From 0257992b8d791ed483fbbb7d9892afcc584fdfcc Mon Sep 17 00:00:00 2001 From: Marius Ciepluch <11855163+norandom@users.noreply.github.com> Date: Sat, 18 May 2024 19:09:47 +0200 Subject: [PATCH] Added Reduce logic for GCN vector embedding --- ...otype_Beta_GCN_BERT_Embedding_Sysmon.ipynb | 1019 ++++++++++++++++- 1 file changed, 1006 insertions(+), 13 deletions(-) diff --git a/Prototype_Beta_GCN_BERT_Embedding_Sysmon.ipynb b/Prototype_Beta_GCN_BERT_Embedding_Sysmon.ipynb index 19673a1..b098281 100644 --- a/Prototype_Beta_GCN_BERT_Embedding_Sysmon.ipynb +++ b/Prototype_Beta_GCN_BERT_Embedding_Sysmon.ipynb @@ -118,7 +118,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 12, "id": "ee9877b3-ece3-4f37-bc1b-73e0994325c7", "metadata": { "tags": [] @@ -128,15 +128,964 @@ "name": "stdout", "output_type": "stream", "text": [ - "GCN embeddings shape: torch.Size([7, 768])\n", - "tensor([[-0.2238, -0.1572, 0.0377, ..., -0.1945, -0.2714, 0.7107],\n", - " [-0.0212, -0.0117, 0.0010, ..., -0.0172, -0.0238, 0.0644],\n", - " [-0.0212, -0.0117, 0.0010, ..., -0.0172, -0.0238, 0.0644],\n", - " ...,\n", - " [-0.0212, -0.0117, 0.0010, ..., -0.0172, -0.0238, 0.0644],\n", - " [-0.0212, -0.0117, 0.0010, ..., -0.0172, -0.0238, 0.0644],\n", - " [-0.0212, -0.0117, 0.0010, ..., -0.0172, -0.0238, 0.0644]])\n" + "Node information and GCN embeddings:\n", + "Node: Event_0\n", + "Embedding: tensor([-2.3119e-01, -1.6862e-01, 4.4349e-02, -5.0699e-02, -2.8104e-01,\n", + " -3.9275e-01, 8.4402e-02, 1.0447e-01, 1.9368e-01, -3.5476e-01,\n", + " -3.1395e-01, 8.9203e-02, -6.6652e-01, 1.6511e-01, -4.1936e-01,\n", + " 5.4424e-02, 1.6919e-01, 4.2394e-02, -4.1758e-01, -9.3900e-02,\n", + " -5.4258e-01, -7.2341e-01, 1.5994e-01, -3.9458e-01, 3.7100e-01,\n", + " -7.0741e-02, -1.3362e-02, -4.5131e-01, -3.5793e-01, 1.0730e-01,\n", + " -3.3780e-01, 2.1790e-01, -1.3532e-01, -1.6432e-01, 4.2228e-01,\n", + " 4.2277e-02, 4.9105e-02, -2.6514e-01, 3.0174e-01, 1.1747e-02,\n", + " -5.4978e-01, 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2.2755e-01,\n", + " -2.4025e-01, 2.0792e-01, -1.3338e+00, -8.4396e-02, -1.4627e-01,\n", + " -4.3543e-01, -3.5452e-01, -5.7280e-01, 2.6163e-01, -5.2129e-01,\n", + " 1.3515e-01, -5.0758e-01, -8.3909e-02, 9.5998e-01, -2.5117e-01,\n", + " -2.0369e-01, -2.8565e-01, 7.3449e-01])\n", + "\n", + "Node: \n", + "Embedding: tensor([-3.8586e-02, -2.8364e-02, 7.7572e-03, -8.4333e-03, -4.7116e-02,\n", + " -6.5246e-02, 1.4845e-02, 1.8454e-02, 3.3703e-02, -5.8469e-02,\n", + " -5.2116e-02, 1.5825e-02, -1.1374e-01, 2.8140e-02, -6.9949e-02,\n", + " 1.1335e-02, 2.9190e-02, 8.7221e-03, -6.9413e-02, -1.5888e-02,\n", + " -9.1704e-02, -1.2275e-01, 2.8149e-02, -6.5600e-02, 6.1470e-02,\n", + " -1.2031e-02, -2.8596e-03, -7.5755e-02, -5.9553e-02, 1.8498e-02,\n", + " -5.5995e-02, 3.6313e-02, -2.3882e-02, -2.8184e-02, 7.0511e-02,\n", + " 6.8126e-03, 8.3883e-03, -4.4675e-02, 5.0769e-02, 1.9657e-03,\n", + " -9.3035e-02, -7.2008e-02, 6.1952e-03, 1.6926e-02, -8.3383e-02,\n", + " 2.5604e-02, -5.4528e-01, -2.4617e-02, 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1.2432e-01])\n", + "\n", + "Node: 0x3E7\n", + "Embedding: tensor([-3.8586e-02, -2.8364e-02, 7.7572e-03, -8.4333e-03, -4.7116e-02,\n", + " -6.5246e-02, 1.4845e-02, 1.8454e-02, 3.3703e-02, -5.8469e-02,\n", + " -5.2116e-02, 1.5825e-02, -1.1374e-01, 2.8140e-02, -6.9949e-02,\n", + " 1.1335e-02, 2.9190e-02, 8.7221e-03, -6.9413e-02, -1.5888e-02,\n", + " -9.1704e-02, -1.2275e-01, 2.8149e-02, -6.5600e-02, 6.1470e-02,\n", + " -1.2031e-02, -2.8596e-03, -7.5755e-02, -5.9553e-02, 1.8498e-02,\n", + " -5.5995e-02, 3.6313e-02, -2.3882e-02, -2.8184e-02, 7.0511e-02,\n", + " 6.8126e-03, 8.3883e-03, -4.4675e-02, 5.0769e-02, 1.9657e-03,\n", + " -9.3035e-02, -7.2008e-02, 6.1952e-03, 1.6926e-02, -8.3383e-02,\n", + " 2.5604e-02, -5.4528e-01, -2.4617e-02, -4.2885e-02, -7.7809e-02,\n", + " -6.1972e-02, 3.4263e-02, -7.2126e-02, 9.9356e-02, 5.5890e-02,\n", + " -4.7779e-02, -3.1965e-02, -1.0972e-01, 6.0339e-02, -8.8014e-02,\n", + " 8.8114e-02, -4.1862e-02, 3.3611e-02, 9.8043e-03, -1.6090e-02,\n", + " 4.4591e-02, 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6.9012e-02,\n", + " 1.4412e-02, -5.6607e-02, 6.0877e-02, -4.8792e-03, 5.5228e-02,\n", + " 1.5390e-02, 1.9907e-02, -2.4034e-03, 1.6219e-02, -1.0205e-03,\n", + " 1.5612e-01, 6.2441e-02, 8.4819e-02, -4.7069e-02, 3.5991e-02,\n", + " 5.0003e-02, -6.0731e-02, 9.9596e-02, 9.6428e-02, -3.4941e-02,\n", + " 5.2633e-02, -1.7093e-01, 6.0091e-02, -5.3557e-02, -2.3053e-02,\n", + " 6.2420e-03, 5.1430e-02, -9.2739e-03, -8.1851e-03, 1.0972e-01,\n", + " -1.9864e-02, -2.2659e-01, -1.3877e-03, -9.8243e-02, 4.8640e-02,\n", + " -6.7777e-02, -1.1494e-01, -2.2236e-02, -3.3317e-03, 8.2843e-03,\n", + " -7.0327e-02, 5.8232e-02, 1.0699e-03, -1.0408e-02, 3.9004e-02,\n", + " 3.4889e-02, -6.6534e-02, -3.6101e-03, 1.8802e-02, 1.1491e-02,\n", + " 7.1708e-02, 3.3958e-02, -8.2581e-02, -4.2154e-03, 9.1659e-02,\n", + " -6.8585e-02, 9.0577e-02, -2.2156e-02, 9.1520e-02, -8.6720e-03,\n", + " 5.9090e-04, -1.7769e-01, -5.0731e-02, 6.6907e-03, -5.5843e-02,\n", + " -1.9082e-02, 1.2766e-01, -2.8337e-02, -1.8303e-02, 4.1025e-02,\n", + " -1.9997e-02, -3.0688e-02, 6.1464e-02, -4.7882e-02, -5.3297e-04,\n", + " 1.1946e-01, 9.7536e-02, -6.6076e-02, 1.0256e-02, 1.0248e-02,\n", + " -1.7453e-02, 8.9518e-03, -5.0985e-02, -1.5329e-02, 1.0449e-02,\n", + " -1.8060e-02, -1.5532e-02, -3.1546e-02, 2.0783e-02, 7.4755e-02,\n", + " -1.4488e-01, 4.1367e-02, 1.2601e-01, -1.4707e-02, 3.0759e-02,\n", + " 2.0402e-02, 3.9427e-02, 1.9334e-03, 1.1888e-01, 3.0331e-02,\n", + " -6.5899e-02, 4.9232e-02, 1.9182e-02, 6.5827e-02, 7.8371e-03,\n", + " 5.7706e-02, 4.5214e-02, 7.8503e-02, 3.5484e-02, 2.2645e-02,\n", + " -1.6532e-02, 2.3728e-02, 2.7630e-02, 4.1081e-02, -5.4536e-02,\n", + " -6.3680e-02, -1.5581e-02, -9.5518e-02, 6.6667e-02, 4.9040e-02,\n", + " -2.2304e-01, -9.5720e-03, 4.3189e-02, 3.2714e-02, -3.0859e-02,\n", + " -5.4798e-02, 8.3072e-03, 1.0947e-01, -9.5002e-02, -6.2065e-02,\n", + " -7.1122e-02, 1.3208e-02, 2.2921e-02, 8.2308e-02, 7.9319e-02,\n", + " -4.1337e-02, 2.9532e-02, -2.7857e-02, 6.0590e-02, -4.1249e-02,\n", + " 3.8525e-02, -1.5444e-02, 5.2577e-02, -9.3051e-02, 3.9267e-02,\n", + " 1.0859e-02, -5.7904e-02, -9.1968e-03, 2.7508e-02, 2.1696e-02,\n", + " 2.9113e-03, -3.2325e-03, -3.8932e-02, 6.3298e-02, 6.6077e-02,\n", + " 6.3639e-02, -2.5115e-03, -8.4729e-03, 6.3028e-02, 8.2823e-02,\n", + " -9.8232e-02, 1.8996e-02, 2.5732e-02, 9.9882e-02, 1.0215e-01,\n", + " 3.9862e-02, 8.3747e-03, -2.6712e-02, -6.0273e-02, 5.2394e-02,\n", + " -1.5731e-02, -1.0859e-01, -5.3687e-02, -2.9068e-02, -3.0732e-02,\n", + " -1.8983e-02, -1.0005e-01, 4.2413e-02, -7.8822e-02, 5.2183e-02,\n", + " 8.3061e-02, 2.7600e-02, -4.6616e-02, -8.5862e-02, -4.9263e-02,\n", + " -1.0680e-01, -1.2142e-02, 4.8734e-02, -6.9671e-02, -1.2624e-02,\n", + " 6.6395e-02, -1.3812e-02, -1.2350e-01, -5.0158e-02, -2.3613e-02,\n", + " -1.0594e-02, -1.8519e-02, -4.5846e-02, -1.2227e-02, 3.3178e-02,\n", + " -6.7356e-02, 1.3417e-02, 1.2423e-01, -8.4528e-04, -4.6071e-02,\n", + " 3.6863e-02, 1.0760e-02, 5.9475e-03, 5.8635e-02, 1.2607e-01,\n", + " -3.2951e-02, 2.8297e-02, -7.6171e-03, 8.9051e-03, 1.5587e-02,\n", + " -4.2500e-02, -8.3398e-03, -7.0182e-03, 4.4050e-02, -9.8269e-02,\n", + " 3.8998e-02, 4.1572e-02, 5.9459e-02, 5.5570e-02, -4.6288e-03,\n", + " 6.5259e-02, 3.2574e-02, 4.2216e-02, 8.7766e-03, 6.7109e-03,\n", + " 8.1520e-02, 2.1542e-02, 1.5494e-02, 1.1832e-04, -9.7646e-04,\n", + " -1.3930e-01, 8.5223e-03, 2.2907e-02, 5.8150e-02, -5.9184e-02,\n", + " -8.7970e-02, 6.5753e-02, 7.1052e-02, -4.7336e-02, -1.7822e-02,\n", + " 3.3150e-02, -2.8569e-02, 1.4560e-02, -8.7949e-02, 1.0971e-01,\n", + " 9.9295e-03, -6.5559e-03, 4.8330e-02, -1.7060e-03, -4.6296e-02,\n", + " -1.0629e-03, -1.7845e-01, 9.4424e-02, -1.9536e-02, 8.0643e-02,\n", + " 1.7387e-01, 5.3827e-03, -5.5155e-02, 6.8476e-02, -2.2952e-02,\n", + " -9.7438e-02, -4.0389e-02, 4.8615e-02, -6.1901e-02, -2.3169e-02,\n", + " 3.1784e-02, -6.6086e-02, -1.3974e-01, -1.2158e-01, -6.9467e-02,\n", + " 7.1097e-03, -4.5920e-02, -8.1100e-02, 4.5189e-02, 2.4155e-02,\n", + " 6.6592e-02, -1.4030e-02, -2.5580e-02, 3.8433e-02, 5.3588e-02,\n", + " -4.4694e-02, 2.9309e-02, 7.7342e-02, 9.9601e-02, -2.1941e-02,\n", + " 2.9146e-02, 3.3596e-02, 4.8089e-02, 1.1041e-02, 2.7985e-02,\n", + " -4.3420e-02, -1.0670e-01, 3.5781e-02, -9.8180e-02, 8.0267e-02,\n", + " -9.0816e-03, -5.1750e-02, -3.7311e-02, 3.8888e-02, -7.9238e-03,\n", + " 1.0253e-01, 5.8119e-02, 2.4325e-02, 4.7443e-02, 4.9959e-02,\n", + " 2.2897e-02, 8.8195e-02, 5.5952e-03, 8.2229e-02, 4.7240e-02,\n", + " 1.2188e-03, -8.0431e-02, -2.0149e-02, 1.2731e-02, 4.5429e-02,\n", + " 9.7162e-02, -6.0380e-02, -2.9244e-03, 6.2496e-02, 9.2194e-03,\n", + " 3.4119e-02, 2.3940e-02, 7.1828e-02, 4.2675e-02, 1.3506e-01,\n", + " -8.8608e-02, -2.5284e-02, -3.7072e-02, 3.0327e-02, 4.2145e-02,\n", + " 5.8454e-02, -2.4474e-02, 1.5238e-02, 6.9064e-03, -6.4415e-03,\n", + " -1.0281e-01, -1.3888e-01, -1.1128e-01, 2.0790e-02, 7.6132e-02,\n", + " 7.2102e-03, 4.2542e-04, -1.0044e-01, 7.0981e-03, -7.9493e-02,\n", + " 1.0928e-01, 3.8517e-02, 2.3197e-02, -6.6179e-02, -5.9795e-02,\n", + " -6.3351e-02, 1.4908e-01, 1.3025e-02, 9.9178e-02, -5.7917e-02,\n", + " 7.4289e-02, -9.4654e-03, -9.7420e-02, 1.1500e-01, 1.3689e-02,\n", + " 6.4025e-02, -4.2491e-02, -1.9202e-02, 1.0885e-01, 8.0133e-02,\n", + " 2.6378e-02, -5.3364e-02, -6.1843e-02, 9.2499e-03, 2.2564e-03,\n", + " 4.3381e-02, -2.4500e-02, -3.8350e-02, -9.6087e-02, -2.2432e-02,\n", + " 2.8459e-02, -3.2197e-02, 2.6522e-02, -5.0198e-02, 6.9351e-02,\n", + " -3.9908e-02, -5.6814e-02, -3.4956e-02, -1.6584e-02, -1.3013e-02,\n", + " 3.4765e-02, 6.7459e-02, -7.0984e-02, -1.2404e-02, 3.9214e-02,\n", + " 3.6752e-02, -5.1732e-02, 1.9082e-03, -3.5173e-02, 3.8254e-02,\n", + " -4.0463e-02, 3.5459e-02, -2.1930e-01, -1.3807e-02, -2.5059e-02,\n", + " -7.2549e-02, -5.9049e-02, -9.6847e-02, 4.3074e-02, -8.7843e-02,\n", + " 2.3317e-02, -8.5235e-02, -1.4703e-02, 1.6026e-01, -4.1970e-02,\n", + " -3.5022e-02, -4.7688e-02, 1.2432e-01])\n", + "\n", + "Node: Enumerate Credentials This event occurs when a user performs a read operation on stored credentials in Credential Manager.\n", + "Embedding: tensor([-3.8586e-02, -2.8364e-02, 7.7572e-03, -8.4333e-03, -4.7116e-02,\n", + " -6.5246e-02, 1.4845e-02, 1.8454e-02, 3.3703e-02, -5.8469e-02,\n", + " -5.2116e-02, 1.5825e-02, -1.1374e-01, 2.8140e-02, -6.9949e-02,\n", + " 1.1335e-02, 2.9190e-02, 8.7221e-03, -6.9413e-02, -1.5888e-02,\n", + " -9.1704e-02, -1.2275e-01, 2.8149e-02, -6.5600e-02, 6.1470e-02,\n", + " -1.2031e-02, -2.8596e-03, -7.5755e-02, -5.9553e-02, 1.8498e-02,\n", + " -5.5995e-02, 3.6313e-02, -2.3882e-02, -2.8184e-02, 7.0511e-02,\n", + " 6.8126e-03, 8.3883e-03, -4.4675e-02, 5.0769e-02, 1.9657e-03,\n", + " -9.3035e-02, -7.2008e-02, 6.1952e-03, 1.6926e-02, -8.3383e-02,\n", + " 2.5604e-02, -5.4528e-01, -2.4617e-02, -4.2885e-02, -7.7809e-02,\n", + " -6.1972e-02, 3.4263e-02, -7.2126e-02, 9.9356e-02, 5.5890e-02,\n", + " -4.7779e-02, -3.1965e-02, -1.0972e-01, 6.0339e-02, -8.8014e-02,\n", + " 8.8114e-02, -4.1862e-02, 3.3611e-02, 9.8043e-03, -1.6090e-02,\n", + " 4.4591e-02, -8.6020e-03, 4.2879e-02, -3.9267e-02, -1.0442e-02,\n", + " -7.9020e-02, 5.2385e-04, 1.2906e-03, -1.1091e-01, 5.7191e-02,\n", + " 2.4405e-02, -9.6260e-02, 6.3495e-02, -4.2692e-02, 3.0885e-02,\n", + " -4.2549e-02, 3.8816e-02, 5.9316e-02, 1.1293e-02, 1.8521e-02,\n", + " 5.5908e-03, -4.4925e-02, 2.8684e-02, -4.0305e-02, 1.0757e-01,\n", + " -7.4813e-06, 5.0363e-02, -9.6683e-02, 6.3197e-02, -2.7937e-02,\n", + " 8.4831e-02, -2.9750e-02, -3.4151e-02, 9.3881e-02, 2.6553e-02,\n", + " -4.6859e-02, -8.3575e-02, -5.9222e-02, -8.8638e-02, 3.5184e-02,\n", + " 4.6418e-02, -3.6950e-02, 2.3462e-02, -4.4178e-02, 6.7830e-03,\n", + " 6.5268e-02, 5.3528e-02, -1.5237e-02, -2.3095e-02, 2.0955e-02,\n", + " -4.2359e-02, 1.0320e-02, -1.5865e-02, 1.0790e-02, -1.0188e-01,\n", + " 3.6947e-02, 1.0097e-01, 4.5591e-03, -5.1843e-02, 4.5158e-02,\n", + " -2.3706e-02, 3.8816e-02, 7.5799e-02, 2.2895e-02, -4.3212e-02,\n", + " 2.1964e-02, 4.3876e-02, -1.3141e-02, 5.2850e-02, -8.5862e-02,\n", + " 1.0565e-01, -5.1351e-02, -4.2234e-02, -5.2232e-02, 2.5073e-02,\n", + " -6.9116e-03, -1.1915e-01, -1.3339e-01, 3.0547e-02, 6.8972e-02,\n", + " -1.0619e-02, 4.7038e-02, 8.3274e-03, -3.2852e-02, 1.4130e-01,\n", + " 1.0038e-01, 7.3802e-02, -5.5650e-03, -5.6066e-02, -3.6078e-02,\n", + " 5.7793e-02, -6.9978e-02, -2.5807e-02, -8.9850e-02, 6.6585e-02,\n", + " 9.4560e-02, 1.2939e-01, -1.6775e-01, 1.7793e-02, 6.0206e-02,\n", + " 4.8610e-02, -1.0050e-02, 4.7521e-02, -1.5192e-02, -1.0855e-01,\n", + " 3.7042e-02, 4.7582e-02, 1.4991e-01, 4.0887e-02, 2.6278e-02,\n", + " 8.7376e-02, 1.3517e-02, -9.2140e-03, 1.6303e-01, -5.8793e-03,\n", + " -1.0571e-01, 4.0416e-02, 2.9807e-02, 4.5717e-02, 9.8401e-03,\n", + " 2.6819e-02, 2.9473e-02, -4.2832e-02, 3.5501e-02, -2.3819e-02,\n", + " -2.1105e-02, -6.7501e-02, -9.5727e-03, -5.9749e-02, 4.4262e-02,\n", + " -3.8762e-02, -1.5376e-02, -1.9444e-02, -6.3395e-02, -7.7670e-02,\n", + " -1.7598e-02, 3.6900e-02, 1.9356e-02, -6.0049e-02, -3.3085e-02,\n", + " 3.4686e-01, 1.1386e-02, -4.3656e-02, 4.4840e-02, 3.5319e-02,\n", + " -9.1470e-02, 8.5132e-02, 2.4707e-02, -1.6579e-02, 1.2681e-01,\n", + " 6.7833e-02, 1.6571e-02, 2.1453e-02, -1.0163e-02, 2.4787e-02,\n", + " 2.3504e-02, 5.7378e-02, -9.1742e-02, 9.7881e-02, -1.4732e-01,\n", + " 9.2781e-02, -6.7964e-02, 1.2273e-01, 1.7592e-01, -2.4703e-01,\n", + " 7.1042e-02, -3.4124e-02, 3.0534e-02, -5.6894e-02, 1.4596e-02,\n", + " 5.2834e-02, 7.4640e-02, 9.4881e-03, 6.8504e-02, 4.8564e-03,\n", + " -8.8052e-02, 1.4591e-01, 5.7021e-03, -2.3085e-02, 1.2960e-02,\n", + " -5.7551e-02, 3.7094e-02, -6.4790e-02, -1.2069e-02, -6.7411e-02,\n", + " 2.6982e-02, 6.6136e-02, -3.2721e-02, 9.0566e-03, -7.6499e-03,\n", + " -9.7795e-03, -4.4807e-02, -3.0553e-02, -1.2047e-01, -2.7125e-03,\n", + " -8.3060e-02, -1.0790e-02, -6.2980e-02, 9.2915e-02, -8.1434e-02,\n", + " -6.2701e-02, 1.2897e-02, 1.0898e-01, 1.2215e-02, -6.8713e-02,\n", + " -4.2269e-02, -3.6835e-02, -5.4274e-02, -7.9496e-02, -4.4437e-02,\n", + " -7.5512e-02, 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -148,8 +1097,10 @@ "from torch_geometric.data import Data\n", "from torch_geometric.nn import GCNConv\n", "from torch_geometric.utils import from_networkx\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.manifold import TSNE\n", "\n", - "# Sample CSV data\n", + "# Sample data\n", "data = {\n", " \"Timestamp\": [\"2024-05-15T16:00:15.887Z\"],\n", " \"OS\": [\"win10\"],\n", @@ -174,6 +1125,20 @@ "# Convert to DataFrame\n", "df = pd.DataFrame(data)\n", "\n", + "\n", + "# Reduce the data\n", + "def data_reduce(key):\n", + " \n", + " match(key):\n", + " case \"Account Domain\":\n", + " return None\n", + " \n", + " # If an exact match is not confirmed, this last case will be used if provided\n", + " case _:\n", + " return key\n", + "\n", + "\n", + "\n", "# Function to parse hierarchical data from description\n", "def parse_description(description):\n", " hierarchy = {}\n", @@ -186,6 +1151,11 @@ " if ':' in line:\n", " parts = line.split(':', 1)\n", " key = parts[0].strip()\n", + " \n", + " # Call data_reduce and skip the key if None is returned\n", + " if data_reduce(key) is None:\n", + " continue\n", + " \n", " value = parts[1].strip()\n", " hierarchy[key] = value\n", " elif key:\n", @@ -263,9 +1233,32 @@ "with torch.no_grad():\n", " embeddings = model(data)\n", "\n", - "# Print the embeddings\n", - "print(\"GCN embeddings shape:\", embeddings.shape)\n", - "print(embeddings)\n" + "# Print the embeddings and node information side by side\n", + "print(\"Node information and GCN embeddings:\")\n", + "for i, node in enumerate(G.nodes):\n", + " print(f\"Node: {node}\")\n", + " print(f\"Embedding: {embeddings[i]}\\n\")\n", + "\n", + "# Visualize the graph\n", + "plt.figure(figsize=(12, 8))\n", + "pos = nx.spring_layout(G)\n", + "nx.draw(G, pos, with_labels=True, node_color=\"skyblue\", edge_color=\"gray\", node_size=2000, font_size=10, font_weight=\"bold\")\n", + "plt.title(\"Graph Visualization\")\n", + "plt.show()\n", + "\n", + "# Visualize the embeddings using t-SNE\n", + "embeddings_np = embeddings.numpy()\n", + "tsne = TSNE(n_components=2, random_state=42, perplexity=1) # Reduced perplexity to 1\n", + "embeddings_2d = tsne.fit_transform(embeddings_np)\n", + "\n", + "plt.figure(figsize=(10, 8))\n", + "plt.scatter(embeddings_2d[:, 0], embeddings_2d[:, 1], color='blue', alpha=0.7)\n", + "for i, node in enumerate(G.nodes):\n", + " plt.annotate(node, (embeddings_2d[i, 0], embeddings_2d[i, 1]))\n", + "plt.title(\"Embeddings Visualization with t-SNE\")\n", + "plt.xlabel(\"t-SNE component 1\")\n", + "plt.ylabel(\"t-SNE component 2\")\n", + "plt.show()\n" ] }, {