forked from kubapok/retroc2
second
This commit is contained in:
korne
parent
5389bd1d5b
commit
14fbbecf94
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@ -2,7 +2,7 @@
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 15,
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"execution_count": 10,
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"id": "greenhouse-technician",
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"metadata": {},
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"outputs": [],
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@ -19,90 +19,48 @@
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"execution_count": 11,
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"id": "acoustic-dividend",
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"metadata": {},
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"outputs": [],
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"source": [
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"def predict_year(x, path_out, model):\n",
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" \n",
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" results = model.predict(x)\n",
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"\n",
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" with open(path_out, 'wt') as file:\n",
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" for r in results:\n",
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" #if r%1==0:\n",
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" # r = r+0,5\n",
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" file.write(str(r) + '\\n')\n",
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" "
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" file.write(str(r) + '\\n') "
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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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"execution_count": 12,
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"id": "senior-harassment",
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"metadata": {},
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"outputs": [],
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"source": [
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"with open('train.tsv', 'r', encoding='utf8') as file:\n",
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" train = pd.read_csv(file, sep='\\t', names=['Begin', 'End', 'Title', 'Publisher', 'Text'])\n",
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"with open('train/train.tsv', 'r', encoding='utf8') as file:\n",
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" train = pd.read_csv(file, sep='\\t', names=['Date1', 'Date2', 'Title', 'Author', 'Text'])\n",
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" \n",
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"#with open('train/train.tsv', 'r', encoding='utf8') as file:\n",
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"# train = pd.read_csv(file, sep='\\t', names=['Begin', 'End', 'Title', 'Publisher', 'Text'])\n",
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"train = train[0:2000]\n",
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"train_x=train['Text'] \n",
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"train_y=train['Begin']"
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"#train = train[0:10000]\n",
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"train_x = train['Text']\n",
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"train['Date'] = (train['Date1'].astype(float) + train['Date2'].astype(float))/2\n",
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"train_y=train['Date1']"
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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": "beneficial-traveler",
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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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"0 nowią część kultury. U nas już nikt ich nie ch...\n",
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||||
"1 hlstorja znana w okresie piramid, jak wlaśclcl...\n",
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||||
"2 działek. Idąc dalej w swych hipotetycznych roz...\n",
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"3 w Warszawie o stosunkach domowych dziatwy szko...\n",
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"4 \\\\'iykład: \"Cywilizacyjna Koncepcja dziejów ¥e...\n",
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" ... \n",
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"1995 i' padną dobitne rozkazy. Od modlitwy nie poni...\n",
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"1996 WOJEWÖDZKI roz. 28 N'r. 7 III. IW slüsUlnlku d...\n",
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"1997 główną wagę kłaść należy na wspomniane w Bibli...\n",
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"1998 dniu 1'3 tym marca rozchwytywali broil z miejs...\n",
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"1999 ubezpieczenie społeczne, po rozpoznaniu na pos...\n",
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"Name: Text, Length: 2000, dtype: object"
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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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"train_x"
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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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"execution_count": 13,
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"id": "polyphonic-coach",
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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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"Pipeline(memory=None,\n",
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" steps=[('tfidfvectorizer', TfidfVectorizer(analyzer='word', binary=False, decode_error='strict',\n",
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" dtype=<class 'numpy.float64'>, encoding='utf-8', input='content',\n",
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" lowercase=True, max_df=1.0, max_features=None, min_df=1,\n",
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" ngram_range=(1, 1), norm='l2', preprocessor=None, smooth...ression', LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
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" normalize=False))])"
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"Pipeline(steps=[('tfidfvectorizer', TfidfVectorizer()),\n",
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" ('linearregression', LinearRegression())])"
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]
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},
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"execution_count": 9,
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -114,140 +72,67 @@
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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": 14,
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"id": "varying-wright",
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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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"0 Gazet, a tam o osobie zamformuie się. Uwiadomi...\n",
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"1 materiały, która wyniesie na rok w przybliżeni...\n",
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"2 były nawet w posiadaniu miejscowego polskiego ...\n",
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"3 Usuwanie nawarstwień... 105 powania nieudowodn...\n",
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"4 nie słyszał odC .S' źnniefAle'ObJ—A.\" \"hOdZI Ś...\n",
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" ... \n",
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"19993 wypoczęci! wzmocnieni, pełni najiepszych chęci...\n",
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"19994 ten krok draltyczny, ahby na tirazniejllzej je...\n",
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"19995 47 ust. 1 pkt 2 tej ustawy, obowiązkiem nałożo...\n",
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"19996 w, mmm ”w\" w „|. ..no-ń r. .I. Lennobrovi jako...\n",
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"19997 lat, przy czym za kolejny rok wnoszona jest do...\n",
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"Name: 0, Length: 19998, dtype: object"
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]
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},
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"execution_count": 10,
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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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"outputs": [],
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"source": [
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"with open('devin.tsv', 'r', encoding='utf8') as file:\n",
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" x_dev = pd.read_csv(file, header=None, sep='\\t')\n",
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"x_dev = x_dev[0] \n",
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"x_dev"
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"with open('dev-0/in.tsv', 'r', encoding='utf8') as file:\n",
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" x_dev0 = pd.read_csv(file, header=None, sep='\\t')\n",
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"x_dev0 = x_dev0[0] \n",
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"x_dev0[19999] = 'nie jest'\n",
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"x_dev0[20000] = 'nie wiem'"
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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": 24,
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"execution_count": 15,
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"id": "frozen-ticket",
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"metadata": {},
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"outputs": [],
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"source": [
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"predict_year(x_dev, 'devout.tsv', model)"
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"with open('dev-1/in.tsv', 'r', encoding='utf8') as file:\n",
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" x_dev1 = pd.read_csv(file, header=None, sep='\\t')\n",
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"x_dev1 = x_dev1[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": 25,
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"execution_count": 16,
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"id": "8e3a18db-f966-45e4-b881-4b336f188055",
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"metadata": {},
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"outputs": [],
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"source": [
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"with open('test-A/in.tsv', 'r', encoding='utf8') as file:\n",
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" x_test = pd.read_csv(file, header=None, sep='\\t')\n",
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"x_test = x_test[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": 17,
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"id": "traditional-amount",
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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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"0 1862.531349\n",
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"1 1962.791429\n",
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"2 1950.953131\n",
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"3 1965.496217\n",
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"4 1920.848072\n",
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" ... \n",
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"19993 1914.228771\n",
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"19994 1902.264257\n",
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"19995 2009.252595\n",
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"19996 1918.643586\n",
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"19997 1963.890277\n",
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"Name: 0, Length: 19998, dtype: float64"
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]
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},
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"execution_count": 25,
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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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"y_dev = pd.read_csv('devout.tsv',header = None, sep = '/t',engine = 'python')\n",
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"y_dev = y_dev[0]\n",
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"y_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": 26,
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"id": "cordless-maker",
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"metadata": {},
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"outputs": [],
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"source": [
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"y_dev_exp = pd.read_csv('expected.tsv',header = None, sep = '/t',engine = 'python')\n",
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"y_dev_exp = y_dev_exp[0:19998]\n",
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"y_dev_exp = y_dev_exp[0]"
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"#y_dev = pd.read_csv('dev-0/out.tsv',header = None, sep = '/t',engine = 'python')\n",
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"#y_dev = y_dev[0]\n",
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"#y_dev_exp = pd.read_csv('dev-0/expected.tsv',header = None, sep = '/t',engine = 'python')\n",
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"#y_dev_exp = y_dev_exp[0]\n",
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"#RMSE_dev = mean_squared_error(y_dev_exp, y_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": 27,
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"id": "authorized-basics",
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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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"3631.1358243407444"
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]
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},
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"execution_count": 27,
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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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"RMSE_dev = mean_squared_error(y_dev_exp, y_dev)\n",
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"RMSE_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": null,
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"id": "congressional-requirement",
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"metadata": {},
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"outputs": [],
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"source": [
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"x_dev = pd.read_csv('dev-0/in.tsv',header = None, sep = '/t',engine = 'python')\n",
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"x_dev = x_dev[0]\n",
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"x_dev = pd.read_csv('dev-1/in.tsv',header = None, sep = '/t',engine = 'python')\n",
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"x_dev = x_dev[0]\n",
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"x_test = pd.read_csv('test-A/in.tsv',header = None, sep = '/t',engine = 'python')\n",
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"x_test = x_test[0]\n",
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"\n"
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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": null,
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"execution_count": 18,
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"id": "close-clinton",
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"metadata": {},
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"outputs": [],
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"source": []
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"source": [
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"predict_year(x_dev0, 'dev-0/out.tsv', model)\n",
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"predict_year(x_dev1,'dev-1/out.tsv', model)\n",
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"predict_year(x_test,'test-A/out.tsv', model)"
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]
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},
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{
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"cell_type": "code",
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@ -255,15 +140,12 @@
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"id": "official-sweet",
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"metadata": {},
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"outputs": [],
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"source": [
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"#evaluation(x_dev,'dev-0/out.tsv', model)\n",
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"#evaluation(x_test,'test-A/out.tsv', model)"
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]
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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@ -277,7 +159,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.3"
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"version": "3.9.12"
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}
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},
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"nbformat": 4,
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@ -0,0 +1,167 @@
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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": 10,
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"id": "greenhouse-technician",
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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 sklearn\n",
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"import pandas as pd\n",
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"from gzip import open as open_gz\n",
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"from sklearn.feature_extraction.text import TfidfVectorizer\n",
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"from sklearn.pipeline import make_pipeline\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": 11,
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"id": "acoustic-dividend",
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"metadata": {},
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"outputs": [],
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"source": [
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"def predict_year(x, path_out, model):\n",
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" results = model.predict(x)\n",
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" with open(path_out, 'wt') as file:\n",
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" for r in results:\n",
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" file.write(str(r) + '\\n') "
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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": 12,
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"id": "senior-harassment",
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"metadata": {},
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"outputs": [],
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"source": [
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"with open('train/train.tsv', 'r', encoding='utf8') as file:\n",
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" train = pd.read_csv(file, sep='\\t', names=['Date1', 'Date2', 'Title', 'Author', 'Text'])\n",
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" \n",
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"#train = train[0:10000]\n",
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"train_x = train['Text']\n",
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"train['Date'] = (train['Date1'].astype(float) + train['Date2'].astype(float))/2\n",
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"train_y=train['Date1']"
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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": 13,
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"id": "polyphonic-coach",
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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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"Pipeline(steps=[('tfidfvectorizer', TfidfVectorizer()),\n",
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" ('linearregression', LinearRegression())])"
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]
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},
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"execution_count": 13,
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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 = make_pipeline(TfidfVectorizer(), LinearRegression())\n",
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"model.fit(train_x, train_y)"
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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": 14,
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"id": "varying-wright",
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"metadata": {},
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"outputs": [],
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"source": [
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"with open('dev-0/in.tsv', 'r', encoding='utf8') as file:\n",
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" x_dev0 = pd.read_csv(file, header=None, sep='\\t')\n",
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"x_dev0 = x_dev0[0] \n",
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"x_dev0[19999] = 'nie jest'\n",
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"x_dev0[20000] = 'nie wiem'"
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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": 15,
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"id": "frozen-ticket",
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"metadata": {},
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"outputs": [],
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"source": [
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"with open('dev-1/in.tsv', 'r', encoding='utf8') as file:\n",
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" x_dev1 = pd.read_csv(file, header=None, sep='\\t')\n",
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"x_dev1 = x_dev1[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": 16,
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"id": "8e3a18db-f966-45e4-b881-4b336f188055",
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"metadata": {},
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"outputs": [],
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"source": [
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"with open('test-A/in.tsv', 'r', encoding='utf8') as file:\n",
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" x_test = pd.read_csv(file, header=None, sep='\\t')\n",
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"x_test = x_test[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": 17,
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"id": "traditional-amount",
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"metadata": {},
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"outputs": [],
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"source": [
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"#y_dev = pd.read_csv('dev-0/out.tsv',header = None, sep = '/t',engine = 'python')\n",
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"#y_dev = y_dev[0]\n",
|
||||
"#y_dev_exp = pd.read_csv('dev-0/expected.tsv',header = None, sep = '/t',engine = 'python')\n",
|
||||
"#y_dev_exp = y_dev_exp[0]\n",
|
||||
"#RMSE_dev = mean_squared_error(y_dev_exp, y_dev) "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"id": "close-clinton",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"predict_year(x_dev0, 'dev-0/out.tsv', model)\n",
|
||||
"predict_year(x_dev1,'dev-1/out.tsv', model)\n",
|
||||
"predict_year(x_test,'test-A/out.tsv', model)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "official-sweet",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"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": "3.9.12"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
40000
dev-0/out.tsv
40000
dev-0/out.tsv
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Load Diff
23124
dev-1/out.tsv
23124
dev-1/out.tsv
File diff suppressed because it is too large
Load Diff
|
|
@ -2,7 +2,7 @@
|
|||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"execution_count": 10,
|
||||
"id": "greenhouse-technician",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -19,7 +19,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"execution_count": 11,
|
||||
"id": "acoustic-dividend",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -33,7 +33,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"execution_count": 12,
|
||||
"id": "senior-harassment",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -41,15 +41,15 @@
|
|||
"with open('train/train.tsv', 'r', encoding='utf8') as file:\n",
|
||||
" train = pd.read_csv(file, sep='\\t', names=['Date1', 'Date2', 'Title', 'Author', 'Text'])\n",
|
||||
" \n",
|
||||
"train = train[0:10000]\n",
|
||||
"#train = train[0:10000]\n",
|
||||
"train_x = train['Text']\n",
|
||||
"train['Date'] = (train['Date1'].astype(float) + train['Date2'].astype(float))/2\n",
|
||||
"train_y=train['Date']"
|
||||
"train_y=train['Date1']"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"execution_count": 13,
|
||||
"id": "polyphonic-coach",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
|
|
@ -60,7 +60,7 @@
|
|||
" ('linearregression', LinearRegression())])"
|
||||
]
|
||||
},
|
||||
"execution_count": 4,
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
|
|
@ -72,7 +72,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"execution_count": 14,
|
||||
"id": "varying-wright",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -80,13 +80,13 @@
|
|||
"with open('dev-0/in.tsv', 'r', encoding='utf8') as file:\n",
|
||||
" x_dev0 = pd.read_csv(file, header=None, sep='\\t')\n",
|
||||
"x_dev0 = x_dev0[0] \n",
|
||||
"x_dev0[19999] = 'jest'\n",
|
||||
"x_dev0[20000] = 'nie'"
|
||||
"x_dev0[19999] = 'nie jest'\n",
|
||||
"x_dev0[20000] = 'nie wiem'"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"execution_count": 15,
|
||||
"id": "frozen-ticket",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -98,7 +98,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"execution_count": 16,
|
||||
"id": "8e3a18db-f966-45e4-b881-4b336f188055",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -110,7 +110,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"execution_count": 17,
|
||||
"id": "traditional-amount",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -124,7 +124,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"execution_count": 18,
|
||||
"id": "close-clinton",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
|
|||
|
|
@ -0,0 +1,167 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"id": "greenhouse-technician",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import os\n",
|
||||
"import sklearn\n",
|
||||
"import pandas as pd\n",
|
||||
"from gzip import open as open_gz\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"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"id": "acoustic-dividend",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def predict_year(x, path_out, model):\n",
|
||||
" results = model.predict(x)\n",
|
||||
" with open(path_out, 'wt') as file:\n",
|
||||
" for r in results:\n",
|
||||
" file.write(str(r) + '\\n') "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"id": "senior-harassment",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"with open('train/train.tsv', 'r', encoding='utf8') as file:\n",
|
||||
" train = pd.read_csv(file, sep='\\t', names=['Date1', 'Date2', 'Title', 'Author', 'Text'])\n",
|
||||
" \n",
|
||||
"#train = train[0:10000]\n",
|
||||
"train_x = train['Text']\n",
|
||||
"train['Date'] = (train['Date1'].astype(float) + train['Date2'].astype(float))/2\n",
|
||||
"train_y=train['Date1']"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"id": "polyphonic-coach",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"Pipeline(steps=[('tfidfvectorizer', TfidfVectorizer()),\n",
|
||||
" ('linearregression', LinearRegression())])"
|
||||
]
|
||||
},
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"model = make_pipeline(TfidfVectorizer(), LinearRegression())\n",
|
||||
"model.fit(train_x, train_y)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"id": "varying-wright",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"with open('dev-0/in.tsv', 'r', encoding='utf8') as file:\n",
|
||||
" x_dev0 = pd.read_csv(file, header=None, sep='\\t')\n",
|
||||
"x_dev0 = x_dev0[0] \n",
|
||||
"x_dev0[19999] = 'nie jest'\n",
|
||||
"x_dev0[20000] = 'nie wiem'"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"id": "frozen-ticket",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"with open('dev-1/in.tsv', 'r', encoding='utf8') as file:\n",
|
||||
" x_dev1 = pd.read_csv(file, header=None, sep='\\t')\n",
|
||||
"x_dev1 = x_dev1[0] "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"id": "8e3a18db-f966-45e4-b881-4b336f188055",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"with open('test-A/in.tsv', 'r', encoding='utf8') as file:\n",
|
||||
" x_test = pd.read_csv(file, header=None, sep='\\t')\n",
|
||||
"x_test = x_test[0] "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"id": "traditional-amount",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#y_dev = pd.read_csv('dev-0/out.tsv',header = None, sep = '/t',engine = 'python')\n",
|
||||
"#y_dev = y_dev[0]\n",
|
||||
"#y_dev_exp = pd.read_csv('dev-0/expected.tsv',header = None, sep = '/t',engine = 'python')\n",
|
||||
"#y_dev_exp = y_dev_exp[0]\n",
|
||||
"#RMSE_dev = mean_squared_error(y_dev_exp, y_dev) "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"id": "close-clinton",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"predict_year(x_dev0, 'dev-0/out.tsv', model)\n",
|
||||
"predict_year(x_dev1,'dev-1/out.tsv', model)\n",
|
||||
"predict_year(x_test,'test-A/out.tsv', model)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "official-sweet",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"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": "3.9.12"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
28438
test-A/out.tsv
28438
test-A/out.tsv
File diff suppressed because it is too large
Load Diff
Loading…
Reference in New Issue