try linear models (marginal improvement)
This commit is contained in:
Arthur Lu
parent
5b9a700123
commit
45986a9568
151
linear_bow.ipynb
151
linear_bow.ipynb
@ -140,79 +140,63 @@
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 10,
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"execution_count": 12,
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"metadata": {},
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"metadata": {},
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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"from sklearn.model_selection import train_test_split\n",
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"import numpy as np\n",
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"import numpy as np\n",
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"X = []\n",
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"Y1 = []\n",
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"Y2 = []\n",
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"for review in reviews:\n",
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" X.append(feature(review))\n",
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" Y1.append(review[\"funny\"])\n",
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" Y2.append(review[\"helpful_n\"])\n",
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"\n",
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"\n",
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"X = np.array(X)\n",
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"X_train, X_test, Y_funny_train, Y_funny_test, Y_helpful_train, Y_helpful_test = [], [], [], [], [], []\n",
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"Y1 = np.array(Y1)\n",
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"\n",
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"Y2 = np.array(Y2)"
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"train, test = train_test_split(reviews, test_size=0.25, random_state=0)\n",
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"\n",
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"for review in train:\n",
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" X_train.append(feature(review))\n",
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" #Y1.append(review[\"funny\"])\n",
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" if review[\"funny\"] == 0:\n",
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" Y_funny_train.append(0)\n",
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" else:\n",
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" Y_funny_train.append(np.log(review[\"funny\"]))\n",
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" #Y2.append(review[\"helpful_n\"])\n",
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" if review[\"helpful_n\"] == 0:\n",
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" Y_helpful_train.append(0)\n",
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" else:\n",
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" Y_helpful_train.append(np.log(review[\"helpful_n\"]))\n",
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"\n",
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"for review in test:\n",
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" X_test.append(feature(review))\n",
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" #Y1.append(review[\"funny\"])\n",
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" if review[\"funny\"] == 0:\n",
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" Y_funny_test.append(0)\n",
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" else:\n",
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" Y_funny_test.append(np.log(review[\"funny\"]))\n",
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" #Y2.append(review[\"helpful_n\"])\n",
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" if review[\"helpful_n\"] == 0:\n",
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" Y_helpful_test.append(0)\n",
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" else:\n",
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" Y_helpful_test.append(np.log(review[\"helpful_n\"]))\n"
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]
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]
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},
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},
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 11,
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"execution_count": 13,
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"metadata": {},
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"metadata": {},
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"outputs": [
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"outputs": [
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{
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{
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"name": "stdout",
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"name": "stdout",
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"output_type": "stream",
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"output_type": "stream",
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"text": [
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"text": [
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"baseline 294.7309048565537 4.604634941766926\n"
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"baseline 0.17818192454918605 0.557911382661004\n"
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]
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]
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}
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}
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],
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],
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"source": [
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"source": [
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"from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
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"from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
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"guess_mean1 = np.mean(Y1)\n",
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"guess_mean_funny = np.mean(Y_funny_train)\n",
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"guess_mean2 = np.mean(Y2)\n",
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"guess_mean_helpful = np.mean(Y_helpful_train)\n",
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"\n",
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"\n",
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"print(\"baseline\", mean_squared_error(Y1, [guess_mean1]*len(Y1)), mean_absolute_error(Y2, [guess_mean2]*len(Y2)))"
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"print(\"baseline\", mean_squared_error(Y_funny_test, [guess_mean_funny]*len(Y_funny_test)), mean_squared_error(Y_helpful_test, [guess_mean_helpful]*len(Y_helpful_test)))"
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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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"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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"0.001 282.2541467007739 4.179655704428717\n",
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"0.01 282.25415246942805 4.179600740282743\n",
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"0.1 282.2546345232787 4.179072864682249\n",
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"1 282.2692492511399 4.175349141167781\n",
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"10 282.3721909589884 4.147935437500891\n",
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"100 283.13132181376034 3.9883973026815065\n",
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"1000 286.86570062121467 3.620101916467935\n"
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]
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}
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],
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"source": [
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"from sklearn import linear_model\n",
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"\n",
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"for C in [0.001, 0.01, 0.1, 1, 10, 100, 1000]:\n",
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"\n",
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" model1 = linear_model.Ridge(C, fit_intercept=True)\n",
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" model1.fit(X, Y1)\n",
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"\n",
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" model2 = linear_model.Ridge(C, fit_intercept=True)\n",
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" model2.fit(X, Y2)\n",
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"\n",
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" predictions1 = model1.predict(X)\n",
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" predictions2 = model1.predict(X)\n",
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"\n",
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" print(C, mean_squared_error(Y1, predictions1), mean_absolute_error(Y2, predictions2))"
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]
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]
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},
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{
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{
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@ -224,26 +208,65 @@
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"name": "stdout",
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"name": "stdout",
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"output_type": "stream",
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"output_type": "stream",
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"text": [
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"text": [
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"1 294.7309048565537 3.2338225122785453\n",
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"0.01 0.24665170508912013 0.7702414041912456\n",
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"10 294.7309048565537 3.2338225122785453\n",
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"0.1 0.24578924150085898 0.7681419094613451\n",
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"100 294.7309048565537 3.2338225122785453\n",
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"1 0.24248804203997093 0.7584811772506682\n",
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"1000 294.7309048565537 3.2338225122785453\n"
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"10 0.24888382029075776 0.7518311372299598\n",
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"100 0.23060394844562843 0.6419885405134674\n"
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]
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]
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}
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}
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],
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],
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"source": [
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"source": [
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"for C in [1, 10, 100, 1000]:\n",
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"from sklearn import linear_model\n",
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"\n",
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"\n",
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" model1 = linear_model.Lasso(alpha=C, fit_intercept=True)\n",
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"Cs = [0.01, 0.1, 1, 10, 100]\n",
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" model1.fit(X, Y1)\n",
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"\n",
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"\n",
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" model2 = linear_model.Lasso(alpha=C, fit_intercept=True)\n",
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"for C in Cs:\n",
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" model2.fit(X, Y2)\n",
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"\n",
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"\n",
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" predictions1 = model1.predict(X)\n",
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" model1 = linear_model.Ridge(C, fit_intercept=True)\n",
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" predictions2 = model1.predict(X)\n",
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" model1.fit(X_train, Y_funny_train)\n",
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"\n",
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"\n",
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" print(C, mean_squared_error(Y1, predictions1), mean_absolute_error(Y2, predictions2))"
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" model2 = linear_model.Ridge(C, fit_intercept=True)\n",
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" model2.fit(X_train, Y_helpful_train)\n",
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"\n",
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" pred_funny_test = model1.predict(X_test)\n",
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" pred_helpful_test = model2.predict(X_test)\n",
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"\n",
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" print(C, mean_squared_error(Y_funny_test, pred_funny_test), mean_squared_error(Y_helpful_test, pred_helpful_test))"
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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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"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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"0.01 0.17730058785614386 0.539258189636067\n",
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"0.1 0.17818192454918605 0.543156420319067\n",
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"1 0.17818192454918605 0.557911382661004\n",
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"10 0.17818192454918605 0.557911382661004\n",
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"100 0.17818192454918605 0.557911382661004\n"
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]
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}
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],
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"source": [
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"Cs = [0.01, 0.1, 1, 10, 100]\n",
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"\n",
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"for C in Cs:\n",
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"\n",
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" model1 = linear_model.Lasso(C, fit_intercept=True)\n",
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" model1.fit(X_train, Y_funny_train)\n",
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"\n",
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" model2 = linear_model.Lasso(C, fit_intercept=True)\n",
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" model2.fit(X_train, Y_helpful_train)\n",
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"\n",
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" pred_funny_test = model1.predict(X_test)\n",
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" pred_helpful_test = model2.predict(X_test)\n",
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"\n",
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" print(C, mean_squared_error(Y_funny_test, pred_funny_test), mean_squared_error(Y_helpful_test, pred_helpful_test))"
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]
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]
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},
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},
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{
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{
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@ -225,14 +225,6 @@
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"plt.xlabel(\"Num. Total Ratings\")\n",
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"plt.xlabel(\"Num. Total Ratings\")\n",
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"plt.title(\"Num. Helpful Ratings vs Num. Total Ratings\")"
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"plt.title(\"Num. Helpful Ratings vs Num. Total Ratings\")"
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]
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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": "79ea84a9",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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}
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],
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],
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"metadata": {
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"metadata": {
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