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fix example.ipynb
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‎Example.ipynb‎

Lines changed: 198 additions & 39 deletions
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@@ -9,7 +9,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"execution_count": 19,
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"metadata": {},
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"outputs": [
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{
@@ -49,7 +49,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"metadata": {},
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"outputs": [
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{
@@ -89,7 +89,7 @@
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},
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{
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"cell_type": "code",
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"source": [
@@ -114,7 +114,7 @@
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},
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{
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"cell_type": "code",
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"source": [
@@ -133,7 +133,7 @@
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},
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{
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"cell_type": "code",
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{
@@ -164,8 +164,10 @@
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},
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{
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"cell_type": "code",
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"data": {
@@ -184,7 +186,7 @@
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" 'opt3': False}"
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]
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},
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"execution_count": 24,
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"metadata": {},
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"output_type": "execute_result"
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}
@@ -195,6 +197,156 @@
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"EmbMethodClass.__PARAMS__"
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]
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}
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],
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"source": [
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"# This shows the inside structure of the returned embedding file\n",
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"# The key is the node_id, the value is the embedding in list()\n",
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"list(dict_struc2vec_emb.items())[:1]"
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]
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},
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{
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"cell_type": "markdown",
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@@ -204,7 +356,7 @@
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},
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{
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"source": [
@@ -222,7 +374,7 @@
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},
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{
@@ -244,45 +396,45 @@
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},
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{
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{
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"data": {
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"text/plain": [
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"{'overall': {'accuracy': {'mean': 0.786, 'std': 0.0759},\n",
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" 'f1_macro': {'mean': 0.7791, 'std': 0.0752},\n",
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" 'f1_micro': {'mean': 0.786, 'std': 0.0759},\n",
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" 'auc_micro': {'mean': 0.9288, 'std': 0.0255},\n",
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" 'auc_macro': {'mean': 0.9413, 'std': 0.0182}},\n",
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" 'detailed': {0: {'accuracy': 0.8148,\n",
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" 'f1_macro': 0.805,\n",
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" 'auc_micro': 0.9374,\n",
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" 1: {'accuracy': 0.6538,\n",
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" 'f1_micro': 0.6538,\n",
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" 'auc_micro': 0.8817,\n",
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" 'auc_macro': 0.9083},\n",
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"{'overall': {'accuracy': {'mean': 0.7863, 'std': 0.0622},\n",
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" 'f1_macro': {'mean': 0.7774, 'std': 0.0612},\n",
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" 'f1_micro': {'mean': 0.7863, 'std': 0.0622},\n",
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" 'auc_micro': {'mean': 0.9234, 'std': 0.0288},\n",
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" 'auc_macro': {'mean': 0.9314, 'std': 0.0261}},\n",
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" 'detailed': {0: {'accuracy': 0.7778,\n",
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" 'f1_macro': 0.747,\n",
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" 'f1_micro': 0.7778,\n",
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" 'auc_micro': 0.9255,\n",
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" 'auc_macro': 0.9362},\n",
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" 1: {'accuracy': 0.6923,\n",
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" 'f1_micro': 0.6923,\n",
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" 'auc_micro': 0.8728,\n",
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" 'auc_macro': 0.8881},\n",
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" 2: {'accuracy': 0.7692,\n",
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" 'f1_macro': 0.7448,\n",
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" 'f1_micro': 0.7692,\n",
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" 'auc_micro': 0.9438,\n",
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" 'auc_micro': 0.9359,\n",
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" 'auc_macro': 0.9297},\n",
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" 3: {'accuracy': 0.8846,\n",
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" 'f1_macro': 0.8769,\n",
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" 'f1_micro': 0.8846,\n",
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" 'auc_micro': 0.9556,\n",
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" 'auc_macro': 0.9585},\n",
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" 'auc_micro': 0.961,\n",
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" 'auc_macro': 0.97},\n",
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" 4: {'accuracy': 0.8077,\n",
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" 'f1_macro': 0.8148,\n",
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" 'f1_micro': 0.8077,\n",
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" 'auc_micro': 0.9255,\n",
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" 'auc_micro': 0.9216,\n",
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" 'auc_macro': 0.9332}}}"
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]
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},
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}
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},
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{
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"outputs": [
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{
@@ -314,10 +466,10 @@
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{
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"data": {
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"text/plain": [
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"{'overall': {'purity': [0.6793893129770993], 'nmi': [0.4854751062047489]}}"
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"{'overall': {'purity': [0.6641221374045801], 'nmi': [0.4661859253253555]}}"
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]
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{
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"execution_count": 28,
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"execution_count": 30,
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{
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"0.9379255572546902"
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"0.9482621398890526"
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"execution_count": 30,
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}
@@ -356,6 +508,13 @@
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" centrality='clustering_coeff', \n",
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" similarity='euclidean')"
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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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