final

.ipynb_checkpoints/run-checkpoint.py

@@ -0,0 +1,49 @@
+import pandas as pd
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.pipeline import make_pipeline
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import mean_squared_error
+
+with open('train/train.tsv', 'r', encoding='utf8') as file:
+    train_data = pd.read_csv(file, sep='\t', names=['Begin', 'End', 'Title', 'Publisher', 'Text'])
+    
+    
+def readFile(filename):
+    result = []
+    with open(filename, 'r', encoding="utf-8") as file:
+        for line in file:
+            text = line.split("\t")[0].strip()
+            result.append(text)
+    return result
+
+def write_pred(filename, predictions):
+    with open(filename, "w") as file:
+        for pred in predictions:
+            file.write(str(pred) + "\n")
+
+            
+            
+train_data = train_data[:10000]
+    
+X = train_data['Text']
+Y = train_data['Begin']
+
+
+model = make_pipeline(TfidfVectorizer(), LinearRegression())
+model.fit(X, Y)
+
+
+
+dev_0 = readFile('dev-0/in.tsv')
+predict_dev_0 = model.predict(dev_0)
+write_pred('dev-0/out.tsv', predict_dev_0)
+
+dev_1 = readFile('dev-1/in.tsv')
+predict_dev_1 = model.predict(dev_1)
+write_pred('dev-1/out.tsv', predict_dev_1)
+
+test_A = readFile('test-A/in.tsv')
+predict_test_A = model.predict(test_A)
+write_pred('test-A/out.tsv', predict_test_A)
+
+

model.ipynb

@@ -0,0 +1,118 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "id": "8f5480f9-fa82-4150-acff-9309fdc43690",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "107463\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Pipeline(steps=[('tfidfvectorizer', TfidfVectorizer()),\n",
+       "                ('linearregression', LinearRegression())])"
+      ]
+     },
+     "execution_count": 99,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "\n",
+    "with open('train/train.tsv', 'r', encoding='utf8') as file:\n",
+    "    train_data = pd.read_csv(file, sep='\\t', names=['Begin', 'End', 'Title', 'Publisher', 'Text'])\n",
+    "\n",
+    "print(len(train_data))    \n",
+    "train_data = train_data[:10000]\n",
+    "    \n",
+    "X = train_data['Text']\n",
+    "Y = train_data['Begin']\n",
+    "\n",
+    "\n",
+    "model = make_pipeline(TfidfVectorizer(), LinearRegression())\n",
+    "model.fit(X, Y)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "id": "02e89f1c-a2d0-4d41-94a2-aa86b257069d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def readFile(filename):\n",
+    "    result = []\n",
+    "    with open(filename, 'r', encoding=\"utf-8\") as file:\n",
+    "        for line in file:\n",
+    "            text = line.split(\"\\t\")[0].strip()\n",
+    "            result.append(text)\n",
+    "    return result\n",
+    "\n",
+    "def write_pred(filename, predictions):\n",
+    "    with open(filename, \"w\") as file:\n",
+    "        for pred in predictions:\n",
+    "            file.write(str(pred) + \"\\n\")\n",
+    "        \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "id": "b85f5e22-eafb-41ee-aa2c-20c338d42701",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "dev_0 = readFile('dev-0/in.tsv')\n",
+    "predict_dev_0 = model.predict(dev_0)\n",
+    "write_pred('dev-0/out.tsv', predict_dev_0)\n",
+    "\n",
+    "dev_1 = readFile('dev-1/in.tsv')\n",
+    "predict_dev_1 = model.predict(dev_1)\n",
+    "write_pred('dev-1/out.tsv', predict_dev_1)\n",
+    "\n",
+    "test_A = readFile('test-A/in.tsv')\n",
+    "predict_test_A = model.predict(test_A)\n",
+    "write_pred('test-A/out.tsv', predict_test_A)\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

run.py

@@ -0,0 +1,49 @@
+import pandas as pd
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.pipeline import make_pipeline
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import mean_squared_error
+
+with open('train/train.tsv', 'r', encoding='utf8') as file:
+    train_data = pd.read_csv(file, sep='\t', names=['Begin', 'End', 'Title', 'Publisher', 'Text'])
+    
+    
+def readFile(filename):
+    result = []
+    with open(filename, 'r', encoding="utf-8") as file:
+        for line in file:
+            text = line.split("\t")[0].strip()
+            result.append(text)
+    return result
+
+def write_pred(filename, predictions):
+    with open(filename, "w") as file:
+        for pred in predictions:
+            file.write(str(pred) + "\n")
+
+            
+            
+train_data = train_data[:10000]
+    
+X = train_data['Text']
+Y = train_data['Begin']
+
+
+model = make_pipeline(TfidfVectorizer(), LinearRegression())
+model.fit(X, Y)
+
+
+
+dev_0 = readFile('dev-0/in.tsv')
+predict_dev_0 = model.predict(dev_0)
+write_pred('dev-0/out.tsv', predict_dev_0)
+
+dev_1 = readFile('dev-1/in.tsv')
+predict_dev_1 = model.predict(dev_1)
+write_pred('dev-1/out.tsv', predict_dev_1)
+
+test_A = readFile('test-A/in.tsv')
+predict_test_A = model.predict(test_A)
+write_pred('test-A/out.tsv', predict_test_A)
+
+