forked from kubapok/retroc2
20k train data
This commit is contained in:
parent
0faa3da61f
commit
5a6487da37
@ -43,7 +43,7 @@
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"source": [
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"train = pd.read_csv('train/train.tsv', header=None, sep='\\t', error_bad_lines=False)\n",
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"print(len(train))\n",
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"train = train.head(1000)"
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"train = train.head(2000)"
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]
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},
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{
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@ -128,7 +128,21 @@
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"execution_count": 9,
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"id": "cc1270d5-29dc-4f03-82c1-dc03f3e4fa00",
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"ename": "MemoryError",
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"evalue": "Unable to allocate 32.2 GiB for an array with shape (20000, 216394) and data type float64",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[1;31mMemoryError\u001b[0m Traceback (most recent call last)",
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"\u001b[1;32mC:\\Users\\SEBAST~1\\AppData\\Local\\Temp/ipykernel_17784/3948937349.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdev_predicted\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_dev\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtoarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'dev-0/out.tsv'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'wt'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdev_predicted\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m'\\n'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
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"\u001b[1;32mD:\\Programy\\anaconda3\\lib\\site-packages\\scipy\\sparse\\compressed.py\u001b[0m in \u001b[0;36mtoarray\u001b[1;34m(self, order, out)\u001b[0m\n\u001b[0;32m 1029\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mout\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0morder\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1030\u001b[0m \u001b[0morder\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_swap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'cf'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1031\u001b[1;33m \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_process_toarray_args\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1032\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mout\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflags\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mc_contiguous\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflags\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf_contiguous\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1033\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Output array must be C or F contiguous'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
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"\u001b[1;32mD:\\Programy\\anaconda3\\lib\\site-packages\\scipy\\sparse\\base.py\u001b[0m in \u001b[0;36m_process_toarray_args\u001b[1;34m(self, order, out)\u001b[0m\n\u001b[0;32m 1200\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1201\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1202\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1203\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1204\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
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"\u001b[1;31mMemoryError\u001b[0m: Unable to allocate 32.2 GiB for an array with shape (20000, 216394) and data type float64"
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]
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}
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],
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"source": [
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"dev_predicted = model.predict(x_dev.toarray())\n",
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"\n",
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@ -142,25 +156,17 @@
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"execution_count": null,
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"id": "223de995-5e91-4254-9214-4fc871c985e9",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"3486.2683285642797\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"print(mean_squared_error(dev_out, dev_expected))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"execution_count": null,
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"id": "3bc8418b-64f1-4163-a0ec-8e3293032341",
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"metadata": {},
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"outputs": [],
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@ -180,19 +186,10 @@
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"execution_count": null,
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"id": "a18aea56-7fa1-40bd-8aa3-bbaf9d66d6b7",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"[NbConvertApp] Converting notebook run.ipynb to script\n",
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"[NbConvertApp] Writing 1629 bytes to run.py\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"!jupyter nbconvert --to script run.ipynb"
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]
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219
.ipynb_checkpoints/run-rr-checkpoint.ipynb
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219
.ipynb_checkpoints/run-rr-checkpoint.ipynb
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@ -0,0 +1,219 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "d103a6c5-a9b4-4547-9e10-f384d716972d",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"import sklearn\n",
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"from sklearn.feature_extraction.text import TfidfVectorizer\n",
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"from sklearn.linear_model import LinearRegression\n",
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"from sklearn.metrics import mean_squared_error"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "6a2785e6-36b0-4649-91d1-aea8fd3599c1",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"D:\\Programy\\anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3444: FutureWarning: The error_bad_lines argument has been deprecated and will be removed in a future version.\n",
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"\n",
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"\n",
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" exec(code_obj, self.user_global_ns, self.user_ns)\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"107463\n"
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]
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}
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],
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"source": [
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"train = pd.read_csv('train/train.tsv', header=None, sep='\\t', error_bad_lines=False)\n",
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"print(len(train))\n",
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"train = train.head(1500)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "8cc00b89-1007-4c4a-8ba7-c62b57459b79",
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"metadata": {},
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"outputs": [],
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"source": [
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"x_train = train[4]\n",
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"y_train = train[0]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "dd454ce5-a06e-4fbd-a546-83fb94ad0390",
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"metadata": {},
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"outputs": [],
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"source": [
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"x_dev_data = pd.read_csv('dev-0/in.tsv', header=None, sep='\\t')\n",
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"x_dev = x_dev_data[0]\n",
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"x_dev[19999] = \"to jest tekst testowy\"\n",
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"x_dev[20000] = \"a ten tekst jest najbardziej testowy\"\n",
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"y_dev = pd.read_csv('dev-0/expected.tsv', header=None, sep='\\t')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "79099730-c5bd-4c5c-a0b0-788512d44226",
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"metadata": {},
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"outputs": [],
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"source": [
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"vectorizer = TfidfVectorizer()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "0a1cce75-86a1-4f76-9416-e876e01699e3",
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"metadata": {},
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"outputs": [],
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"source": [
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"x_train = vectorizer.fit_transform(x_train)\n",
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"x_dev = vectorizer.transform(x_dev)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "ef405093-6b4c-4558-add4-40bd0ced244e",
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"metadata": {},
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"outputs": [],
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"source": [
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"model = LinearRegression()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "4354553c-6143-43c7-8845-3b2327819481",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"LinearRegression()"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"model.fit(x_train.toarray(), y_train)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "cc1270d5-29dc-4f03-82c1-dc03f3e4fa00",
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"metadata": {},
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"outputs": [
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{
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"ename": "MemoryError",
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"evalue": "Unable to allocate 20.6 GiB for an array with shape (20000, 138052) and data type float64",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[1;31mMemoryError\u001b[0m Traceback (most recent call last)",
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"\u001b[1;32mC:\\Users\\SEBAST~1\\AppData\\Local\\Temp/ipykernel_7064/3948937349.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdev_predicted\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_dev\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtoarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'dev-0/out.tsv'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'wt'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdev_predicted\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m'\\n'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;32mD:\\Programy\\anaconda3\\lib\\site-packages\\scipy\\sparse\\compressed.py\u001b[0m in \u001b[0;36mtoarray\u001b[1;34m(self, order, out)\u001b[0m\n\u001b[0;32m 1029\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mout\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0morder\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1030\u001b[0m \u001b[0morder\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_swap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'cf'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1031\u001b[1;33m \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_process_toarray_args\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1032\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mout\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflags\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mc_contiguous\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflags\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf_contiguous\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1033\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Output array must be C or F contiguous'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;32mD:\\Programy\\anaconda3\\lib\\site-packages\\scipy\\sparse\\base.py\u001b[0m in \u001b[0;36m_process_toarray_args\u001b[1;34m(self, order, out)\u001b[0m\n\u001b[0;32m 1200\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1201\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1202\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1203\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1204\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;31mMemoryError\u001b[0m: Unable to allocate 20.6 GiB for an array with shape (20000, 138052) and data type float64"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"dev_predicted = model.predict(x_dev.toarray())\n",
|
||||
"\n",
|
||||
"with open('dev-0/out.tsv', 'wt') as f:\n",
|
||||
" for i in dev_predicted:\n",
|
||||
" f.write(str(i)+'\\n')\n",
|
||||
"\n",
|
||||
"dev_out = pd.read_csv('dev-0/out.tsv', header=None, sep='\\t')\n",
|
||||
"dev_expected = pd.read_csv('dev-0/expected.tsv', header=None, sep='\\t')\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "223de995-5e91-4254-9214-4fc871c985e9",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(mean_squared_error(dev_out, dev_expected))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "3bc8418b-64f1-4163-a0ec-8e3293032341",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"with open('test-A/in.tsv', 'r', encoding = 'utf-8') as f:\n",
|
||||
" x_test = f.readlines()\n",
|
||||
" \n",
|
||||
"x_test = pd.Series(x_test)\n",
|
||||
"x_test = vectorizer.transform(x_test)\n",
|
||||
"\n",
|
||||
"test_predicted = model.predict(x_test.toarray())\n",
|
||||
"\n",
|
||||
"with open('test-A/out.tsv', 'wt') as f:\n",
|
||||
" for i in test_predicted:\n",
|
||||
" f.write(str(i)+'\\n')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "a18aea56-7fa1-40bd-8aa3-bbaf9d66d6b7",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"!jupyter nbconvert --to script run.ipynb"
|
||||
]
|
||||
}
|
||||
],
|
||||
"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
|
||||
}
|
||||
40000
dev-0/out.tsv
40000
dev-0/out.tsv
File diff suppressed because it is too large
Load Diff
219
run-rr.ipynb
Normal file
219
run-rr.ipynb
Normal file
@ -0,0 +1,219 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "d103a6c5-a9b4-4547-9e10-f384d716972d",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import os\n",
|
||||
"import pandas as pd\n",
|
||||
"import numpy as np\n",
|
||||
"import sklearn\n",
|
||||
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
|
||||
"from sklearn.linear_model import LinearRegression\n",
|
||||
"from sklearn.metrics import mean_squared_error"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "6a2785e6-36b0-4649-91d1-aea8fd3599c1",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"D:\\Programy\\anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3444: FutureWarning: The error_bad_lines argument has been deprecated and will be removed in a future version.\n",
|
||||
"\n",
|
||||
"\n",
|
||||
" exec(code_obj, self.user_global_ns, self.user_ns)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"107463\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"train = pd.read_csv('train/train.tsv', header=None, sep='\\t', error_bad_lines=False)\n",
|
||||
"print(len(train))\n",
|
||||
"train = train.head(2000)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "8cc00b89-1007-4c4a-8ba7-c62b57459b79",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"x_train = train[4]\n",
|
||||
"y_train = train[0]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "dd454ce5-a06e-4fbd-a546-83fb94ad0390",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"x_dev_data = pd.read_csv('dev-0/in.tsv', header=None, sep='\\t')\n",
|
||||
"x_dev = x_dev_data[0]\n",
|
||||
"x_dev[19999] = \"to jest tekst testowy\"\n",
|
||||
"x_dev[20000] = \"a ten tekst jest najbardziej testowy\"\n",
|
||||
"y_dev = pd.read_csv('dev-0/expected.tsv', header=None, sep='\\t')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "79099730-c5bd-4c5c-a0b0-788512d44226",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"vectorizer = TfidfVectorizer()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"id": "0a1cce75-86a1-4f76-9416-e876e01699e3",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"x_train = vectorizer.fit_transform(x_train)\n",
|
||||
"x_dev = vectorizer.transform(x_dev)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"id": "ef405093-6b4c-4558-add4-40bd0ced244e",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"model = LinearRegression()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"id": "4354553c-6143-43c7-8845-3b2327819481",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"LinearRegression()"
|
||||
]
|
||||
},
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"model.fit(x_train.toarray(), y_train)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"id": "cc1270d5-29dc-4f03-82c1-dc03f3e4fa00",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"ename": "MemoryError",
|
||||
"evalue": "Unable to allocate 32.2 GiB for an array with shape (20000, 216394) and data type float64",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[1;31mMemoryError\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[1;32mC:\\Users\\SEBAST~1\\AppData\\Local\\Temp/ipykernel_17784/3948937349.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdev_predicted\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_dev\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtoarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'dev-0/out.tsv'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'wt'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdev_predicted\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m'\\n'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;32mD:\\Programy\\anaconda3\\lib\\site-packages\\scipy\\sparse\\compressed.py\u001b[0m in \u001b[0;36mtoarray\u001b[1;34m(self, order, out)\u001b[0m\n\u001b[0;32m 1029\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mout\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0morder\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1030\u001b[0m \u001b[0morder\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_swap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'cf'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1031\u001b[1;33m \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_process_toarray_args\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1032\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mout\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflags\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mc_contiguous\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflags\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf_contiguous\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1033\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Output array must be C or F contiguous'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;32mD:\\Programy\\anaconda3\\lib\\site-packages\\scipy\\sparse\\base.py\u001b[0m in \u001b[0;36m_process_toarray_args\u001b[1;34m(self, order, out)\u001b[0m\n\u001b[0;32m 1200\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1201\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1202\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1203\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1204\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;31mMemoryError\u001b[0m: Unable to allocate 32.2 GiB for an array with shape (20000, 216394) and data type float64"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"dev_predicted = model.predict(x_dev.toarray())\n",
|
||||
"\n",
|
||||
"with open('dev-0/out.tsv', 'wt') as f:\n",
|
||||
" for i in dev_predicted:\n",
|
||||
" f.write(str(i)+'\\n')\n",
|
||||
"\n",
|
||||
"dev_out = pd.read_csv('dev-0/out.tsv', header=None, sep='\\t')\n",
|
||||
"dev_expected = pd.read_csv('dev-0/expected.tsv', header=None, sep='\\t')\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "223de995-5e91-4254-9214-4fc871c985e9",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(mean_squared_error(dev_out, dev_expected))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "3bc8418b-64f1-4163-a0ec-8e3293032341",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"with open('test-A/in.tsv', 'r', encoding = 'utf-8') as f:\n",
|
||||
" x_test = f.readlines()\n",
|
||||
" \n",
|
||||
"x_test = pd.Series(x_test)\n",
|
||||
"x_test = vectorizer.transform(x_test)\n",
|
||||
"\n",
|
||||
"test_predicted = model.predict(x_test.toarray())\n",
|
||||
"\n",
|
||||
"with open('test-A/out.tsv', 'wt') as f:\n",
|
||||
" for i in test_predicted:\n",
|
||||
" f.write(str(i)+'\\n')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "a18aea56-7fa1-40bd-8aa3-bbaf9d66d6b7",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"!jupyter nbconvert --to script run.ipynb"
|
||||
]
|
||||
}
|
||||
],
|
||||
"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
|
||||
}
|
||||
73
run.ipynb
73
run.ipynb
@ -13,7 +13,8 @@
|
||||
"import sklearn\n",
|
||||
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
|
||||
"from sklearn.linear_model import LinearRegression\n",
|
||||
"from sklearn.metrics import mean_squared_error"
|
||||
"from sklearn.metrics import mean_squared_error\n",
|
||||
"from sklearn.pipeline import make_pipeline"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -43,7 +44,7 @@
|
||||
"source": [
|
||||
"train = pd.read_csv('train/train.tsv', header=None, sep='\\t', error_bad_lines=False)\n",
|
||||
"print(len(train))\n",
|
||||
"train = train.head(1000)"
|
||||
"train = train.head(20000)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -74,63 +75,34 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "79099730-c5bd-4c5c-a0b0-788512d44226",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"vectorizer = TfidfVectorizer()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"id": "0a1cce75-86a1-4f76-9416-e876e01699e3",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"x_train = vectorizer.fit_transform(x_train)\n",
|
||||
"x_dev = vectorizer.transform(x_dev)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"id": "ef405093-6b4c-4558-add4-40bd0ced244e",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"model = LinearRegression()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"id": "4354553c-6143-43c7-8845-3b2327819481",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"LinearRegression()"
|
||||
"Pipeline(steps=[('tfidfvectorizer', TfidfVectorizer()),\n",
|
||||
" ('linearregression', LinearRegression())])"
|
||||
]
|
||||
},
|
||||
"execution_count": 8,
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"model.fit(x_train.toarray(), y_train)"
|
||||
"model = make_pipeline(TfidfVectorizer(), LinearRegression())\n",
|
||||
"model.fit(x_train, y_train)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"execution_count": 6,
|
||||
"id": "cc1270d5-29dc-4f03-82c1-dc03f3e4fa00",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"dev_predicted = model.predict(x_dev.toarray())\n",
|
||||
"dev_predicted = model.predict(x_dev)\n",
|
||||
"\n",
|
||||
"with open('dev-0/out.tsv', 'wt') as f:\n",
|
||||
" for i in dev_predicted:\n",
|
||||
@ -142,7 +114,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"execution_count": 7,
|
||||
"id": "223de995-5e91-4254-9214-4fc871c985e9",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@ -150,7 +122,7 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"3486.2683285642797\n"
|
||||
"4214.6524419302405\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@ -160,7 +132,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"execution_count": null,
|
||||
"id": "3bc8418b-64f1-4163-a0ec-8e3293032341",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@ -168,10 +140,10 @@
|
||||
"with open('test-A/in.tsv', 'r', encoding = 'utf-8') as f:\n",
|
||||
" x_test = f.readlines()\n",
|
||||
" \n",
|
||||
"x_test = pd.Series(x_test)\n",
|
||||
"x_test = vectorizer.transform(x_test)\n",
|
||||
"# x_test = pd.Series(x_test)\n",
|
||||
"# x_test = vectorizer.transform(x_test)\n",
|
||||
"\n",
|
||||
"test_predicted = model.predict(x_test.toarray())\n",
|
||||
"test_predicted = model.predict(x_test)\n",
|
||||
"\n",
|
||||
"with open('test-A/out.tsv', 'wt') as f:\n",
|
||||
" for i in test_predicted:\n",
|
||||
@ -180,19 +152,10 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"execution_count": null,
|
||||
"id": "a18aea56-7fa1-40bd-8aa3-bbaf9d66d6b7",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"[NbConvertApp] Converting notebook run.ipynb to script\n",
|
||||
"[NbConvertApp] Writing 1629 bytes to run.py\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"!jupyter nbconvert --to script run.ipynb"
|
||||
]
|
||||
|
||||
41
run.py
41
run.py
@ -11,6 +11,7 @@ import sklearn
|
||||
from sklearn.feature_extraction.text import TfidfVectorizer
|
||||
from sklearn.linear_model import LinearRegression
|
||||
from sklearn.metrics import mean_squared_error
|
||||
from sklearn.pipeline import make_pipeline
|
||||
|
||||
|
||||
# In[2]:
|
||||
@ -18,7 +19,7 @@ from sklearn.metrics import mean_squared_error
|
||||
|
||||
train = pd.read_csv('train/train.tsv', header=None, sep='\t', error_bad_lines=False)
|
||||
print(len(train))
|
||||
train = train.head(1000)
|
||||
train = train.head(20000)
|
||||
|
||||
|
||||
# In[3]:
|
||||
@ -41,32 +42,14 @@ y_dev = pd.read_csv('dev-0/expected.tsv', header=None, sep='\t')
|
||||
# In[5]:
|
||||
|
||||
|
||||
vectorizer = TfidfVectorizer()
|
||||
model = make_pipeline(TfidfVectorizer(), LinearRegression())
|
||||
model.fit(x_train, y_train)
|
||||
|
||||
|
||||
# In[6]:
|
||||
|
||||
|
||||
x_train = vectorizer.fit_transform(x_train)
|
||||
x_dev = vectorizer.transform(x_dev)
|
||||
|
||||
|
||||
# In[7]:
|
||||
|
||||
|
||||
model = LinearRegression()
|
||||
|
||||
|
||||
# In[8]:
|
||||
|
||||
|
||||
model.fit(x_train.toarray(), y_train)
|
||||
|
||||
|
||||
# In[9]:
|
||||
|
||||
|
||||
dev_predicted = model.predict(x_dev.toarray())
|
||||
dev_predicted = model.predict(x_dev)
|
||||
|
||||
with open('dev-0/out.tsv', 'wt') as f:
|
||||
for i in dev_predicted:
|
||||
@ -76,7 +59,7 @@ dev_out = pd.read_csv('dev-0/out.tsv', header=None, sep='\t')
|
||||
dev_expected = pd.read_csv('dev-0/expected.tsv', header=None, sep='\t')
|
||||
|
||||
|
||||
# In[10]:
|
||||
# In[7]:
|
||||
|
||||
|
||||
print(mean_squared_error(dev_out, dev_expected))
|
||||
@ -88,12 +71,18 @@ print(mean_squared_error(dev_out, dev_expected))
|
||||
with open('test-A/in.tsv', 'r', encoding = 'utf-8') as f:
|
||||
x_test = f.readlines()
|
||||
|
||||
x_test = pd.Series(x_test)
|
||||
x_test = vectorizer.transform(x_test)
|
||||
# x_test = pd.Series(x_test)
|
||||
# x_test = vectorizer.transform(x_test)
|
||||
|
||||
test_predicted = model.predict(x_test.toarray())
|
||||
test_predicted = model.predict(x_test)
|
||||
|
||||
with open('test-A/out.tsv', 'wt') as f:
|
||||
for i in test_predicted:
|
||||
f.write(str(i)+'\n')
|
||||
|
||||
|
||||
# In[ ]:
|
||||
|
||||
|
||||
get_ipython().system('jupyter nbconvert --to script run.ipynb')
|
||||
|
||||
|
||||
28440
test-A/out.tsv
28440
test-A/out.tsv
File diff suppressed because it is too large
Load Diff
Loading…
Reference in New Issue
Block a user