sklearn_offline.ipynb

{"cells":[{"cell_type":"markdown","source":"This notebook was made to test out the model locally before training and deploying it on SageMaker","metadata":{"tags":[],"cell_id":"00000-b5a82f4f-fa6f-4b68-8b27-612c5890b967"}},{"cell_type":"code","metadata":{"tags":[],"cell_id":"00001-9769254a-ac5a-4745-9fbd-77c3d61322ac"},"source":"# imports\nimport pandas as pd\nfrom sklearn import svm\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.preprocessing import StandardScaler","execution_count":8,"outputs":[]},{"cell_type":"markdown","source":"Creation of model:","metadata":{"tags":[],"cell_id":"00002-1f4bd368-b1cb-428e-a3b0-28fee86629c2"}},{"cell_type":"code","metadata":{"tags":[],"cell_id":"00000-e060d0f2-c75a-4ecf-abc9-07610f4c68da"},"source":"# Read in csv training file\ntrain_data = pd.read_csv(\"plagiarism_data/train.csv\", header=None, names=None)\n\n# Labels are in the first column\ntrain_y = train_data.iloc[:,0]\ntrain_x = train_data.iloc[:,1:]\n\n# define model\n# model = svm.SVC(kernel='poly', C=1, gamma=0.01)\n# model = svm.SVC()\n# model = svm.LinearSVC()\n# model = svm.SVC(kernel='linear', C=1, gamma=0.001)\n# model = GaussianNB()\n# model.fit(train_x, train_y)\nmodel = svm.LinearSVC()\n\n# train model\nmodel.fit(train_x, train_y)\n\n","execution_count":46,"outputs":[{"output_type":"execute_result","execution_count":46,"data":{"text/plain":"LinearSVC()"},"metadata":{}}]},{"cell_type":"markdown","source":"Prediction:","metadata":{"tags":[],"cell_id":"00004-5e7083a9-b553-4699-bdec-b9bdc7a32596"}},{"cell_type":"code","metadata":{"tags":[],"cell_id":"00001-ae19d9df-6a55-4669-a29b-be555e888c36"},"source":"test_data = pd.read_csv(\"plagiarism_data/test.csv\", header=None, names=None)\n# labels are in the first column\ntest_y = test_data.iloc[:,0]\ntest_x = test_data.iloc[:,1:]\npred = model.predict(test_x)\n\nfor value in zip(pred, test_y.values):\n    print(value)\n# print(\"Predictions: \", pred)\n# print(\"Actual: \", test_y.values)","execution_count":47,"outputs":[{"name":"stdout","text":"(1, 1)\n(1, 1)\n(1, 1)\n(1, 1)\n(1, 1)\n(1, 1)\n(0, 0)\n(0, 0)\n(0, 0)\n(0, 0)\n(0, 0)\n(0, 0)\n(1, 1)\n(1, 1)\n(1, 1)\n(1, 1)\n(1, 1)\n(1, 1)\n(0, 0)\n(1, 1)\n(0, 0)\n(1, 1)\n(1, 1)\n(0, 0)\n(0, 0)\n","output_type":"stream"}]},{"cell_type":"markdown","source":"Accuracy:","metadata":{"tags":[],"cell_id":"00006-8dc456b2-b56f-4065-bbd0-8d269f7eb8d6"}},{"cell_type":"code","metadata":{"tags":[],"cell_id":"00006-fce4bc8f-1add-4d59-82a9-8705cf4c655d"},"source":"from sklearn.metrics import accuracy_score\nacc = accuracy_score(pred, test_y)\nprint(\"Accuracy: \", acc)","execution_count":49,"outputs":[{"name":"stdout","text":"Accuracy:  1.0\n","output_type":"stream"}]}],"nbformat":4,"nbformat_minor":2,"metadata":{"orig_nbformat":2,"deepnote_notebook_id":"930723b1-475e-436c-a11a-f80d5fb08acc","deepnote_execution_queue":[]}}