ML_AI/grid_search.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"grid_search.ipynb","provenance":[],"collapsed_sections":[],"toc_visible":true,"authorship_tag":"ABX9TyM7dp8pb4qDtalXRx2+Gtjm"},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"taLo6Q7NxKRE","colab_type":"text"},"source":["# Grid Search"]},{"cell_type":"markdown","metadata":{"id":"xQ1Zs9MKxXJ1","colab_type":"text"},"source":["## Importing the libraries"]},{"cell_type":"code","metadata":{"id":"D3x14CwtxcV5","colab_type":"code","colab":{}},"source":["import numpy as np\n","import matplotlib.pyplot as plt\n","import pandas as pd"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"1L1wq9evxg83","colab_type":"text"},"source":["## Importing the dataset"]},{"cell_type":"code","metadata":{"id":"TMeZ6FKbxkU6","colab_type":"code","colab":{}},"source":["dataset = pd.read_csv('Social_Network_Ads.csv')\n","X = dataset.iloc[:, :-1].values\n","y = dataset.iloc[:, -1].values"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"i3FBTRwzxzl7","colab_type":"text"},"source":["## Splitting the dataset into the Training set and Test set"]},{"cell_type":"code","metadata":{"id":"V5TZ-0qSx5mX","colab_type":"code","colab":{}},"source":["from sklearn.model_selection import train_test_split\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"WPt8zMSIxrJ7","colab_type":"text"},"source":["## Feature Scaling"]},{"cell_type":"code","metadata":{"id":"c60y-wPDxvY-","colab_type":"code","colab":{}},"source":["from sklearn.preprocessing import StandardScaler\n","sc = StandardScaler()\n","X_train = sc.fit_transform(X_train)\n","X_test = sc.transform(X_test)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"29ry3GSGx9P_","colab_type":"text"},"source":["## Training the Kernel SVM model on the Training set"]},{"cell_type":"code","metadata":{"id":"BUM0MSyGyCjF","colab_type":"code","outputId":"60c2a200-eb87-414e-92d2-57009dcc5a27","executionInfo":{"status":"ok","timestamp":1590794428964,"user_tz":-240,"elapsed":1927,"user":{"displayName":"Hadelin de Ponteves","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64","userId":"15047218817161520419"}},"colab":{"base_uri":"https://localhost:8080/","height":86}},"source":["from sklearn.svm import SVC\n","classifier = SVC(kernel = 'rbf', random_state = 0)\n","classifier.fit(X_train, y_train)"],"execution_count":5,"outputs":[{"output_type":"execute_result","data":{"text/plain":["SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n","    decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n","    max_iter=-1, probability=False, random_state=0, shrinking=True, tol=0.001,\n","    verbose=False)"]},"metadata":{"tags":[]},"execution_count":5}]},{"cell_type":"markdown","metadata":{"id":"fBbur5RQyTcL","colab_type":"text"},"source":["## Making the Confusion Matrix"]},{"cell_type":"code","metadata":{"id":"_1llVHNPyWkZ","colab_type":"code","outputId":"db224f96-fd7d-4e5b-da84-53ae1aacc922","executionInfo":{"status":"ok","timestamp":1590794428965,"user_tz":-240,"elapsed":1923,"user":{"displayName":"Hadelin de Ponteves","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64","userId":"15047218817161520419"}},"colab":{"base_uri":"https://localhost:8080/","height":68}},"source":["from sklearn.metrics import confusion_matrix, accuracy_score\n","y_pred = classifier.predict(X_test)\n","cm = confusion_matrix(y_test, y_pred)\n","print(cm)\n","accuracy_score(y_test, y_pred)"],"execution_count":6,"outputs":[{"output_type":"stream","text":["[[64  4]\n"," [ 3 29]]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["0.93"]},"metadata":{"tags":[]},"execution_count":6}]},{"cell_type":"markdown","metadata":{"id":"U-eGh0nuyabx","colab_type":"text"},"source":["## Applying k-Fold Cross Validation"]},{"cell_type":"code","metadata":{"id":"N5puHSW9ydyi","colab_type":"code","outputId":"1b9131fb-e262-4204-8415-e0ba740c4e93","executionInfo":{"status":"ok","timestamp":1590794428965,"user_tz":-240,"elapsed":1918,"user":{"displayName":"Hadelin de Ponteves","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64","userId":"15047218817161520419"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["from sklearn.model_selection import cross_val_score\n","accuracies = cross_val_score(estimator = classifier, X = X_train, y = y_train, cv = 10)\n","print(\"Accuracy: {:.2f} %\".format(accuracies.mean()*100))\n","print(\"Standard Deviation: {:.2f} %\".format(accuracies.std()*100))"],"execution_count":7,"outputs":[{"output_type":"stream","text":["Accuracy: 90.33 %\n","Standard Deviation: 6.57 %\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"EHE5GWnf0hZJ","colab_type":"text"},"source":["## Applying Grid Search to find the best model and the best parameters"]},{"cell_type":"code","metadata":{"id":"KU4I-ALV0kj0","colab_type":"code","outputId":"59af0d43-e2cc-45f1-d07c-e4bffa3124e4","executionInfo":{"status":"ok","timestamp":1590794430907,"user_tz":-240,"elapsed":3855,"user":{"displayName":"Hadelin de Ponteves","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64","userId":"15047218817161520419"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["from sklearn.model_selection import GridSearchCV\n","parameters = [{'C': [0.25, 0.5, 0.75, 1], 'kernel': ['linear']},\n","              {'C': [0.25, 0.5, 0.75, 1], 'kernel': ['rbf'], 'gamma': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]}]\n","grid_search = GridSearchCV(estimator = classifier,\n","                           param_grid = parameters,\n","                           scoring = 'accuracy',\n","                           cv = 10,\n","                           n_jobs = -1)\n","grid_search.fit(X_train, y_train)\n","best_accuracy = grid_search.best_score_\n","best_parameters = grid_search.best_params_\n","print(\"Best Accuracy: {:.2f} %\".format(best_accuracy*100))\n","print(\"Best Parameters:\", best_parameters)"],"execution_count":8,"outputs":[{"output_type":"stream","text":["Best Accuracy: 90.67 %\n","Best Parameters: {'C': 0.5, 'gamma': 0.6, 'kernel': 'rbf'}\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"M6HUu8z8ygbT","colab_type":"text"},"source":["## Visualising the Training set results"]},{"cell_type":"code","metadata":{"id":"wkjkHN6Qykw3","colab_type":"code","outputId":"132603b8-7014-4522-9f1d-bf9c283ddc32","executionInfo":{"status":"ok","timestamp":1590794432060,"user_tz":-240,"elapsed":5003,"user":{"displayName":"Hadelin de Ponteves","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64","userId":"15047218817161520419"}},"colab":{"base_uri":"https://localhost:8080/","height":349}},"source":["from matplotlib.colors import ListedColormap\n","X_set, y_set = X_train, y_train\n","X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n","                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n","plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n","             alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n","plt.xlim(X1.min(), X1.max())\n","plt.ylim(X2.min(), X2.max())\n","for i, j in enumerate(np.unique(y_set)):\n","    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n","                c = ListedColormap(('red', 'green'))(i), label = j)\n","plt.title('Kernel SVM (Training set)')\n","plt.xlabel('Age')\n","plt.ylabel('Estimated Salary')\n","plt.legend()\n","plt.show()"],"execution_count":9,"outputs":[{"output_type":"stream","text":["'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n","'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"],"name":"stderr"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"9rLgiI19yn8m","colab_type":"text"},"source":["## Visualising the Test set results"]},{"cell_type":"code","metadata":{"id":"PSuas5JgyrpC","colab_type":"code","outputId":"dc6a7b97-42b3-4aa4-f1ea-c878dcfb6f0f","executionInfo":{"status":"ok","timestamp":1590794432662,"user_tz":-240,"elapsed":5601,"user":{"displayName":"Hadelin de Ponteves","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64","userId":"15047218817161520419"}},"colab":{"base_uri":"https://localhost:8080/","height":349}},"source":["from matplotlib.colors import ListedColormap\n","X_set, y_set = X_test, y_test\n","X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n","                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n","plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n","             alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n","plt.xlim(X1.min(), X1.max())\n","plt.ylim(X2.min(), X2.max())\n","for i, j in enumerate(np.unique(y_set)):\n","    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n","                c = ListedColormap(('red', 'green'))(i), label = j)\n","plt.title('Kernel SVM (Test set)')\n","plt.xlabel('Age')\n","plt.ylabel('Estimated Salary')\n","plt.legend()\n","plt.show()"],"execution_count":10,"outputs":[{"output_type":"stream","text":["'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n","'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"],"name":"stderr"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]}]}