From 4852c580a0a5777bde0943822420d3ba2c9ccb0e Mon Sep 17 00:00:00 2001 From: Iver Band Date: Sun, 25 Nov 2018 13:51:39 -0800 Subject: [PATCH 1/2] Put all required (idempotent) nltk downloads in one cell --- notebooks/best_multiclass_model.ipynb | 467 +++----------------------- 1 file changed, 50 insertions(+), 417 deletions(-) diff --git a/notebooks/best_multiclass_model.ipynb b/notebooks/best_multiclass_model.ipynb index 2cb5006..94ae874 100644 --- a/notebooks/best_multiclass_model.ipynb +++ b/notebooks/best_multiclass_model.ipynb @@ -2,22 +2,15 @@ "cells": [ { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "This notebook contains different models that attempt to classify hate speech from Twitter. It was built as part of this research: https://arxiv.org/pdf/1703.04009.pdf " ] }, { "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", @@ -42,24 +35,17 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ - "#nltk.download('averaged_perceptron_tagger')" + "nltk.download('averaged_perceptron_tagger')\n", + "nltk.download('stopwords')" ] }, { "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "df = pd.read_csv('../data/twitter-hate-speech2.csv', encoding='latin-1')" @@ -67,286 +53,37 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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1130301!!!!! RT @mleew17: boy dats cold...tyga dwn ba...
2230301!!!!!!! RT @UrKindOfBrand Dawg!!!! RT @80sbaby...
3330211!!!!!!!!! RT @C_G_Anderson: @viva_based she lo...
4460601!!!!!!!!!!!!! RT @ShenikaRoberts: The shit you...
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MGlN3cRjlIuD5iHhR0nh9FgK9EXEEeEHSAHBeahuIiL0AknpTXxcHM7M2maw1h0XAQ4X7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\nAAAAAElFTkSuQmCC\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "df['class'].hist()" ] }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import cross_val_score, StratifiedShuffleSplit\n", @@ -371,12 +108,8 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "stopwords=stopwords = nltk.corpus.stopwords.words(\"english\")\n", @@ -420,12 +153,8 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "from sklearn.base import BaseEstimator, TransformerMixin\n", @@ -471,12 +200,8 @@ }, { "cell_type": "code", - "execution_count": 38, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "sentiment_analyzer = VS()\n", @@ -553,12 +278,8 @@ }, { "cell_type": "code", - "execution_count": 44, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "from sklearn.pipeline import FeatureUnion, Pipeline\n", @@ -587,91 +308,40 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "# Running the model" ] }, { "cell_type": "code", - "execution_count": 45, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/media/scott/BigHD/venvs/base/lib/python3.5/re.py:203: FutureWarning: split() requires a non-empty pattern match.\n", - " return _compile(pattern, flags).split(string, maxsplit)\n" - ] - }, - { - "data": { - "text/plain": [ - "Pipeline(steps=[('features', FeatureUnion(n_jobs=1,\n", - " transformer_list=[('tfidf', TfidfVectorizer(analyzer='word', binary=False, decode_error='replace',\n", - " dtype=, encoding='utf-8', input='content',\n", - " lowercase=True, max_df=0.75, max_features=10000, min_df=5,\n", - " ngra...ty='l2', random_state=None,\n", - " solver='liblinear', tol=0.0001, verbose=0, warm_start=False))])" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "model.fit(X_train,y_train)" ] }, { "cell_type": "code", - "execution_count": 46, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/media/scott/BigHD/venvs/base/lib/python3.5/re.py:203: FutureWarning: split() requires a non-empty pattern match.\n", - " return _compile(pattern, flags).split(string, maxsplit)\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "y_preds = model.predict(X_test)" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Evaluating the results on the test set" ] }, { "cell_type": "code", - "execution_count": 47, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "report = classification_report( y_test, y_preds)" @@ -679,62 +349,18 @@ }, { "cell_type": "code", - "execution_count": 48, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 0.38 0.49 0.43 214\n", - " 1 0.96 0.91 0.94 2879\n", - " 2 0.82 0.96 0.88 625\n", - "\n", - "avg / total 0.91 0.89 0.90 3718\n", - "\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "print(report)" ] }, { "cell_type": "code", - "execution_count": 49, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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deep0G38jD2xZy8ykP5kSv5zqcY2DHX7gRAL7hIhjk9i0h2ZQqlQpln3xGTOm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\ngZQilhsoItU9y1UWkXqFfZG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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "from sklearn.metrics import confusion_matrix\n", "\n", @@ -748,6 +374,13 @@ "plt.figure(figsize=(5, 5))\n", "seaborn.heatmap(confusion_df, annot=True, annot_kws={\"size\": 12}, square=True, cmap=\"Reds\")" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -766,7 +399,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.7.0" } }, "nbformat": 4, From 59d269612d0a8934d2a0eff086bfea999efa2b94 Mon Sep 17 00:00:00 2001 From: Iver Band Date: Sun, 25 Nov 2018 14:18:54 -0800 Subject: [PATCH 2/2] Changed path to data file twice so notebook works unchanged with cloned repository --- notebooks/Data Exploration.ipynb | 598 ++----------------------------- 1 file changed, 29 insertions(+), 569 deletions(-) diff --git a/notebooks/Data Exploration.ipynb b/notebooks/Data Exploration.ipynb index d1876a6..bd8f8e6 100644 --- a/notebooks/Data Exploration.ipynb +++ b/notebooks/Data Exploration.ipynb @@ -9,10 +9,8 @@ }, { "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": true - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", @@ -27,483 +25,38 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": true - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ - "df = pd.read_csv('twitter-hate-speech.csv', encoding='latin-1')" + "df = pd.read_csv('../data/twitter-hate-speech.csv', encoding='latin-1')" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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It was a joke, faggot.\n", - "9 I'm tired of people saying I look like my brot...\n", - "Name: tweet_text, dtype: object" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "text[:10]" ] }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": true - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "def remove_handles(content):\n", @@ -571,40 +99,17 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 Warning: penny boards will make you a faggot\n", - "1 Fuck dykes\n", - "2 __ _chulo at least i dont look like jefree sta...\n", - "3 \" : \" : Is a fag\" jackie jealous\" Neeeee\n", - "4 You heard me bitch but any way I'm back th tex...\n", - "5 your a dirty terrorist and your religion is a ...\n", - "6 RT _: @_WhitePonyJr_ looking like faggots?\n", - "7 Well I thought you knew actually RT : Man why ...\n", - "8 I know. It was a joke, faggot.\n", - "9 I'm tired of people saying I look like my brot...\n", - "Name: tweet_text, dtype: object" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "text.apply(remove_handles)[:10]" ] }, { "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": true - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "data = df[~df['_golden']].dropna(axis=1)" @@ -612,75 +117,30 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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YBnS6SItZ32XhRL72huuqHOVo1dQJvp4u+DP/Mv4q1teqj9ZuKqx4KgD3uChv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\nGo2WQ3s1/U2GDBmC8vJybNi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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "sns.stripplot(x=\"label\", y=\"confidence\", data=data, size=6, jitter=True);" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Not offensive 7253\n", - "Offensive 4807\n", - "Hate speech 2382\n", - "Name: label, dtype: int64" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "data['label'].value_counts()" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "data['confidence'].hist(bins=10);" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] } ], "metadata": { @@ -699,7 +159,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.7" + "version": "3.7.0" } }, "nbformat": 4,