update

.ipynb_checkpoints/run-checkpoint.ipynb

@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "id": "5e807436",
+   "id": "c333fcd7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -15,7 +15,7 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "fa414610",
+   "id": "e550c221",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -36,7 +36,7 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "id": "180143ed",
+   "id": "57585a3e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -57,8 +57,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "id": "3f5ddebd",
+   "execution_count": 35,
+   "id": "a3a5ea9a",
    "metadata": {},
    "outputs": [
     {
@@ -88,7 +88,7 @@
     "    \n",
     "data['train_input'] = data.apply(lambda row: to_vowpalwabbit(row, categories), axis=1)\n",
     "\n",
-    "model = vowpalwabbit.Workspace('--oaa 7 --learning_rate 0.5')\n",
+    "model = vowpalwabbit.Workspace('--oaa 7 --learning_rate 0.95')\n",
     "\n",
     "for example in data['train_input']:\n",
     "    model.learn(example)\n",
@@ -100,8 +100,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
-   "id": "5c1c1cb1",
+   "execution_count": 36,
+   "id": "ee80e14d",
    "metadata": {},
    "outputs": [
     {
@@ -109,7 +109,7 @@
      "output_type": "stream",
      "text": [
       "[NbConvertApp] Converting notebook run.ipynb to script\n",
-      "[NbConvertApp] Writing 1950 bytes to run.py\n"
+      "[NbConvertApp] Writing 1951 bytes to run.py\n"
      ]
     }
    ],

run.ipynb

@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "id": "5e807436",
+   "id": "c333fcd7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -15,7 +15,7 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "fa414610",
+   "id": "e550c221",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -36,7 +36,7 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "id": "180143ed",
+   "id": "57585a3e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -57,8 +57,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "id": "3f5ddebd",
+   "execution_count": 35,
+   "id": "a3a5ea9a",
    "metadata": {},
    "outputs": [
     {
@@ -88,7 +88,7 @@
     "    \n",
     "data['train_input'] = data.apply(lambda row: to_vowpalwabbit(row, categories), axis=1)\n",
     "\n",
-    "model = vowpalwabbit.Workspace('--oaa 7 --learning_rate 0.5')\n",
+    "model = vowpalwabbit.Workspace('--oaa 7 --learning_rate 0.95')\n",
     "\n",
     "for example in data['train_input']:\n",
     "    model.learn(example)\n",
@@ -100,8 +100,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
-   "id": "5c1c1cb1",
+   "execution_count": 36,
+   "id": "ee80e14d",
    "metadata": {},
    "outputs": [
     {
@@ -109,7 +109,7 @@
      "output_type": "stream",
      "text": [
       "[NbConvertApp] Converting notebook run.ipynb to script\n",
-      "[NbConvertApp] Writing 1950 bytes to run.py\n"
+      "[NbConvertApp] Writing 1951 bytes to run.py\n"
      ]
     }
    ],

run.py

@@ -44,7 +44,7 @@ def to_vowpalwabbit(row, categories):
     return vw
 
 
-# In[31]:
+# In[ ]:
 
 
 x_train = pd.read_csv('train/in.tsv', header=None, sep='\t')
@@ -65,7 +65,7 @@ print(categories)
     
 data['train_input'] = data.apply(lambda row: to_vowpalwabbit(row, categories), axis=1)
 
-model = vowpalwabbit.Workspace('--oaa 7 --learning_rate 0.5')
+model = vowpalwabbit.Workspace('--oaa 7 --learning_rate 0.95')
 
 for example in data['train_input']:
     model.learn(example)
@@ -75,7 +75,7 @@ prediction('test-A/in.tsv', 'test-A/out.tsv', model, categories)
 prediction('test-B/in.tsv', 'test-B/out.tsv', model, categories)
 
 
-# In[32]:
+# In[34]:
 
 
 get_ipython().system('jupyter nbconvert --to script run.ipynb')