Sklearn Logistic regression 1-3gram 1000iter
This commit is contained in:
unknown
parent
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commit
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@ -14,6 +14,37 @@
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}
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},
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"cells": [
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{
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"cell_type": "code",
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"metadata": {
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"id": "dZUIeB9Q8rv3",
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"colab_type": "code",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 136
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},
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"outputId": "95512ec2-2ea3-4ff5-bc08-09ee3b99c39c"
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},
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"source": [
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"!git clone git://gonito.net/paranormal-or-skeptic "
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],
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"execution_count": 5,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"Cloning into 'paranormal-or-skeptic'...\n",
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"remote: Enumerating objects: 3583, done.\u001b[K\n",
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"remote: Counting objects: 100% (3583/3583), done.\u001b[K\n",
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"remote: Compressing objects: 100% (3188/3188), done.\u001b[K\n",
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"remote: Total 3583 (delta 789), reused 2704 (delta 338)\n",
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"Receiving objects: 100% (3583/3583), 202.38 MiB | 4.18 MiB/s, done.\n",
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"Resolving deltas: 100% (789/789), done.\n"
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],
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"name": "stdout"
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}
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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@ -30,7 +61,7 @@
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"metadata": {
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"id": "NQFKg_czGeRA",
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"colab_type": "code",
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"outputId": "60d1c52a-8b42-4a26-d878-67f284589917",
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"outputId": "4cf38154-be9f-48b4-e0ea-cfac084e795a",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 34
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@ -39,7 +70,7 @@
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"source": [
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"!xzcat train/in.tsv.xz | wc -l"
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],
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"execution_count": 0,
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"execution_count": 11,
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"outputs": [
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{
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"output_type": "stream",
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@ -79,8 +110,10 @@
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"source": [
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"from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer\n",
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"from sklearn.pipeline import Pipeline\n",
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"from sklearn.linear_model import SGDClassifier\n",
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"from sklearn.naive_bayes import MultinomialNB,ComplementNB,BernoulliNB,GaussianNB"
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"from sklearn.linear_model import SGDClassifier, LogisticRegression\n",
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"from sklearn.svm import LinearSVC\n",
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"from sklearn.naive_bayes import MultinomialNB,ComplementNB,BernoulliNB,GaussianNB\n",
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"from sklearn.neural_network import MLPClassifier"
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],
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"execution_count": 0,
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"outputs": []
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@ -174,7 +207,7 @@
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"metadata": {
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"id": "dcjUSa7f7Wex",
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"colab_type": "code",
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"outputId": "9fa0ca70-0516-4656-a1d5-641e5b0f41ff",
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"outputId": "f9ade29f-f746-4dd2-eb79-25845095a9f6",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 204
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@ -183,7 +216,7 @@
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"source": [
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"train_set.sample(5)"
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],
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"execution_count": 0,
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"execution_count": 18,
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"outputs": [
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{
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"output_type": "execute_result",
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@ -216,43 +249,43 @@
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>103770</th>\n",
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" <td>Holy crap. I don't think I've seen or heard o...</td>\n",
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" <td>2010-07-16 19:27:08</td>\n",
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" <td>16</td>\n",
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" <td>7</td>\n",
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" <td>2010</td>\n",
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" <th>112652</th>\n",
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" <td>As i hovered over that link I was expecting r/...</td>\n",
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" <td>2012-03-23 13:34:29</td>\n",
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" <td>23</td>\n",
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" <td>3</td>\n",
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" <td>2012</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>240391</th>\n",
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" <td>You lost all pretense of civility with your ar...</td>\n",
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" <td>2010-09-30 12:18:36</td>\n",
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" <td>30</td>\n",
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" <td>9</td>\n",
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" <td>2010</td>\n",
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" <th>172265</th>\n",
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" <td>Caesarean section is now the new natural child...</td>\n",
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" <td>2012-04-19 14:28:59</td>\n",
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" <td>19</td>\n",
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" <td>4</td>\n",
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" <td>2012</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>220910</th>\n",
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" <td>What do people think of ghost adventures? Cur...</td>\n",
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" <td>2012-08-21 19:59:56</td>\n",
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" <td>21</td>\n",
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" <th>150100</th>\n",
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" <td>The Somerton Man reminds me of the [Lead Masks...</td>\n",
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" <td>2012-08-04 21:21:56</td>\n",
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" <td>4</td>\n",
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" <td>8</td>\n",
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" <td>2012</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>39644</th>\n",
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" <td>Congrats on getting the joke.</td>\n",
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" <td>2011-07-29 18:19:46</td>\n",
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" <td>29</td>\n",
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" <td>7</td>\n",
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" <td>2011</td>\n",
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" <th>153335</th>\n",
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" <td>As a skeptic, I demand this man provide eviden...</td>\n",
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" <td>2012-06-20 04:44:02</td>\n",
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" <td>20</td>\n",
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" <td>6</td>\n",
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" <td>2012</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>220867</th>\n",
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" <td>We live in a world where any media can be copi...</td>\n",
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" <td>2012-07-18 08:53:24</td>\n",
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" <td>18</td>\n",
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" <td>7</td>\n",
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" <th>149621</th>\n",
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" <td>It's a fucking bug.</td>\n",
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" <td>2012-11-15 02:29:24</td>\n",
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" <td>15</td>\n",
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" <td>11</td>\n",
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" <td>2012</td>\n",
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" </tr>\n",
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" </tbody>\n",
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@ -261,11 +294,11 @@
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],
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"text/plain": [
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" text ... year\n",
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"103770 Holy crap. I don't think I've seen or heard o... ... 2010\n",
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"240391 You lost all pretense of civility with your ar... ... 2010\n",
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"220910 What do people think of ghost adventures? Cur... ... 2012\n",
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"39644 Congrats on getting the joke. ... 2011\n",
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"220867 We live in a world where any media can be copi... ... 2012\n",
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"112652 As i hovered over that link I was expecting r/... ... 2012\n",
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"172265 Caesarean section is now the new natural child... ... 2012\n",
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"150100 The Somerton Man reminds me of the [Lead Masks... ... 2012\n",
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"153335 As a skeptic, I demand this man provide eviden... ... 2012\n",
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"149621 It's a fucking bug. ... 2012\n",
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"\n",
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"[5 rows x 5 columns]"
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]
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@ -273,7 +306,7 @@
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"metadata": {
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"tags": []
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},
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"execution_count": 12
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"execution_count": 18
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}
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]
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},
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@ -320,29 +353,33 @@
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"metadata": {
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"id": "CeYlhwda9Sa7",
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"colab_type": "code",
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"outputId": "607d4f8f-f632-4d41-a1ab-e5d020cc00ae",
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"outputId": "61a66f28-85b6-452d-bdd0-180772498e34",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 34
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"height": 102
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}
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},
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"source": [
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"bayes = MultinomialNB(alpha=0.4)\n",
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"bayes = LogisticRegression(max_iter=1000)\n",
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"bayes.fit(X,y)"
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],
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"execution_count": 0,
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"execution_count": 176,
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"MultinomialNB(alpha=0.4, class_prior=None, fit_prior=True)"
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"LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
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" intercept_scaling=1, l1_ratio=None, max_iter=1000,\n",
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" multi_class='auto', n_jobs=None, penalty='l2',\n",
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" random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
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" warm_start=False)"
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]
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},
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"metadata": {
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"tags": []
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},
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"execution_count": 15
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"execution_count": 176
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}
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]
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},
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@ -367,7 +404,9 @@
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"def predict_data(data):\n",
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" prepared = prepare_data(data)\n",
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" vectorized = vectorize.transform(data[\"text\"])\n",
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" predicted = bayes.predict(vectorized)\n",
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" predicted = bayes.predict_proba(vectorized)[:,1]\n",
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" predicted[predicted < 0.05] = 0.05\n",
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" predicted[predicted > 0.95] = 0.95\n",
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" return predicted"
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],
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"execution_count": 0,
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@ -391,28 +430,29 @@
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"metadata": {
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"id": "yigVrrVJHkob",
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"colab_type": "code",
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"outputId": "9491f926-94a3-4310-9f63-be937663489d",
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"outputId": "42a53652-60ed-4a11-85cf-683ba4b91d23",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 34
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"height": 51
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}
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},
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"source": [
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"np.mean(dev_predicted == expected_dev[\"class\"])"
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"dev_predicted"
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],
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"execution_count": 0,
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"execution_count": 195,
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"0.8201820940819423"
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"array([0.05 , 0.75847969, 0.86484399, ..., 0.0650311 , 0.95 ,\n",
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" 0.37791457])"
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]
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},
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"metadata": {
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"tags": []
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},
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"execution_count": 19
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"execution_count": 195
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}
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]
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},
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@ -471,8 +511,8 @@
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"colab": {}
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},
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"source": [
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"np.savetxt('test-A/out.tsv', test_predicted, '%c')\n",
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"np.savetxt('dev-0/out.tsv', dev_predicted, '%c')"
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"np.savetxt('test-A/out.tsv', test_predicted, '%f')\n",
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"np.savetxt('dev-0/out.tsv', dev_predicted, '%f')"
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],
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"execution_count": 0,
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"outputs": []
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@ -506,21 +546,25 @@
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"metadata": {
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"id": "oEkjIcwe8zef",
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"colab_type": "code",
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"outputId": "cdb6473e-4eb9-48a7-cc25-25a193cc9194",
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"outputId": "16433b8f-9e3a-4e49-db5d-dc7373c3c675",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 34
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"height": 102
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}
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},
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"source": [
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"!./geval -t \"dev-0\""
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],
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"execution_count": 0,
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"execution_count": 214,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"0.8202\n"
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"Likelihood\t0.6707\n",
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"Accuracy\t0.8151\n",
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"F1.0\t0.7197\n",
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"Precision\t0.7762\n",
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"Recall\t0.6710\n"
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],
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"name": "stdout"
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}
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10544
dev-0/out.tsv
10544
dev-0/out.tsv
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@ -5,10 +5,12 @@ Automatically generated by Colaboratory.
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Original file is located at
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https://colab.research.google.com/drive/1JI_RWapDbABFZPc4NDhU-zQlZiIiXk58
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# Loading Data
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"""
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!git clone git://gonito.net/paranormal-or-skeptic
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"""# Loading Data"""
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!xzcat train/in.tsv.xz | wc -l
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import matplotlib.pyplot as plt
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@ -21,8 +23,10 @@ import datetime
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from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
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from sklearn.pipeline import Pipeline
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from sklearn.linear_model import SGDClassifier
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from sklearn.linear_model import SGDClassifier, LogisticRegression
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from sklearn.svm import LinearSVC
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from sklearn.naive_bayes import MultinomialNB,ComplementNB,BernoulliNB,GaussianNB
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from sklearn.neural_network import MLPClassifier
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def load_set(path, isTest):
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dataset = pd.read_csv(path+"/in.tsv.xz", delimiter="\t",header=None,names=["text","date"],quoting=csv.QUOTE_NONE)
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@ -58,7 +62,7 @@ vectorized = vectorize.fit_transform(train_set["text"])
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X = vectorized
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y = expected_train["class"]
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bayes = MultinomialNB(alpha=0.4)
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bayes = LogisticRegression(max_iter=1000)
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bayes.fit(X,y)
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"""# Predict and evaluate"""
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def predict_data(data):
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prepared = prepare_data(data)
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vectorized = vectorize.transform(data["text"])
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predicted = bayes.predict(vectorized)
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predicted = bayes.predict_proba(vectorized)[:,1]
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predicted[predicted < 0.05] = 0.05
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predicted[predicted > 0.95] = 0.95
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return predicted
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dev_predicted = predict_data(dev_set)
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np.mean(dev_predicted == expected_dev["class"])
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dev_predicted
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test_predicted = predict_data(test_set)
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@ -82,8 +88,8 @@ dev_predicted = np.array([item.strip() for item in dev_predicted])
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"""**Save to file**"""
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np.savetxt('test-A/out.tsv', test_predicted, '%c')
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np.savetxt('dev-0/out.tsv', dev_predicted, '%c')
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np.savetxt('test-A/out.tsv', test_predicted, '%f')
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np.savetxt('dev-0/out.tsv', dev_predicted, '%f')
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"""**Check geval output**"""
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10304
test-A/out.tsv
10304
test-A/out.tsv
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Load Diff
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Block a user