update

.ipynb_checkpoints/run-checkpoint.ipynb

@@ -2,8 +2,8 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "4206eb3f",
+   "execution_count": 11,
+   "id": "5182690b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -14,8 +14,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "fde46276",
+   "execution_count": 12,
+   "id": "6ebd5310",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -29,14 +29,14 @@
     "    with open(path_out, 'w', encoding='utf-8') as file:\n",
     "        for example in data['train_input']:\n",
     "            predicted = model.predict(example)\n",
-    "            text_predicted = dict((value, key) for key, value in map_dict.items()).get(predicted)\n",
+    "            text_predicted = dict((value, key) for key, value in categories.items()).get(predicted)\n",
     "            file.write(str(text_predicted) + '\\n')\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "27e69709",
+   "execution_count": 13,
+   "id": "e5d2de9f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -57,8 +57,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "c406b425",
+   "execution_count": null,
+   "id": "f6ba46b9",
    "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 3 --quiet')\n",
+    "model = vowpalwabbit.Workspace('--oaa 7 --quite)\n",
     "\n",
     "for example in data['train_input']:\n",
     "    model.learn(example)\n",
@@ -97,6 +97,25 @@
     "prediction('test-A/in.tsv', 'test-A/out.tsv', model, categories)\n",
     "prediction('test-B/in.tsv', 'test-B/out.tsv', model, categories)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "0cdf4eaa",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[NbConvertApp] Converting notebook run.ipynb to script\n",
+      "[NbConvertApp] Writing 1933 bytes to run.py\n"
+     ]
+    }
+   ],
+   "source": [
+    "!jupyter nbconvert --to script run.ipynb"
+   ]
   }
  ],
  "metadata": {

run.ipynb

@@ -2,8 +2,8 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "4206eb3f",
+   "execution_count": 23,
+   "id": "4618be1f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -14,8 +14,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "fde46276",
+   "execution_count": 24,
+   "id": "fc616309",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -29,14 +29,14 @@
     "    with open(path_out, 'w', encoding='utf-8') as file:\n",
     "        for example in data['train_input']:\n",
     "            predicted = model.predict(example)\n",
-    "            text_predicted = dict((value, key) for key, value in map_dict.items()).get(predicted)\n",
+    "            text_predicted = dict((value, key) for key, value in categories.items()).get(predicted)\n",
     "            file.write(str(text_predicted) + '\\n')\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "27e69709",
+   "execution_count": 25,
+   "id": "9553e9b0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -57,8 +57,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "c406b425",
+   "execution_count": 28,
+   "id": "96e97cdf",
    "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 3 --quiet')\n",
+    "model = vowpalwabbit.Workspace('--oaa 7')\n",
     "\n",
     "for example in data['train_input']:\n",
     "    model.learn(example)\n",
@@ -97,6 +97,25 @@
     "prediction('test-A/in.tsv', 'test-A/out.tsv', model, categories)\n",
     "prediction('test-B/in.tsv', 'test-B/out.tsv', model, categories)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "e1fe9b4c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[NbConvertApp] Converting notebook run.ipynb to script\n",
+      "[NbConvertApp] Writing 1933 bytes to run.py\n"
+     ]
+    }
+   ],
+   "source": [
+    "!jupyter nbconvert --to script run.ipynb"
+   ]
   }
  ],
  "metadata": {

run.py

@@ -1,7 +1,7 @@
 #!/usr/bin/env python
 # coding: utf-8
 
-# In[6]:
+# In[23]:
 
 
 import vowpalwabbit
@@ -9,7 +9,7 @@ import pandas as pd
 import re
 
 
-# In[7]:
+# In[24]:
 
 
 def prediction(path_in, path_out, model, categories):
@@ -22,11 +22,11 @@ def prediction(path_in, path_out, model, categories):
     with open(path_out, 'w', encoding='utf-8') as file:
         for example in data['train_input']:
             predicted = model.predict(example)
-            text_predicted = dict((value, key) for key, value in map_dict.items()).get(predicted)
+            text_predicted = dict((value, key) for key, value in categories.items()).get(predicted)
             file.write(str(text_predicted) + '\n')
 
 
-# In[8]:
+# In[25]:
 
 
 def to_vowpalwabbit(row, categories):
@@ -44,7 +44,7 @@ def to_vowpalwabbit(row, categories):
     return vw
 
 
-# In[9]:
+# In[28]:
 
 
 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 3 --quiet')
+model = vowpalwabbit.Workspace('--oaa 7')
 
 for example in data['train_input']:
     model.learn(example)
@@ -74,3 +74,9 @@ prediction('dev-0/in.tsv', 'dev-0/out.tsv', model, categories)
 prediction('test-A/in.tsv', 'test-A/out.tsv', model, categories)
 prediction('test-B/in.tsv', 'test-B/out.tsv', model, categories)
 
+
+# In[15]:
+
+
+get_ipython().system('jupyter nbconvert --to script run.ipynb')
+