forked from kubapok/retroc2
2nd attempt
This commit is contained in:
parent
f130909428
commit
c3d349bf38
@ -13,7 +13,8 @@
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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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"from sklearn.metrics import mean_squared_error\n",
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"from sklearn.pipeline import make_pipeline"
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]
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},
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{
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@ -43,7 +44,7 @@
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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(2000)"
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"train = train.head(30000)"
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]
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},
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{
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@ -74,77 +75,34 @@
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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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"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": 8,
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"execution_count": 5,
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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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"model = make_pipeline(TfidfVectorizer(), LinearRegression())\n",
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"model.fit(x_train, 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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"execution_count": 6,
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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 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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"outputs": [],
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"source": [
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"dev_predicted = model.predict(x_dev.toarray())\n",
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"dev_predicted = model.predict(x_dev)\n",
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"\n",
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"with open('dev-0/out.tsv', 'wt') as f:\n",
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" for i in dev_predicted:\n",
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@ -156,17 +114,25 @@
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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": 7,
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"id": "223de995-5e91-4254-9214-4fc871c985e9",
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"metadata": {},
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"outputs": [],
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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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"4261.093474053155\n"
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]
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}
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],
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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": null,
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"execution_count": 8,
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"id": "3bc8418b-64f1-4163-a0ec-8e3293032341",
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"metadata": {},
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"outputs": [],
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@ -174,10 +140,10 @@
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"with open('test-A/in.tsv', 'r', encoding = 'utf-8') as f:\n",
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" x_test = f.readlines()\n",
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" \n",
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"x_test = pd.Series(x_test)\n",
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"x_test = vectorizer.transform(x_test)\n",
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"# x_test = pd.Series(x_test)\n",
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"# x_test = vectorizer.transform(x_test)\n",
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"\n",
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"test_predicted = model.predict(x_test.toarray())\n",
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"test_predicted = model.predict(x_test)\n",
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"\n",
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"with open('test-A/out.tsv', 'wt') as f:\n",
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" for i in test_predicted:\n",
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@ -186,10 +152,19 @@
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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": 9,
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"id": "a18aea56-7fa1-40bd-8aa3-bbaf9d66d6b7",
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"metadata": {},
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"outputs": [],
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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 1607 bytes to run.py\n"
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]
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}
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],
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"source": [
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"!jupyter nbconvert --to script run.ipynb"
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]
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40000
dev-0/out.tsv
40000
dev-0/out.tsv
File diff suppressed because it is too large
Load Diff
@ -44,7 +44,7 @@
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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(20000)"
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"train = train.head(30000)"
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]
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},
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{
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@ -122,7 +122,7 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"4214.6524419302405\n"
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"4261.093474053155\n"
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]
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}
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],
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@ -161,7 +161,7 @@
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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 1608 bytes to run.py\n"
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"[NbConvertApp] Writing 1607 bytes to run.py\n"
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]
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}
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],
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2
run.py
2
run.py
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train = pd.read_csv('train/train.tsv', header=None, sep='\t', error_bad_lines=False)
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print(len(train))
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train = train.head(100000)
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train = train.head(30000)
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# In[3]:
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28440
test-A/out.tsv
28440
test-A/out.tsv
File diff suppressed because it is too large
Load Diff
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Block a user