From 85129a311715329c1e9a8bfc5cf8f99d5bb2c9ab Mon Sep 17 00:00:00 2001 From: Elena Chatzimichali Date: Wed, 26 Aug 2015 06:19:18 +0100 Subject: [PATCH] Final iPython notebooks --- EuroScipy.ipynb | 293 ++------ ...yExecuted.ipynb => EuroScipySolution.ipynb | 696 +++--------------- 2 files changed, 157 insertions(+), 832 deletions(-) rename EuroScipyExecuted.ipynb => EuroScipySolution.ipynb (67%) diff --git a/EuroScipy.ipynb b/EuroScipy.ipynb index 7f5e353..12300d3 100644 --- a/EuroScipy.ipynb +++ b/EuroScipy.ipynb @@ -91,10 +91,7 @@ }, "outputs": [], "source": [ - "### Write your code here ###\n", - "\n", - "# Solution #\n", - "wine.head() " + "### Write your code here ###" ] }, { @@ -135,11 +132,7 @@ "outputs": [], "source": [ "print \"X dimensions:\", ### Write your code here ###\n", - "print \"y dimensions:\", ### Write your code here ###\n", - "\n", - "# Solution #\n", - "# print \"X dimensions:\", X.shape \n", - "# print \"y dimensions:\", y.shape " + "print \"y dimensions:\", ### Write your code here ### " ] }, { @@ -179,11 +172,7 @@ }, "outputs": [], "source": [ - "### Write your code here ###\n", - "\n", - "# Solution #\n", - "# X = preprocessing.StandardScaler().fit_transform(X) \n", - "X = preprocessing.scale(X)" + "### Write your code here ###" ] }, { @@ -358,16 +347,7 @@ "# 2. Train (fit) the model\n", "# 3. Test (predict)\n", "# 4. Report the performance metrics\n", - "############################################\n", - "\n", - "\n", - "### Solution ### \n", - "knn99 = KNeighborsClassifier(n_neighbors=99)\n", - "knn99.fit(XTrain, yTrain)\n", - "yPredK99 = knn99.predict(XTest)\n", - "\n", - "print metrics.classification_report(yTest, yPredK99)\n", - "print \"Overall Accuracy:\", round(metrics.accuracy_score(yTest, yPredK99), 2)" + "############################################" ] }, { @@ -385,10 +365,7 @@ }, "outputs": [], "source": [ - "### Write your code here ### \n", - "\n", - "### Solution ### \n", - "visplots.knnDecisionPlot(XTrain, yTrain, XTest, yTest, n_neighbors= 99, weights=\"uniform\")" + "### Write your code here ### " ] }, { @@ -598,15 +575,7 @@ "# 2. Train (fit) the model\n", "# 3. Test (predict)\n", "# 4. Report the performance metrics\n", - "#############################################################\n", - "\n", - "## Solution ## \n", - "clf = RandomForestClassifier()\n", - "clf.fit(XTrain, yTrain)\n", - "predRF = clf.predict(XTest)\n", - "\n", - "print metrics.classification_report(yTest, predRF)\n", - "print \"Overall Accuracy:\", round(metrics.accuracy_score(yTest, predRF),2)" + "#############################################################" ] }, { @@ -627,10 +596,7 @@ "# 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)" + "### Write your code here ### " ] }, { @@ -642,6 +608,18 @@ "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, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# View the list of arguments to be optimised\n", + "help(RandomForestClassifier())" + ] + }, { "cell_type": "code", "execution_count": null, @@ -650,15 +628,23 @@ }, "outputs": [], "source": [ - "parameters = [{'n_estimators': [5, 10, 20, 50, 100], \n", - " 'max_depth': [5, 10, 15]}] \n", + "# Parameters you could investigate include: \n", + "n_estimators = [5, 10, 20, 50, 100]\n", + "max_depth = [5, 10, 15]\n", "\n", - "grid = GridSearchCV(RandomForestClassifier(), parameters, cv=10) \n", - "grid.fit(XTrain, yTrain)\n", + "# Also, you may choose any of the following\n", + "# max_features = [1, 3, 10]\n", + "# min_samples_split = [1, 3, 10]\n", + "# min_samples_leaf = [1, 3, 10]\n", + "# bootstrap = [True, False]\n", + "# criterion = [\"gini\", \"entropy\"]\n", "\n", - "best_n_estim = grid.best_params_['n_estimators']\n", - "best_max_depth = grid.best_params_['max_depth']\n", - "print \"The best parameters are: n_estimators=\", best_n_estim, \" and max_depth=\", best_max_depth" + "############################################################################################## \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" ] }, { @@ -672,7 +658,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": true + "collapsed": false }, "outputs": [], "source": [ @@ -682,16 +668,7 @@ "# 2. Train (fit) the model\n", "# 3. Test (predict)\n", "# 4. Report the performance metrics\n", - "#################################################################################### \n", - "\n", - "\n", - "## Solution ## \n", - "clfRDF = RandomForestClassifier(n_estimators=best_n_estim, max_depth=best_max_depth)\n", - "clfRDF.fit(XTrain, yTrain)\n", - "predRF = clfRDF.predict(XTest)\n", - "\n", - "print metrics.classification_report(yTest, predRF)\n", - "print \"Overall Accuracy:\", round(metrics.accuracy_score(yTest, predRF),2)" + "#################################################################################### " ] }, { @@ -723,15 +700,7 @@ "# 2. Train (fit) the model\n", "# 3. Test (predict)\n", "# 4. Report the performance metrics\n", - "##################################################################\n", - "\n", - "## Solution ## \n", - "linearSVM = SVC(kernel='linear', C=1.0)\n", - "linearSVM.fit(XTrain, yTrain)\n", - "yPredLinear = linearSVM.predict(XTest)\n", - "\n", - "print metrics.classification_report(yTest, yPredLinear)\n", - "print \"Overall Accuracy:\", round(metrics.accuracy_score(yTest, yPredLinear),2)" + "##################################################################" ] }, { @@ -752,11 +721,7 @@ "# 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')" + "### Write your code here ### " ] }, { @@ -791,15 +756,7 @@ "# 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", - "rbfSVM.fit(XTrain, yTrain)\n", - "yPredRBF = rbfSVM.predict(XTest)\n", - "\n", - "print metrics.classification_report(yTest, yPredRBF)\n", - "print \"Overall Accuracy:\", round(metrics.accuracy_score(yTest, yPredRBF),2)" + "################################################################# " ] }, { @@ -820,10 +777,7 @@ "# 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')" + "### Write your code here ### " ] }, { @@ -853,18 +807,7 @@ "# 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", - "\n", - "# Solution \n", - "parameters = [{'gamma': g_range, 'C': C_range}] \n", - "\n", - "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" + "############################################################################################## " ] }, { @@ -887,24 +830,7 @@ "# 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", - "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", - "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", - "plt.show()\n" + "##########################################" ] }, { @@ -928,16 +854,7 @@ "# 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", - "print \"Overall Accuracy:\", round(metrics.accuracy_score(yTest, predictions),2)" + "#################################################################################### " ] }, { @@ -965,15 +882,7 @@ "# 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", - "l_prediction = l_regression.predict(XTest)\n", - "\n", - "print metrics.classification_report(yTest, l_prediction)\n", - "print \"Overall Accuracy:\", round(metrics.accuracy_score(yTest, l_prediction),2)" + "#############################################################################" ] }, { @@ -994,10 +903,7 @@ "# 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)" + "### Write your code here ### " ] }, { @@ -1033,18 +939,7 @@ "# 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", - "grid = GridSearchCV(LogisticRegression(), parameters, cv= 10)\n", - "grid.fit(XTrain, yTrain)\n", - "\n", - "bestC = grid.best_params_['C']\n", - "bestP = grid.best_params_['penalty']\n", - "print \"The best parameters are: cost=\", bestC , \" and penalty=\", bestP" + "############################################################################################## " ] }, { @@ -1067,25 +962,7 @@ "# 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", - "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()" + "##########################################" ] }, { @@ -1109,16 +986,7 @@ "# 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", - "print metrics.classification_report(yTest, l_prediction)\n", - "print \"Overall Accuracy:\", round(metrics.accuracy_score(yTest, l_prediction),2)" + "#################################################################################### " ] }, { @@ -1170,17 +1038,7 @@ "# 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", - "\n", - "print metrics.classification_report(yTest, net_prediction)\n", - "print \"Overall Accuracy:\", round(metrics.accuracy_score(yTest, net_prediction),2)" + "#####################################################################################" ] }, { @@ -1202,12 +1060,7 @@ "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)" + "### Try arguments such as hidden_layer = 2 or (2,3,6) and learning_rate = .5" ] }, { @@ -1249,18 +1102,7 @@ "# 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", - "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" + "############################################################################################## " ] }, { @@ -1283,25 +1125,7 @@ "# 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", - "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()" + "##########################################" ] }, { @@ -1325,16 +1149,7 @@ "# 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)" + "#################################################################################### " ] }, { @@ -1361,7 +1176,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.8" + "version": "2.7.9" } }, "nbformat": 4, diff --git a/EuroScipyExecuted.ipynb b/EuroScipySolution.ipynb similarity index 67% rename from EuroScipyExecuted.ipynb rename to EuroScipySolution.ipynb index 70aeae1..64fe71f 100644 --- a/EuroScipyExecuted.ipynb +++ b/EuroScipySolution.ipynb @@ -23,7 +23,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": { "collapsed": false }, @@ -66,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": { "collapsed": false }, @@ -85,7 +85,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": { "collapsed": false }, @@ -209,15 +209,12 @@ "4 0 " ] }, - "execution_count": 4, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "### Write your code here ###\n", - "\n", - "# Solution #\n", "wine.head() " ] }, @@ -230,7 +227,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": { "collapsed": false }, @@ -252,7 +249,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": { "collapsed": false }, @@ -261,17 +258,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "X dimensions: y dimensions:\n" + "X dimensions: (1000, 10)\n", + "y dimensions: (1000,)\n" ] } ], "source": [ - "print \"X dimensions:\", ### Write your code here ###\n", - "print \"y dimensions:\", ### Write your code here ###\n", - "\n", - "# Solution #\n", - "# print \"X dimensions:\", X.shape \n", - "# print \"y dimensions:\", y.shape " + "print \"X dimensions:\", X.shape \n", + "print \"y dimensions:\", y.shape " ] }, { @@ -284,7 +278,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": { "collapsed": false }, @@ -314,17 +308,14 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "### Write your code here ###\n", - "\n", - "# Solution #\n", - "# X = preprocessing.StandardScaler().fit_transform(X) \n", - "X = preprocessing.scale(X)" + "X = preprocessing.scale(X)\n", + "# X = preprocessing.StandardScaler().fit_transform(X) " ] }, { @@ -336,7 +327,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": { "collapsed": false }, @@ -345,7 +336,7 @@ "data": { "image/png": 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iek/C9msauXBvrS7XvW7hTS7kKu7E1TdefPzQ+eqCmKPaaoXeOWCP6+tzCtsS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\nNlhbnJZG7pnDmlFEF0w+G+dENEu+kthWXFKU0Fl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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -388,7 +379,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": { "collapsed": false }, @@ -413,7 +404,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": { "collapsed": false }, @@ -458,7 +449,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": { "collapsed": false }, @@ -502,7 +493,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": { "collapsed": false }, @@ -521,7 +512,7 @@ "data": { "image/png": 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fYDA4rlkwd+7C9Oz5qcPK0zI2pRS7dy8nT54gus2Yz43IV7BYK/DLoUmc//cG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nMfbyGKR8iXrxy+jXKnun3VQoWoWkgSW0jQVufUvXsIgsD5hw8ZNaocBoMeURSoWbM3NjaOmJvb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\nI2N0OX4nXHj4kIbe3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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -545,7 +536,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": { "collapsed": false }, @@ -566,16 +557,6 @@ } ], "source": [ - "############################################ \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", "knn99 = KNeighborsClassifier(n_neighbors=99)\n", "knn99.fit(XTrain, yTrain)\n", "yPredK99 = knn99.predict(XTest)\n", @@ -593,7 +574,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": { "collapsed": false }, @@ -602,7 +583,7 @@ "data": { "image/png": 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8zZ13vpQlZSml0ic9vRxvDT4dYoyZizXekI63qFIVFubE798GXB2cspn8+c9/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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -610,9 +591,6 @@ } ], "source": [ - "### Write your code here ### \n", - "\n", - "### Solution ### \n", "visplots.knnDecisionPlot(XTrain, yTrain, XTest, yTest, n_neighbors= 99, weights=\"uniform\")" ] }, @@ -636,7 +614,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": { "collapsed": false }, @@ -689,7 +667,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": { "collapsed": false }, @@ -730,7 +708,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": { "collapsed": false }, @@ -739,7 +717,7 @@ "data": { "image/png": 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HWHrpPMS8ch5i3jT+mADL5yHmfFx/gKvmIea8vK/m6VrNx9BQ8zXc1Ly8r+br\nvM7Dv4Hvjj/kvJxTmJ/Pq/k4fpin/1vm6z2wlg7lNkwfp5pOsgjYIsky4As0I5u9oKqeNF2ZaRPk\nJIcm+XSSH9HcIPdsmpEnngc8cIz1Xif9eun8jNkyL/+Rz+mWyg5xfzkPMdelBPmqeYi5LiXI83D9\nwQTZBHn8McEEeT4+r+YtQZ6P/1tMkO/StwQ5yTnA12hGPHtZVe1aVe+qqqFfG4eNYrEI+Azw+qpa\nB/8JSJIkaVDfbtID/qqqzk6yObOYE2NYH+TXj6VakiRJ0r3jZ0m+QzMBHkmuA15SVZcNK5Qq58sY\nlMQTIkmSxqqqOrdezpck9cV6xkgx9s+pa8WxdJXk68CHquqL7fL+wGuqat9h5bpMFNIr69JFlyRJ\nmo11sR/xiLaaSI4BquqLSd4xUyETZEmSpJ7oYYK8MskmVfVbgCQbw8wnYegwb5IkSdI67AWsnu8u\naNcNZYI8S0mOSXJDkgvHGPM+Sc5MsjzJ95K8a4yxr0pyQZLzkpw1hng7tbEmHj9P8tox1fXQJBcm\nuSjJnMbanur6JHlhkouTrEry2DHG/Yck57fX7etJFo4h5pIk1wyc3/3GEPOEgXhXJjlvNjGHxN0t\nyXfaf18nJdliljEXJjm9vTYXTfw7GvV6DYk75+s1JOacr9eQmJ8e5XoNiTvn6zXdZ9QYrtV0cUe5\nVtPFHOVaTRdz1Gs1XdyR3lttjAVtvb7ULo/8OThN3JE+B6eJOdLn4DQxR7pWQ+KOfK3WtAXcMdJj\nXVNVVwG/n+S9Sd4LLJ5piDfwJr1ZS7IP8Evg+Kp6zBjjblpVv06yIfC/wBur6n/HEPdKYM+qunnk\nSt4z9gY0U5DvVVVXjxjr94BPAY8HVgIn0wzNcvks49zj+iR5FHAncCTwhqo6dw71myruFlV1a/v8\nNcBuVfUXI8Z8O3BrVb1/tnWcLuak7e8Fbqmqfxw1bpKzgb+pqjOSvBzYbjYzbCbZBtimqpanGX7n\nHOC5QDHC9RoS95q5Xq8hMV/EHK/XdDGrasXAPrO+XkPqejyjXa97fEYBP2X099ZUcc8f8b01Vcyn\nMdp7a+hn9Ajvranq+i+McK3auH8D7AlsUVXPGcfn4DRxR/ocnCbmSJ+DU8WctG1O12qauo70Obim\nJalvTD83RidPzXfWqfu1krwJeA5wLM3/L68ATqqq9wwrZwvyLFXVGcDP5iHur9unG9M0/48zoZ2v\nf8j7ApcNXGSjAAAYv0lEQVSPmhy3HgWcWVW3VdUqmslp/mS2Qaa6PlV1SVV9f5TKTRN3cL6nzWmS\nhZFituZ8vYb9+0wSmoTuU2OKu2O7HppB2J8/y5g/rqrl7fNf0kxEtO2o12tI3Dlfr2liPrTdPKfr\nNV09J7bP9XoNqeuo1+sen1Fjem9NFXfU99bkmBP/dkd5b037GT3ie2uquo50rZI8DPgj4GO0xzyO\nazVN3JGu1VQx279zvlbTxJzYNudrNU3cka7VvaFvE4UALwX2raqPVdXRNF+WXzJTIRPktUSSDZIs\np5kf/PSq+t6YQhfwtSTLkrxqTDEn/CnwyTHFugjYJ8kDk2wK/DHtmIVrsyTvTDPb5MuAd48p7Gva\nnyyPTnL/McUE2Ae4Ybat8kNcnGa4HIAXArP+aXVCmmlA9wDOHL1a08cdx/UaiDkxkdjI12ua4x/5\nek2KO9L1mq/PqOnijnKtpoh5cbtpztdqhuOf87Wapq6jvrf+BfhbmhbjcZoy7ojvq6liFqO9r4Yd\n/yjvq6niju1zcE3pYYJ8e1X9ZmKhqm6jw3vDBHktUVV3VtXuNEnh7ydZPKbQT66qPYBnAq9ufyof\nWZq7QJ8N/Nc44lXVJcB7gFOBrwLnMf4P97Grqr+rqocDx9F8eI7q34HtgN2B64H3jSHmhAMY3xca\naH6mOjjN3PabA3OazLftBvBZ4NC2xXMspoo76vWaIubI12vI8Y90vSbFvZURr9d8fUZNF3eUazVN\nzJGu1QzHP+drNU3cOV+rJM8Cbqyq8xjjr4fD4s71Wg2JOedr1eH453SthsQdy+eg5tWXkzxgYqH9\nwvWVmQqZIK9lqurnwJeBx40p3vXt358Anwf2GkdcmoT7nDbuWFTVMVX1uKp6CnALcOm4Yq8Bn6Tp\nPz2SqrqxWjQ/443lerX9G58HfHoc8QCq6tKq+sOqehxwAjCX1rONgM8B/1lVXxhX3TrEnfX1mirm\nqNdrunqOer2mqevI16uNM9bPqA5x5/zeGow5rvfW5HqO6701qa6jXKu9geekuf/kU8BTkxw/St1m\nEXe212rKmCNeq2nrOeK1mq6uY3lfrUl9a0GuqrdW1c8Glm+pqsNnKmeCvBZIstXET0hJ7gs8naYF\nddS4m6a9ozbJZsAzgHGNvnEAc+jDNUySh7R/H07zITbO1s67XmZsgZIdBxb3ZzzX7HcGFp/H+K7X\nvsCKqrpuTPFI8uD27wbAW2lafWZTPsDRwPeq6gPT7TaHek0Zd5TrNSTmnK/XDMc/5+s1pK5zvl4d\nP6Pmcq2mjJvkEQO7zfZaTRdzm4HdZnuthh3/KNdqurrO+VpV1eFVtbCqtqPpBveNqnrp5JeebV2n\nizvK+2pIzDm/r2Y4/jlfqyF1Helz8N7Qt1Es0oy+dVSS09KM8HN6kqUzlXOikFlK8ingKcCDklwN\nvK2qjh0x7O8AH2/fYBsA/6+qvj5iTICtgc83/1+yIfCJqjp11KBtsr0vMO4+zZ9N8iCaUSwOrqpf\nzKFuE9dnq/b6vJ3mZpp/Bbai+anlvKp65hji/lGSnWgGHL8c+OsxxFycZHeaPnhXAgfNMebkf58v\nZoQvNNPUdfMkr253+VxVHTfLsE8G/gy4IHcPuXQ4sAmjXa/p4r5yhOs1XcwDRrheU8V8S1WdzGjX\na7q67jjC9ZryMyrJ84APMfdrNV3cz45wraaLefwI12rYZ/Qo12q6uh6a5OB2n7m8twYVwBiu1aBM\nxAXeNcrn4DQx/ynJbszxc3CSwaG6RvocnCbu/zfGa7VGbLgOtgKP6DM0X1yO5O6umzN+SXSYN0mS\npB5IUufWziPFeGxWrGvDvJ1bVbMe+9sWZEmSpJ5YF/sRj+jLSV5Hc9PybRMrq2rokIQmyJIkST3R\nwwT5JTRdKibP0LvdsEImyJIkST2xLt5oN4qq2n4u5UyQJUmStF5K8jKmuClvphsqHeZNkiSpJzZk\n1UiPqSTZL8klSX6Q5E1TbN8qyclJlie5KMmB7fqdkpw38Ph5kte22x7YDs32/SSnZu4zy+458NgH\n+AfgT2Yq5CgWkiRJPZCkrqjfmXnHIbbP9auNYpFkAc3EXvsC1wJnAwdU1YqBfZYAm1TVW5Js1e6/\ndVXdMbDPBm35varq6iT/BPy0qv6pTbofUFVvHqny3DWT3ueq6mnD9rMFWRIASe5M8t6B5TcmefuY\nYh+X5PnjiDXD67wwyfeSfH3S+kVJLhxYflWSZUnuN991moskL5s0WcIosbZNMuOU8EmmnOZ7TV07\nSWvGPMyktxdwWVVdVVUraWYU3H/SPtcDW7bPtwRuGkyOW/sCl1fV1e3yc4CPt88/Djx3xEMHmpn0\ngA3SzKw4LRNkSRNuB57XTtYCqw+wP6o5x5rpQ2ySVwJ/MaxlIMmfA4cAz2in9513bcvIbBwIbDuO\n166q66rqhV12neX6Gc3y2klaNz0UuHpg+Zp23aCjgF2SXAeczz1HlIBmhsLBGXS3rqob2uc30Ex+\nNmtJHpTktUkOTLJR2+L9rCkS9NWYIEuasBL4KPD6yRsmtyJOtDYmWZzkm0m+kOTyJO9O8udJzkpy\nQZLBu4f3TXJ2kkuT/HFbfkGSf273Pz/JXw7EPSPJF4GLp6jPAW38C5O8u133NppZ5I5pf5q7hyQv\nAt4EPL2qbp7mOD+Y5Fvt8Qwe898O1HPJwPrPt63RFyV51cD6XyZ5b5LlwJOS/FmSM9t+dv+RZIP2\n+I9rj+OCJK9rX/NxwCeSnJvkPpPquLQ9z2e25/L/zHAu72o9TzP9/GeSXJzkxCTfTfLYgdj/mKaP\n4HfSTv0+5NrdJ8mxbb3PTbK4XX9gkpPaVvzTkmyT5H/a475wor6S7h2zbTE+c+ltfGDJrXc9ptDl\nS/ThwPKq2hbYHfhIki0mNibZGHg2MOWvXdX0B57rl/UvAY8A9gP+BdgU+OJMhfx2L2nQv9FMUTw5\nwZz8wTS4vCvwKOBnNNPCHlVVe6W50eI1NAl3gN+tqscneQRwevv3ZcAt7f6bAP+bZGI69D2AXarq\nh4MvnGRb4N3AY4FbgFOT7F9V/zfJHwBvqKpzpzi2RTRTWO9eVTdOc/wFbFNVT06yM3AS8LkkzwAe\n0dZzA+CLSfapqjOAV1TVz5LcFzgryWer6mc0H8Lfrao3trHeBOxdVauSfIRmbM6LgW2r6jHtsW1Z\nVb9IcsiQ4yhgQVU9Ickzaab9fjpN6/l053LCwTQ/be6SZBdg+cC2zYDvVNVbk7yHZir5dzL9tXs1\nsKqqdk0z1fCpSR7ZxtoDeExV3ZLkDcDJVXVEkrSvI+leMtth3vZZHPZZvPFdy//yjt9M3uVaYOHA\n8kKaVuRBe9N8nlBVlye5EtgJWNZufyZwTlX9ZKDMDUm2qaofp+lyNt3n9kw2r6rXpmk5Preqbk3y\ngJkK2YIs6S5VdStwPPDaWRQ7u6puqKrbgcuAU9r1F9EkpdAkdZ9pX+My4AqapPoZwEuTnAd8F3gg\nzTd9gLMmJ8etxwOnV9VNVbUK+ATw+wPbp5sC9Ubgh8CLZzieL7T1XMHdP+k9A3hGW89zaD7YJ+p5\naNtK/B2a/xh2bNevAj7XPn8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av7EMG/QAAAAASUVORK5CYII=\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -777,7 +755,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "metadata": { "collapsed": false }, @@ -822,7 +800,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "metadata": { "collapsed": false }, @@ -831,14 +809,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "Best parameters: n_neighbors= 61\n" + "Best parameters: n_neighbors= 13\n" ] }, { "data": { - "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -878,7 +856,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 20, "metadata": { "collapsed": false }, @@ -889,26 +867,17 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.85 0.89 0.87 149\n", - " 1 0.88 0.85 0.86 151\n", + " 0 0.85 0.91 0.88 149\n", + " 1 0.90 0.84 0.87 151\n", "\n", - "avg / total 0.87 0.87 0.87 300\n", + "avg / total 0.88 0.87 0.87 300\n", "\n", "Overall Accuracy: 0.87\n" ] } ], "source": [ - "############################################################# \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", - "clf = RandomForestClassifier(n_estimators=5)\n", + "clf = RandomForestClassifier()\n", "clf.fit(XTrain, yTrain)\n", "predRF = clf.predict(XTest)\n", "\n", @@ -925,7 +894,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 21, "metadata": { "collapsed": false }, @@ -942,9 +911,9 @@ }, { "data": { - "image/png": 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qiMrOKMeVxpjLRWQV0NwYkyoi640xeT5GuriMcizorFGOe6hYsTYREVFZbj9k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\n/g/M4+fqaYT4ogAAAABJRU5ErkJggg==\n", 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BqFLlUu6/fxgzZrTAZquB13uIwYO/ON3bMCdut5OhQzuyf/8WwEKrVjfz7LPn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\n/MXEif04dGgzbnck8CDwGL7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wlVpAPBZLKS5vfgvxUVcwduxHlC5dgTff7E5S0t80aHAN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\n5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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -954,10 +923,7 @@ "source": [ "# Check the arguments of the function\n", "help(visplots.rfDecisionPlot)\n", - "\n", - "### Write your code here ### \n", - "\n", - "### Solution ### \n", + " \n", "visplots.rfDecisionPlot(XTrain, yTrain, XTest, yTest)" ] }, @@ -972,7 +938,19 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# View the list of arguments to be optimised\n", + "help(RandomForestClassifier())" + ] + }, + { + "cell_type": "code", + "execution_count": 23, "metadata": { "collapsed": false }, @@ -981,15 +959,26 @@ "name": "stdout", "output_type": "stream", "text": [ - "The best parameters are: n_estimators= 100 and max_depth= 10\n" + "The best parameters are: n_estimators= 50 and max_depth= 15\n" ] } ], "source": [ - "parameters = [{'n_estimators': [5, 10, 20, 50, 100], \n", - " 'max_depth': [5, 10, 15]}] \n", + "# Parameters you could investigate include: \n", + "n_estimators = [5, 10, 20, 50, 100]\n", + "max_depth = [5, 10, 15]\n", + "\n", + "# Also, you may choose any of the following\n", + "# max_features = [1, 3, 10]\n", + "# min_samples_split = [1, 3, 10]\n", + "# min_samples_leaf = [1, 3, 10]\n", + "# bootstrap = [True, False]\n", + "# criterion = [\"gini\", \"entropy\"]\n", + "\n", + "parameters = [{'n_estimators': n_estimators, \n", + " 'max_depth': max_depth}] \n", "\n", - "grid = GridSearchCV(RandomForestClassifier(), parameters, cv=10) \n", + "grid = GridSearchCV(RandomForestClassifier(), param_grid=parameters, cv=10)\n", "grid.fit(XTrain, yTrain)\n", "\n", "best_n_estim = grid.best_params_['n_estimators']\n", @@ -1006,7 +995,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 24, "metadata": { "collapsed": false }, @@ -1017,26 +1006,16 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.87 0.91 0.89 149\n", - " 1 0.91 0.86 0.88 151\n", + " 0 0.86 0.96 0.91 149\n", + " 1 0.96 0.85 0.90 151\n", "\n", - "avg / total 0.89 0.89 0.89 300\n", + "avg / total 0.91 0.90 0.90 300\n", "\n", - "Overall Accuracy: 0.89\n" + "Overall Accuracy: 0.9\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", "clfRDF = RandomForestClassifier(n_estimators=best_n_estim, max_depth=best_max_depth)\n", "clfRDF.fit(XTrain, yTrain)\n", "predRF = clfRDF.predict(XTest)\n", @@ -1083,15 +1062,6 @@ } ], "source": [ - "################################################################### \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', C=1.0)\n", "linearSVM.fit(XTrain, yTrain)\n", "yPredLinear = linearSVM.predict(XTest)\n", @@ -1128,7 +1098,7 @@ "data": { "image/png": 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bgEdcWxri5dXMtRKDEFd3vYkX8sJ110NzJ7mHVrilpiYSE7ODIkWKExraIE97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ThVqRJH582j5kujOXflB0DbizTo+zOlt61IszZLIuEnT7J23z483d3p3779TUXdUkriEhJyJFl8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\n3yaLhZ9CQ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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1139,10 +1109,6 @@ "# 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')" ] }, @@ -1187,15 +1153,6 @@ } ], "source": [ - "################################################################# \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", "rbfSVM.fit(XTrain, yTrain)\n", "yPredRBF = rbfSVM.predict(XTest)\n", @@ -1232,7 +1189,7 @@ "data": { "image/png": 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P8EWLKFuoELXbzM10q8+mxrFjO5k9eyBXrlykUqX6tG373n0tLeFyuVi4cBhH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RzUaJM4Tlrg7NmdDBxYFJPJyPTp5yhb9u330NM6SlCIHRjWujU1z53jljUoZKVGw+7Wrdl84gQe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\nLMaHEU/IZ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BcHBxUmU8z0nrRAmgwKqeznx9/Dh9Nn4UKu3L9vb3PSPF1nzcLn4kWems1s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\nqgDKSyn1rze0Op94KyMIIfIBA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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1243,9 +1200,6 @@ "# 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')" ] }, @@ -1279,15 +1233,6 @@ "g_range = 2. ** np.arange(-15, 5, step=2)\n", "C_range = 2. ** np.arange(-5, 15, step=2)\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 (don't forget to use np.log2() this time)\n", - "############################################################################################## \n", - "\n", - "\n", - "# Solution \n", "parameters = [{'gamma': g_range, 'C': C_range}] \n", "\n", "grid = GridSearchCV(SVC(), parameters, cv= 10) \n", @@ -1316,7 +1261,7 @@ "data": { "image/png": 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BVwcPRsTTjSo58ZqZWa56OPF+kuTW8herjjdcSMiJ18zMbBgi4u3DqefEa2Zm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\nJl5JfynpNkmvSvpy1bn5ku6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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1324,16 +1269,6 @@ } ], "source": [ - "##########################################\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", @@ -1379,16 +1314,6 @@ } ], "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", "rbfSVM = SVC(kernel='rbf', C = bestC, gamma = bestG)\n", "rbfSVM.fit(XTrain, yTrain)\n", "predictions = rbfSVM.predict(XTest) \n", @@ -1431,15 +1356,6 @@ } ], "source": [ - "#############################################################################\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", "l_prediction = l_regression.predict(XTest)\n", @@ -1476,7 +1392,7 @@ "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": {}, @@ -1487,9 +1403,6 @@ "# 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)" ] }, @@ -1528,16 +1441,6 @@ "pen = ['l1','l2']\n", "C_range = 2. ** np.arange(-5, 15, step=2)\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 = [{'C': C_range, 'penalty': pen}]\n", "\n", "grid = GridSearchCV(LogisticRegression(), parameters, cv= 10)\n", @@ -1566,7 +1469,7 @@ "data": { "image/png": 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5o9NiVIQKRQi5cRARQnaJCCEU4l3dIOn7pEiV/6pdt33XEMsWBGHFKUmzr+lkFrXybVd3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\ncP43238bRpkGhKR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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1574,15 +1477,6 @@ } ], "source": [ - "##########################################\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", @@ -1630,16 +1524,6 @@ } ], "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", "l_regression = LogisticRegression(C=bestC, penalty=bestP)\n", "l_regression.fit(XTrain, yTrain)\n", "l_prediction = l_regression.predict(XTest)\n", @@ -1672,347 +1556,18 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "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", - " | In multi-label classification, this is the subset accuracy\n", - " | which is a harsh metric since you require for each sample that\n", - " | each label set be correctly predicted.\n", - " | \n", - " | Parameters\n", - " | ----------\n", - " | X : array-like, shape = (n_samples, n_features)\n", - " | Test samples.\n", - " | \n", - " | y : array-like, shape = (n_samples) or (n_samples, n_outputs)\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" - ] - } - ], + "outputs": [], "source": [ "help(multilayer_perceptron.MultilayerPerceptronClassifier)" ] }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 37, "metadata": { "collapsed": false, "scrolled": true @@ -2024,27 +1579,16 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.89 0.88 0.89 149\n", - " 1 0.88 0.89 0.89 151\n", + " 0 0.89 0.87 0.88 149\n", + " 1 0.87 0.89 0.88 151\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": [ - "##################################################################################### \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", @@ -2063,7 +1607,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 38, "metadata": { "collapsed": false }, @@ -2080,9 +1624,9 @@ }, { "data": { - "image/png": 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VjNs6npVkJ/ydUupOjFPMpzBOxeYIcoQnhBAiR5AjPCGEEDmC33SkmxSllBx+CiGEuI7W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\ne6xxDo2d7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nw1JK5XLp3pYgIsHGGEeO6PL6bQnJNX3lU9q06Uv79rdlaj5jDPfdWZy58adp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MhNHg/TjaE91lXhNmADUBRoEhbGuCFDzutgfqEDx4/TeOhQ7k5JIcQY5gYFsWzs2LR2RgXNmH8v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\n2d4Ua4z5G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zp/WzOiwhhEiTJNAc4GOzUa1aE378cQDR0SetDidX2r9/I5H/LiHcEctwNKsd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pRT58/7/Bkk6Vq/PhOffZYnvTNpp77YmSolduJvyYm/XwQvN47iqZo1r9NK1ud0uTGqymrgMaAl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\ntcf7cAMQY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VUhUyNqzsp3DevJQsWYNNm+bgcERbHU6WExiYn0EjNzC+2L1U8svJkrIPMHDk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\ni0vNFStWm2LFaicdHz/+JzNm/IQozkL9CX4LtCM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narR4eGZLdcmtx49jCA3di9UaDeiR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\n01qvNTmsb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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2103,11 +1647,6 @@ "# 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)" ] @@ -2134,7 +1673,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 39, "metadata": { "collapsed": false }, @@ -2143,7 +1682,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The best parameters are: hidden_layer_sizes= (3, 2) and learning_rate_init= 0.1\n" + "The best parameters are: hidden_layer_sizes= 5 and learning_rate_init= 1.0\n" ] } ], @@ -2153,16 +1692,6 @@ "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", @@ -2182,16 +1711,16 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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xjlvLVNXlVfVyOuuz/j3wriTfSvKqJMum62/SKkmS1EYLkLQmWZXk0iSXJXnz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\nWZFk0gXCkxye5BmTHF+Z5NQ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kyZgvUFVJ2rzOaO5M8kbgL4H9k4QWuarJrCRJUgctHL1UYEy/XvZdfr3se+N1uQFY1He8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\nsjaJ8jzW/339Enh1VT0RWAr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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2199,15 +1728,6 @@ } ], "source": [ - "##########################################\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", @@ -2234,7 +1754,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 41, "metadata": { "collapsed": false }, @@ -2245,26 +1765,16 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.84 0.89 0.87 149\n", - " 1 0.89 0.83 0.86 151\n", + " 0 0.86 0.88 0.87 149\n", + " 1 0.88 0.86 0.87 151\n", "\n", - "avg / total 0.86 0.86 0.86 300\n", + "avg / total 0.87 0.87 0.87 300\n", "\n", - "Overall Accuracy: 0.86\n" + "Overall Accuracy: 0.87\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", @@ -2297,7 +1807,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.8" + "version": "2.7.9" } }, "nbformat": 4,