update
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 23,
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"execution_count": 23,
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"id": "c333fcd7",
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"id": "3312dc2a",
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"metadata": {},
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"metadata": {},
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 24,
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"execution_count": 24,
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"id": "e550c221",
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"id": "a5d2718d",
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"metadata": {},
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"metadata": {},
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"outputs": [],
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"outputs": [],
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"source": [
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 25,
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"execution_count": 25,
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"id": "57585a3e",
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"id": "2273a549",
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"metadata": {},
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"metadata": {},
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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@ -57,8 +57,8 @@
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},
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},
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 35,
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"execution_count": 37,
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"id": "a3a5ea9a",
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"id": "83f7c5b5",
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"metadata": {},
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"metadata": {},
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"outputs": [
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"outputs": [
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{
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{
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@ -88,7 +88,7 @@
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" \n",
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" \n",
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"data['train_input'] = data.apply(lambda row: to_vowpalwabbit(row, categories), axis=1)\n",
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"data['train_input'] = data.apply(lambda row: to_vowpalwabbit(row, categories), axis=1)\n",
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"\n",
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"\n",
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"model = vowpalwabbit.Workspace('--oaa 7 --learning_rate 0.95')\n",
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"model = vowpalwabbit.Workspace('--oaa 7 --learning_rate 0.99')\n",
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"\n",
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"\n",
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"for example in data['train_input']:\n",
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"for example in data['train_input']:\n",
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" model.learn(example)\n",
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" model.learn(example)\n",
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@ -100,8 +100,8 @@
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},
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},
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 36,
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"execution_count": 38,
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"id": "ee80e14d",
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"id": "caa9bb3b",
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"metadata": {},
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"metadata": {},
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"outputs": [
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"outputs": [
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{
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{
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@ -109,7 +109,7 @@
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"output_type": "stream",
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"output_type": "stream",
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"text": [
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"text": [
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"[NbConvertApp] Converting notebook run.ipynb to script\n",
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"[NbConvertApp] Converting notebook run.ipynb to script\n",
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"[NbConvertApp] Writing 1951 bytes to run.py\n"
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"[NbConvertApp] Writing 1952 bytes to run.py\n"
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]
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]
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}
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}
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],
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],
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870
dev-0/out.tsv
870
dev-0/out.tsv
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18
run.ipynb
18
run.ipynb
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 23,
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"execution_count": 23,
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"id": "c333fcd7",
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"id": "3312dc2a",
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"metadata": {},
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"metadata": {},
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 24,
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"execution_count": 24,
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"id": "e550c221",
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"id": "a5d2718d",
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"metadata": {},
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"metadata": {},
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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@ -36,7 +36,7 @@
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 25,
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"execution_count": 25,
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"id": "57585a3e",
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"id": "2273a549",
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"metadata": {},
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"metadata": {},
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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@ -57,8 +57,8 @@
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},
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},
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 35,
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"execution_count": 37,
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"id": "a3a5ea9a",
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"id": "83f7c5b5",
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"metadata": {},
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"metadata": {},
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"outputs": [
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"outputs": [
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{
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{
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@ -88,7 +88,7 @@
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" \n",
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" \n",
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"data['train_input'] = data.apply(lambda row: to_vowpalwabbit(row, categories), axis=1)\n",
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"data['train_input'] = data.apply(lambda row: to_vowpalwabbit(row, categories), axis=1)\n",
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"\n",
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"\n",
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"model = vowpalwabbit.Workspace('--oaa 7 --learning_rate 0.95')\n",
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"model = vowpalwabbit.Workspace('--oaa 7 --learning_rate 0.99')\n",
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"\n",
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"\n",
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"for example in data['train_input']:\n",
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"for example in data['train_input']:\n",
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" model.learn(example)\n",
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" model.learn(example)\n",
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@ -100,8 +100,8 @@
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},
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},
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 36,
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"execution_count": 38,
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"id": "ee80e14d",
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"id": "caa9bb3b",
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"metadata": {},
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"metadata": {},
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"outputs": [
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"outputs": [
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{
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{
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@ -109,7 +109,7 @@
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"output_type": "stream",
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"output_type": "stream",
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"text": [
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"text": [
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"[NbConvertApp] Converting notebook run.ipynb to script\n",
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"[NbConvertApp] Converting notebook run.ipynb to script\n",
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"[NbConvertApp] Writing 1951 bytes to run.py\n"
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"[NbConvertApp] Writing 1952 bytes to run.py\n"
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]
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]
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}
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}
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],
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],
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6
run.py
6
run.py
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return vw
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return vw
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# In[ ]:
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# In[37]:
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x_train = pd.read_csv('train/in.tsv', header=None, sep='\t')
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x_train = pd.read_csv('train/in.tsv', header=None, sep='\t')
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data['train_input'] = data.apply(lambda row: to_vowpalwabbit(row, categories), axis=1)
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data['train_input'] = data.apply(lambda row: to_vowpalwabbit(row, categories), axis=1)
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model = vowpalwabbit.Workspace('--oaa 7 --learning_rate 0.95')
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model = vowpalwabbit.Workspace('--oaa 7 --learning_rate 0.99')
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for example in data['train_input']:
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for example in data['train_input']:
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model.learn(example)
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model.learn(example)
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prediction('test-B/in.tsv', 'test-B/out.tsv', model, categories)
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prediction('test-B/in.tsv', 'test-B/out.tsv', model, categories)
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# In[34]:
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# In[36]:
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get_ipython().system('jupyter nbconvert --to script run.ipynb')
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get_ipython().system('jupyter nbconvert --to script run.ipynb')
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886
test-A/out.tsv
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test-A/out.tsv
File diff suppressed because it is too large
Load Diff
510
test-B/out.tsv
510
test-B/out.tsv
File diff suppressed because it is too large
Load Diff
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