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0, 0], dtype=int64))], 'db_size': 1066, 'db_n_attr': 11, 'imbalanced_ratio': 23.790697674418606}\n" ] } ], "source": [ "datasetName = \"folding_flare-F\"\n", "pickle_in = open(f\"{datasetName}.pickle\",\"rb\")\n", "example_dict1 = pickle.load(pickle_in)\n", "\n", "print(example_dict1)" ] }, { "cell_type": "code", "execution_count": 5, "id": "abb675c9", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "dict_keys(['folding', 'db_size', 'db_n_attr', 'imbalanced_ratio'])" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "example_dict1.keys()" ] }, { "cell_type": "code", "execution_count": 6, "id": "850de26e", "metadata": {}, "outputs": [], "source": [ "myData = example_dict1[\"folding\"]" ] }, { "cell_type": "code", "execution_count": 7, "id": "ace501e9", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([[0, 3, 0, ..., 0, 0, 0],\n", " [4, 2, 0, ..., 0, 0, 0],\n", " [3, 3, 0, ..., 0, 0, 0],\n", " ...,\n", " [4, 3, 0, ..., 1, 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" ...,\n", " [0, 2, 0, ..., 1, 0, 0],\n", " [3, 1, 0, ..., 0, 0, 0],\n", " [1, 2, 0, ..., 0, 0, 0]], dtype=int64),\n", " array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n", " 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int64))" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "myData[0]" ] }, { "cell_type": "code", "execution_count": 8, "id": "4ca86b2f", "metadata": {}, "outputs": [], "source": [ "k = myData[0]" ] }, { "cell_type": "code", "execution_count": 9, "id": "3d3161ed", "metadata": {}, "outputs": [], "source": [ "data_con = np.concatenate((k[0],k[2]),axis = 0)" ] }, { "cell_type": "code", "execution_count": 10, "id": "c991a993", "metadata": {}, "outputs": [], "source": [ "target = np.concatenate((k[1],k[3]),axis = 0)" ] }, { "cell_type": "code", "execution_count": 11, "id": "eb41a063", "metadata": {}, "outputs": [], "source": [ "target = target.astype(float)" ] }, { "cell_type": "code", "execution_count": 12, "id": "079ee297", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1066,)" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "target.shape" ] }, { "cell_type": "code", "execution_count": 13, "id": "9f2b899b", "metadata": {}, "outputs": [], "source": [ "data_con = data_con.astype(float)" ] }, { "cell_type": "code", "execution_count": 14, "id": "8f387254", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1066, 11)" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_con.shape" ] }, { "cell_type": "code", "execution_count": 15, "id": "30d4b238", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0., 0., 0., ..., 0., 0., 0.])" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "target" ] }, { "cell_type": "code", "execution_count": 16, "id": "8f968ead", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[0., 3., 0., ..., 0., 0., 0.],\n", " [4., 2., 0., ..., 0., 0., 0.],\n", " [3., 3., 0., ..., 0., 0., 0.],\n", " ...,\n", " [0., 2., 0., ..., 1., 0., 0.],\n", " [3., 1., 0., ..., 0., 0., 0.],\n", " [1., 2., 0., ..., 0., 0., 0.]])" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_con" ] }, { "cell_type": "code", "execution_count": 17, "id": "d45ed2ab", "metadata": {}, "outputs": [], "source": [ "def loadDataset():\n", " labels = target\n", " features = data_con\n", " label_1 = list(np.where(labels == 1)[0])\n", " label_0 = list(np.where(labels == 0)[0])\n", " features_1 = features[label_1]\n", " features_0 = features[label_0]\n", " \n", " data0 = np.array(range(100)).reshape((25,4))\n", " return DataSet(data0=features_0, data1=features_1)" ] }, { "cell_type": "code", "execution_count": 18, "id": "7b18e7b0", "metadata": {}, "outputs": [], "source": [ "data = loadDataset()" ] }, { "cell_type": "code", "execution_count": 19, "id": "079c63c2", "metadata": {}, "outputs": [], "source": [ "def getRandGen(initValue, incValue=257, multValue=101, modulus=65537):\n", " value = initValue\n", " while True:\n", " value = ((multValue * value) + incValue) % modulus\n", " yield value\n", " \n", "randGen = getRandGen(2021)\n", "random.seed(2021)" ] }, { "cell_type": "code", "execution_count": 20, "id": "001aa1bf", "metadata": {}, "outputs": [], "source": [ "def shuffler(data):\n", " data = list(data)\n", " size = len(data)\n", " shuffled = []\n", " while size > 0:\n", " p = next(randGen) % size\n", " size -= 1\n", " shuffled.append(data[p])\n", " data = data[0:p] + data[(p + 1):]\n", " return np.array(shuffled)" ] }, { "cell_type": "code", "execution_count": 21, "id": "71fbb17e", "metadata": {}, "outputs": [], "source": [ "ganName = \"SimpleGAN\"\n", "gan = SimpleGan(numOfFeatures=k[2].shape[1])" ] }, { "cell_type": "code", "execution_count": 22, "id": "014e5633", "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-> Shuffling data\n", "### Start exercise for synthetic point generator\n", "\n", "====== Step 1/5 =======\n", "-> Shuffling data\n", "-> Spliting data to slices\n", "\n", "------ Step 1/5: Slice 1/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "Epoch 1/3\n", "Epoch 2/3\n", "Epoch 3/3\n", "-> create 784 synthetic samples\n" ] }, { "data": { 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 202, 3\n", "LR fn, tp: 8, 1\n", "LR f1 score: 0.154\n", "LR cohens kappa score: 0.131\n", "LR average precision score: 0.189\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 199, 6\n", "GB fn, tp: 7, 2\n", "GB f1 score: 0.235\n", "GB cohens kappa score: 0.204\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 200, 5\n", "KNN fn, tp: 9, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: -0.031\n", "\n", "\n", "------ Step 1/5: Slice 2/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "Epoch 1/3\n", "Epoch 2/3\n", "Epoch 3/3\n", "-> create 784 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 204, 1\n", "LR fn, tp: 6, 3\n", "LR f1 score: 0.462\n", "LR cohens kappa score: 0.447\n", "LR average precision score: 0.491\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 203, 2\n", "GB fn, tp: 8, 1\n", "GB f1 score: 0.167\n", "GB cohens kappa score: 0.149\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 204, 1\n", "KNN fn, tp: 9, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: -0.008\n", "\n", "\n", "------ Step 1/5: Slice 3/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "Epoch 1/3\n", "Epoch 2/3\n", "Epoch 3/3\n", "-> create 784 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 204, 1\n", "LR fn, tp: 7, 2\n", "LR f1 score: 0.333\n", "LR cohens kappa score: 0.319\n", "LR average precision score: 0.335\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 205, 0\n", "GB fn, tp: 8, 1\n", "GB f1 score: 0.200\n", "GB cohens kappa score: 0.193\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 205, 0\n", "KNN fn, tp: 9, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "------ Step 1/5: Slice 4/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "Epoch 1/3\n", "Epoch 2/3\n", "Epoch 3/3\n", "-> create 784 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 204, 1\n", "LR fn, tp: 9, 0\n", "LR f1 score: 0.000\n", "LR cohens kappa score: -0.008\n", "LR average precision score: 0.474\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 204, 1\n", "GB fn, tp: 7, 2\n", "GB f1 score: 0.333\n", "GB cohens kappa score: 0.319\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 205, 0\n", "KNN fn, tp: 9, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "------ Step 1/5: Slice 5/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "Epoch 1/3\n", "Epoch 2/3\n", "Epoch 3/3\n", "-> create 784 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 198, 5\n", "LR fn, tp: 6, 1\n", "LR f1 score: 0.154\n", "LR cohens kappa score: 0.127\n", "LR average precision score: 0.211\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 202, 1\n", "GB fn, tp: 6, 1\n", "GB f1 score: 0.222\n", "GB cohens kappa score: 0.211\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 203, 0\n", "KNN fn, tp: 7, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "====== Step 2/5 =======\n", "-> Shuffling data\n", "-> Spliting data to slices\n", "\n", "------ Step 2/5: Slice 1/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "Epoch 1/3\n", "Epoch 2/3\n", "Epoch 3/3\n", "-> create 784 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 203, 2\n", "LR fn, tp: 5, 4\n", "LR f1 score: 0.533\n", "LR cohens kappa score: 0.517\n", "LR average precision score: 0.553\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 203, 2\n", "GB fn, tp: 8, 1\n", "GB f1 score: 0.167\n", "GB cohens kappa score: 0.149\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 205, 0\n", "KNN fn, tp: 8, 1\n", "KNN f1 score: 0.200\n", "KNN cohens kappa score: 0.193\n", "\n", "\n", "------ Step 2/5: Slice 2/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "Epoch 1/3\n", "Epoch 2/3\n", "Epoch 3/3\n", "-> create 784 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 203, 2\n", "LR fn, tp: 6, 3\n", "LR f1 score: 0.429\n", "LR cohens kappa score: 0.411\n", "LR average precision score: 0.398\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 203, 2\n", "GB fn, tp: 8, 1\n", "GB f1 score: 0.167\n", "GB cohens kappa score: 0.149\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 205, 0\n", "KNN fn, tp: 9, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "------ Step 2/5: Slice 3/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "Epoch 1/3\n", "Epoch 2/3\n", "Epoch 3/3\n", "-> create 784 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 203, 2\n", "LR fn, tp: 8, 1\n", "LR f1 score: 0.167\n", "LR cohens kappa score: 0.149\n", "LR average precision score: 0.424\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 205, 0\n", "GB fn, tp: 8, 1\n", "GB f1 score: 0.200\n", "GB cohens kappa score: 0.193\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 205, 0\n", "KNN fn, tp: 9, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "------ Step 2/5: Slice 4/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "Epoch 1/3\n", "Epoch 2/3\n", "Epoch 3/3\n", "-> create 784 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 202, 3\n", "LR fn, tp: 8, 1\n", "LR f1 score: 0.154\n", "LR cohens kappa score: 0.131\n", "LR average precision score: 0.288\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 204, 1\n", "GB fn, tp: 8, 1\n", "GB f1 score: 0.182\n", "GB cohens kappa score: 0.169\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 205, 0\n", "KNN fn, tp: 9, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "------ Step 2/5: Slice 5/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "Epoch 1/3\n", "Epoch 2/3\n", "Epoch 3/3\n", "-> create 784 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 199, 4\n", "LR fn, tp: 4, 3\n", "LR f1 score: 0.429\n", "LR cohens kappa score: 0.409\n", "LR average precision score: 0.452\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 201, 2\n", "GB fn, tp: 6, 1\n", "GB f1 score: 0.200\n", "GB cohens kappa score: 0.184\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 203, 0\n", "KNN fn, tp: 7, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "====== Step 3/5 =======\n", "-> Shuffling data\n", "-> Spliting data to slices\n", "\n", "------ Step 3/5: Slice 1/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "Epoch 1/3\n", "Epoch 2/3\n", "Epoch 3/3\n", "-> create 784 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 205, 0\n", "LR fn, tp: 5, 4\n", "LR f1 score: 0.615\n", "LR cohens kappa score: 0.605\n", "LR average precision score: 0.798\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 205, 0\n", "GB fn, tp: 9, 0\n", "GB f1 score: 0.000\n", "GB cohens kappa score: 0.000\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 205, 0\n", "KNN fn, tp: 9, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "------ Step 3/5: Slice 2/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "Epoch 1/3\n", "Epoch 2/3\n", "Epoch 3/3\n", "-> create 784 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 199, 6\n", "LR fn, tp: 6, 3\n", "LR f1 score: 0.333\n", "LR cohens kappa score: 0.304\n", "LR average precision score: 0.268\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 198, 7\n", "GB fn, tp: 6, 3\n", "GB f1 score: 0.316\n", "GB cohens kappa score: 0.284\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 205, 0\n", "KNN fn, tp: 9, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "------ Step 3/5: Slice 3/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "Epoch 1/3\n", "Epoch 2/3\n", "Epoch 3/3\n", "-> create 784 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 204, 1\n", "LR fn, tp: 8, 1\n", "LR f1 score: 0.182\n", "LR cohens kappa score: 0.169\n", "LR average precision score: 0.374\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 204, 1\n", "GB fn, tp: 8, 1\n", "GB f1 score: 0.182\n", "GB cohens kappa score: 0.169\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 205, 0\n", "KNN fn, tp: 9, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "------ Step 3/5: Slice 4/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "Epoch 1/3\n", "Epoch 2/3\n", "Epoch 3/3\n", "-> create 784 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 204, 1\n", "LR fn, tp: 8, 1\n", "LR f1 score: 0.182\n", "LR cohens kappa score: 0.169\n", "LR average precision score: 0.251\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 204, 1\n", "GB fn, tp: 8, 1\n", "GB f1 score: 0.182\n", "GB cohens kappa score: 0.169\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 205, 0\n", "KNN fn, tp: 9, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "------ Step 3/5: Slice 5/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "Epoch 1/3\n", "Epoch 2/3\n", "Epoch 3/3\n", "-> create 784 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 199, 4\n", "LR fn, tp: 6, 1\n", "LR f1 score: 0.167\n", "LR cohens kappa score: 0.143\n", "LR average precision score: 0.288\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 198, 5\n", "GB fn, tp: 6, 1\n", "GB f1 score: 0.154\n", "GB cohens kappa score: 0.127\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 203, 0\n", "KNN fn, tp: 7, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "====== Step 4/5 =======\n", "-> Shuffling data\n", "-> Spliting data to slices\n", "\n", "------ Step 4/5: Slice 1/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "Epoch 1/3\n", "Epoch 2/3\n", "Epoch 3/3\n", "-> create 784 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 200, 5\n", "LR fn, tp: 9, 0\n", "LR f1 score: 0.000\n", "LR cohens kappa score: -0.031\n", "LR average precision score: 0.200\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 201, 4\n", "GB fn, tp: 9, 0\n", "GB f1 score: 0.000\n", "GB cohens kappa score: -0.027\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 205, 0\n", "KNN fn, tp: 9, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "------ Step 4/5: Slice 2/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "Epoch 1/3\n", "Epoch 2/3\n", "Epoch 3/3\n", "-> create 784 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 205, 0\n", "LR fn, tp: 6, 3\n", "LR f1 score: 0.500\n", "LR cohens kappa score: 0.489\n", "LR average precision score: 0.579\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 204, 1\n", "GB fn, tp: 8, 1\n", "GB f1 score: 0.182\n", "GB cohens kappa score: 0.169\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 205, 0\n", "KNN fn, tp: 9, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "------ Step 4/5: Slice 3/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "Epoch 1/3\n", "Epoch 2/3\n", "Epoch 3/3\n", "-> create 784 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 200, 5\n", "LR fn, tp: 6, 3\n", "LR f1 score: 0.353\n", "LR cohens kappa score: 0.326\n", "LR average precision score: 0.290\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 201, 4\n", "GB fn, tp: 7, 2\n", "GB f1 score: 0.267\n", "GB cohens kappa score: 0.241\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 203, 2\n", "KNN fn, tp: 9, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: -0.016\n", "\n", "\n", "------ Step 4/5: Slice 4/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "Epoch 1/3\n", "Epoch 2/3\n", "Epoch 3/3\n", "-> create 784 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 202, 3\n", "LR fn, tp: 6, 3\n", "LR f1 score: 0.400\n", "LR cohens kappa score: 0.379\n", "LR average precision score: 0.474\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 203, 2\n", "GB fn, tp: 7, 2\n", "GB f1 score: 0.308\n", "GB cohens kappa score: 0.289\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 205, 0\n", "KNN fn, tp: 9, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "------ Step 4/5: Slice 5/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "Epoch 1/3\n", "Epoch 2/3\n", "Epoch 3/3\n", "-> create 784 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 202, 1\n", "LR fn, tp: 6, 1\n", "LR f1 score: 0.222\n", "LR cohens kappa score: 0.211\n", "LR average precision score: 0.395\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 203, 0\n", "GB fn, tp: 6, 1\n", "GB f1 score: 0.250\n", "GB cohens kappa score: 0.244\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 203, 0\n", "KNN fn, tp: 7, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "====== Step 5/5 =======\n", "-> Shuffling data\n", "-> Spliting data to slices\n", "\n", "------ Step 5/5: Slice 1/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "Epoch 1/3\n", "Epoch 2/3\n", "Epoch 3/3\n", "-> create 784 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 204, 1\n", "LR fn, tp: 8, 1\n", "LR f1 score: 0.182\n", "LR cohens kappa score: 0.169\n", "LR average precision score: 0.207\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 203, 2\n", "GB fn, tp: 8, 1\n", "GB f1 score: 0.167\n", "GB cohens kappa score: 0.149\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 205, 0\n", "KNN fn, tp: 9, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "------ Step 5/5: Slice 2/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "Epoch 1/3\n", "Epoch 2/3\n", "Epoch 3/3\n", "-> create 784 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 205, 0\n", "LR fn, tp: 7, 2\n", "LR f1 score: 0.364\n", "LR cohens kappa score: 0.354\n", "LR average precision score: 0.511\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 205, 0\n", "GB fn, tp: 9, 0\n", "GB f1 score: 0.000\n", "GB cohens kappa score: 0.000\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 205, 0\n", "KNN fn, tp: 9, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "------ Step 5/5: Slice 3/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "Epoch 1/3\n", "Epoch 2/3\n", "Epoch 3/3\n", "-> create 784 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 202, 3\n", "LR fn, tp: 5, 4\n", "LR f1 score: 0.500\n", "LR cohens kappa score: 0.481\n", "LR average precision score: 0.538\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 205, 0\n", "GB fn, tp: 8, 1\n", "GB f1 score: 0.200\n", "GB cohens kappa score: 0.193\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 205, 0\n", "KNN fn, tp: 9, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "------ Step 5/5: Slice 4/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "Epoch 1/3\n", "Epoch 2/3\n", "Epoch 3/3\n", "-> create 784 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 203, 2\n", "LR fn, tp: 8, 1\n", "LR f1 score: 0.167\n", "LR cohens kappa score: 0.149\n", "LR average precision score: 0.328\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 203, 2\n", "GB fn, tp: 9, 0\n", "GB f1 score: 0.000\n", "GB cohens kappa score: -0.016\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 205, 0\n", "KNN fn, tp: 9, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "------ Step 5/5: Slice 5/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "Epoch 1/3\n", "Epoch 2/3\n", "Epoch 3/3\n", "-> create 784 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 203, 0\n", "LR fn, tp: 6, 1\n", "LR f1 score: 0.250\n", "LR cohens kappa score: 0.244\n", "LR average precision score: 0.341\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 199, 4\n", "GB fn, tp: 5, 2\n", "GB f1 score: 0.308\n", "GB cohens kappa score: 0.286\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 200, 3\n", "KNN fn, tp: 7, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: -0.020\n", "\n", "### Exercise is done.\n", "\n", "-----[ LR ]-----\n", "maximum:\n", "LR tn, fp: 205, 6\n", "LR fn, tp: 9, 4\n", "LR f1 score: 0.615\n", "LR cohens kappa score: 0.605\n", "LR average precision score: 0.798\n", "\n", "\n", "average:\n", "LR tn, fp: 202.36, 2.24\n", "LR fn, tp: 6.68, 1.92\n", "LR f1 score: 0.289\n", "LR cohens kappa score: 0.272\n", "LR average precision score: 0.386\n", "\n", "\n", "minimum:\n", "LR tn, fp: 198, 0\n", "LR fn, tp: 4, 0\n", "LR f1 score: 0.000\n", "LR cohens kappa score: -0.031\n", "LR average precision score: 0.189\n", "\n", "\n", "-----[ GB ]-----\n", "maximum:\n", "GB tn, fp: 205, 7\n", "GB fn, tp: 9, 3\n", "GB f1 score: 0.333\n", "GB cohens kappa score: 0.319\n", "\n", "\n", "average:\n", "GB tn, fp: 202.6, 2.0\n", "GB fn, tp: 7.48, 1.12\n", "GB f1 score: 0.183\n", "GB cohens kappa score: 0.168\n", "\n", "\n", "minimum:\n", "GB tn, fp: 198, 0\n", "GB fn, tp: 5, 0\n", "GB f1 score: 0.000\n", "GB cohens kappa score: -0.027\n", "\n", "\n", "-----[ KNN ]-----\n", "maximum:\n", "KNN tn, fp: 205, 5\n", "KNN fn, tp: 9, 1\n", "KNN f1 score: 0.200\n", "KNN cohens kappa score: 0.193\n", "\n", "\n", "average:\n", "KNN tn, fp: 204.16, 0.44\n", "KNN fn, tp: 8.56, 0.04\n", "KNN f1 score: 0.008\n", "KNN cohens kappa score: 0.005\n", "\n", "\n", "minimum:\n", "KNN tn, fp: 200, 0\n", "KNN fn, tp: 7, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: -0.031\n", "\n" ] } ], "source": [ "exercise = Exercise(shuffleFunction=shuffler, numOfShuffles=5, numOfSlices=5)\n", "exercise.run(gan, data)\n", "exercise.saveResultsTo(f\"{datasetName}-{ganName}.csv\")" ] }, { "cell_type": "code", "execution_count": null, "id": "8f96107d", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.8" } }, "nbformat": 4, "nbformat_minor": 5 }