full train results

retroc.ipynb

@@ -1,38 +1,13 @@
 {
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-    "version": 3
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-   "mimetype": "text/x-python",
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- },
- "nbformat": 4,
- "nbformat_minor": 2,
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
     "import pandas as pd\n",
+    "import csv\n",
     "from sklearn.linear_model import LinearRegression\n",
     "from stop_words import get_stop_words\n",
     "from sklearn.feature_extraction.text import TfidfVectorizer"
@@ -40,37 +15,48 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#trening\n",
     "\n",
     "#dane treningowe\n",
-    "train_data = pd.read_csv('train/train.tsv.xz', compression='xz', sep='\\t')\n",
+    "train_data = pd.read_csv('train/train.tsv.xz', compression='xz', header=None, sep='\\t')\n",
     "\n",
     "#regresja liniowa\n",
     "LR = LinearRegression()\n",
     "#vectorizer\n",
     "VEC = TfidfVectorizer(stop_words=get_stop_words('polish'))\n",
     "#wektoryzacja danych treningowych\n",
-    "train_x = VEC.fit_transform(train_data[2])\n",
+    "train_x = VEC.fit_transform(train_data[4])\n",
     "#średnia dat\n",
-    "dm = mean([train_data[0],train_data[1]])\n",
+    "dm = (train_data[0] + train_data[1])/2\n",
     "#trening\n",
     "LR.fit(train_x, dm)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
     "#dev-0 predict\n",
     "\n",
     "#dane treningowe\n",
-    "dev0_data = pd.read_csv('dev-0/in.tsv', sep='\\t')\n",
+    "dev0_data = pd.read_csv('dev-0/in.tsv', header=None, error_bad_lines=False, quoting=csv.QUOTE_NONE, sep='\\t')\n",
     "\n",
     "#wektoryzacja danych treningowych\n",
     "dev0_x = VEC.transform(dev0_data[0])\n",
@@ -82,7 +68,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {
     "tags": []
    },
@@ -91,7 +77,7 @@
     "#dev-1 predict\n",
     "\n",
     "#dane treningowe\n",
-    "dev1_data = pd.read_csv('dev-1/in.tsv', sep='\\t')\n",
+    "dev1_data = pd.read_csv('dev-1/in.tsv', header=None, error_bad_lines=False, quoting=csv.QUOTE_NONE, sep='\\t')\n",
     "\n",
     "#wektoryzacja danych treningowych\n",
     "dev1_x = VEC.transform(dev1_data[0])\n",
@@ -103,22 +89,48 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
     "#test-A predict\n",
     "\n",
     "#dane treningowe\n",
-    "testA_data = pd.read_csv('testA/in.tsv', sep='\\t')\n",
+    "testA_data = pd.read_csv('test-A/in.tsv', header=None, error_bad_lines=False, quoting=csv.QUOTE_NONE, sep='\\t')\n",
     "\n",
     "#wektoryzacja danych treningowych\n",
     "testA_x = VEC.transform(testA_data[0])\n",
     "#predykcja\n",
     "testA_y = LR.predict(testA_x)\n",
     "#zapis wyników\n",
-    "testA_y.tofile('testA/out.tsv', sep='\\n')"
+    "testA_y.tofile('test-A/out.tsv', sep='\\n')"
    ]
   }
- ]
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
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+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
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+ "nbformat_minor": 2
 }