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Merge pull request #1 from GemsLab/evaluation
Evaluation --> Master
2 parents 4384f72 + 514e85b commit 261a4d2

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‎.DS_Store‎

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‎.gitignore‎

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# backup files
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*.bak
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# Mac OS file
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*.DS_Store
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# struc2vec pickles and random walks
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semb/methods/struc2vec/pickles/
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semb/methods/struc2vec/random_walks.txt
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# drne intermediate files
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semb/methods/drne/eni/
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# riwalk intermediate files
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semb/methods/riwalk/walks/

‎Untitled.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"graphwave\n",
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"drne\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:493: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
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" _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
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"/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:494: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
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" _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
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"/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:495: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
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" _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
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"/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:496: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
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" _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
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"/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:497: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
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" _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
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"/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:502: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
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" np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"node2vec\n",
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"role2vec\n",
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"line\n",
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"struc2vec\n",
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"xnetmf\n",
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"multilens\n",
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"segk\n",
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"riwalk\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/externals/joblib/__init__.py:15: DeprecationWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
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" warnings.warn(msg, category=DeprecationWarning)\n"
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]
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}
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],
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"source": [
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"from semb.methods import load as load_method\n",
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"from semb.methods import get_method_ids\n",
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"for mid in get_method_ids():\n",
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" print(mid)\n",
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" load_method(mid)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"airports\n"
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]
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}
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],
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"source": [
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"from semb.datasets import load as load_dataset\n",
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"from semb.datasets import get_dataset_ids\n",
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"for did in get_dataset_ids():\n",
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" print(did)\n",
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" load_dataset(did)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"ename": "AttributeError",
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"evalue": "'Graph' object has no attribute 'N'",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m<ipython-input-3-b934f906c1fe>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0mnode2vec\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNode2VecMethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbrazil_airport_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m128\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mnode2vec\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 11\u001b[0m \u001b[0memb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnode2vec\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_embeddings\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m~/GoogleDrive/UM/S4/GEMS/Git/StrucEmbeddingLibrary/semb/methods/xnetmf/method.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 15\u001b[0m alpha=self.params['discount'], gammastruc=self.params['gamma']) \n\u001b[1;32m 16\u001b[0m \u001b[0;31m# FIXME: this doesnt look like a standard embeddings format\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m \u001b[0mrepresentations\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_representations\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrep_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 18\u001b[0m \u001b[0mlist_nodes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0membeddings\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m~/GoogleDrive/UM/S4/GEMS/Git/StrucEmbeddingLibrary/semb/methods/xnetmf/internal/xnetmf.py\u001b[0m in \u001b[0;36mget_representations\u001b[0;34m(graph, rep_method, verbose)\u001b[0m\n\u001b[1;32m 165\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget_representations\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgraph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrep_method\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 166\u001b[0m \u001b[0;31m#Node identity extraction\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 167\u001b[0;31m \u001b[0mfeature_matrix\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_features\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgraph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrep_method\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 168\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 169\u001b[0m \u001b[0;31m#Efficient similarity-based representation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m~/GoogleDrive/UM/S4/GEMS/Git/StrucEmbeddingLibrary/semb/methods/xnetmf/internal/xnetmf.py\u001b[0m in \u001b[0;36mget_features\u001b[0;34m(graph, rep_method, verbose)\u001b[0m\n\u001b[1;32m 100\u001b[0m \u001b[0mbefore_khop\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 101\u001b[0m \u001b[0;31m#Get k-hop neighbors of all nodes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 102\u001b[0;31m \u001b[0mkhop_neighbors_nobfs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_khop_neighbors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgraph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrep_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 104\u001b[0m \u001b[0mgraph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkhop_neighbors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkhop_neighbors_nobfs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m~/GoogleDrive/UM/S4/GEMS/Git/StrucEmbeddingLibrary/semb/methods/xnetmf/internal/xnetmf.py\u001b[0m in \u001b[0;36mget_khop_neighbors\u001b[0;34m(graph, rep_method)\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0;31m#only 0-hop neighbor of a node is itself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0;31m#neighbors of a node have nonzero connections to it in adj matrix\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mnode\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgraph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mN\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 17\u001b[0m \u001b[0mneighbors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnonzero\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgraph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mG_adj\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m###\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mneighbors\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m#disconnected node\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;31mAttributeError\u001b[0m: 'Graph' object has no attribute 'N'"
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]
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}
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],
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"source": [
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"from semb.methods import load as load_method\n",
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"from semb.datasets import load as load_dataset\n",
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"\n",
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"Node2VecMethod = load_method(\"xnetmf\")\n",
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"AirportDataProvider = load_dataset(\"airports\")\n",
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"airport_datasets = AirportDataProvider().get_datasets()\n",
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"brazil_airport_graph = AirportDataProvider().load_dataset(airport_datasets[0])\n",
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"\n",
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"node2vec = Node2VecMethod(brazil_airport_graph, dim=128)\n",
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"node2vec.train()\n",
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"emb = node2vec.get_embeddings()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "SEMB",
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"language": "python",
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"name": "semb"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎Untitled1.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"graphwave\n",
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"degree2\n",
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"drne\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:493: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
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" _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
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"/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:494: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
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" _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
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"/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:495: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
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" _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
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"/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:496: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
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" _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
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"/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:497: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
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" _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
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"/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:502: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
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" np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"node2vec\n",
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"degree\n",
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"role2vec\n",
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"line\n",
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"degree1\n",
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"struc2vec\n",
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"xnetmf\n",
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"multilens\n",
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"segk\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/externals/joblib/__init__.py:15: DeprecationWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
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" warnings.warn(msg, category=DeprecationWarning)\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"riwalk\n"
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]
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}
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],
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"source": [
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"from semb.methods import load as load_method\n",
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"from semb.methods import get_method_ids\n",
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"for mid in get_method_ids():\n",
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" print(mid)\n",
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" load_method(mid)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"airports\n"
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]
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}
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],
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"source": [
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"from semb.datasets import load as load_dataset\n",
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"from semb.datasets import get_dataset_ids\n",
90+
"for did in get_dataset_ids():\n",
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" print(did)\n",
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" load_dataset(did)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
101+
"from semb.methods import load as load_method\n",
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"from semb.datasets import load as load_dataset\n",
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"\n",
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"Node2VecMethod = load_method(\"segk\")\n",
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"AirportDataProvider = load_dataset(\"airports\")\n",
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"airport_datasets = AirportDataProvider().get_datasets()\n",
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"brazil_airport_graph = AirportDataProvider().load_dataset(airport_datasets[0])\n",
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"\n",
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"node2vec = Node2VecMethod(brazil_airport_graph)\n",
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"node2vec.train()\n",
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"emb = node2vec.get_embeddings()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "SEMB",
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"language": "python",
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"name": "semb"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}

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