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extract lasso features

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ltcptgeneral 2023-12-04 10:22:45 -08:00
parent 4cf85a15dd
commit e5e37ebea9
2 changed files with 52 additions and 18 deletions
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66
linear_bow.ipynb
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@ -2,7 +2,7 @@
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@ -64,7 +64,7 @@
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@ -73,7 +73,7 @@
"97248"
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@ -97,7 +97,7 @@
},
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@ -111,7 +111,7 @@
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@ -129,7 +129,7 @@
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@ -169,7 +169,7 @@
},
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{
@ -190,18 +190,18 @@
},
{
"cell_type": "code",
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.01 0.21632227671715168 0.6847807364903296\n",
"0.1 0.2156867944836758 0.6829965387241808\n",
"1 0.21316700811628655 0.6747810400313006\n",
"10 0.2161776145305841 0.6681779252365153\n",
"100 0.20723445731519957 0.5973124724751776\n"
"0.01 0.21632227671897006 0.6847807364939246\n",
"0.1 0.21568679448554554 0.6829965387278908\n",
"1 0.21316700811782532 0.6747810400344023\n",
"10 0.21617761453133164 0.6681779252378663\n",
"100 0.20723445731522736 0.5973124724752429\n"
]
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@ -226,7 +226,7 @@
},
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"execution_count": 9,
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@ -236,7 +236,7 @@
"0.01 0.17702951629340366 0.538690243296189\n",
"0.1 0.177432503566242 0.5387345171140366\n",
"1 0.17743138596037397 0.538778156304091\n",
"10 0.17786269625555318 0.5396020974919651\n",
"10 0.17786269625555318 0.539602097491965\n",
"100 0.17818192454918605 0.557911382661004\n"
]
}
@ -257,6 +257,40 @@
"\n",
" print(C, mean_squared_error(Y_funny_test, pred_funny_test), mean_squared_error(Y_helpful_test, pred_helpful_test))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-0.008666338118757945: you\n",
"-0.0033743830081447994: shit\n",
"0.002818066343305709: i\n",
"-0.0009495127061038011: 3\n",
"-0.0007321653144316716: it\n",
"0.0006758513650775456: bad\n",
"-0.0005113655228402811: nyan\n",
"-0.00045892382707229636: of\n",
"0.0003103226245059089: *review length*\n",
"-0.00026518360080170943: ͡°\n"
]
}
],
"source": [
"model = linear_model.Lasso(alpha=0.01, fit_intercept=True)\n",
"model.fit(X_train, Y_helpful_train)\n",
"idxs = np.argsort(np.abs(model.coef_))[::-1][:10]\n",
"\n",
"for idx in idxs:\n",
" if (idx < len(words)):\n",
" print(f\"{model.coef_[idx]}: {words[idx]}\")\n",
" else:\n",
" print(f\"{model.coef_[idx]}: *review length*\")"
]
}
],
"metadata": {

4
preliminary.ipynb
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@ -16,7 +16,7 @@
},
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