJVjqVv3Hp98shmAgwfXcPr0L/kJJ56ejUqV9l/Gi0uZh1ZCQmNj\n6aDT5StjdJdlJt6794+u4UhoKF2mT8dEksgE3ubPSi1Bp2PtwYP8duIE5uXKsXvKFDwcHAqMvxYd\nTT+dLn9MN52Or6OfrVJCS19f+nXqhPdvv2GlUGAtiuT5ObUAH5WKqPv3sbKwoPaHH3I/PR0FYBDU\n1MEUgXIoBC3b+/YtNPed+HimYvzShgHtMRozgHeBPomJbDp+nM9/+il/ay7Ay4varq6oVQW/6nHJ\nyVQQBAJzP/sAvioVadnZpfJgx3buyImwpRwJdUIQ1Pg4VmRJv7EAHLt2jXfmzsVckkiRJCzK2ZOe\nrUcvZTKodWu+7t8r3wibq9U4PcIDfphKuR3QYxISCImMxEahQJNrzAA8AG8xh4UL36JevU65nlzj\n/CJstdqcihWduHHjFLNnv4XBYIokpTJ48DKaNetV5DnzxpSGiIjTLJrVHlO9jjRJz/uDvqF5i76l\nmqtt2w+4di2Y8+ddUCjKYWdnz5Ahu/N/37DhG5Qvb0dERDDbts3i5s3z2Nm50LPnnMd2JCjj5afM\noD2C6k5OTDMxYbJWiyVGZYzqlf/ZNhrdZ81ikiQxEKN24isYMwAF4BBwE3DQalkkivSaN4/FgwdT\n280N89xYn0+1amyLjKRLroe2TaWierVqxTp3pkaDLMulerh/9u67DH7tNaITEmg/ZQohokgdjF7Q\nDb0e98qVeWvOHNzT07mGUReijWwFhCDjhCQvY8z6JXSqX7/AvFUqV2ZrRgbdAG9gLnAf45f4IOBV\nsSI9mzalvrt7vjbjqj/+IDohgRnduzOqY8f8uZwqViRZkjgDuAPJQKhej0cp/8ZqlYpdn44kJjER\nvcGAm709CoUCjSjSbe5c1ufk0A5oRDnOpvbC2AculXVHXqVZjSC65ab6P47UrCzMTEzyY7kXo6OZ\nsWkTl/V6HIDKwCmM1Xy3gZsmJsxoF0Bs0g2Cdu9kRWQk9tbW+bG4Bh4e9J39NdnZqzHWj4WyalUL\nfHwCqVzZvVT3oSgkycDi2R1Yk5VKF4wvI6+sGYa3zytUqeL1pOGFUCiUjB79PUlJcYhiDpUruxfw\nGK2s7GjU6A0aNXoDMNa8xcRceaTSfmxsKBUqVCl1D7kXBYNBT1ZWKpaWFV/oDubPkjKD9gi6NmrE\nH4GBeJ06haNSyQOVij0fl071u7Qk6XT8AMzEmEreEOiuUKBSKHg39yEGUEOWuRQXR6vJk5GBAe3a\nsXTAAD7v0YMON25Q/d49BMDW3p49vXs/9pw6vZ4Pli7lx9OnAXijbl3WjR5dyMN5EpWsrKhkZcWq\nYcNovXw5HioVUXo9C/v3x6liRW7GxDADo+LIVUBJRwy5VW4yg4iIH1Eoy/GXCRPwGz4cZ60WFZCE\nUSA5ryf2wrfeQqFQUKNqVWpUrUq/li0BSM/ORpNr1POwNDPj03feoeWmTQiAARjcqhWuf0NdJa8W\n7WHikpMxlyTySosjkYGhGF9LKpCl7c7ZqHOPNWhJGRm0n7mES7cjkTEw7vUuzOr5Npdu36a1IOCa\ne9wWoA1Qy9ycm3o903r2zO8VB2CQJK7HxREUHk5wRASLfv+d7GwtRmMGUAuFogGxsaFP1aClpt5H\nEnPokvu5OtBIaUJs7NVSGbQ8bG2rPvkgQKlU4eb26K7re/cu5cSJTVSo4FhAr9PFxbdUW6vPg7Nn\nd/DVV30xGGQsLKyZNGnbY6/530pZDO0JhN+9S0pWFrWcnR+bgPEssOnWjRHAVCAGo0Gr4+dH2zp1\n2PzTT5zUatEBbhj7QLfDGK/qDATPn49/tWoYJIkrMTHIucoZTyoUnv3zzxzavp3toogC6KZWU/e1\n15jWq+htKJ1ej0qpRBAEDLmNSv8a+0lISyPq/n1cK1WiSoUKADQZNw7P27f5HtgNdMUVHVcxyh7/\njoPNEO6tWlTofKJez/YzZzh09So//fEHZzBur80DFqpUJGze/OQbizGm5T54MMuzsngDOAG8Jgj0\naNWKNn5+tK1dG5typdMQfJhMjYaqAwZwXKfDD/DDkqvMw2jUdFioW/Pl+9UZ0KrVI/82neZ8zf5L\nfugMXwMPsDB9lY0fdcHW0pL+s2dzPjfOewToZm7O9okTcbGzo6rt43t/aUSRiv2HkiMeBepi9Hd9\ncKygoHnNmvmeXKTrmFIr7MuyjFabxfCB9hwSc2iAUWXGX23B6BmncHWtXap5nzaSZCA6OoSbN8/l\nd/7+7LP9L0UpQGJiDB9/XB+tdjfGp8QWypefwKpVUYXUWP4tlMXQSon3c8pqBNAqFIyRJGOGIsY4\nUVVfX8Z06kRoZCQ1L1zA2mDAWq/P9wDaYOxotTUoCP9q1VAqFNQpohXLowi+epUhokjeo3yoKLI4\nNLTQcYnp6fScN4/DERGYK5UEenpyNDwcGejbtCnLhg7Nf0DbW1sXUg35Yfx46o0YgY9ejwqQiQdc\nARcUQhgrBg4rcn1qlYpuTZrw6+nTdAby0h1GA5/q9Yh6fbG8yduJiVgYDLyR+7kp4K9WI8sym06c\noGbVqk/FoFmambFy6FBaffMNtVUq7uq0mAufYqLagMEQT6XyBkauPcmItWt5t2FDVo8YgalJwQzB\n4PAIdIZ1gAKoTLa2LyfCzrCwTy/eaNGCWocP46NScdlgYMuYMcWSGwOjVuf6DwfRZ2kr1Cp/RP01\nxr3egZ6vNjZ6ceHhrD18mBvxM3F1rY2XV2C+wklxYmnnzu3k668HotEkYWNTjTbyXWqbmHJdL9Kq\n08cvjDGLj4/k69kduRkfgbW5FR+M2MSwYd8VeawkGfjmm4G4uzfA2zsAFxf/UrW2eZrcvn0ZhaIB\n5Eere6DVjiEl5V6+fNp/hTKD9gJTzcaG48nJdAJ0wHlTU5pUqoQgCHw7ahQXoqM5cPky0zZvJg6j\nIbsL3AEae5VuKydNFDkEvJX7+QiQrtEUOm7gkiVUj4pilywzRa9nd1gYdzF+od4MDma2nR2Tu3d/\n5Hlc7Oy49e23fHfoELcSE3mwZw9LDYlYkch+BJb8+itvNGr0yPFWFhYcAbQYty1PAuZQ7K3RytbW\nPDAYuIkxhvYAiJJlvu3c+bGlGZ3mzMHd3p4Ab28Cvb1xzf17PI53mzYlsHp1rsXF4WZvT2Vra85F\nRXH46lX27N7N6dxyhe4XLvD5xo3M7devwHinirYkZR7LXamEufoYbvZGg7JgwADe/9//uJucjH+1\navkecHF5OyCAxp6ehMbFUc3uDWpUNW7j+Tg60rdFCwAycnI4m9tOJ+jIbL5fHYG5Wk1gXl2ctzfR\nbmMLNEWNj49k8eL+iOJOoBGpqYuwtV1D0w8W84adC1Wr1ijROp8Vsizz5fT/MSLxNiNlmeDsNDp9\n2Y3pX17F3r5wCYjBoKd69aaEhwdx4MAKEhJu4e5eDz+/Nrz99ufP4QrA1tYZg+EqkAJUAMKQ5cz/\nZHfuMoP2HJAkic82bmTF/v0IwNB27Zjeq1ehQO6qESN4a/ZsXlEoiJRlvH188mMtgiBQ392d+u7u\n/HDkCH537xIAnAY8HRwKJVQUFwuVit+BaxjjUjcA9yKMxLEbN7hhMGACXAdcUeGCEgloK8ocDQlh\ncvfuXI+L462FK4mIv4WLnRM/jx6cXytmaWbGRx06sGzvXt5SKnnXYACgtSxjER39WKUQPxcXjgF1\ngBoYpaI0GONExSketylXjvl9+tBkwwZeUSo5YzAwrGPHIo3Z7gsX+HDFCuIzM6nr5EQFd3d+CQ5m\n7IYNSJLEKz4+bB09+rGBeBc7uwLdB9r4+7N+/34+FEXyonbjdTrGXb5caOy64X1oMWUM8COSfJea\nVQUGte5T4F74uZR+a8zZzg7nx3RGKG9uTitfX1r5+gK5Isr37+d7cRuOH+fa3Vm4uPjlenGBpKXd\nR6FoiVGLBGR5NCkpX+DtHVBqseFnQWZmMokpd/k4N/TSBGiqVBIZebZIg2ZiYkqrVgNo1cpYjpGd\nnUZExGlSUorOgNbptAiC8MyaogK4utamTZteHDxYF4WiPgbDCQYOXPqflAMrM2jPga937eLggQNc\nzi0afnv/fhwqVuSjh7LwAJrVrMnFxYsJjojA1tKS5jVrFvnQDFm8mIU7dnAoNJQJNWvySZcuhY4p\nLi6VK1Pjxg1ayDIycEYQiC0i88+hfHnOJyXRHniAQDDu6NkPqNhNR2qJaWhEkRZT5/MgbTIy73Hz\n/nZaTxtN9LL5WFv8+Z/NwcaGDQoFeoxfyPOAvYXFYw1EVVtbrExNmaPVkoSxJm2ohUWJlFA+aNeO\nprVqcTUmhokODtR3L5wIEXbnDu9/+SVbRZH6wJQ7dzgREsLBWbOQZZnYpCSuxsQUudac3Nozj8qV\ni/TiHOzsOK9U0j/XkJ8XBByKSNmv6+ZG+JI5nLxxA0uz+rTy9S22aPKzQBAEPB0c8HRw4L3cIvks\njYZzN28aNSpPzOfo9etoNbYYXzPMgDBUgkxvs70oUbCVd57b+h/G3NwKA8YXNx8gB7gmSdS3cXj8\nwFwsLKypXbvtI38fGnqYhQvfwtW1Tom3bEtC375zaNr0TRISblGt2kycnKo/1flfFsqSQp4DHSdP\nxu7GDY5izHVrBiRXr87OaX+/NXyWRsOHK1aw79IlKlhYMH/gQDrUMzaT3H/pEmNWrSIxK4s2vr4s\nHz68UFr+neRkmo4fj39uJuFZExOOz5lTKHvv4JUrdJ83jw7AdtGMdHklxigfwB78q01g84j3CZy0\nnAxNZP44E6UvVqpI7C0taeLnx6ELF9AbDFhbWGCWkUEtWWa3LLN65MgilTremjOHPy5eNJYUWFhQ\nxWDAD9gly6z86CO6Nm5MjigyauVKfj9/HiszM2b170/XIrYvYxITGbR4MZdiY3GvVImqlSpx8vp1\nrMzMmNmvH282bsyagwc5uW4d32mN2od6wFwQyN60CZMnbG9ejYmhw+zZ5IiisSYud5uyoYcH5c3N\nSUxP55VPPsEjO5vyssxxpZJDM2c+9dY/ACv2/8HUrbsQ9Tr6tmjK/Pe6lVoG7V5KCj2WrOVidCRO\nFe3ZNKJfIYUWSZLoMHsJR64lYZDqIUl7UCqyqe3qQkMPD87e1XMr6hxmpuV4o9ccmjXv84izlR69\nXscP60YRfHILpiZmvN5jJi1a9it03JGDa9j63UjaCXBOUOBYtwODR/3w1LRRc3IyiIo6+5AQczAt\nW/ajd+95T2X+/yKPSgopM2jPgSaffELqrVtsBaOHBti5u3Nizpy/PXfvBQvQX7zIPJ2OG0AvtZoD\nM2agUipp8emnbBBFagETTUwQ/fz4ccKEQnOkZGay68IFZOC1OnUKqVnkERkf///2zj0+5/L/489r\n9717M8OcbU6zk9NEchgqFNJJ+VGIcq4kRepb5FRSOVQiEVJIIanImZKKYczkuM1sTsOY2fk+Xr8/\nPvfuNnZw2HbPXM/Hw4PPfV/X5/P+3Lb7/Xlf1/v9erPjyBGm/bqZ4/G90QoMAKYTXOtrNo9/g3rD\n38JoiUHrpp2CjjpsJgnQ1PrnoS0b9jUYuK9dO0Lq16dtUJBjLyc7r8yfz7qtW1mDtm/WEyjv58eg\nzp1pW78+jexzXpw9m4TQUGaazZwEehsMrJk0KYdAssVqpdlrr9Hn8mUG2GysBd5BW7pMBHoZDPw6\ncSLnrlxh2hdf8HdmJjo0tZP7DQYSly694S+8s4mJWuRiV9gPqF6dxa++CmglBev278dksfBIs2bU\n8Cp8RYtf9+7luVkrSDf+BFTAw+15Rj/hx/u9ut/0uaSUNH5jPFHx3bHYRgDbqODxBlGzplL1mp8T\nKSUbDxzgbGIiLf39CfLxYX9MDBOXLcMcGck3UnIB6CYET3XsSL8HHqClv79Dk/N2o7jlS0ZzafM8\nFpnSSQB6GDwY8ObqXPuzxcYe4MSJMCpXrkXTpo8UqdC3lBKTKSPXJcGIiM2kpiYSFNSGKlXqqNY5\neaCyHEsQHjodb6EpZ4DmBr6yPy3vjY7m2SlTSEpPx8vDg5XvvkvLbF/EWfx97BjvLFzI5dRUOjdr\nxrRBg3A3GPgtPJwos5mqaLJTz1mtbD54EINezzM2G1ndtb4wm/GOiMjVvoqenvS7Rm8xN7KWnVb9\n+Sdn4j/FSiSgx4VfqOBSDe+KFXm1a2fmbWmNyfI4ZutvPEOGQ9V+KFpxeB9gisnEp6dOsWDYsDyv\nt23PHj4AsnLjPgGGnDlzXYuX3/btI9Rspg5aduggs5mN4eE5HFpsQgKpycmMtWeRvgwsQatt6wAM\nMZvZsH8/43r2ZG7dujwUG8u9FgsrdTpmDh58U180NStVokdICD3sCvvZKe/hQZ/7NcX5dfv3E3bi\nBG2CgmgdGJhjWTYvwk+eZOhX3xN/5QoPNgpi/ovPXxd1r9h5gHTjO0ALANKNn7By19Bbcmjnk5I4\neTEBi+0jtPWFF5ByMbujoq7btxVC8Oi9OWuh2jVowLmEBJZLiR9amstbUvLjiRMcPn2ag6dOEeTt\nTUhgILqgDIKC2lCjRsAtfbFHhP7ESlM6/milHaNN6fy+J3eH5uvbDF/fZjd9jVtBCJHn/lZGRgo7\nd67g229H4uLi4qiJa9euzw3X3d3NKIfmBKpXrEhMtuMYoEalSiSnp9N53Dhet9noDSxPS6PzuHGc\nWrSI8tm+3I6fO0f3KVOYYzTSCBj311+8kpnJopEjKe/mRqzdoQGc1Olo5OGBq05HrE6HtFgQwEmg\ngpsbhYF3pUq8xlFqsQobWq7Vv/a9oBkv9KLTPfU5dOoUn6w+z/B0o2NeNFqZAVn2FNCQ083dnZjk\nZMdxDGDIpQNCeXd3Tqank5UmcVKvp/U1zqF8mTIkWa1cBbzQsiXPgkPq7KROx31ly6IOWcM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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -945,6 +1108,13 @@ } ], "source": [ + "# Check the arguments of the function\n", + "help(visplots.svmDecisionPlot)\n", + "\n", + "### Write your code here ### \n", + "\n", + "\n", + "### Solution ### \n", "visplots.svmDecisionPlot(XTrain, yTrain, XTest, yTest, 'linear')" ] }, @@ -952,14 +1122,23 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Non-linear SVMs\n", + "**Tuning:** For more details and examples on how to tune linear SVM models using grid search and cross-validation you can use as a reference the following link: http://scikit-learn.org/stable/modules/grid_search.html" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Non-linear (RBF) SVMs\n", + "\n", + "In addition to C, which is common for all types of SVM, the gamma hyperparameter in the RBF kernel controls the nonlinearity of the SVM bounaries. The larger the gamma, the more nonlinear the boundaries surrounding individual samples. Lower values of gamma lead to broader, more linear boundaries.

\n", "\n", - "In addition to C, which is common for all types of SVM, the gamma parameter in the RBF kernel controls the nonlinearity of the SVM bounaries. The larger the gamma, the more nonlinear the boundaries surrounding individual samples. Lower values of gamma lead to broader, more linear boundaries.

In this example, we will use non-linear SVMs with the default values for C and gamma" + "At first, let us build an RBF SVM model using the default values for the hypeparameters `C` (`C=1.0`) and `gamma` (`gamma=0.0`). Thorough documentation on how to implement SVMs with scikit-learn can be found at http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 25, "metadata": { "collapsed": false }, @@ -980,7 +1159,13 @@ } ], "source": [ - "### Write your code here ### \n", + "################################################################# \n", + "# Write your code here \n", + "# 1. Build the RBF SVM classifier using the default parameters\n", + "# 2. Train (fit) the model\n", + "# 3. Test (predict)\n", + "# 4. Report the performance metrics\n", + "################################################################# \n", "\n", "## Solution ## \n", "rbfSVM = SVC(kernel='rbf', C=1.0, gamma=0.0)\n", @@ -995,21 +1180,31 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can visualise the classification boundary created by the RBF SVM using the following function. Once more, for easier visualisation, only the test samples have been included in the plot. And remember that the decision boundary has been built using the _training_ data!" + "We can visualise the classification boundary created by the RBF SVM using the `visplots.svmDecisionPlot` function. You can check the arguments passed in this function by using the `help` command. For easier visualisation, only the test samples have been included in the plot. And remember that the decision boundary has been built using the _training_ data!" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on function svmDecisionPlot in module visplots:\n", + "\n", + "svmDecisionPlot(XTrain, yTrain, XTest, yTest, kernel)\n", + "\n" + ] + }, { "data": { "image/png": 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P8EWLKFuoELXbzM10q8+mxrFjO5k9eyBXrlykUqX6tG373n0tLeFyuVi4cBhH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RzUaJM4Tlrg7NmdDBxYFJPJyPTp5yhb9u330NM6SlCIHRjWujU1z53jljUoZKVGw+7Wrdl84gQe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\nqgDKSyn1rze0Op94KyMIIfIBA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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1017,6 +1212,12 @@ } ], "source": [ + "# Check the arguments of the function\n", + "help(visplots.svmDecisionPlot)\n", + "\n", + "### Write your code here ### \n", + "\n", + "### Solution ### \n", "visplots.svmDecisionPlot(XTrain, yTrain, XTest, yTest, 'rbf')" ] }, @@ -1024,14 +1225,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Hyperparameter Tuning\n", + "#### Hyperparameter Tuning for non-linear SVMs\n", "\n", - "Proper choice of C and gamma is critical for the performance of SVMs. Optimisation (tuning) of the hyperparameters can be achieved by applying a coarse tuning (often followed by a finer-tuning in the \"neighborhood\" of good parameters)" + "Proper choice of `C` and `gamma` is critical for the performance of SVMs. Optimisation (tuning) of the hyperparameters can be achieved by applying a coarse tuning (often followed by a finer-tuning in the \"neighborhood\" of good parameters)" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 27, "metadata": { "collapsed": false }, @@ -1040,45 +1241,54 @@ "name": "stdout", "output_type": "stream", "text": [ - "The best parameters are: gamma= -11.0 and Cost= 13.0\n" + "The best parameters are: gamma= -9.0 and Cost= 9.0\n" ] } ], "source": [ + "# Define the parameters to be optimised and their values/ranges\n", "# Range for gamma and Cost hyperparameters\n", "g_range = 2. ** np.arange(-15, 5, step=2)\n", "C_range = 2. ** np.arange(-5, 15, step=2)\n", "\n", - "grid = [{'gamma': g_range, 'C': C_range}]\n", + "############################################################################################## \n", + "# Write your code here \n", + "# 1. Construct a dictionary of hyperparameters (see task 4.3)\n", + "# 2. Conduct a grid search with 10-fold cross-validation using the dictionary of parameters\n", + "# 3. Print the optimal parameters (don't forget to use np.log2() this time)\n", + "############################################################################################## \n", "\n", - "gridcv = GridSearchCV(SVC(), param_grid=grid, cv= cv.KFold(n=XTrain.shape[0], n_folds=5))\n", - "gridcv.fit(XTrain, yTrain)\n", "\n", - "bestG = np.log2(gridcv.best_params_['gamma']);\n", - "bestC = np.log2(gridcv.best_params_['C']);\n", + "# Solution \n", + "parameters = [{'gamma': g_range, 'C': C_range}] \n", "\n", - "print \"The best parameters are: gamma=\", bestG, \" and Cost=\", bestC" + "grid = GridSearchCV(SVC(), parameters, cv= 10) \n", + "grid.fit(XTrain, yTrain)\n", + "\n", + "bestG = grid.best_params_['gamma']\n", + "bestC = grid.best_params_['C']\n", + "print \"The best parameters are: gamma=\", np.log2(bestG), \" and Cost=\", np.log2(bestC)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Plot the results of the grid search using a heatmap" + "Plot the results of the grid search using a heatmap (see task 4.3)." ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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CjtsQAuxBtrTnNsDFwF+R3f10bkSc1UIo6yLiOyOp0GgJUTMzs1FPUh9wNjAd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\ngiGpxPLcwC1lh7BBMrE8MFB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BVwcPRsTTjSo58ZqZWa56OPF+kuTW8herjjdcSMiJ18zMbBgi4u3DqefEa2Zm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\nJl5JfynpNkmvSvpy1bn5ku6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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1086,37 +1296,41 @@ } ], "source": [ - "# plot the scores of the grid\n", - "# grid_scores_ contains parameter settings and scores\n", - "score_dict = gridcv.grid_scores_\n", - "scores = [x[1] for x in score_dict]\n", + "##########################################\n", + "# Write your code here \n", + "# 1. Fix the scores \n", + "# 2. Make a heatmap with the performance\n", + "# 3. Add the colorbar\n", + "##########################################\n", + "\n", + "\n", + "\n", + "### Solution ### \n", + "scores = [x[1] for x in grid.grid_scores_]\n", "scores = np.array(scores).reshape(len(C_range), len(g_range))\n", "\n", - "# Make a heatmap with the performance\n", "plt.figure(figsize=(10, 6))\n", - "plt.subplots_adjust(left=0.15, right=0.95, bottom=0.15, top=0.95)\n", "plt.imshow(scores, interpolation='nearest', origin='higher', cmap=plt.cm.get_cmap('jet_r'))\n", - "plt.xlabel('gamma (log2)')\n", - "plt.ylabel('Cost (log2)')\n", "plt.xticks(np.arange(len(g_range)), np.log2(g_range))\n", "plt.yticks(np.arange(len(C_range)), np.log2(C_range))\n", + "plt.xlabel('gamma (log2)')\n", + "plt.ylabel('Cost (log2)')\n", "\n", "cbar = plt.colorbar()\n", "cbar.set_label('Classification Accuracy', rotation=270, labelpad=20)\n", - "\n", - "plt.show()" + "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Finally, testing with the optimised model (best hyperparameters):" + "Finally, testing our independent XTest dataset using the optimised model: " ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 32, "metadata": { "collapsed": false }, @@ -1127,8 +1341,8 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.87 0.87 0.87 149\n", - " 1 0.87 0.87 0.87 151\n", + " 0 0.87 0.88 0.87 149\n", + " 1 0.88 0.87 0.87 151\n", "\n", "avg / total 0.87 0.87 0.87 300\n", "\n", @@ -1137,9 +1351,18 @@ } ], "source": [ - "rbfSVM = SVC(kernel='rbf', C=gridcv.best_params_['C'], gamma=gridcv.best_params_['gamma'])\n", - "rbfSVM.fit(XTrain, yTrain)\n", + "#################################################################################### \n", + "# Write your code here \n", + "# 1. Build the classifier using the optimal parameters detected by grid search \n", + "# 2. Train (fit) the model\n", + "# 3. Test (predict)\n", + "# 4. Report the performance metrics\n", + "#################################################################################### \n", + "\n", "\n", + "## Solution ## \n", + "rbfSVM = SVC(kernel='rbf', C = bestC, gamma = bestG)\n", + "rbfSVM.fit(XTrain, yTrain)\n", "predictions = rbfSVM.predict(XTest) \n", "\n", "print metrics.classification_report(yTest, predictions)\n", @@ -1150,14 +1373,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Logistic Regression\n", + "### 5.3 Logistic Regression\n", + "\n", + "Logistic regression is based on linear regression, but rather than the predicted output being a continuous value, it predicts the probability that a sample belongs to a class based on the values of the input variables. In the case of classification, we can use this to then assign the sample to the most likely class. For more details, see: http://www.omidrouhani.com/research/logisticregression/html/logisticregression.htm \n", "\n", - "Logistic regression predicts the probability that a sample belongs to a class based on the values of the input variables, based on a linear model. In the case of classification, we can use this to then assign the sample to the most likely class." + "In scikit-learn, you can learn a logistic regression model using the LogisticRegression object (http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html). As with linear regression, there are certain assumpttions that you might make or constraints that you wish your model to fulfil, e.g. whether or not you want a constant to be included in the function. You can also specify the way you wish learning to take place by using different solvers or how you wish errors to be penalised." ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 33, "metadata": { "collapsed": false }, @@ -1178,36 +1403,52 @@ } ], "source": [ - "### Write your code here ### \n", + "#############################################################################\n", + "# Write your code here \n", + "# 1. Build the Logistic Regression classifier using the default parameters\n", + "# 2. Train (fit) the model\n", + "# 3. Test (predict)\n", + "# 4. Report the performance metrics\n", + "#############################################################################\n", "\n", "## Solution ## \n", "l_regression = LogisticRegression()\n", "l_regression.fit(XTrain, yTrain)\n", - "net_prediction = l_regression.predict(XTest)\n", + "l_prediction = l_regression.predict(XTest)\n", "\n", - "print metrics.classification_report(yTest, net_prediction)\n", - "print \"Overall Accuracy:\", round(metrics.accuracy_score(yTest, net_prediction),2)" + "print metrics.classification_report(yTest, l_prediction)\n", + "print \"Overall Accuracy:\", round(metrics.accuracy_score(yTest, l_prediction),2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We can visualise the classification boundary created by the logistic regression model using the built in visualisation function `logregDecisionPlot`. As with the above examples, only the test samples have been included in the plot. And remember that the decision boundary has been built using the _training_ data!" + "We can visualise the classification boundary created by the logistic regression model using the built-in function `visplots.logregDecisionPlot`.
As with the above examples, only the test samples have been included in the plot. Remember that the decision boundary has been built using the _training_ data!" ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on function logregDecisionPlot in module visplots:\n", + "\n", + "logregDecisionPlot(XTrain, yTrain, XTest, yTest, pen_val='l2', c_val=10)\n", + "\n" + ] + }, { "data": { "image/png": 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Db7/9OGXLVuX++19Nc6LMrVtX8/ffq3G7YwEXbnc/Zs2qwc03DyAqyprC5b77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\nM4HeuFwradiwJgMHflno7x8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t2+dYtuwnnj9/QqlStT6IluOHJiQkkLNnd2Nv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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1215,9 +1456,22 @@ } ], "source": [ + "# Check the arguments of the function\n", + "help(visplots.logregDecisionPlot)\n", + "\n", + "### Write your code here ### \n", + "\n", + "## Solution ## \n", "visplots.logregDecisionPlot(XTrain, yTrain, XTest, yTest)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Tuning Logistic Regression" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1227,7 +1481,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 35, "metadata": { "collapsed": false }, @@ -1236,109 +1490,134 @@ "name": "stdout", "output_type": "stream", "text": [ - "The best parameters are: cost= 8.0 and penalty= l2\n" + "The best parameters are: cost= 0.5 and penalty= l1\n" ] } ], "source": [ - "# Range for gamma and Cost hyperparameters\n", + "# Define the parameters to be optimised and their values/ranges\n", + "# Range for pen and C hyperparameters\n", "pen = ['l1','l2']\n", "C_range = 2. ** np.arange(-5, 15, step=2)\n", "\n", - "grid = [{'C': C_range, 'penalty': pen}]\n", "\n", - "gridcv = GridSearchCV(LogisticRegression(), param_grid=grid, cv= cv.KFold(n=XTrain.shape[0], n_folds=5))\n", - "gridcv.fit(XTrain, yTrain)\n", + "############################################################################################## \n", + "# Write your code here \n", + "# 1. Construct a dictionary of hyperparameters (see task 4.3)\n", + "# 2. Conduct a grid search with 10-fold cross-validation using the dictionary of parameters\n", + "# 3. Print the optimal parameters\n", + "############################################################################################## \n", + "\n", + "\n", + "# Solution\n", + "parameters = [{'C': C_range, 'penalty': pen}]\n", "\n", - "best_c = gridcv.best_params_['C']\n", - "best_penalty = gridcv.best_params_['penalty']\n", + "grid = GridSearchCV(LogisticRegression(), parameters, cv= 10)\n", + "grid.fit(XTrain, yTrain)\n", "\n", - "print \"The best parameters are: cost=\", best_c, \" and penalty=\", best_penalty" + "bestC = grid.best_params_['C']\n", + "bestP = grid.best_params_['penalty']\n", + "print \"The best parameters are: cost=\", bestC , \" and penalty=\", bestP" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Now try these out to see how the performance metrics are affected." + "Plot the results of the grid search with a heatmap (see task 4.3)" ] }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 0.88 0.90 0.89 149\n", - " 1 0.90 0.88 0.89 151\n", - "\n", - "avg / total 0.89 0.89 0.89 300\n", - "\n", - "Overall Accuracy: 0.89\n" - ] + "data": { + "image/png": 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1SSGJNrX9L0m/BfZj4ReLpD2Bt9reUNI7gV8AO3RA3GCAtNn6XCbF7mmk9ESz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\niTFyKSrXtS4RrnQTSbdKek3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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "l_regression = LogisticRegression(C=0.5, penalty='l2')\n", - "l_regression.fit(XTrain, yTrain)\n", - "net_prediction = l_regression.predict(XTest)\n", + "##########################################\n", + "# Write your code here \n", + "# 1. Fix the scores \n", + "# 2. Make a heatmap with the performance\n", + "# 3. Add the colorbar\n", + "##########################################\n", "\n", - "print metrics.classification_report(yTest, net_prediction)\n", - "print \"Overall Accuracy:\", round(metrics.accuracy_score(yTest, net_prediction),2)" + "\n", + "# Solution\n", + "scores = [x[1] for x in grid.grid_scores_]\n", + "scores = np.array(scores).reshape(len(pen), len(C_range))\n", + "scores = np.transpose(scores)\n", + "\n", + "plt.figure(figsize=(12, 6))\n", + "plt.imshow(scores, interpolation='nearest', origin='higher', cmap=plt.cm.get_cmap('jet_r'))\n", + "plt.xticks(np.arange(len(pen)), pen)\n", + "plt.yticks(np.arange(len(C_range)), C_range)\n", + "plt.xlabel('penalisation norm')\n", + "plt.ylabel('inv regularisation strength')\n", + "\n", + "cbar = plt.colorbar()\n", + "cbar.set_label('Classification Accuracy', rotation=270, labelpad=20)\n", + "\n", + "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Plot the results of the grid search with a heatmap." + "Finally, testing our independent XTest dataset using the optimised model: " ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.88 0.89 0.89 149\n", + " 1 0.89 0.88 0.89 151\n", + "\n", + "avg / total 0.89 0.89 0.89 300\n", + "\n", + "Overall Accuracy: 0.89\n" + ] } ], "source": [ - "# plot the scores of the grid\n", - "# grid_scores_ contains parameter settings and scores\n", - "score_dict = gridcv.grid_scores_\n", - "scores = [x[1] for x in score_dict]\n", - "scores = np.array(scores).reshape(len(pen), len(C_range))\n", - "scores = np.transpose(scores)\n", + "#################################################################################### \n", + "# Write your code here \n", + "# 1. Build the classifier using the optimal parameters detected by grid search \n", + "# 2. Train (fit) the model\n", + "# 3. Test (predict)\n", + "# 4. Report the performance metrics\n", + "#################################################################################### \n", "\n", - "# Make a heatmap with the performance\n", - "plt.figure(figsize=(12, 6))\n", - "plt.imshow(scores, interpolation='nearest', origin='higher', cmap=plt.cm.get_cmap('jet_r'))\n", - "plt.xticks(np.arange(len(pen)), pen)\n", - "plt.yticks(np.arange(len(C_range)), C_range)\n", - "plt.xlabel('penalisation norm')\n", - "plt.ylabel('inv regularisation strength')\n", "\n", - "cbar = plt.colorbar()\n", - "cbar.set_label('Classification Accuracy', rotation=270, labelpad=20)\n", + "## Solution ## \n", + "l_regression = LogisticRegression(C=bestC, penalty=bestP)\n", + "l_regression.fit(XTrain, yTrain)\n", + "l_prediction = l_regression.predict(XTest)\n", "\n", - "plt.show()" + "print metrics.classification_report(yTest, l_prediction)\n", + "print \"Overall Accuracy:\", round(metrics.accuracy_score(yTest, l_prediction),2)" ] }, { @@ -1355,14 +1634,353 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Neural Networks\n", + "### 5.4 Neural Networks\n", + "\n", + "A neural network is a set of connected input-output units. During training, the connections are assigned different weights. This allows the classification function to take on highly complex \"shapes\" (equivalent to complicated mathematical expressions that go beyond the linear or polynomial models of logistic regression). This might also mean that the resulting model is difficult to interpret and map to domain knowledge. (NB. even though you might think of the second layer of a neural network as just a logistic regression model, the non-linear transformation in the hidden units gives the input to output mapping a non-linear decision boundary.)\n", + "
\n", "\n", - "A neural network is a set of connected input-output units. During training, the connections are assigned different weights. This allows the classification function to take on highly complex \"shapes\" (equivalent to complicated mathematical expressions that go beyond the linear or polynomial models of logistic regression). This might also mean that the resulting model is difficult to interpret and map to domain knowledge. (NB. even though you might think of the second layer of a neural network as just a logistic regression model, the non-linear transformation in the hidden units gives the input to output mapping a non-linear decision boundary.)\n" + "We will build our Neural Network classifier using the `multilayer_perceptron.MultilayerPerceptronClassifier` function. Further details on the `multilayer_perceptron` library can be found at https://github.com/IssamLaradji/NeuralNetworks. " ] }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 38, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on class MultilayerPerceptronClassifier in module multilayer_perceptron.multilayer_perceptron:\n", + "\n", + "class MultilayerPerceptronClassifier(BaseMultilayerPerceptron, sklearn.base.ClassifierMixin)\n", + " | Multi-layer Perceptron classifier.\n", + " | \n", + " | This algorithm optimizes the logistic loss function using l-bfgs or\n", + " | gradient descent.\n", + " | \n", + " | Parameters\n", + " | ----------\n", + " | hidden_layer_sizes : tuple, length = n_layers - 2, default (100,)\n", + " | The ith index in list contains the number of neurons in the ith\n", + " | hidden layer.\n", + " | \n", + " | activation : {'logistic', 'tanh', 'relu'}, default 'relu'\n", + " | Activation function for the hidden layer.\n", + " | \n", + " | - 'logistic', the logistic sigmoid function,\n", + " | returns f(x) = 1 / (1 + exp(x)).\n", + " | \n", + " | - 'tanh', the hyperbolic tan function,\n", + " | returns f(x) = tanh(x).\n", + " | \n", + " | - 'relu', the rectified linear unit function,\n", + " | returns f(x) = max(0, x)\n", + " | \n", + " | algorithm : {'l-bfgs', 'sgd'}, default 'l-bfgs'\n", + " | The algorithm for weight optimization. Defaults to 'l-bfgs'\n", + " | \n", + " | - 'l-bfgs' is an optimization algorithm in the family of\n", + " | quasi-Newton methods.\n", + " | \n", + " | - 'sgd' refers to stochastic gradient descent.\n", + " | \n", + " | alpha : float, optional, default 0.00001\n", + " | L2 penalty (regularization term) parameter.\n", + " | \n", + " | batch_size : int, optional, default 200\n", + " | Size of minibatches in SGD optimizer.\n", + " | If you select the algorithm as 'l-bfgs',\n", + " | then the classifier will not use minibatches.\n", + " | \n", + " | learning_rate : {'constant', 'invscaling'}, default 'constant'\n", + " | Base learning rate for weight updates.\n", + " | \n", + " | -'constant', as it stands, keeps the learning rate\n", + " | 'learning_rate_init' constant throughout training.\n", + " | learning_rate_ = learning_rate_init\n", + " | \n", + " | -'invscaling' gradually decreases the learning rate 'learning_rate_' at\n", + " | each time step 't' using an inverse scaling exponent of 'power_t'.\n", + " | learning_rate_ = learning_rate_init / pow(t, power_t)\n", + " | \n", + " | max_iter : int, optional, default 200\n", + " | Maximum number of iterations. The algorithm\n", + " | iterates until convergence (determined by 'tol') or\n", + " | this number of iterations.\n", + " | \n", + " | random_state : int or RandomState, optional, default None\n", + " | State of or seed for random number generator.\n", + " | \n", + " | shuffle : bool, optional, default False\n", + " | Whether to shuffle samples in each iteration before extracting\n", + " | minibatches.\n", + " | \n", + " | tol : float, optional, default 1e-5\n", + " | Tolerance for the optimization. When the loss at iteration i+1 differs\n", + " | less than this amount from that at iteration i, convergence is\n", + " | considered to be reached and the algorithm exits.\n", + " | \n", + " | learning_rate_init : double, optional, default 0.5\n", + " | The initial learning rate used. It controls the step-size\n", + " | in updating the weights.\n", + " | \n", + " | power_t : double, optional, default 0.5\n", + " | The exponent for inverse scaling learning rate.\n", + " | It is used in updating learning_rate_init when the learning_rate\n", + " | is set to 'invscaling'.\n", + " | \n", + " | verbose : bool, optional, default False\n", + " | Whether to print progress messages to stdout.\n", + " | \n", + " | warm_start : bool, optional, default False\n", + " | When set to True, reuse the solution of the previous\n", + " | call to fit as initialization, otherwise, just erase the\n", + " | previous solution.\n", + " | \n", + " | Attributes\n", + " | ----------\n", + " | `classes_` : array or list of array of shape (n_classes,)\n", + " | Class labels for each output.\n", + " | \n", + " | `cost_` : float\n", + " | The current cost value computed by the loss function.\n", + " | \n", + " | `label_binarizer_` : LabelBinarizer\n", + " | A LabelBinarizer object trained on the training set.\n", + " | \n", + " | `layers_coef_` : list, length n_layers - 1\n", + " | The ith element in the list represents the weight matrix corresponding\n", + " | to layer i.\n", + " | \n", + " | `layers_intercept_` : list, length n_layers - 1\n", + " | The ith element in the list represents the bias vector corresponding to\n", + " | layer i + 1.\n", + " | \n", + " | `learning_rate_` : float\n", + " | The current learning rate.\n", + " | \n", + " | n_iter_ : int,\n", + " | The current number of iterations the algorithm has ran.\n", + " | \n", + " | n_layers_ : int\n", + " | Number of layers.\n", + " | \n", + " | `n_outputs_` : int\n", + " | Number of outputs.\n", + " | \n", + " | `out_activation_` : string\n", + " | Name of the output activation function.\n", + " | \n", + " | Notes\n", + " | -----\n", + " | MultilayerPerceptronClassifier trains iteratively since at each time step\n", + " | the partial derivatives of the loss function with respect to the model\n", + " | parameters are computed to update the parameters.\n", + " | \n", + " | It can also use regularizer as a penalty term added to the loss function\n", + " | that shrinks model parameters towards zero.\n", + " | \n", + " | This implementation works with data represented as dense and sparse numpy\n", + " | arrays of floating point values for the features.\n", + " | \n", + " | References\n", + " | ----------\n", + " | Hinton, Geoffrey E.\n", + " | \"Connectionist learning procedures.\" Artificial intelligence 40.1\n", + " | (1989): 185-234.\n", + " | \n", + " | Glorot, Xavier, and Yoshua Bengio. \"Understanding the difficulty of\n", + " | training deep feedforward neural networks.\" International Conference\n", + " | on Artificial Intelligence and Statistics. 2010.\n", + " | \n", + " | Method resolution order:\n", + " | MultilayerPerceptronClassifier\n", + " | BaseMultilayerPerceptron\n", + " | abc.NewBase\n", + " | sklearn.base.BaseEstimator\n", + " | sklearn.base.ClassifierMixin\n", + " | __builtin__.object\n", + " | \n", + " | Methods defined here:\n", + " | \n", + " | __init__(self, hidden_layer_sizes=(100,), activation='relu', algorithm='l-bfgs', alpha=1e-05, batch_size=200, learning_rate='constant', learning_rate_init=0.5, power_t=0.5, max_iter=200, shuffle=False, random_state=None, tol=1e-05, verbose=False, warm_start=False)\n", + " | \n", + " | decision_function(self, X)\n", + " | Decision function of the elm model\n", + " | \n", + " | Parameters\n", + " | ----------\n", + " | X : {array-like, sparse matrix}, shape (n_samples, n_features)\n", + " | The input data.\n", + " | \n", + " | Returns\n", + " | -------\n", + " | y : array-like, shape (n_samples,) or (n_samples, n_classes)\n", + " | The predicted values.\n", + " | \n", + " | partial_fit(self, X, y, classes=None)\n", + " | Fit the model to the data X and target y.\n", + " | \n", + " | Parameters\n", + " | ----------\n", + " | X : {array-like, sparse matrix}, shape (n_samples, n_features)\n", + " | The input data.\n", + " | \n", + " | y : array-like, shape (n_samples,)\n", + " | The predicted values.\n", + " | \n", + " | classes : array, shape (n_classes)\n", + " | Classes across all calls to partial_fit.\n", + " | Can be obtained by via `np.unique(y_all)`, where y_all is the\n", + " | target vector of the entire dataset.\n", + " | This argument is required for the first call to partial_fit\n", + " | and can be omitted in the subsequent calls.\n", + " | Note that y doesn't need to contain all labels in `classes`.\n", + " | \n", + " | Returns\n", + " | -------\n", + " | self : returns a trained MLP model.\n", + " | \n", + " | predict(self, X)\n", + " | Predict using the extreme learning machines model\n", + " | \n", + " | Parameters\n", + " | ----------\n", + " | X : {array-like, sparse matrix}, shape (n_samples, n_features)\n", + " | The input data.\n", + " | \n", + " | Returns\n", + " | -------\n", + " | y : array-like, shape (n_samples,) or (n_samples, n_classes)\n", + " | The predicted classes, or the predicted values.\n", + " | \n", + " | predict_log_proba(self, X)\n", + " | Return the log of probability estimates.\n", + " | \n", + " | Parameters\n", + " | ----------\n", + " | X : array-like, shape (n_samples, n_features)\n", + " | The input data.\n", + " | \n", + " | Returns\n", + " | -------\n", + " | y_prob : array-like, shape (n_samples, n_classes)\n", + " | The predicted log-probability of the sample for each class\n", + " | in the model, where classes are ordered as they are in\n", + " | `self.classes_`. Equivalent to log(predict_proba(X))\n", + " | \n", + " | predict_proba(self, X)\n", + " | Probability estimates.\n", + " | \n", + " | Parameters\n", + " | ----------\n", + " | X : {array-like, sparse matrix}, shape (n_samples, n_features)\n", + " | The input data.\n", + " | \n", + " | Returns\n", + " | -------\n", + " | y_prob : array-like, shape (n_samples, n_classes)\n", + " | The predicted probability of the sample for each class in the\n", + " | model, where classes are ordered as they are in `self.classes_`.\n", + " | \n", + " | ----------------------------------------------------------------------\n", + " | Data and other attributes defined here:\n", + " | \n", + " | __abstractmethods__ = frozenset([])\n", + " | \n", + " | ----------------------------------------------------------------------\n", + " | Methods inherited from BaseMultilayerPerceptron:\n", + " | \n", + " | fit(self, X, y)\n", + " | Fit the model to the data X and target y.\n", + " | \n", + " | Parameters\n", + " | ----------\n", + " | X : {array-like, sparse matrix}, shape (n_samples, n_features)\n", + " | The input data.\n", + " | \n", + " | y : array-like, shape (n_samples,)\n", + " | The target values.\n", + " | \n", + " | Returns\n", + " | -------\n", + " | self : returns a trained MLP model.\n", + " | \n", + " | ----------------------------------------------------------------------\n", + " | Methods inherited from sklearn.base.BaseEstimator:\n", + " | \n", + " | __repr__(self)\n", + " | \n", + " | get_params(self, deep=True)\n", + " | Get parameters for this estimator.\n", + " | \n", + " | Parameters\n", + " | ----------\n", + " | deep: boolean, optional\n", + " | If True, will return the parameters for this estimator and\n", + " | contained subobjects that are estimators.\n", + " | \n", + " | Returns\n", + " | -------\n", + " | params : mapping of string to any\n", + " | Parameter names mapped to their values.\n", + " | \n", + " | set_params(self, **params)\n", + " | Set the parameters of this estimator.\n", + " | \n", + " | The method works on simple estimators as well as on nested objects\n", + " | (such as pipelines). The former have parameters of the form\n", + " | ``__`` so that it's possible to update each\n", + " | component of a nested object.\n", + " | \n", + " | Returns\n", + " | -------\n", + " | self\n", + " | \n", + " | ----------------------------------------------------------------------\n", + " | Data descriptors inherited from sklearn.base.BaseEstimator:\n", + " | \n", + " | __dict__\n", + " | dictionary for instance variables (if defined)\n", + " | \n", + " | __weakref__\n", + " | list of weak references to the object (if defined)\n", + " | \n", + " | ----------------------------------------------------------------------\n", + " | Methods inherited from sklearn.base.ClassifierMixin:\n", + " | \n", + " | score(self, X, y, sample_weight=None)\n", + " | Returns the mean accuracy on the given test data and labels.\n", + " | \n", + " | Parameters\n", + " | ----------\n", + " | X : array-like, shape = (n_samples, n_features)\n", + " | Test samples.\n", + " | \n", + " | y : array-like, shape = (n_samples,)\n", + " | True labels for X.\n", + " | \n", + " | sample_weight : array-like, shape = [n_samples], optional\n", + " | Sample weights.\n", + " | \n", + " | Returns\n", + " | -------\n", + " | score : float\n", + " | Mean accuracy of self.predict(X) wrt. y.\n", + "\n" + ] + } + ], + "source": [ + "help(multilayer_perceptron.MultilayerPerceptronClassifier)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, "metadata": { "collapsed": false, "scrolled": true @@ -1374,17 +1992,28 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.89 0.87 0.88 149\n", - " 1 0.87 0.89 0.88 151\n", + " 0 0.87 0.87 0.87 149\n", + " 1 0.87 0.87 0.87 151\n", "\n", - "avg / total 0.88 0.88 0.88 300\n", + "avg / total 0.87 0.87 0.87 300\n", "\n", - "Overall Accuracy: 0.88\n" + "Overall Accuracy: 0.87\n" ] } ], "source": [ - "nnet = multilayer_perceptron.MultilayerPerceptronClassifier(activation='logistic',\n", + "##################################################################################### \n", + "# Write your code here \n", + "# 1. Build the Neural Net classifier classifier ... you can use parameters such as \n", + "# activation='logistic', hidden_layer_sizes=2, learning_rate_init=.5\n", + "# 2. Train (fit) the model\n", + "# 3. Test (predict)\n", + "# 4. Report the performance metrics\n", + "#####################################################################################\n", + "\n", + "\n", + "# Solution #\n", + "nnet = multilayer_perceptron.MultilayerPerceptronClassifier(activation='logistic', \n", " hidden_layer_sizes=2, learning_rate_init=.5)\n", "nnet.fit(XTrain, yTrain)\n", "net_prediction = nnet.predict(XTest)\n", @@ -1397,43 +2026,41 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can visualise the classification boundary created by the neural network using the built in visualisation function `nnDecisionPlot`. As with the above examples, only the test samples have been included in the plot. And remember that the decision boundary has been built using the _training_ data!" + "We can visualise the classification boundary of the neural network using the built-in visualisation function `visplots.nnDecisionPlot`. As with the above examples, only the test samples have been included in the plot. And remember that the decision boundary has been built using the _training_ data!" ] }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on function nnDecisionPlot in module visplots:\n", + "\n", + "nnDecisionPlot(XTrain, yTrain, XTest, yTest, hidden_layer, learning_rate)\n", + "\n" + ] + }, { "data": { - "image/png": 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Bd2O78r/3epGRFra5SfPm9xAcvAKbbTAwlaCg7tx550CrwxJC+Ll0b0tQSgVo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\nNHaWN8UaY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hT1LSwjY7adbsIQIC1mKzDQG+wt+/G/ffP8jqsIQQmVyytyUopXy11pYc0WX1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CBW+RmFhiXhErhChhck2gSqlKgQiktIioVImIiKZ4PNLAvL/k5Hhq2uy4zP8r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\n7OuJFju+0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/3HIBZctGsm3bSqdDKVASEmKpGhBIiP1/GSAiwKUtcpVSBVZ2zkBXici3ItJd\n9ILUORMRQkPDOXp0n9OhFCi1a7dimwTwOXAAeDkgkLBSFSlXrrrDkSmlVPqyk0AbAB8DdwDbReQ1\nEdFnc52DN65uxo8/jmTTpgVOh1JghIeX4alhixhZtRENQ4ozvU4bBg9bREBAoNOhKaVUurJsRGSM\n8QHzgHki0hWYDDxoP4bsWWPMijyOsdC5pFEjGjfuQmzsQadDKVBq1mzBS6M3Ox2GUkplS3augZYT\nkcdEZB0wCHgYKAc8CXyVx/EVWuHhpVmyZCLx8bFOh6KUUuosZKcKdwVQErjOGNPdGPODMSbZGLMW\n+DBvwyu8fryjAx5PIuvWzXA6lPPSzp2/8+KL1zBwYHumTBlOSorX6ZCUUkVMdhLoC8aYl4wxp+67\nEJE+AMaY1/MsskIuNDiY2rVb8+uv4zl+/JDT4ZxXDhzYwdChV7J16/Xs3fs6s2Yt5NNPBzsdllKq\niMlOAn0mnWHP5nYgRdG021oTGhrOunUznQ7lvLJ27Y+kpNwE3AN0wuOZzJIlE50OSylVxGTYiEhE\nrga6A5Ei8i6QegtLBKBPh84FrsBASpWqjNsdn63pDxzYzvbtv1GqVCWaNOlSZLu5CwwMQiTOb0gc\ngYHBjsWjlCqaMjsDjQHWAUn239TXT4B25JpL2rW7ie++e4moqEWZTrf2tx8ZOqgF0R/fx1cje/Dh\n6D5F9tFo7dvfTGjoYgICBgMTCAnpxfXXP+l0WEqpIibLzuRFJMgY48gZZ2HrTD4jl3/0C3XqtOby\ny+9Nd7wxhvvuLMncpJNchHVEc2FoODcM/K7Idkp/9Og+pk17i9jYo1x00ZV07HiL0yGpfHL0aAzT\npo3i2LEjtGlzBZ063VZka2OKqgLfmbyIfGuM6Q38ns7GaYwxzfM0siIkKCiUP//8lY4dbyM0tPgZ\n471eD3HueNra/4cCLY0p0r0ZlSlTlbvuetvpMFQ+O3HiME89dTFxcb3x+Trzxx+v8++/e7nppvSa\naiiVtzKrwn3M/tsjnVfPPI6rSPmyz4UcOxbDsmXp31YbFBRCzUp1eVsEA2wG5hlD3bpt8jVOpZy2\natW3JCV1wOcbBQzA7Z7OTz/pgZRyRoYJ1BgTY//dnd4r3yIsAsqEh9OgQXt++21ahh0rPPrsbD4o\nX4vwwCDaBoXS955xVK/eLJ8jLVxiYw+yffsaTp48ku74uLijbN++htjYA/kcmcqI15uMMeF+Q8JJ\nSfE4Fo8wlC28AAAgAElEQVQq2jKrwo0DMrpAaowxJc6lYBGpBkwEKtjljDfGvHsuyzyffXlTM9q/\nuZ6lS7/kqqseOmN8pUp1eO297SQmniA0NFz7iD1H8+d/xqefPonLVYuUlN08+uintG173anxa9fO\nZMyYfgQE1MDr3UW/fm9wxRV3OxixAmjduidff/0KycktgcYEBw/jkkvucDosVURldgYaboyJAMYA\nTwNV7ddT9rBzlQw8YYxpArQDHhKRRrmw3PNSeGgokZGN8XozPpoWEYoVK6nJ8xwdPhzNZ58NJjl5\nFYmJ6/B45vLuu/1JTDwJQFJSPGPG3InbPZPExHUkJ6/hiy+e5dChXQ5HripUqMlLL/1CkyaziYx8\nlu7dO3H33VqFq5yRZWfyQM80DYbGicifwJBzKdgYcwDryVUYY+JEZAtQBdhyLss9nzVs2JFPPnmQ\nunXb0LBhR6fDKbQOHNiBy9UEjyf1oUJtCAgoz5Eje4iMbMyxYzGIlMI6rgOoi8vVjAMHtlOhQi2H\nolapatZswdChPzkdhlLZ6okoXkRuF5FA+3UbEJflXDkgIjWBC4HVubnc883bF8Mll9zOtm2rnA6l\nUKtUqQ5ebxSwzR7yG8YcpmzZagCULl0FY2KB1O9hO17vRipVqudAtEqpgio7Z6C3YlXZvmP/v9we\nlitEJBz4DnjMGHNGYh7mdx9olyZN6NKkSW4VXSC5XMFER2/E603G5QpyOpxCqVy56gwYMIoJE9rh\nctXE54vm0Uc/IywsAoDQ0OI8/vhE3nnnWgICquP17ubOO9+kQoWazgauVBEVFbUoy85mnJBlRwp5\nWrhIEDATmGOMeSed8UWiIwV/e48cocuoCXTufCdXXfWw0+EUaAcP7iQu7iiRkY0JCSmW4/ljYw9y\n+HA0FSvWJiKi7Bnj4+KOcuDADsqVq06pUhVzI2SlVC44HzpSeNoYM1JE3ktntDHGPHouBYvVO8ME\nYHN6ybOoiixblqZNLyMpKVdryQsVYwzjxz/GkiVf43JVweU6xvDhc4iMbJyj5ZQqVTHTxBgeXoa6\ndcuca7hKqUIqs2ugm+2/64C1fq/UPnHPVQfgduBSEVlvv67KheWe98qXr8n8+Z+wd+/mrCcugtau\n/YllyxaSnLydxMQNnDz5PG+91c/psJRSRUyGZ6DGmBn238/zomBjzDKy14ipyPm4W1m6/XMFq1f/\nkOOzqqJg374tJCdfBaTeityHgwe1M3mlVP7KMoGJyC9itelP/b+MiPyct2GpBg3a8+uvH7Fjx1qn\nQzmDz+fj++9HMnBgB4YMuZpt21bma/lVqzYiKGgucMIeMpWKFYvsLcRKKYdk5wywvLHa9ANgjDkK\naIuKPPZep1D6XXwB69bNcDqUM0yZMozp06ezd+8I/vqrLy+/3JM9e6JytYwFCz7j7rtrc+edlfjw\nw0dO62CideueXHJJV4KC6hIW1oKIiBE8+eTnuVq+UkplJTu3saSISA1jzD9w6p5NX14GpSwRYWEc\nPRKDMcbRxzUdO7afI0f2UKlSPcLDS7NgwUTc7jmAddbn8Wxm5crvqFYtd24x2rDhZz79dBgez/dA\nBZYtu4eQkBfo3/8NwOqR6d57x3D99Y8TF3eUqlUbnVUrXKWUOhfZOQN9HlgqIpNFZDKwBHgub8NS\nALd27MiBbXP5+eexjsUwa9ZYHn64CS+//AAPPFCfP/6YR2BgEP59aQQEnMTlCs61Mn/7bTYezyNA\na6A6Hs9I1qyZfcZ0FSrUonbtVpo8lVKOyPIM1BgzV0RaYfVrZoDHjTGH8zwyRb3Klbm1Y0fWHdvv\nSPkxMX8xZcpLJCevJzm5BrCEt966kb59hzFlSl/c7mcQ2U1o6HQ6d16Ta+VGRJQiMHAHKSmpQ3ZQ\nvHipzGZRSql8l50qXAAvcAjrWc6NRQRjzJK8C0ulKl28OL+vnMVll92d7/2wxsRsw+VqjcdTwx7S\nCZ8viLZte1KmTGVWrJhBeHgE11+/grJlI3Ot3Kuvfoj589uRkPA/UlIq4nJ9Qb9+RatDDaVUwZdl\nAhWRe4BHgUhgA9aZ6Eqga96GpgDuv+IK1u/axbx5H3L77SPztewqVerj9a4FdgM1gcUEBCRTqlRF\n2rW7gXbtbsiTckuWrMDbb//GkiWT8HgSadVqgT77VClV4GTnDPQxoA2w0hhzqYg0BF7L27BUqoCA\nAOpVrkzMMXe+l12lSgNuvXUoX37ZEperBj7fXgYN+irL651er4dnn+3CP//8CQTQps1VDB6cszPI\niIiyXHPN46cNO3hwJ6NH38W+fRupUKEejz32MdWrN83paimlVK7ITiOiJGNMIoCIhBpjtgIN8jYs\n5a9z48YsX/41S5d+me9ld+/+IO+/H8WQIeMZN24bzZtfkeU8w4dfwz//eIGNwFJ++20FEycOPqc4\nvF4PQ4deza5d1+B2R7Fnz10MG3Y1CQknTpsuIeE427f/xpEje8+pvOxISDjB9u2/cfjwnjwvSylV\n8GQnge4RkdLAdOAXEfkJq05P5ZOL6tVjSM9ubNmy1JHyS5euTN26bQgPL52t6bdv3wi8BdQCWgBD\nWLly7jnFcODADhISfBgzCOs25Lvx+aoSHf3nqWm2bFnKAw/U5+WX7+PRR1vw/fdvnFOZmdm2beWp\nsh577AK++eaVPCtLKVUwZZlAjTG9jDHHjDHDsB6i/QlwfV4Hpk53RfPmrF8/m19/He90KFkKCgoG\ndvgN2Ubx4lnfauLzpbBkySSmTh3O2rU/4f+koGLFSpKScgRI7dMjgZSUGIoVs1rnGmN4442bSUz8\ngsTE30lO3sS0ae+yc+fvubZeqYwxjBzZl8TE8XZZW5g582N9jqtSRUx2W+ECYIxZlEdxqCw0r1GD\nF3tcxpzoTU6HkqV+/Yby4YcPYT1z4DjwA/ffvzDTeYwxvPnmbWzaFI3b3ZWQkGe44orV3HHHCADK\nlKnCpZf2Z/HiS/B4ehIc/AstW3Y91XlDQsJx3O44IPV5BJUJCOhATMxWatdueUZ5bncC06a9yZ49\n26lbtzk9ejye7eevejyJxMcfBHraQyoAndm3bwv167fL1jKUUue/HCVQ5azQ4GB27FhDbOzBAv18\nyq5d76J06SrMnj2GgIBAgoOv4/33H6FChercffcb6T6YeseOtWzatBa3OwoIwe1+grlza3HDDU8S\nHm49Uuyuu0bRvPl0oqM3UrnyQC6+uM+pHpqKFStJSEg4Xu8c4GpgPz7fcqpUefqMslJSvAwbdg3R\n0eVJTu7OH398zV9//cbTT3+TrR6fgoPDCA+vyIkTP2El0YPAYiIjHzrbj6zQMMawYP7HrJg7Fpcr\niG69h9Gq1bVOh6VUntCnoZxH7ujUibYVg5g27VWnQ8nShRdezfPPz8XjCeD3333ExLzFn39ewHPP\ndSE+PvaM6RMSYgkIiARC7CFlCAgoQULC8VPTiAht2/bipptepEOHvgQEBJw27umnpxIW1p+wsAsJ\nCmrKDTc8lu7Z565dv/PPP9tITl4EPIjH8wd//DGPI0ey1xhIRHjqqW8oVuw+u6zG9OhxL/XqXZSD\nT6hwWjj/E3794gnejP6TF3au49PRN7Nx43ynw1IqT2TrDNTu/7auMeZXESkGuIwxJzKfS+W20OBg\nOjRsyA+78/+WlrMRHx/L1q2LSEk5CgTh83UgOXkRW7YsoXXrnqdNW7t2K0S2AV8AVxEQ8AmlSpWm\nXLnq2S6vYcOOjBv3F/v3/03p0pUpU6ZqutMdPrwXrzcWGAP0AD4jJeUVEhJigeyVV79+Oz744C/2\n799GqVKVcrUjifPZip/H8r47gSvt/w94Evhh/sc0a3aZo3EplRey8zize4FvgY/sQZHAtLwMSmWs\nabVqrF79HWvWFPyvIDDQBaQAifYQgzHp95sbHl6GYcPmULXqB4SENKFOnUUMGzaLgIDAHJVZrFhJ\n6tRpnWHyBDh0aBdQG7gbq0XvM0A4hw/n7NaXYsVKUKdOa02eflyuYL9ekq0HzrmCQjKaXKnzWnbO\nQB8C2gKrAIwx20SkQp5GpTJ0SaNGvNb7On74fTZt2/ZyOpxMhYaG07HjnSxffhVe7z0EBi6kTBk3\njRt3SXf6mjVbMHr06gyXZ4xhzZpp/PPPRqpUqUf79qdX42ZXuXLVsK5bJgJhwDHgRIFLhIcO7WLF\niqmICO3b30z58jWynMcYw8qV37J37xYiIxtx8cW98/VJPt16D+OBt2/igCeRk8CbIcV59pqB+Va+\nUvkpOwnUbYxxp+6EIuLC6lReOSQkKIjDh3eSnOwmqIAf3VeoEIkxPwDvY8wxSpSol+3WrmlNmPAk\nixf/itvdk5CQMaxZ8zNPPPF5ugkiLu4Y338/kn//jaFZs/ZcccW9p5Jtu3Y3Ubr0ixw7dhFwDTCV\n6tVbUKNG87Nf0Vy2Z08UL7zQFY/nJsDwww9tee21JVSpknkfJuPGPcTKlatwu7sTEjKS9esX8tBD\n4/InaKBly+48+Mwsfpr/MQGuYJ695glq1myRb+UrlZ/E/167dCcQeRPr5rs7gIeBB4HNxpjn8zw4\nEWOmaifiaR2Ni+Pm0aMJrnYZ/fqNdjqcDHk8Sdx5ZxlSUnYAlQEvoaEteeqpMTRtemmOlnX0aAyP\nPNKU5OSdQCkgkZCQBowYMfuM7vySkuIZNKgdR4+2x+ttR0jIR3TqdDH33PPfZ+X1evniiyfYuzeK\nunUv4pZbRpzV2WxeeeON21i7thVgnb2JjOSii7YwcODnGc5z8OBOBg68mOTkHUA4EEdwcF1GjVpO\npUp1Mi1vxYpvmT79A8DQo8e9XHLJrbm1Kkrluj59BGOMcw9JtmXnDPQZ4C6sftnuA2ZjdaagHFIm\nPJw7OndmxC+/kZh4krCwCKdDSldSUhwiwUAle4gLkRp2Y52cSUg4TmBgWZKTUx9rFkZgYJV0l/Xn\nn/M4caI8Xu+HgOB2X88vv1Rg2bJvuPzy/tx228u4XC7uuuu9s121PHfyZCzwX9Izpg4nT67MdJ74\n+FhcrgokJ4fbQ8IJDKyY5ef9228/8sEHA/F4PgAC+OijhwgMdNG+fZ9zWwmlCrns9ESUYowZb4y5\nyX59bLI6bVV57vo2bWhUwsPkyU85HUqGIiLKUqlSAwICngcOAFMxZg316uW8s4FKlepQrFgAIqOw\nrl9+TEDAPqpXP7Pa1etNxjoDSz1ADQMCSUycx7x5C5g+/a2zXaV80779NYSEDAf+ArYQEvIyF1/c\nPdN5IiMbERwcj8h7wEFE3ic4+ARVqzbKdL558ybj8byK1SL5GjyeN/n550m5tCbKKQcO7GDnznW4\n3QlOh1JoZZhARWRjJq8/M5pP5Y+IsDBuvOgiEhNP5luZHk8Sa9fOYOXKbzlx4t8spxcRhgyZToMG\nfxAS0pRKlV7nxRdnULp05SznTU52s27dTFasmEps7EFcrmCGD59DrVqzCAlpQrVqnzF8+FyKFStx\nxrzNml2Gy7Ue6+rDUuBmrA4PmuJ2v8SqVbNzvO757aqrHuDaa3tRvPhlFC/ejeuu68sVV9yT6TzB\nwWEMHz6XGjW+JSSkCTVqfMPw4T8TEpJ5N4rWNWn/trPpt5RW5wdjDB988CBPPtme4cMH8PDDTYiJ\n+cvpsAqlDK+B2vd+ZsgYszv3wzkjBr0GmonFmzfT8+2xPProl9l6Ssq5SEw8yXPPXcqRI2FAaQID\n1/HKK/OpWrVhrpeVlBTPq8+3I+Lf3ZQXYbUE8OxLy3L06LIDB7YzYcIz/P33OhISIoF5WGeiH9Ks\n2c8MGZL+bUD//PMna9fOIDS0OJ06/Y+IiLK5sk4F2bZtK3nppZ54PM8AgQQHv8pzz31L48adnQ5N\nnYVVq75j7NgRuN1LgAhExlK9+hTefHOZ06HlmoJyDTTLRkR5WrjIp1jNIA8ZY854YrIm0Ky9P3cu\nU7bG8/jjX+dpOVOnvsz06Vvwer/Eqhp9l0aNfmb48Fmnpjl8OJpp097ixIlYqlatzoEDMQQFBdOj\nx4PZeiB2UlI806a9wW9r5hIWs54OJoB4AqlAAivqXcSzI3LeWfuhQ7t4+ukOuN3XYkwoLtcUXnpp\nHrVqXXjGtBs3zmfkyL54vf0IDDxA8eLLGTVqNSVKlD81zZYtS5k37wsCAwO5+up7qFOndY5jSrVh\nw1wWLpxKSEgoPXs+QmRk5lWteWn79jXMmfMJxhiuvLI/DRq0dywWdW6+++5lvv02CWNG2EP+JTi4\nAZMnH3U0rtxUUBJoho2IRGS5MaaDiMRx5m0rxhhzZt1Zzn0GvAdMzIVlFUnlIiKIj9+Lz+fL01ak\nu3dH4fV24r/rih3Yt++/RjjHju3nqafak5DwP3w+H/A+MAI4wapVXRkxYkGmSdTrTebFF69k377q\nJCffAGzmbwYBlQjlOcJjtmUZo9udwKRJLxAVtYJy5SK5666RVKpUh7feWsuyZV/h86XQrt1KKlWq\ny7Fj+/nkk8Hs27edOnWa07//SD777AU8nvFAL3w+OHnyPubOHUefPi8CqQn2Vjye5wEPq1dfzdCh\ns6hbt22OP88VK77lgw+ewON5AZEjrFrVmddfX5rlbSp5pW7dtjzyyOnrceTIXj75ZDD79++ifv2W\n9Ov3erpV5qpgsa6Fj8DtfgbrDHQqlSs3djqsQinDBGqM6WD/Dc9omnNljFmaVVWxylyXJk14e+ZM\nJk9+ijvuGJVn5SQmHgPGYV1PLAG8hdfrYf/+vxk9+i727v0Dr7cUcCfwANaxkfXUO7fbx+zZH3H/\n/e9nuPzt21dz4MAJkpMnA89h9d8xFIAk6iIpd2YZ41tv3UFUlJCcPIqYmOU89tgFBAcHU69eRx57\n7GNKlrT6//B4Ennhhcs5erQHKSkPcOjQRPbs6cHJk0ewOt16AChGSkprfvhhCitXzuKxx8bz3Xfv\n4PGMAv5nr1cwP/44liefzHkC/fbbt/B4JgBXYgwkJSXx888f079/3n2H2REfH8un793On1GLiPME\n4OMxjHmYQ4c+Yd++Xrzyyq/52jGDyrmLLrqR9esXsnx5XQIDKxIcfJInnji35/Gq9GV5G4uITDLG\n/C+rYcoZlUqV4unrruONZbvytJyKFesRFZUMVMVqe9aS8PAyDB16FcePP4ox3wLfYT1OrApWK9hU\n4SQnezJdvtebjEhxe9nJQJnT5i+excO8k5Li+fPPWfh8sUAIxnQEFuJ238SWLZt59dXejBy5GICd\nO38nLi6UlJTX7bIvJiamBoGBBogB1mC19O2Oz/cI+/bVY/jw7lSs2DDH65XZ+p65LOe7l/7o7d40\n3rKEh7weBtCUBF4GwOttx+7dlTh2LCbTbhKV80SEBx4Yyw03PEl8fCxVqzbMsiGZOjvZqfM7reWG\n3RNRq7wJR52N8NBQdu36neg8fFZop059EFkPlMZKkFG0aNEetzsUYx7D6lP2IaykUAurn9mfge8I\nDn6Vyy67LdPl163blrCwowQEDAEaAm8CXwHzCQm5l27d+mU6v9VnrsG/313rfQVSUkbxzz9rSEqK\nB6xWp8YkYPXTC+DBGLc9/l2sDuXbAIPtZfTDmDo0b96O4OAnsW6Fnk5w8BC6dTu748hu3e4gJOR+\n4Ffga4KD36JLl1vOalm5xRjD2qiFvOf1UAkIIAHw2WPdGJOsrXPPIxUr1qZ27ZaaPPNQZtdAnwOe\nBcJExP9eiWRgfF4HlmqYXyOiLk2a0KVJk/wqOkeSvV4mLVnCniNHad+gPlc0z79u4bq1aMGDnbcx\ndeqLDBr0Q56UsXPn7wQGtsLrnQ0EIfI0MTFb8XoPYXUZXgKIQ2Q/FSoEU7VqSw4fHkFQUAi9e3+a\nZYvO0NDivPrqAiZMeJqYmF8pV64zcXETSE5Opnnzq/F6E5k9ewydOt1BeJqz0f37/2b16u+pXbsd\n0dFX4fHcByzC6uP2cmAPIkJwcCgAtWq1pGrVSkRH9yU5uTvBwd/QtGkXfv/9V2AHkPrdbcd6dkIS\nKSl7adfuRqpUacjs2W8QEBBIr17v0bJl5vdmZuTaax8lMNDFggUvERISxs03f+X4w7hFhPDgMHYk\nxdEeqM0BNtIbw7WEhEzmwgt7ntagKj8ZY/jttx/ZvfsPKleuS4cOtxSonqNU3oqKWkRU1CKnwzhD\ndrrye90Y80yeBWBdA51xPrfCTfH56Dz0DdbvDifR04Gw4C8ZcmMXnrk+/x4kvHjzZnqNGc/gwdPO\nqlFLVt577z6WLr0A6/ogwO+UL9+fZs06sXz5UjyeawgOnkvbtq145JHcO77asGEuo0bdYbeM3UdE\nxG+MGrXq1EO2d+5cx9ChV+H13ooxCQQGfkvDhpeyefMiUlIaAF2Az2nZsgPPPPP9qeW63QlMnz6K\n6Oi/qVu3OT16PM6AAdVISvIC/YA9wBygPyEha2jevDaDBk0u9Nf/Fi6YwLRPH+WO5CTWuULYEFqK\nmvW70KhRG6655hH7CTv577PPnmLBgtm43dcTEvIrLVrU5cknJxX670Olr6C0ws3WbSwiUhqoB4Sm\nDjPGLDnnwkWmAJ2BssAh4EVjzGd+48+LBDp3wwZ6vz2DuKT1QCCwB1dgPRImfUaQK/9+cN6eOZMv\nNx3hmWdm5vqyZ858h6+/noPHMwMIIjDwWVq02M3TT09h9eof2Ls3ioAAFzs2zCEh7ijN2/XmupuG\n5PhxZKk2bVrIV1+9xq5dG0lJuQ6rAZPgcvWnd+8G9OplHdMNH96TqKhrgXsBEHmBZs028Ndf+3G7\n78fqAakOgYF3M3nyiUwTwEsvXU9UVGWMqW6v4ye0aFGPDh36OnrGk5zs5ssvh/LHH4spXboiAwa8\nlqe3vGzZspTNmxdTokR5One+g+DgMDZunM+UKa/jdifStWtfund/KN+SV2zsQR58sAFe7y6sSwhJ\nhIQ05OWXp1Oz5gX5EoMqWApKAs1OI6J7gEeBasB6oB2wEuh6roUbY5y96JNLYuPjEWpiJU9IbWiT\n6PGkm0C/XLKEoZMmEefxcH2bNrxz772EBp/7taWGVaqQvD7mnJeTls+XQseOt7JhwxK2bq1DQEA4\nJUsGcf/98xAR2rW7kZiYpgx/uhWj3PHUAZ6e8SZfJ8Rya793cliWj6iohbz+el+Sk98FKgBPAG8B\ng/B6a3P06IFTt+2c2WdsXU6eXIhIbSC15x4fxtxFcrI70wT60EPvM2RIN+LjhZSUWJo378SgQZPP\n+iAgt7z//n2sW3cYj+cNYmLW88ILXRk9+vds9eh0Nho1uoRGjS459f+2basYOfIWPJ53gfJ8/fUT\npKQk07PnE3lSflpWP8il8XpTq+5DCQyMJD4+530qK5WbsnN69BhWi4qVxphLRaQh8FrehnV+6diw\nIYaJwA9Ae1yBb9C0Wm1KFDvz4v2iqCieGj+e7z0eqgIPrlzJYJeL9+6//5zjaF6jBocOfcs337zI\nzTe/dM7LA1i7dgZjxtxJSoqX4sXL8eij71GhQk0iIxuf1qBkzZpp3O51M8D+/yt3Ahct+jxHCXTD\nhrmMfbs3HncCLhNGMg2AlsDHwACss4+R/PKLsGTJVwwaNIWLL76GgweH4HbXBBIICXmdzp0fYsqU\nl7Ce+34xgYFvUL16W0JDi2daftmykYwZs559+7YQHFyMypXrOV5F6POlsHr1FHy+w0AExlxCSspK\nNmyYy6WX9s+XGBYv/hqP5wmgLwBu94fMm/dQviXQChVqERERisczEmP6AbMQ2Z1uZxhK5afs1Ekl\nGWMSAUQk1BizFXDmbu8CKrJsWX5+fiB1Kj5FeGgjOjZczs/PP57utHN//537PR7aYZ3Sj0pOZvba\ntQBsio7mq2XLWLUt604DMorji7tu5u+/c95jT3oOH45mzJgBuN1z8HpPcPz4S4wf/zjVqjU9ozVm\nYGAQcfLf5hQHuHJwvezo0RjGvXUTs5LiSDA+PieeMLpgtcSNxrq95DHgS3y+kyQmTuXNN2/hssvu\npEuXdoSEXExoaDd69RpA9+6P8Pzz06lQYRihoc1o1GgHzz//XbbiCAoKoWbNC6hSpb7jydMiiAQC\n8X7D8rev2qCgtH3lxuVr+S5XEMOGzaFOnXmEhDQhMnI8w4fPoVixkvkWg1Lpyc4v3B77Guh04BcR\nOQbsztOozkPtGzRg+3tZn5iXCg9nq8sFXi9gtfksVawYH8+bx5CJE+kcEMAaY+h7+eW8dmfWnQek\nFexycfDgTg4fjqZcueo5nt/f7t1/EBjYBrjIHnI7SUmDiY09QNmykadN27Hjrbww7VWeSjhOfV8K\nr4UUo/sNL2S7rOjojTQLdNHB/r8P8DAnaUF//iCZrtc8zs8/T8frvc6eogtebzU2b17MihXfExDQ\nBmMSWLBgIt263U3Dhh14//0/zmn9C4KAgACuuWYgP//cHbf7YQIDf6d48W20atUj32Lo1u0eFizo\naN+yVI7g4BH07p2/T7SpUKEmr746P1/LVCorOeoLV0S6YN2vMNcYc3Z3kOfA+dKIKCeOxsXRbtAg\nWp08SWRKCl+4XIx75BH6jxnDeq+XOlg3XzQNDubnV1+lafWcJcEUn4/BkyaxKMbw7LOzsp4hHcYY\nFi6YwNplX7F+y2ZSfH8BJYG/CApqw2efHSQ4OOyM+Q4fjmbWtNdIPHmY5u1uon37m7Nd5p49UYx8\ntg1bPImUwTqwuBDYB0wG3q/egs3RfwFbgRrAv0Ad6tZtzc6dl+PzPQcYXK4H6NatBDfd9Czffz+S\nf/+NoVmz9lxxxb353gho+fJvWLVqNiVKlKZXrycpV67aWS3HGMP8+Z+yYcNiypatyI03PpXvt5Ps\n3buFGTPeJykpkS5denPhhVfna/lK+SsojYgyexpLmXRH2Iwxed4zcWFMoADH4uKYtGQJcUlJdG/Z\nkojQULoOHsw/bvepaS4vVozBjz/OlRfkvJXhgk2bePT7pQwduuCs4pv65TNsnfseA90JvE0YUZQg\nJE9/qdMAACAASURBVPQSfL4lDBgwkq5d+53VcrPy9cQnWfPLh7RI9rDK5+U1rGZAG4ErwkpyJLmY\nfeLeEViFy+WiTJmSHDr0DlZjboBJXHDBDGJitnD0aHu83naEhHxEp04Xc889o/Mk7vTMmPEOU6eO\nw+1+ioCAvylW7Evefvs3SpWqlPXMSqlMFZQEmlkV7u+c2Yl8KgPUzv1wiobS4eE82v2/G/A9Xi++\noCC+drvpCywH/khJoVkOzz5TVS9XjujoP/n11/Fcfvm9OZrXGMPMWaPZ6fVQGehPIu2DDOGXlOPq\nqxcSGZmzTqmPHdvPx6Nv5u9dv1OhdGX6PTKZevUuOjU+IeE47713H1FR8ylevDzX9h3BokUTCftn\nI9fjxQu8jguPD4KCDF7vU1i9Hl1KYOBQGja8nNjYMXg8FwFuQkI+olSpRmzdWh6v90NAcLuvZ/78\nyvTvPzLfrt398MNbuN1zgKb4fJCUdIjly7/mmmvSvzaulDr/ZFinZYypaYyplcFLk2cuCna5+GnI\nEJ4tWZKIwECuCw1l4sCBVCmTaSVAhupWqsTUR+5j8eKcP+TGGIPP58O/vWq1gADq1Gmd4+T5f/bO\nMjCKqwvDz8xanBAI7m7F3YK7Fy1WoKWlSHFvseDBraVI6Ye7FCjukOJOCASChUAghOju7OzM92OS\nlBRilAJt9/m3O/feObMyZ+6957xHVVVmTahLbf9T3LRE4R18mxkT6vLixeP4NjNnduPSJSfM5ss8\nfz6HNWsmki1bMYKpQCZ0OKJjCw68sERjsbzAaJyITvcVBsNIrNaXHD++ElH8HVFMiyhmpHz5wmTM\nmBNV1fFH5RhtuVlRbK8b+TehKAm1blXVBVn+23c9/jZUVeXRIz/u3r3wj74OO3beJSkKkxQEoTlQ\nHW3meURV1R1/q1X/QUrlzs2dxYsJi4oijZPTX96vK5otG8HBt9m+3YdmzQanuJ8oilSv1IY2Z7Yy\nSorhIgL7RT2TSqZ+zysi4jmPgm8xWZER0AKDlgkCt275Ur58S1RV5erVXbEpGi5AZlS1NWnSOGIT\ntqOq6VFwRCYauIqimBCEWjRt2pydO1ehKBeAnEjSaPLk8aV9+xHMmNERyILFcgtoCgzFYJjNJ580\ne+O+7d9FjRpdOHiwCxbLBOAWev0aypc/+d7O/y6x2WTmTWvGnWtHcBV1WF3TMXzCCTw8snxo0+zY\n+aAke5cWBGEKmpDCNeAG0E8QBHse6N+AIAikdXF5J8Eu2dOnZ2v/r/n9903JN/4T3Xv/jEu9XnyT\nrQhrP6nN6Imn3upm6eDgglVViZtvysA9VcHJyR3QrtdkSoMWMgSgIoq3iYx8jk5XArgH3EVTGRoA\nZMJi6Yyf3ylstrZALkBAUQYTGHiaWbO6EBOzgpiYC8AtBMGXDBl6U6tWbgYOXJEq2+O0V9evH8ex\nY6tia5ymnK5dJ9O0aX2yZx9F4cJbGTt2N5kz50/Q5sGDa2zePIlff51FeHhIqsZPLQ8f3mDLlsns\n2DGDsLAnqeq7d89CjNeOEChFc9McQc1nD5g01otdu+bYxQzs/KdJiRbuFaCkqqq22Nc64OKbtGvf\nuXH/0iCi94Wvvz8t5i1n8uQzrwmwvy+2bZzAiW1TaC/FcMzohDVfeQZ+tz/+IeHQoRUsXToSq7UL\nBsNVMmZ8So4cRThxoiRa3ifAVaAZcBujsQUVKqTh9OlALJZDaIsov5I27UAiIoKR5T9Kgjk4tKNn\nz+ZUrfpZqu1esWIE+/dvx2Jpicl0gE8+yc2QIaveWW7o9etHmTO5IV2tFp6JevY7pWG8z6W/Jcjo\n5s2TeHs3x2rtjCiG4eCwDx8f3xSXJVu2sDuNDi+nL1odmq5AF+CBwQFf1/SM87n8wX5fdv6bfCxB\nRCmZ6qiA+yuv3Uk8uOg/RYwk0XvJSooMGEt979ncDg7+0CYloEyePDQqnJ3Zs1OeTgJgNkcyclgV\nOn2WgS+65eDs2e0JjiuKwqZNUxk4sArffdcQf/9TiY7VvPV3fDZoE3fajKVEj/kMGL0XURTx9d3E\n0KE12bVrGTlz5sHFZSXp0t2jV695REY+AdYDZrSf2mrgBYKQjfTp7/PFF4vIl88dB4eyODi0xmTq\nRr9+izGZXNAE4AEeoygnyJKl0BvtCgsLZqFPK8b2L8ySeZ2IjHwRfyw8/Bl79izEYjkGeGOxzOHc\nuX306VOaVau+j63l+dfY/PO3LLJEM0ux8T/ZQqvIUH7b+dejhMPDn/HjrHaM7V+YH2e1Izw8hBUr\nxmCxzERRZiLLy4iObsu2bSlXiMqcqySbjU5Y0Eqd/w9NWHG91Uy18KccOPDTX7bbjp1/IinZA50M\nnBcE4XDsay/gb6vO8k+izcxFHLiSHrP1R24GnaTCyAncnD2Z9G5uyfaVZBmL1Yqr49+3L2fQ6/mm\nfn3a/ZQyFZ44hgwsR8izjChsQpLPM31aeyZPOU6ePKUBWLNmLL/9theLZQpwjwkTmjFp0mGyZ39z\nqbmSJRtQsmSD+Nfnzv3K/PnfIkkL0X6CXwCdiYjIxvjxTcibtwTarDMX4AyEAj+iqhLPng3gyZMA\nvvtuK1euHCAyMpSCBWeSPn0Ohg1bz+TJrYHMyPJ9WrUaHm/zq0hSDBNHVaRt6COa22SWP73DjAfX\n+G7KOURRjNVeTYMse6AtIzdBVccSElKc3bvHEx7en169FqTqM/0zUVFhryj4QgFF5m74s1SPI0lm\nFEXGwcEFWbYy7ftq1HsSwDiblXVPApgSeJFI1ZVX9YIVJR8REedTfI569b9h/qU95Lp2mGgpOqHd\nssSliOepttuOnX8DSdUDXQisVlV1jSAIR9D0cFVguKqqjxPr918hRpL47eIZbMpLwAFFrYpVPsyB\nq1dpV7lykn0nrF3LpG3bEIGKuXOzYeRIPFxckuzztqRzdSU4+Da+vhupWLF1su1lWebJs1vA72ia\nGdUQOcTu3XPp3ftnAA4e/CU2RUOrCCJJ1zl1amOiDvTP7NnzPyTJG21ZFmAeWsZnNGazjmvXjqMF\nFS1E2/vci/bzA0m6yYkT68mVqwQlStRLMG6hQlVZtOgmjx/fIm3azIkuUd65cx63yBdMs2lqUJVk\niWxB/jx9eodMmfLh6ZkTNzdXnj+fjKKoQEO0YuEgSas5dizPX3agJSq0YsjeH/hZiuYZ4GN0olOF\nVinur6oqq5f1Y/e+RQgIlChSnWbtJ2INfchcmxUBqGyzsutFEIUrdSI0dASS9DMQhtHoQ8WKPik+\nl06np9/wX3n82J9NK4cy4NJefrCauQ8sNDrSu8z7K9tnx87HRFJLuP7AdEEQ7gH9gfuqqm63O08N\nnSjGJknExL6johKJMZnyZVtOn2b1zp3ctdkIt9koGBhI7wUpuxnfDg5mzfHjHLp6lZQqSOXOkIF1\nfb9m0ybvFLXX9iYF/qy9ajA4IElmzp7dgc1m41VtVFFMnTbqm7RVNQfpguYsY4D5aHugzsAfaROi\nGInBkPi5nJzSkDdv2ST39/R6AzGqQlxSiwRYVCX+GnQ6PWPH7iJPngPo9ZMQhFcDZSLR6f56Lmnr\nz6bg6tWFMo5uNHPzpPHnsyhdunGK+x86sIR7h5fxWLERrsjkvnmCvdunYVEV5Ng2MmBRVRo16kWt\nWuVwcqqCq2tLOnUaTPnyLVNlryAIZMlSkK/6r0Ou2IYSjm60TZOR9l8vSVC5xY6d/xKJ3u1VVZ0N\nzI4teN0eWCYIghPahtQaVVXfTvH8X4JRr6dnnfr8fKQu0ZbeGPUn8HR7SL3iXybZ79SNG3SxWIgL\nFRkoy9RNgXj89rNn6TD7J3Q6LxRlFw1LZWf9gK9TFNRSKndunj27x8GDy6hVq3uSbUVRpGC+ivjf\n9kJlOCKnUYXTNGo0j2HDqvL8uSOynA5oDoxDEO7h4LAFL6/TydoRR4sWfbl0qQmSZEH7CU4EBgE2\niFfDbYf23FYIaAVMQBAeYTKtoUaNvyaWnzt3aZyyFqbt/Ss0tZpZaXSicLGapEv3h9Sep2dOJk3a\nT0TEcwYOLEtk5CBstqKYTDNp1mzQXzo/aE6885eL6PzlorfqH3DtEF9boonLFB5ktdAx8BJZ81Wg\nxS1f2kgxbDQ6kilvWbJnL0b37j50757yWWdiGI0O9Oj7Cz3+8kh27PzzSa0WbilgOfCJqmWq/618\n7FG4iqLww7797L8SQG7PNIz+tBlpk1mKnbtrF/tXr2arJCECvwCLc+bk+PTpb2x/ws+P5Xv28Ivv\nBay2g2jC7mZcHEqxcWCrFEv9Hb52jS7LNjJjxtWUXdeiHly9fAJXtzT06fczp05tZtu2m1it/0Ob\noXZHr99JxozZ6Nlz9muzkCtXDnDgwGoMBiNNm35DjhwJg7YPH17Bxo0zCAl5gKp2QVvCrYe29xmn\nhvsJ8CnwHBcXPypUaEzz5v3JlCkvb4Oqqhw7tpozZ/bi4uKKs8lAREggWfOVp1HTwej1hjf2e/Hi\nMZs3Tycs7DllytTBy6vTB6/UsmHtaAzbfVgpWxCAWYLAuiI16DdyNzu3TePx3fNkyl2KJs2HYTCY\nPqitduy8az6WKNyUpLHogUZos9DawCG0Gei2v924j9yBvg1mSaL+998TExREVkHgFLBrzBhK53ld\n3OnQ1au0mzKFYZLEEERUZOLUdZxNHZnbzY3utVJW1/zq/fvUmjqf2bP93uqGOm/eVxw7VgL4Jvad\nc0BbBKEDTk7L8PE5HV+h5ezZHcye3RNJ+g4Ix2SawcSJB+Od6K1bvzNuXBMkaThgAEZhMJRGUW7H\nFsouh812El5Rw/XwaMcPP1xPtd2vsnnzdLZsWY7FMghR9MPFZT0zZ55978Ls74Lo6HAmjixPutAg\n0gDn9QZGeZ8kSxZ7pUE7/34+FgeaVBBRPTSn2Rg4DawBeqqqGplYHzvJ42A0st/bm32XLxNpNrOw\ncGEyp31zDt2cTZuYLkl0BRbjyC18UBkM3EBV91I277AUnzd/5sxUypme6dNbMHLk7uQ7/IlixSpx\n+vQ8LJZ2aMFFM4G6qKo3MTHPOXLkf7RqNQKA9etnIEmLgBYAWCw2du36ka+/ng/A1q3zkKQxQJ/Y\n0dOQMeMPfP75CkwmZ44fX8nBg+mwWlsAMnr9LAoVqphqm//M1q3TsViOAwVQlKuo4T/R/+ts5M1Z\ngi8HrCNDhtx/+RzvCycnN8ZMu8jly/uIiQnnxe+7GDGiOiaTG59/PonKldt8aBPt2PnXk1QQ0XDg\nFFBYVdWmqqqutjvPd4NBr6dR6dK0rVw5gfMMjYzkzO3bBIdpQStWWY5XU91FFOkZh4AjDobyLPqy\nPcVz5kzxOU0GA9M6deLevcuYzan/GmvU6EqtWrURxaxogT2PAW1PTVGcCQ6+Hd/2RegjXtWBBVeC\ngm7Fv7JarX867oKzc1qKF69DwYKV6N59Pg0atEEUs6PTuZE/fzA9e/71HMk/9Glf4ogXM3jJbVmi\nw51zTB3j9U7yO98nRqMDZcs25cKFw1y4YCEm5jxhYctZuLBvkrm5/1aio8MJCDjL8+cPP7Qpdv4j\nJBVElLK1QTvvhF3nz9Nl1ixyiCKBssyULl3o0qABg+/exUGSkAGDQWJN7960rlgR3VvI/eXNmJHG\nxfIyeXIjxo07mqq+giDQrds0OnWaQI8u6RFsIcRwBriHgYUY9J3j2zoKUVjohpklQAQGRuOsKxt/\nvEGDLly//hWSlBbQYzQOpn79yQnO1bmzNx06fI8sSzg4vJsUn+rVu3L0aCckqTm5iSYu3GuYqrAw\n6gVPn975Ry6Bnj+/E6v1FJAVyIrV2oOLF/dSoEClD23ae+PmzZNMmtSKuBzgFi0G06bNiA9tlp1/\nOSkSk7fz9xJtsdB51ix2WCxUBu4AFf73P3x9fPDu2ZPpv/6KKAjMbdmSTysmvpR5+vZt9l68iLuL\nC129vF4TadDrdIxt04bSoycQEfEcV9d0ydqmKDaOHVtFyNO75MlbltKlG5PJPS01nvtxlpakQcFB\nJ5P5FceTJUMu6r305RLtMKKSTYhCfiVHtHTpxvTpM4ctW+agqgpNmnhTtWr7186t1xvR640EBl7k\n/PmdODi4UL16l7eWjevRwwdX14mcOLGckBArMapWp+UFECZbcXJK81bjfmgcHd2Jjg5AKzQOen0A\nLi7/XOf5+PEtVq4citkcRZ06PalUKen8ZVVVmTq1HTExS9F2nILZvr08pUrVJl++8u/FZjv/TVIV\nhfu++TcGEb2JgOBgag8ZQuArBbXrOjkxqH9/GqQwynazry/fzJ9PV6uVO3o9fmnTcnL69NecqKIo\nfPvzz+y8+ZSpU88lOaaiKMyZ3AjV7zg1LdGsMzlRrtG35MxfkaWz2/O5bCFQZ+BMmgyMm34JZ2dN\n8fHWrd/xGV+bTlYz4aKOXQ4ujJ9+KT7IKDVcvPgbP/h8yueyhYc6Aydd0zHe5zIuLm9X6g20G+6i\nma0Jv7iH+lI0W4xOFKzZjY7d5731mB+SM2e2MWdOT2T5c/T6u7i5XcXH59Q/8oHg0aObDBxYDlVt\nCGQDFvPZZ9/RosXQRPvExETQrVtGFCU6/j0Hh4706FEfL68uf7/Rdt47H0sQkd2BfgREWyxk/+KL\n+BloAFDRaMTXx4e8mVImLl7gq69Y8uIF1WNftzYYqNG5M30aNHit7cPnzyk0eAQ+PleSdGp+fidY\nMbE+NyxRGICnQC6dgR+WPefxY38uXvwNR0c3vLy6vHazfvTIjzNntqKq8ODBbe7cuUrGjDnp0WMq\nGTLkStE1AYzul59ZwbeJKz/eVW9EbjOWFi3/2vKcoiicPLmO4Mf+5MhZgnLlmieamhIYeIkVK77j\n5ctnlClTh3btvks05eVDERBwlosX9+DsnIbq1bvg5JS8nGRquHLlAGvWTMViiaFWrXY0atQ72VQe\niyWaX34ZxfXrvnh6ZqNHj6lkzJh0KeGxY2tz/XpOYFnsO9vQ63uyenXiFWRUVaVHjxxERi4CmgDB\nmEzlGTNmo30G+i/lY3Gg9iXcjwCborC8Xz+azZ0bvwc6tUuXFDtPgLCYmAQapflkmbDINwcLZfXw\nYED9Wnh712PWrMRTQ6Kjw8gu6ohzFZ6Ao6gjJiaCPHnKkCdPmUT7Zs1aiKxZhzNuXFP8/V2wWmcQ\nHHyEkSNrMGfOxfjZanJERYfz6i03nyxxPjI0RX2TQhRFqlbtkGy7kJB7fP99XczmcUAxnj7961q4\nsiwhSeYUOTmLRZtVGY2OREe/xMHBBZ3u9b9t3rxlyZy5AEajQ6pUoVKCv78vU6d2QJLmAp6sXTuA\nmJgIWrQYlOS5pk/vyI0bRqzW6QQFHY//7pNaPYiMjABe3YfOi82WdHCXIAgMG7YuwR5os2ZD7M7T\nzt/OB3WggiA0AGYDOmCJqqpTP6Q975uX0dG0nzKFI/7+KMA3devSvnp1cnh6ksk9ZQ4mjsalSjHg\n7FlmWa0EAD8bDGxPZPlXEAT6NGiAz669BAXdTDRwJl++8iwGVqElAC8QdXiky57ikltRUWH4+R3G\nZgsFDChKFazWw9y4cZSyZZsl2x+gZNlmDDy+ih+lGB4BC4yOfJ0Kybu/yrlzO7DZmgK9gL+uhbth\nwyQ2b54AiOTKVZ6RIze+cS9aliV+mtuJE6c3o6oqepMnkjUSQRD44ot51Kr1eXzb8PAQJk5szf37\nZwGFNm3G0apV4kueqeXIkbVI0gC0rDawWH5g/fpGbNo0nhYtRtGu3ejX+sTERHDt2h5stjDAiKpW\nRZaPcO3aYSokoflbtWprVq+eBtQAMgP9yJo1f6Lt4yhYsDKLFvnz+LE/7u6Z3mq7wI6d1PLXKze/\nJbF1RecDDYAiQAdBEAp/KHs+BAMXLybT7duEKwoPFIUDhw9zKzg41c4TYH6vXjiXLUtpR0e+TJuW\nhX37Uj5fvkTbZ3R3x7tNSyZNaphoGzc3T4aOOcj4zAUpZHJmd/6KDB57KNGC3zabzMWLv3HixFqe\nPXsQO1OykUAvWE2Zbq7VauHcuV/JXcSLl8XqUNjgSBMndz7tsYCiRWsk2/9dodcbEYQ/6/bqOH16\na6rTgc6e3c727Suw2e5gs4UTGFiM+fN7vbHtto3j0Z//lReKjTyqIzHm/thsEcjyGZYtG8Hduxfi\n286d25P790tjs0Vgs91my5bFXLiQ+lzfxHizdnEBbLY7/PrrSs6cSaipEhkZytmz22OLkKfuu2/R\nYig1a7ZES0MvTKZML/D23pciO52c3Mibt6zdedp5b3zIGWh54LaqqoEAgiCsRRNYvfEBbXqvnPLz\nY50so0dbHu1mseB7/Todq6VenNvZwYHlAwakqk+3mjUZtX4Td+6cS3Q5Nk+eMnjP8Ut2LFm2Mn58\nUwIDQ9DKkPVj1KitVK3aFV/fxlgs3dHrj+LhYaFIkRpJjmU2RzFqVG1CQkRUNR0Wy0FMpkpEKZHs\n/m05lat0wGh0SNW1vi0VKnzKunWTsdk0LVzwRlUzM3/+PFxcRjBlytEUKxn5+Z3CYumENrMCm20Q\n/v5v/q7vXD3I91IMBsCfaGAImgpVIaARAQFnyJ27FAC3bp3CZpuP9jycFYvlM/z9fSlVKvGHo9RQ\nr96XHDxYFYvFAVX1BCagCWlkxmLpjJ/fKcqVaw7Akyd38B5ZgRKyBU/BQIhaA5V+6PXHcXeP4JNP\naid7vl69fqJXL3uNUTsfPx9sBoqWtPbgldcPY9/7VxIjSYxft47O06czbcsWrLJMtnTpOBF7XAVO\nGgxkzZDhvdnk4eLCou5dmTSpYfxeW2qxWi1s3DiZkSNrc+tWAGbzUczmTZjNPzB//jf06jWfDh3a\nUa7cQRo1ysKkSQeTdX67d88nODgHZvMJLJYdgA8Wi4jZfIpHj9zZv//HBO2vXTvMnDlfsGDB1wlm\nZu8CV9d0TJt2kjp1VNKm9QFKYrNdx2w+wIsXdVm7NmVVbgDSp8+G0XgKUGLfOUHatG/+ybtnyM0x\nUY8ecMMExAnoWxDFswmqzbi7Z4P4X5INk8mXdOne3V8pS5YCTJp0hBo1gnF0nAR8hib2r2AwnMTT\n848Z37qlfegfGcqemAiClEjKC9fInHEODRtmZPLkwxiNb65/azZHsWbNWKZP78zWrT7/OFELO/9N\nPuQMNEXhv2NficKtUbQoNYqmrObkx4RNUWg6bhzugYE0sVpZd+kSv9+4wYyePak3Zgy7FYUQQPH0\nZHnDdzNrSCldvbxYdiWc77+vlmxay59RVZXJk9vg768iSd2BjWji77uAKoSFPUQUdTRq1IdGjfok\nPdgrPH36EKu1MnG6v1AVrWaoiCRVIiTkUXzbixd/w8fncyRpNBCNr289xo/fGz87exekTZuZHj1m\n4u9/kRcvvo63y2arwtOnG1I8Tu3aX3DkyEYePaqAIGRDVU/Su/fON7b9tNN0xl89yGlzFNltMuHW\n+pgcGgDXKV68NKVKNYpv26fPAiZMaAqsBe6RI4cHNWp8/tbX+yayZStMr14LcHf3YMuWmcB14CGy\nfI8SJWbHt3vx7B5VVO0BQQd8rVr5JUduOneelOjYNpvM2LGNePAgM1ZrAy5dWsPNm2cZOnTNBxft\nt/NxcO3aYa5dO/yhzXiND+lAHwHZX3mdHW0WmoCxbdu+N4P+Li4GBvLgwQP2WK3ogA6SRM7r13F1\ndOTi7NkcvXEDR6ORusWLYzK83/QIURTZ9WU90n2xiaiosBRHxwI8fuyPv/95JOkumih8JyA/cAWd\nbiX58iWfzP/s2X1GjKjDy5cPEQQTLi6uxMS8QFvm7AikBaahVaEJRq9fTuHCf8SabdgwKzY6VPud\nWCwiO3YspF+/pJcA79+/ypLZ7XkcEkiubEVSpIVbrFglHj2ahyRVA2SMxkUUK5byYtIGg4kJE/Zy\n5cp+YmIiKFRoAR4eWd7Y1sMjC5Nm+3HlygEEQaBLhjw8fHgdd/dvKFq0ZgLHkj9/BWbPvoCf33Gc\nnNLwySd13hip+y44cmQjmqOOQZNF3Mnx42tp0+Y7APIWqcHMJ3cobzUTAyw0OVG8WM0kxwwIOEtQ\n0DOs1kNoD0ntuXw5Gy9eBCVZ1/VDERHxnLlze3Lz5lFcXTPRq9dciiVzjXb+GkWL1kgQ+7Bx47gP\nZ8wrfEgHehbIH1tvNAhtTSj5vIJ/IFZZxlEQ4tfLDYBJEJBkmVwZMtCm0rtRjYmRJK4/fIi7k1N8\nCoyqqgQ8eUJ4dDSFs2XD0fh6EIeLgwO96tRk4MCi9OixgPLlW6TofLJsRRAc+ONnpANsiGJFsmcv\nw7ffbkp2jKFDaxAZWQU4gqpeJSKiHfArMBbIAojoMKHdsFdiUgw4O/+hRKQt9SXU1U1u+S86+iVT\nx3oxKTKUpsDygLN4j65Mv6HbyJGjeKJLzO3bf09wcDfOnfMAVCpW7EqzZtq+c2joI0JDg8iSpUCS\nAgZ6vSHFe5NOTmkSRKzmylUi0bYeHlmpXLldisb9K2gpJbmBYgCo6vEEaSZtu/iw8Old0lzeh4pK\n3aodqVs/6dUHm82KVmo47h9iRBBMyLKUVLcPxrRpn3H7dn5stiuYzWeZOrUt06f7vnWZPTv/XD6Y\nA1VVVRYEoQ+wB+3Ou1RV1X9lAFHJXLmwuboyTJJoZrOxSq8na8aMqcrzTA7/oCDqjxmDqyTxRJZp\nVbky87/+mq8WLGDn6dN46nREm0zsGT/+tfMKgsCcbt1oXfEGtSa0Y/78u4nOjF4la9ZCeHp68Phx\nP2y2duh0m/H09MDb+zxubumT7a8oCpGR94HLaE4wM9pz1GXgIJANR8ycJ5SsgAmYqsicP78z/mm0\nfv0uLF/+LRaLCERjNI6nTp0VSZ43MPAiOW0yX6DFCJ/GgSdhZsaN+xxnZxsTJuzF0/N1oX6DwcSQ\nIasxm6MQRTF+P2/r1pls2OCNXp8LVX3E8OHrKVLEK9nr/ydSu3YXdu7sjsUyFXiAyfQTlSsfUgx7\nVAAAIABJREFUjD9uNDrSf+Su2M9Il6Jgr7x5y+LsHInFMhJFaYRev4KsWfOSPn3KiyW8L2RZwt//\nEKq6E+322QRowI0bR+0O9D/IB80DVVV1N/Du4u0/UuJKmA1dupTBDx5QPG9ednbr9laC8InRY9Ys\nBoWH00dViQSq+/oywNGRy2fOcFuScAZmms30nDuXA5PevB9VrXBhvm/VgiFDStCx41Rq1eqe5Dl1\nOj3jxu1m6dKhBAYOJmfOwvTosS9R52m1Wjhy5BfCwoIpVKhq7LKXEU17qQTatvgtoDLwHIhAR3ru\nEEqh2DFu6404vZKIX6vW54SFBfPbbwMQRR3t2k2iePG6Sdrt5OTOY0UmBi3H9TcKoXICi8URq3Ui\nCxb0ZuzYX9/Y9+7dC7x4EQRoCkU3b57g0qXjKMo0rNYlQCRjx9bE0dE1SRveRO7cpWjU6Nv4VI9M\nmfK9M3H7hw9vcObMVgwGE1WrdsTdPeNbjdO27WhMJieOH/8OJydXOnbc+lqxdAAHB+cUj2k0OjJx\n4kGWLh3Ko0dDyJu3BN2770g0XepDotMZ0OlMyHIgkA9QEIQ7ODsnnttq59+LXcrvX0L6zp25brEQ\nF8M7WhA4WrAgdfz8+D72vQdABScngn7+Ocmxrt6/T8lhI5gx48o7u4HLspXRo+vy8KEJq7UMBsMq\nPvtsKIGBlzh8eCNa4exzwEWgO7AeQXAH8uKkbuYrQeSh3viaFu6dO+cYM6YBsvwZghCDyfQrU6ee\nTFIu8FUtXMFi4RyT0NJEAG7h4lKbqlWbE/knxaPIyFAePLhKjhzFefo0kKCgu6iqG1pUbXk0oYGm\nGHXZuTFrIuncUi6np6oqq48fZ8n5x/HvBQScoVChquTIUZzmzYdiMjmleLxXuXnzJN7ezbFaOyGK\nL3Fw2IePj+9Hub/4T2D37kWsWjUFq7UjRuN5smaNwdt7/0cn7/hv5mOR8rM70H8J1YYMod39+/RR\nVSIAL5OJqjVr4nvoEIcsFm0GKgjszJs30Rnoqyzau5dh67ZQv35vcuYsQZkyTVIsEacoCovmdcLv\n9BYEQaRa86HkzFmC+fOnYTYfR9vrCkCvL8GqVRHs3bsIX9/NpEnjSdGiNQgLe0LGjHmJiAhBkmLI\nlCkfjx/74+DgSvXqnRNUYxk3rhnXrjUBegIgCKOpWTMsvnh3HFarhbNnt3Ps2Dru3LmMg4MzOXMW\n5N69i7G1SpX4toIgMqRpk9fqrepEkcalS+NsMuHY6XMk+ToQDjREqzmfDdhHGqf2hCyZz9RNm9j9\n+++4u7gwrmtXyuZNfonv2oMHjFy2jKdhYZQvXJgyBQuy+8IF1p06RYYMuenRYyElS9ZP0fcQx8iR\ndbl9uwuglZwTxUHUry/Srdv0VI2TElRV5cCBZezZ8ws6nYE2bfpTpkzKA60Arl8/wqpVkzCbo/Dy\nak3Tpt9+dNG4V68ews/vOO7umfDy6oLBYPrQJv2nsDvQFGB3oCknqT3QX2P3QGMS2QOVbTYizWbS\nODkluFHdCwnhiy2XefToOpGRoXz55Y/kzl0q2ZnQ/NkduHdyLYuBCLT55CdVOnD2rIjFsjLurAiC\nEytXRqTo5hMTo7X7sxMfPLg69+8PBeL2HFeSP/8G3N3TYLNZsdlkdDoDQUF+mM3RhIfLKEoxIAJR\nvEz58k1xjb7HsRuB6HVpSeMUzYkJI8jpmbg4glmScOnSDZsSg7Z9PxMYg6MxG3rxKTuG9+NXX19+\nP3SICRYLt4HhJhMnp00jf+bMiY77KDSUMgMGMDomhhLAJKORbBUq8FPfvqiqyt5Ll+j802oKFapK\n166zUrTPDNCvXzmCg+egLY0DLKJq1fPJRiq/Dfv3L2XFimlYLNOAGIzGAQwbtjJFAgqgrSh8/30D\nJGk2kBmTaTAtWrTj00+HvXNb7fxz+VgcqF1M/l9CgSxZuL5gATcePsTd2Zk8GbU9rp/69k0yCnfJ\ngcP0XrocVRXI6ZmZfaMHkCtWzCGnpyf7etZGVWux/tQp+sz9jJcvn+Dl1ZU8ecoginrKl2/5Wn1O\nv9ObWQXUiX39CJh2ZT+qqgA7gHLodBPJk8crWecZGRnK5MltCQg4garKlCzZiHLl/tDCVZQXQDPA\nCW0PNYaQkIzUzlmMFYcOoaoq6Z2dmdmtG90XLUNRbqJF94JB/JJ2Ba1822gwt4ODiTSbKZw1Kw5v\niFR+FQejkbJ5inL+bn+stlFAPhyNOtZ+2xSvokVJ4+REu6lT8bVYyIXm2i/JMltOn2Zo8+aJjrvr\n/Hnq2mzExayukSQynzzJj717I4oi9UuW5O6MQny3bh2DBhWjc2cfqlXrmOzsrGzZuuzcORBVXQOE\nIYqTqFBhTpJ93pbdu5djsWRCW85WkaQq7N+fcgd67Ng6JKkPWgoTWCw/sX9/F7sDtfNRYneg/yIc\njUZK50lYLkoQBPIlEu177s4d+i3fgCRfAApw58lUGk+Zz7WZ418bo13lyrSrXJkXkZH02HGbgICz\nREQ8Z82akWTNWii2nUiNGp8jyRL30XKTQHOgz8NfIItGTKYvsNnM5MxZiq++mk9oaBCKYmP//h/x\n8zv+mo33798gMlIHOAO1uHRpH7IcHZ+zWa1aBzK+PMPPh0+g0+kY3qItjUsVx2v4cHxtNooDCyIi\n8F6zBoPOgNn6h6arIEZi1DsjCEKSM8M38evwPnw2dymn/Avj6ebBz98MoHqRIvHHDTpdQvVYQcCo\nT/rvZtDriXzFGQYBqCpbz5yhbvHiuDo64uzgwMyuXelQpQptfpzOsWMr+fLLRej1Rvz8juPo6Ebx\n4nUT5IFGRISh6ddWAQwIgp7w8Ocpuk5//1M8fXqXHDmKkyNHsWTbv3z5BC3FJQwwA3UICQlI0bkA\nDAZNe/iPhbFIdLp3W13Gjp13hX0J9z/Mor17GfSLTIy0PPYdGQET1rWrUxwhfOPhQ568fAlAeEwM\nQ7Yew9//FALgijYnjADAHYhBxIIBAQlwcnaPT3MoXNiLCXUKIP5pNtV82jzCY+YC1dG0NiYzoPHv\nzOzaMVGbVh49ys4lS1hjNkOsDc6iyNBWbZm+4xzRlmHoxeukdVnFtZkT8UxFsE9KmbNjBwvXr2eo\nxcItUWSVkxNnZs5MslBAWFQUZQcOpHF4ODlsNobjhKgvjUmvw80pgLNTvk/Q3yrLjNu4kaUnr/Di\nxQsEoRpxSkRjx+6MX+7u27cMT54sRBOjAFhM5cq/07//0iSvYfnyYRw8uB5BKI+iHOHzzydSp06P\nJPv06lWc588XAHEavz9TtOhmxozZnvQHFsuTJ3cYOrQSZnMvVDUzRuMkvvjCmxo1Oqeov53/BvYl\nXDsfnGweHujEXYAFLcvyFGmcPVKVXlM4WzYKZ/tDC7VZ2bJ4tD1FJeAe2g6hGzpOUY9s7MQPCw6o\nnAYa2WII+eGHJJcgc2fIwqV7p3FiGzJuCIbb5PRMWjEoW7p0XFBVotEWdi8CRr2e7z5tTt5Mnmw9\ns5rM7s6MbDnuNee58/x5Nh4+jJOjI982b06BLMnnw76Jb5s2JZOHhxZE5OrKyZYtX3Oev128yPpD\nh3AwmejbrBmFs2Xj5LRpTNu0iQWnr6K86IAsT0OSwWwdxIhVm1ne+4/UIoNez8iWLZn+614kKRMw\nDihCQEAdxo2oQOkKLWnSfBientl4+vQkqloBUNHrT5IhQw7Cw5+xefM0nj0LpmTJ6tSu3SP+u7h3\n7zIHDqxCkq6gqUHdYtmyMlSt2g4HBxcSI3v2QoSGnkBVqwEqOt1xcuUqkmj7P5MxYx4mTz7G9u1z\niYm5R/XqC1IdhGTHzvvC7kD/wzQuXZraxXw5cLU4AoWxKUdZ1fervzyuXhQZrSjE6SstwcZ57lAG\nlbi0+nJAuMWCxWpNcs+xRdmCPL43lwnAfWC2LNKgRNJKSV5FilCtXDlKnTlDCVHksM3G0j590Ol0\ndK5ejc7V31wBZdXRo4xYvJhRksQTQaCary8np059a8GLdlWq0K5KlTce23DyJAMWLmS0JPFcEPDy\n9eXYlCkUzJIFn+7dOXlnOndDq8e3t9qqcufpydfGcTKZMIkGJDoBLYBr2GxVKHBvIuGPbzLr6kG6\nf/ED339fB1k+CISRNm0U9et7M3RoFV6+rIvNVpNLl+YTFHSHLl20CO3nzx+i1xdBkuL2t/Oj06Uh\nPPxZkg60R48pjBxZA1k+hqpGkSZNKJ9+eiRVn1uWLAVei6K2Y+djxO5A/8OIosiWIb05dO0aT8LC\nqJB/fHzw0V8hR6ZMTAsKYh0QBcwBJOEO+9RorqMV5GoJOAL5vvqKL+rW5fv27d+YOL/h8GE2o+3e\nAUSgsvbECcYkoZEsCAKL+/bl6I0bBIWG4p0nT4pmkjM2bGCFJFETQFWJMptZduAAEzsmvlwM4Ovv\nT+f5P/P4RQhl8hRg3YAvk63pOmPDBpZKEvVjz2U2m/lpzx58unUDoFaxPFwMnEOMVAtQcDLOo1ax\nN8+8KxYoxKGrIchKcaA2TtyhF1BXiiF3gFZke/bsi1y/fhi93kjx4nU5fXoL0dH5YsuggcXSmN27\nc9KpkzeiKJIrVwlstgvAKaASsAajUUg2dzRjxjzMmXORq1cPodPpKV68boKobVVV2bZtJtu3z0FR\nbNSt24MOHcZ+lKIJduwkh92BfgBUVU1V1Oe7QpJlrj98iIPBQMEsWRAEAUEQqFXs9eCQSLMZv0eP\n8HRzSzKl4038Nn485fr3xzlSC6MpnjkzR7/5hrO3b1Nh1Spkm42sqsopVcUQFUXbXbuIkCT6NmoU\nHwEcx/OIiIRKt6rK7cePE7QxSxK/njuH0WCgSoEC3Hn6lMxp0+JVJGVLh6qqcuvxY8ItFl5NhXdR\nVcKtSevqBoWGUtd7BpHmxUA1Tt6cQT3vWVyaPjbJpWmrLP9JwZcE5/q+dXP8g35i82ltBtisbDVG\ntWoWf85HoaHkz5wZd2dnVvXrQcOJc7gY6I9NtVAWgYZoe78mQUCWJdzc0lOxYuv48WXZiqq+aoET\nqqrERkqLeHhkZcCAFcya1QRZlnBx8WTUqO0pEgtwcfGgYsVP33jsyJH/sWnTUiyW3YCR337rhIuL\nO82bD0x2XDt2PjbsDvQ9oygKn8+axf4LF/DQ6bA6OrJn/PjXHMe7JjgsjHrffYf15UsiFYVyhQuz\nbtgwDG+IDD135w7NJkwgg6LwQJbp1aABEzqnPIgjvZsbd5ctIzgsDAe9HncX7UZdqWBBvmnYkJbj\nx/PZ9esUAS4BQZLEjt27+WXfPrrXqcPU2FkYaPIG3YAZaKV6FgCtdLr447ceP6bSoEE4yDJRgAQU\ncHTkviwz/NNPGdIqaYk1m6LQdeZMDl68iIui0DT2HADzjEZ2JVPc/KS/P4JQEdCck6xM5WbQQsKi\nokjrkvhSZ5d69fh6/XpmWSw8A2YYjWyvUSP+uFGvZ/3AXkRbtD1PJ5OW7jPv118Zs2YNufR6Hqgq\na4cMofYnn3B26nc8Dw+n+ogRnAsJ4QSwTmdA9MhKtmyvlwAsWbI+Ot0IBGE2qloGo3EapUu3SxC9\nW7p0I1asCCE6+iXOzu7vRMzg5MmdWCwjAc0mi2Ucp0752B2onX8kdgf6nllx5AgBFy8SIEk4ApMt\nFnrNn8/u8eOT7fs23H/2jIErVnDy2jXqREWxQlWxAhWvXaPplCl8Vq0aHatVSxA41HHaNGZGRdEO\nTZG2wt691C5dOtW1WN+0jKkTRTzTpiVAEEBV6QxMB7qoKi+sViodPEjt0qXJmT49w1evxqyqGIHP\n0QKCyokiBbL+sYzYytub1rLMQjQdoNVAw5gYHgPlNm+mdsmSr6X2vMryQ4e4d+lS/PcxQRAYajRS\nNEcO1n/2WbLqQe5OTqjqPUBG+zsFoao2nEwmTt++zd6LF0nj7IyroyP3Qp5RLHs2WlWoQL8mTdDr\ndIw7cABHk4nV7dtTsUCB18aPc5yHr11jk68va/bv56LNRg6rlcNAWx8fHi1dikGvJ52bG7vHjqXQ\noGE0FfUUL1aLcvnKsW3bNEqUqEe+fOX+sNs9ExMnHmT58pE8f76OEiW86Njx9RJRoii+lud79+4F\nLpzfiYOjG15eXVJVAs/NzR1BCHglTSUAV9eU97dj52PC7kDfMzfu36epxYJj7Ou2isKPjx4l2edt\nuRcSQvG+famlKHQAfkErFHYeeCnL1L98mSU3b7L1+HE2jhiBKIrYFIVboaHELfalA2opCjcePXpn\nxcyHt21LtfPnuW+xcF1RiNvNTAvUtdk4ePUqC7Zvp4mqUgfYgjPwJSKB3FL388Mr5d+ev3jBZ2iC\nepFoonqg1XWpIor4BQUl6UBv3LtHs1e+j/aqynKTiT0TJ6boWmoWK0aZPHs4G1CTaKkqjsY1jGzZ\nmh1nz9JnwQK6ShI/Ck4EkRNVbYGTaTt7L/nz41dd6N2oEb0bNUr2HD/u2cPElSspb7FQAsgR+34N\nQLTZeBoeTlYPTRs4h6cn9+fPJnuf/py/8TsnL4jIch62bGlCv34/JihVlzVrIUaP3pyi64zj/Pld\nLJ7Zmm6yxH2dge+3T2ecz+XXnGxitG49lLNnq2KxPEJVTRiN6+jYcW+qbLBj52PB7kDfM4Vz5GCJ\nycS3sTftDaJI4ax/j6h3v+XLKaMoWABftBjNYWjRrHeADMAli4Xaly7h2b49adzc+H3GDPJ7eLAx\nNDR+BnpQFPksERufR0QwdNkyrt29S6EcOZjWowcZ0iReDxMgf+bMnJkxg3UnT5Jl61bWR0bSBXgB\n7NPpMJ47RwdVZTGQH1e0As6NUACjrgvrT/kyoqXmCNKlTcuakBCqokkttEYTbvAEfpdlhiUTPFQ4\nZ07mGgzss1qJBtJAqr4PnSiyb/QAVh47xoPnd6iQryP1S5Yk35dfskmSSAfMU40onAWciLIM55ej\nuRj1aSNypH9diu/agwf0W76Bxy/CaVSqMBM7tGLoL79wxmpFBmqhfX85gMOAotOR4U+pOOnd3Piy\nRlXm/XYI2ACAJDVk2bJeKa71mhgbl/VllRRDAwDFRsfwpxw48BPNmw9NUf9MmfIyY8YZTpxYi6LY\nqFjx1L+uDJii2Ni0aSqnTu3ExcWdLl3GkC9f+Q9tlp2/AbsDfc909fLi0IUL5D1//o890D5JFxx+\nWx4+e0YAWhRsTmA42s1XRHMwQUADYDRQBhgbHs4nvXuzc/x4mk2YwGRF4aEs06tevTfOPmWbjYZj\nxlD+8WNm2GxsfPKE+oGB/D5zZgLVHVVVCYuKws1Ji8aMiIkhW7p0DG7WjLrFi9N4/Hhm2mw8kmW6\n16rFgcuXiVvMDEcF/phBSnJ+QiPPx7/ePHo0lQYNYocsE42mfTMdOAockGXSOCWt21syVy7u2WwM\nRRP46wM0TWLG+iYMej3datZM8F6Y2UwetFxYA+mJIc4ONww6D4JCQ19zoI9CQ6k8eiIRMWNRKUlg\nyASCwpYRLcvkQiv8Ngpt9zCHwcBTnY61gwe/cR/b1THufD+hVbrJS0xMWKqu689YrRYio1/yqrsr\nIEtcikiZqlEc6dJlo1mzwX/JFptNxmKJSrJ4+Yfif/8bzf79R7FYJgEBjBvXmKlTj7+zykZ2Ph7s\nSkQfgPcVhdtsyhTynz/PjNjXfkAFQSB7+vS0ev4cd0XBF4j7hMMBDyBi5UpsioLfo0dkSJPmjTMl\n0MqeNR05kjuShIAW9VnIaGT1+PGUiXVC1x48oNXEiTwOD0eJ/a0JQK506dg6ejR5M2UiymzGLyiI\n9K6u5PT0ZOLmzcxeu5ZdwEwcWE9lFH4GHmHQNWHv6D4JHLpZklh57Bi9fvyRSDRJCICaQOkmTZjR\npUuin9GI//0Pw44dxO1AXwA+8/Dgxg8/pPyDfgNdZ87Ecu4cE6xWyuHIS3yATxH4HypjcBTNVMyd\nmw0jR5LOVasd+tP+/fT/OZxoaU3sKGHodZmonT83uW/dYozNxnmgs9HIT337UqtYMdyd31x38/db\nt/AaMx2LbAVuYjB8S/nyafj226TVh96Eqqr88stIdu+ehUmRqSaoLFUV7gMtjY58M/I3ihSpnuw4\n74q9u+ey8pfBiECOzAXoP3rPR1Wa7fPPsxIdfQStXiiI4gDatvWkVauRH9awfxEfixKRPfnqAxCn\nvVoqd+6/7DxjJIntZ8+y0deX5xERCY5VKlAggbZqJODp5saeCRM4nT8/owSBV+cOkWjOzajT4eLg\nQNm8eRN1nqClxYRLEnLsaxsQJkmYJQnQIo6be3szLDSUE7KMg83GPJuNxTYbTZ8+pXVsWbWn4eH4\nBwVx6/FjFEVhVKtWNPPyoo4gsA0zJk7iSkE8qUN6wgmNjExgh4PRyKfltSWymNj3VLQHgqhYOb/E\nMBgMRL0SQBWJpmP7KoqisO/yZdYcP07g06dJjhfHwm++waFMGao6OuLuZiBneh8cDPkxCuM5QTQR\nikLRwEB6zf9DMMCg1yMICRR00Qk6Vg0ZQsgnn/CJgwND06dnw/DhtKpQIVHnCVAhf34Wf9URiMbB\noRTly6fh66/npcj2P3Ps2Er279+NojwkhjCOk4vCOgNt02Sk/ddL3qvz9PM7zq7VI7husxJps/Jp\nkB8/+Hxcxax1OgO8ooQsipEpLgVo55+FfQn3H8zL6Gi8hg/HLSwMN2CAXs/BiRPjhdE7e3lRfscO\nPKOjyamqTDEaGdq6NVk9PPhtwgSuPnhAxUGD+AYoC0wDcri7o/uTA0kMVVXRiSKtFIVPgW0Aokic\ny34WEcGLyEi6A0sBFxzpSzpEiiJzDCk4mJ3nztF29mJ0QnVUblKt0EF+Hd6Ppb17s7R3b3L36MG+\niIjYZ3mYbANfPz9aVaiQwBZBFDGhBRH1BE6iLVf3zJ207F+3WrWotHs3rmYzWVSViUYj41v/kS9p\nUxTaTJ7M7Zs3KQj0U1XWDh1K7U8+SXJcZwcHfh6YMDVjxMqVOG3fHl9UbJDNRjV///jjLcqVY+Sa\nbVjkvsi2UjiZZvJtwyakc3Vl48jUz146VavGvN8fEhER8lYzzziuXj2FxdId0B6motXNpHPvyKxF\nV996zLfF39+X1rJM3Lc6VLEx4+6F925HUrRqNZi1a9tgsQxDFAMwmXZRrdrfE2Vv58Nid6D/YKZv\n2UKukBAy2WxYgcbA0CVL2PLdd4CmCXty6lRmbt3KichIpleuTKuKFeP7F8uenV1jx9Jpxgy2mM1k\nyJiRKjly8OXcufRo0ICKBQoQERPDtM2bCQwKokyhQvRt3Dg+5SWLhweSKFJMUTgAFASOiiJZ06UD\nwN3ZGStwDXgK3CcDKn6AA1oB6up0W7iCaMsGoDZgZf+VMtQYNYrKRYvy8uVLFJuNE2iLYQpwymik\n9p+EHWbv3MnSnTsBKAIcQguQcjUaKZw1Kxfu3uWHnTux2Wx0rls3gcBC7gwZWDV4MEOWLEGyWKhY\nsCCHL1zgtJ8f3zZvzqV79wjy8+NcrMjCPqDn3LkE/JT6WprZ0qdnp9GIIkmIwAkga9o/olfdnZ25\nOG0s3pt28Cj0Go1Le9Gtplei473KhlO+rD91EQ8XR0a0aEiuDBkQRZEj37YmTbcemM1RODgkPmO9\nffs0u3cvQVVV6tfvRsGCleOPZcyYDYPhJFZrX7Q1ihOkS5cVWZbYud2HoIAzZMhRnKYth2M0OiZ6\njpQSFOTPb9umYo2JoKxXlwRauOnSZeOk3oDVJmFAe1BK75Y6oY+/m8aN+5A2bUZ8fXfi6pqGli1P\nkTZt6qr92PlnYN8D/QfT3Nubo5cvMwxwA8YDrmnTcuvHH1M91nE/P1p6ezMydvl1stHIuuHDGb58\nOQWCg6lttbLCZCJ3mTIs698/vt/MrVuZsXEjVUWRk6pK7+bNGf7KDG710aMMWLyY7IrCObkZsCn2\niAoYYvdOIyFWJVfkC9qxlHtoAUENgNlATYOBEJ0OU+bM7J0wIX7p23vTJqavW8dENNnA8UBFg4EH\nokjNChX4smFDGo4dy1CLBQfA22jkf0OGUK9ECQDuPn1KpSFD+Dp2Bvod2iw2jyCwwMGBLxo0IGLH\nDubK2kJ1FJBOFDGvXZvqz9hitdLg+++JfPSI7ILACVVl55gxyeaaJsf83/YybNV+oi2jEIUA3JyW\ncHWGN1k9PFBVlVqLdhMQcJYZM668sb+//ynGj2+GJI0AdBiNExk5cgNFimjO22yOZNSo2oSE6IH0\niOIZJkzYx4YVA3HzO0YHKYatBgfu5y7FsPHHEMWUrWC8iSdP7jBmaEn6mqPIoiqMNzrR8ouFVK/R\nFdAiXGdPbED4LV/yI3BUVeg7bAfFitVMZmQ7/yY+lj1Q+wz0H0yk2cxAtOhagKzAIKuVXefP033G\nDMKtVtIYjfwyZAh1Yx0GaNGePWbN4mxgILk8PPjx22+Zu3kzkySJL2PbuEgS3qtWYQsJ4RerFQFo\nY7H8v707j7Ox3gM4/vnOcmYx1rKPMHbKFtlFke0iXaJCKLdSUVwpS1EURSJUaL1FNyPhykXKLiqy\njVKyjeyTZczMWX/3j+fM3JGZwZnRM8P3/Xp5Nec5z3me73PONN/z+z2/3/dHyc2bmZiYSBF/lZ3B\nd99Ny1q12H34MM+UKpU2eCjV/c2bUycmhhfnz2fL+hUY4oBqCJMwhFO7XHm2HxyP1/cC8BvhfM5A\noBbWqNgn/T8/ly8fbz76KK1r1rxg1OnMhQuZBWlzSYOAGeHhfDhkCM2qVePhKVN4zukktTO1qMvF\nG7GxaQn0/a+/5gGnk9H+L5LVgMeBD/y1cONPnmRlUBBPAeWB14KCaFiuXECfV1hoKMtfeomvduzg\nXHIy06pWpZR//mZWEhITeWDqu6z7aRc3RBXmvQG9Lii/+NL8pSQ5FwF18Rk4n3KKT9au5ZnOnRER\nlv3jLiJ6/ovExASioi4+34IFb+JyvQg8BoDLVYD586emJdDw8CjGj1/D9u0rcLmSqV5akXtbAAAg\nAElEQVR9FsnJ5/jtp7UcciXjAHq5U6h4YDsHD+6gXLnaAb0/AN+snEXflPOMNj4AqriS6B/7YloC\nDQoK5qkRy9i5cyXnzp3irsqNKFq0bMDnUyo7dBBRHhZTrBjpZwBGAQXz5aP7hAmMcrvZBzzrcvH3\nl1/mtH/gjc/no+OYMTT45Rd2OZ0MPnKEDmPGkJiSclFtVrfHQz4RjgLfAUlY37jcHg/p1Slfnvub\nNuXWmBj2Hj3KD7/9xpnz59m2fz+74+OpWro07evU4dYQJ2HUJpRwyvICoZJE7JBHqFRiLsFB+RCq\n8hqnaYg1ZcMBuLGm4ESFhtK+bt2Lpmz4fL4L4s4PhIgQU7w4IoLL7b7oulzpas663W6ifL4Lrzv1\nZ2Molj8/I3v25JaQEPIHB7OkZEk+Hjr0Mj6djIWGhNCuTh3ubdz4spInwN2vzmDljhokpuzmwMlp\ndJzwJr+kqwfs8Xr8kVt8Jgqn+/+fUWhwMIPatWXYsLoAnD9/ml9//Y6EBGvJc4/HfcHrIQqPx3Vh\n3KFh3Hrr32jUqBsFCxbD63XjkKC02sFBQIQEXfS6K+V1O4kyf/o8vBceMygoiJo1W9OkSQ9NnspW\n2gLNw3q2akX3zZsp5XJREBgUFkb9ypU5e+wYj/v3GQS8YQzLtm+ne+PGHD19mviTJxnt8yHA/cCH\nQJ2qVXn2t9/I7+/Cfdbh4OWOHRkyezZVsFpf+4DKJUpkWCjBGMPjb73F/PXrKRYczEGXixtCQvCK\nULNiRd564gmGhYUyznOeasDboYYmdeoTU7w4u98Yy+8JCbR47jmOnDnDOp/hbaA48DMwJCyMXq1b\nZ/getKxXj39s2MBsrAT/HJB0PoxKA59l5N870euuu+izbRtF/aX6ngoL4/m2bdNef2/Tpty1bBmV\nXK60eaBNsEoCptbCrVehAg+3bs15p/OS80pzmsfrZf3PP+Iz67G+UrQH2rM6Li5tsFi/ls2Ysbwn\nSc5XgX2Eh35A14aj0o4hIkzq3ZvJS7qzc+fXvPpqD0Si8Xj206PHaNq06cXu3Y/jchXE6sIdSps2\nr2UZV8mSlYgqVp5Hj+yhl8fFguBQUgoWpWzZWlm+7lIaNevJq1+9QyVnEiWBp8Ly0aRV9pfYU+pq\nsOUeqIh0A0ZjrWxV3xizJZP99B7oJSzdupVJn32Gy+2m5113cVPRotz3yivEY1XmOYvVtbt0zBia\nVqvGueRkSvbrx16vl+JYra2aYWHMHjGC+FOneHvhQgzwaKdO1ImJodnQoWx2uykHrAa6hodz+L33\nLiiUADBv40bGTp9OL5eLFKyqQpuBr4GmISEUqFqVO265hU07d3IsIYFmNWvy4gMPXDCN53BCAs/M\nns3e33+nRNGinD13DrfbTbeWLXmyQ4cMi5n3nzqVDevWcRpreEsYQRxgJF4eJdJRl7UvPsWhU6eY\nEhuL1+ejb7t29LnjjguOsSYujpfnzCExOZliN97I8RMnyBcRwXP330+LGjVIcjr5cPVqTp49S5VS\npdh3/DjBQUH0aNKEaP+AqavFGENkz36kuLcAlQFDVHgT3h/QiK7+AWFen49xny/msw3bKJQvgkm9\n76ZBpUoXH+fBh/B6Bbd7IdYs2YM4HLcxfvzXHDoUx4IF0wFDp06P0LTpfZeMLTExgbnvPsHhfVsp\nUeZmejw0jUKFsr8cXlzcahZ/8izOlETq3f4g7ToOyZFC9urakVvugdqVQKtiDap8BxiiCTTn+Hw+\naj/5JJ4TJ+gMLAAiS5Rgy9SpafuMmTuXOV9+SVeXizUOB0WrVk2rhZve4u+/5+1p01iSlJS2rZTD\nwaY33qDMn+aHjpg7lw8WLKARUAH4AKtFOAJ4G7gP2BgWRv6KFfli1KgLitdnR9MhQ3j50CFSZyJ+\nDDxGBxL5D1Hhf+edf0Rzf9OmAR8/2eWi+bBhlDxxgupuNzONoZ4I5YODWeRwsG78+IAX3L5cM5Z9\nxdB//YcUd2/CQ7+nSukjfDtuxEVfYi7lq+3baT12LFbhe2ugT0REZwYMeJAGDXLXXEqlspJbEqgt\nXbjGmJ+A6/5bpTGGN5csYd433xAZHs6w++7LcG3OKxEUFMSKceNo+fzzzEhIoMyNN/Lf0aMxxvD2\nf//L3JUrCXc46Nm5M8YY+hcrxgPNmmW4oHHlUqX43uPhANZ9yDWAOyiIogUKMGH+fBZv2EBwSAiH\nExL44+xZCgMzsKaQdMCqvfsi8CvWgCCP00ndvXtZExdHy2xeZ6pqZcsy7/ffaeb14gU+wUEytwJH\n8Pk2ULXUU5c6RJY+27CBG06eZKG/2lJ3oIMxLPd4KO31MmHePGY++WQOXEnmBrRpRfXokqzZvZsS\nhWLo3bzPFSdPwD99R7A69qcBh/B6N1O69Cs5HLFS1we9B2qjyYsW8WFsLJOcTk4APcaP5z+jR3Nb\nxYqXfG1mXB4PbZ9/nrYnTnCP18tnx47RYcwYet55J7P+/W8mO538ATxx4AAfDR3KXTVrZpg8AaqU\nKsXz991HnTlzKBsSQrzPx5x//pNXYmNZtnQpY51OBmAVOO+NVRKwDbAJa95mCtZkldQZcCFAWRFO\np2vRZtf4vn1pu28fVU+dItnr5bTHR2TYPNzeKTzXpVOWK7F4PB4OnTpF2aJFM30PTiclUcHrTSsO\nUQFIrShb0Rh2nj2bY9eSlRY1amR7NZzQkBD+9cTj9Jo2jfDwtXi9h+jefTTR0Ze38LhS6kJXLYGK\nyAogo76t4caYxVfrvHnJh8uX847TSWppg30uF5+uXp2tBLrz4EGcp08zyf9Hv4nXS+VTp3hv6VJm\nOJ00B7YDwW4397zyChEOBx8MGkTHevUyPN7jHTpwT+PGxJ86RcUSJSgcFUX/KVNY6nTi5f/98II1\n+KYqsAQYjNVRaIBGwOfAOmCzMcz80/257Lghf342TpxIXHw8IcHBlCxUiL3HjlGycOG0Jb4y8uoX\nX/DCnDmA1Zn55mOPXVQQHuDOm29mbFAQXbCKuA8BbscqDjEuLIyh6ZZWywt6Nm9O37dnMWzYFEqW\nrEyRIlmvVqOUytxVS6DGmIyHTV6h0enugebEt/DcxBESwgWVT0VwhIZmuv/lCA0JIcUYvFgfrgdI\n8fkoGBxMIla92s5YnXg3GUOS00m/KVP4YfLkTOvelixcmJLpKuaEBgezD2uE7GmslmaE/9insVqj\nd/ifTwKaY43iLVukCIuGDLngWDkhJDiYmmX/P52hXlRUFntbBe7HzJnDf7GS4edA77feonP9+rg9\nHlbHxREZFsZdtWpx80038dGQITz5zjucSkoiulAhjpw5Q7vgYJ7s2JEHM0i6uV3Dhl15//2BTJiQ\n4dADpXKdXbtWsWvXKrvDuEhu6MLN8kbo6HvvzerpPG1wt270ffttRrhcHBdhdlgY6zOZrnG5akRH\nU6V8ebrt3Utnt5v5Dge1K1fmwdat6T99Oo+7XCQAk7GSx/dAEa+Xbfv3Z1k4Pr2/N29Ot0WLaIl1\nv7MCMAZrwFJoRASRLhfPer1pczkHAhMA3/nzfLJy5UUjRP9qy378kSpY1w9wD1bBhjnr1jHy04X4\nTAOMOU5MscVsHPcc7erUoV02V2fJTdYO7Ex4rwW43SkEB2f9ZUOp3KBGjRbUqNEi7XFs7Bj7gknH\nlgQqIl2AqVjVqZeIyFZjTDs7YrHTfU2bUjAyktjVq4kID2dt585pc/sCFRQUxIKRI5m0cCEr9++n\ncUwMgzt1Iiw0lPwREXy0YgUp333HZqzatWeAih4PZ5OTMzxeRrVwv/7hB2Zjjaz1AXeKMCo8nMrR\n0XQoVYoF69axFqvr1mB13XYHhjmd1Fq/nvvvuIOGlStneL6/Qo0yZdgLnMT6BdyPtXD4zBUbOJP0\nMlY5esOeI/cw7b/LeKZzJ9tizSkrtm/n3a83EhEaypCOrQgJCWPnzm+oV6+j3aGpLBhjWPXNe/y0\nZQn5i0TT8e8jKViwmN1hKT+7RuEuwGqwXPfa161L+7p1c/SY4Q4HI7p1u2h7m9q1qVGmDCu2bKGK\n1wtAQeCWkBAKRFxcBNzl8dBq5Mi0WrgfbNvG9r17if/jD5r49wkCWhhDrdtvJ3bdOprs3ctTPh8v\nAl9izUP1YS3qnR+oGRTE4YSEHL3eK9Wmdm3qVapE9V9+oSHW6OIut93G6l+OAqlTXoQUd1P2Hf/G\nvkBzyBebN/PA1I9Icr2A8Afzvh3LlD7dGfBGd2bPPk54uLZCc6vYucOJWzqVwc4ktgSHMnpTLGNf\njyNfvkJ2h6bQUn7XnZKFC5MvKop/+R9vBnYEB1Mrg/qu6376Ka0Wbh9gictF7ObN1ClXjgki+IDf\ngQ9DQjjndNI2JYVXfD6GYyXPH0NCOBAWxiNYiXoL8FVyMg+8/jox/fpdUI4uMykuF49Nn06JBx+k\nQv/+fLJmTU68DawcN44Jjz1GmTZtmP3008z95z9pWqUiYSGvYd05PkG+sHe5vXrgA7pyixfmLSPJ\nNQsYgKEv552leHT2x3g8bhYvnmh3eCoTxhj+85/XWe5Moh8wzeumdtJZvv9+kd2hKT9NoHnIueRk\nvt+7l0MnTwZ8jOCgIBaOHMnoQoXIHxxM27Aw3hs0KMP7n6m1cFNvUjsAMQaPMSw0higgBvjD5yM0\nJISodEU5ooFIh4M1L7/M5CJFiAoKojHW4KX9QJvERFoMG3bJeJ957z3iN27ku+RkPjlzhqEzZ/L2\n8uUcP3Mm4PcgVd+WLZn+0EN09Y+knfVob+pX3EFIcH5CgqJ5vE0NujdufImj5H5ujxerqqwBOgGd\n8Xj34vONZt68MRw+/LO9AaoMGWPw+nykX4Quyphs1xtWOUeXM8sjNv3yC3ePG0cJYzjo8fBUx46M\n6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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1441,9 +2068,25 @@ } ], "source": [ + "# Check the arguments of the function\n", + "help(visplots.nnDecisionPlot)\n", + "\n", + "### Write your code here ###\n", + "### Try arguments such as hidden_layer = 2 or (2,3,6) and learning_rate = .5\n", + "\n", + "\n", + "# Solution #\n", + "visplots.nnDecisionPlot(XTrain, yTrain, XTest, yTest, 2, .5)\n", "visplots.nnDecisionPlot(XTrain, yTrain, XTest, yTest, (2,3,6), .5)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Tuning Neural Nets " + ] + }, { "cell_type": "markdown", "metadata": { @@ -1459,7 +2102,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 41, "metadata": { "collapsed": false }, @@ -1468,84 +2111,55 @@ "name": "stdout", "output_type": "stream", "text": [ - "The best parameters are: hidden_layer_sizes= (3, 2) and learning_rate_init= 0.55\n" + "The best parameters are: hidden_layer_sizes= 5 and learning_rate_init= 1.0\n" ] } ], "source": [ + "# Define the parameters to be optimised and their values/ranges\n", "# Range for gamma and Cost hyperparameters\n", "layer_size_range = [(3,2),(10,10),(2,2,2),10,5] # different networks shapes\n", "learning_rate_range = np.linspace(.1,1,3)\n", "\n", - "grid = [{'hidden_layer_sizes': layer_size_range, 'learning_rate_init': learning_rate_range}]\n", "\n", - "gridcv = GridSearchCV(multilayer_perceptron.MultilayerPerceptronClassifier(), \n", - " param_grid=grid, cv= cv.KFold(n=XTrain.shape[0], n_folds=5))\n", - "gridcv.fit(XTrain, yTrain)\n", + "############################################################################################## \n", + "# Write your code here \n", + "# 1. Construct a dictionary of hyperparameters (see task 4.3)\n", + "# 2. Conduct a grid search with 10-fold cross-validation using the dictionary of parameters\n", + "# 3. Print the optimal parameters\n", + "############################################################################################## \n", "\n", - "best_size = gridcv.best_params_['hidden_layer_sizes']\n", - "best_best_lr = gridcv.best_params_['learning_rate_init']\n", "\n", - "print \"The best parameters are: hidden_layer_sizes=\", best_size, \" and learning_rate_init=\", best_best_lr" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now try these out to see how the performance metrics are affected." - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 0.88 0.87 0.87 149\n", - " 1 0.87 0.88 0.88 151\n", - "\n", - "avg / total 0.87 0.87 0.87 300\n", - "\n", - "Overall Accuracy: 0.87\n" - ] - } - ], - "source": [ - "nnet = multilayer_perceptron.MultilayerPerceptronClassifier(hidden_layer_sizes=best_size, learning_rate_init=best_best_lr)\n", - "nnet.fit(XTrain, yTrain)\n", - "net_prediction = nnet.predict(XTest)\n", + "# Solution\n", + "parameters = [{'hidden_layer_sizes': layer_size_range, 'learning_rate_init': learning_rate_range}]\n", "\n", - "print metrics.classification_report(yTest, net_prediction)\n", - "print \"Overall Accuracy:\", round(metrics.accuracy_score(yTest, net_prediction),2)" + "grid = GridSearchCV(multilayer_perceptron.MultilayerPerceptronClassifier(), parameters, cv= 10)\n", + "grid.fit(XTrain, yTrain)\n", + "\n", + "best_size = grid.best_params_['hidden_layer_sizes']\n", + "best_best_lr = grid.best_params_['learning_rate_init']\n", + "print \"The best parameters are: hidden_layer_sizes=\", best_size, \" and learning_rate_init=\", best_best_lr" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Plot the results of the grid search using a heatmap." + "Plot the results of the grid search using a heatmap (see task 4.3)." ] }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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1USdHWiVJklpori55BTy0qt6cZB5weVXdleThE3UytEqSJLXQHF49YGWSF1TV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\najK3Jev/+5no9XcDDqMz2jr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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1553,14 +2167,19 @@ } ], "source": [ - "# plot the scores of the grid\n", - "# grid_scores_ contains parameter settings and scores\n", - "score_dict = gridcv.grid_scores_\n", - "scores = [x[1] for x in score_dict]\n", + "##########################################\n", + "# Write your code here \n", + "# 1. Fix the scores \n", + "# 2. Make a heatmap with the performance\n", + "# 3. Add the colorbar\n", + "##########################################\n", + "\n", + "\n", + "# Solution\n", + "scores = [x[1] for x in grid.grid_scores_]\n", "scores = np.array(scores).reshape(len(layer_size_range), len(learning_rate_range))\n", "scores = np.transpose(scores)\n", "\n", - "# Make a heatmap with the performance\n", "plt.figure(figsize=(12, 6))\n", "plt.imshow(scores, interpolation='nearest', origin='higher', cmap=plt.cm.get_cmap('jet_r'))\n", "plt.xticks(np.arange(len(layer_size_range)), layer_size_range)\n", @@ -1578,17 +2197,56 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "- *So, what is your best technique and why?*" + "Finally, testing our independent XTest dataset using the optimised model: " ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "metadata": { - "collapsed": true + "collapsed": false }, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.89 0.89 0.89 149\n", + " 1 0.89 0.89 0.89 151\n", + "\n", + "avg / total 0.89 0.89 0.89 300\n", + "\n", + "Overall Accuracy: 0.89\n" + ] + } + ], + "source": [ + "#################################################################################### \n", + "# Write your code here \n", + "# 1. Build the classifier using the optimal parameters detected by grid search \n", + "# 2. Train (fit) the model\n", + "# 3. Test (predict)\n", + "# 4. Report the performance metrics\n", + "#################################################################################### \n", + "\n", + "\n", + "## Solution ## \n", + "nnet = multilayer_perceptron.MultilayerPerceptronClassifier(hidden_layer_sizes=best_size, learning_rate_init=best_best_lr)\n", + "nnet.fit(XTrain, yTrain)\n", + "net_prediction = nnet.predict(XTest)\n", + "\n", + "print metrics.classification_report(yTest, net_prediction)\n", + "print \"Overall Accuracy:\", round(metrics.accuracy_score(yTest, net_prediction),2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- *So, what is your best technique and why?*" + ] } ], "metadata": { diff --git a/visplots.py b/visplots.py index c03db5f..4312fdb 100755 --- a/visplots.py +++ b/visplots.py @@ -4,6 +4,7 @@ from sklearn.svm import SVC from sklearn.linear_model import LogisticRegression from multilayer_perceptron import multilayer_perceptron +from sklearn.ensemble import RandomForestClassifier import numpy as np @@ -38,6 +39,37 @@ def knnDecisionPlot(XTrain, yTrain, XTest, yTest, n_neighbors, weights): plt.title("2-Class classification (k = %i, weights = '%s')" % (n_neighbors, weights)) plt.show() +def rfDecisionPlot(XTrain, yTrain, XTest, yTest): + plt.figure(figsize=(7,5)) + h = .02 # step size in the mesh + Xtrain = XTrain[:, :2] # we only take the first two features. + + # Create color maps + cmap_light = ListedColormap(["#AAAAFF", "#AAFFAA", "#FFAAAA"]) + cmap_bold = ListedColormap(["#0000FF", "#00FF00", "#FF0000"]) + + clf = RandomForestClassifier() + clf.fit(Xtrain, yTrain) + + # Plot the decision boundary. For that, we will assign a color to each + # point in the mesh [x_min, m_max]x[y_min, y_max]. + x_min, x_max = Xtrain[:, 0].min() - 1, Xtrain[:, 0].max() + 1 + y_min, y_max = Xtrain[:, 1].min() - 1, Xtrain[:, 1].max() + 1 + xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h)) + Z = clf.predict(np.c_[xx.ravel(), yy.ravel()]) + # Put the result into a color plot + Z = Z.reshape(xx.shape) + + plt.pcolormesh(xx, yy, Z, cmap = cmap_light) + plt.scatter(XTest[:, 0], XTest[:, 1], c = yTest, cmap = cmap_bold) + plt.contour(xx, yy, Z, colors=['k'], linestyles=['-'], levels=[0]) + plt.xlim(xx.min(), xx.max()) + plt.ylim(yy.min(), yy.max()) + plt.xlabel('Fixed acidity') + plt.ylabel('Volatile acidity') + plt.title("2-Class classification Random Forests") + plt.show() + def svmDecisionPlot(XTrain, yTrain, XTest, yTest, kernel): plt.figure(figsize=(7, 5))