{ "cells": [ { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "## A Hacker's Guide to Having Your NYTimes Article Comments Noticed\n", "\n", "

\n", "Gavril Bilev\n", "allattention at gmail.com\n", "June 14, 2018\n", "

\n", "\n", "### The problem: predicting the number of upvotes ('recommendations') that a comment posted to an article on the [New York Times](www.nytimes.com) will receive. \n", "\n", "![Imgur](https://i.imgur.com/Vm9tAsj.png?1)\n", "\n", "\n", "### **TL&DR**\n", "If you want upvotes:\n", "* **Time is of the essence, the sooner you comment, the better!**\n", "* **Commenting on comments doesn't bring in the upvotes**\n", "* **There is an optimal article length for getting upvoted comments - at about 800 words.**\n", "* **Print page - most of the important big-headline articles appear in the first 30 pages of the paper, where they attract more readers and more comments.**\n", "* **Getting the coveted 'NYTimes Pick' helps a lot. So does being a trusted user or a NYTimes reporter.**\n", "* **Hot button issues bring about reactions, but not always upvotes** \n", "* **Effort pays off:**\n", " * say more\n", " * use a rich vocabulary \n", " * refer to people, places and organizations, but sparingly \n", " * spell-check\n", "* **Don't be too negative or too positive, be slightly positive.**\n", "\n", "\n", "\n", "\n", "\n", "Readers of the [Gray Lady](www.nytimes.com) are able to post comments on articles and react to the comments of others by either upvoting ('Recommend' button) or replying. For a comment-author, recommendations are desirable because they bring about more visibility - recommended comments float up to the top where they are seen by more readers and can receive even more upvotes. Once a comment 'snowballs' it can be seen by potentially millions of readers. Presumably we write comments because we want others to see them.\n", "\n", "I got curious about what makes some comments rise to the top while most others are completely ignored. [Aashita Kesarwani](https://www.kaggle.com/aashita https://www.kaggle.com/aashita) posted a cool dataset on [Kaggle](www.kaggle.com) of more than 2 million comments geared toward addressing this and similar questions. Be sure to check out the dataset [here](https://www.kaggle.com/aashita/nyt-comments) as well as her wonderful exploratory data analysis of it [here](https://www.kaggle.com/aashita/exploratory-data-analysis-of-comments-on-nyt/data). The data come from two time periods: Jan-May 2017 and Jan-April 2018 and contain features on both the comments (with the full raw text body of each comment) and the more than 9 thousand NYTimes articles the comments were responding to.\n", "\n", "I like this as a prediction task because it is challenging. Presumably what makes people upvote comments or posts, not just at the Times but also in other social media settings (Reddit, Twitter, FB, etc) is the _meaning_ of the comment - they find it helpful, or funny or simply agree with it and want others to see it too. Since we can't easily quantify 'meaning,' we'll have to rely on some feature engineering in order to get any traction with our predictions. The task is also challenging because we don't know much about the readers who are responding to comments and do not have the full text of the articles. Additionally, some comments are pretty short, so they are not easily amenable to a more complicated language model. After extracting what we can from the features already present in the dataset, we will have to wrangle useful information out of noisy and messy raw text - the body of the comments.\n", "\n", "The main point to consider here is that we need to set our expectations low, because this is no handwritten digit recognition exercise with 99%+ accuracy. Even a human scorer would probably not do well in terms of predicting upvotes. Consider these two comments, both made in response to the same article:\n", "\n", "> A. '_Everyone should have walked out. Spicer could have talked to himself._'\n", "\n", "> B. '_If people are \"alarmed\" and \"appalled\" that Trump did this, followed by his cronies justification of it, then they haven't been paying attention. I'm just surprised he took this long to do it. The story here, the one that people should actually be alarmed and appalled by is that the rest of the press stayed. Brietbart, One America, not so surprising but there is absolutely no excuse for rest to have shown so little respect for the one of the most important and defining features of America... The First Amendment, a free press. To echo Mr Kahn's question to President Trump...have you even read the constitution? Here, let me give you a head start... Amendment I: Congress shall make no law respecting an establishment of religion, or prohibiting the free exercise thereof; or abridging the freedom of speech, or of the press; or the right of the people peaceably to assemble, and to petition the Government for a redress of grievances._'\n", "\n", "Without any contextual knowledge, I would have guessed that the two comments would be roughly on par or the first one would receive fewer upvotes. They are generally similar in terms of the reaction to the article, though the second comment is longer and contains more information. The reality is quite different: comment A received over 10,000 upvotes and is the most upvoted comment in the entire dataset, while comment B got ... zero upvotes. \n", "\n", "If you are interested in the takeaways (and one very plausible explanation about the popularity of these two comments, it is not simply brevity!), skip right to the last section. In the sections that follow we will go through the traditional steps of classification, with an emphasis on feature engineering out of the numerical and text data.\n", "\n", "The original target variable here is a count variable of recommendations. For the artificial purpose of using classification algos and metrics, I will discretize it to four meaningful and roughly equal-sized categories. Since the largest of them is about 29%, this is our prediction baseline: if we simply picked that always, our classification would be accurate 29% of the time.\n", "\n", "Categories:\n", "1. None ~29%\n", "2. One or two ~29%\n", "3. Three to eight ~23%\n", "4. More than eight (up to tens of thousands) ~18%\n", "\n", "Naturally, you also choose to keep the original target variable (though cropping the right tail of the distribution might be a good idea due to how skewed it is) and rely on regression and metrics such as mean squared error. (In that case, it would probably be most appropriate to use a negative binomial model for the first simple fit, due to the count nature of the data and the fact that zero has meaning.)\n", "\n", "\n", "### Table of contents:\n", "1. [Data Loading & Preparation](#data)\n", " 1. [Load data files & combine](#dataloading)\n", "\t2. [Discretize **recommendations** (target)](#cut)\n", "\t3. [Turn categorical to dummies](#todummies)\n", "2. [Initial prediction](#initpredict)\n", "\t1. [Simple Multinomial Logistic](#logit)\n", "\t2. [A bag of classifiers](#classifiers)\n", "3. [Feature Engineering](#featureengineering)\n", " 1. [Features based on original variables](#originalvars)\n", " 1. [Replyupvotes](#reply)\n", " 2. [byline](#byline)\n", " 3. [Time](#time)\n", " 2. [Features based on raw text data](#textdata)\n", " 1. [A NYTimes vocabulary & IDF profile](#vocab)\n", "\t\t2. [Basic stats, sentiment analysis, spelling errors & part of speech](#kitchensink)\n", "\t\t3. [Text token features](#texttokens)\n", "4. [Training & Re-evaluation](#training)\n", "\t1. [Simple Multinomial Logistic](#finallogit)\n", "\t2. [A bag of classifiers](#finalclassifiers)\n", "5. [Takeaways](#tldr)\n" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "Data loading \n", "\n", "First, let's prepare the data. We'll load almost all features present in the .csv files in their original shapes into a large dataframe, drop features that cannot be useful (too many missing cases) and convert the categorical features to dummies. This will enable us to do a first-cut prediction without feature engineering - almost as if we know nothing about the dataset and are doing a blind prediction. 'Almost' because we will deliberately exclude one feature that is surely related to the target - the number of replies a comment has received (**replyCount**). We would expect this to be an effect (consequence), rather than a cause of the number of upvotes - upvoted comments are highly visible and therefore much more likely to receive replies.\n", "\n", "We will initially ignore the raw text features (body of comments and keywords of the article) but keep them for later. Finally, to speed up our work here, we will take a random sample of about 10% of the cases (sometimes called 'dev set'). \n", "\n", "Load libraries and already-downloaded data first. There are two separate sets of files, the first containing comments and comment-features and the second containing features about the articles. We read in all of the data, merge them into one Pandas Dataframe where each row is a comment and eliminate all duplicates. \n", " \n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [], "source": [ "import glob\n", "import pandas as pd\n", "import numpy as np\n", "import calendar\n", "import warnings\n", "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "from sklearn.feature_extraction.text import CountVectorizer\n", "from sklearn.feature_extraction.text import TfidfVectorizer\n", "# set a few options:\n", "sns.set(style='whitegrid')\n", "warnings.filterwarnings(\"ignore\")\n", "pd.options.display.max_colwidth = 100\n", "%matplotlib inline\n", "# Kernel to predict upvotes ('recommendations' as target); will cut into intervals to turn this into a classification exercise\n" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "1. Load data files & combine " ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Int64Index: 9335 entries, 0 to 1323\n", "Data columns (total 16 columns):\n", "abstract 167 non-null object\n", "articleID 9335 non-null object\n", "articleWordCount 9335 non-null int64\n", "byline 9335 non-null object\n", "documentType 9335 non-null object\n", "headline 9335 non-null object\n", "keywords 9335 non-null object\n", "multimedia 9335 non-null int64\n", "newDesk 9335 non-null object\n", "printPage 9335 non-null int64\n", "pubDate 9335 non-null object\n", "sectionName 9335 non-null object\n", "snippet 9335 non-null object\n", "source 9335 non-null object\n", "typeOfMaterial 9335 non-null object\n", "webURL 9335 non-null object\n", "dtypes: int64(3), object(13)\n", "memory usage: 1.2+ MB\n", "None\n", "\n", "Int64Index: 9335 entries, 0 to 1323\n", "Data columns (total 7 columns):\n", "articleID 9335 non-null object\n", "byline 9335 non-null object\n", "headline 9335 non-null object\n", "keywords 9335 non-null object\n", "pubDate 9335 non-null object\n", "snippet 9335 non-null object\n", "documentType 9335 non-null object\n", "dtypes: object(7)\n", "memory usage: 583.4+ KB\n", "None\n" ] } ], "source": [ "# small function to read all .csv files containing a string\n", "def read_files(string='Articles', path='./Data/'):\n", " '''Read zipped .csv files starting with string and concat them into a pd.DataFrame'''\n", " files = glob.glob(path + string + '*.csv.zip')\n", " list_dfs = []\n", " for csv in files:\n", " df_ = pd.read_csv(csv)\n", " list_dfs.append(df_)\n", " return pd.concat(list_dfs)\n", "\n", "\n", "# read in the articles files\n", "df_articles = read_files('Articles')\n", "\n", "# brief look inside:\n", "print(df_articles.info())\n", "\n", "# Leave only features which are not already present in the comments data, do not have too many missing values and could potentially be useful (poked around a bit)\n", "useful_columns = ['articleID',\n", " 'byline',\n", " 'headline',\n", " 'keywords',\n", " 'pubDate',\n", " 'snippet',\n", " 'documentType'\n", " ]\n", "\n", "df_articles = df_articles[useful_columns]\n", "print(df_articles.info())" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "Do the same for the comments files, merge the two into one DataFrame and drop all duplicates:\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Int64Index: 2176364 entries, 0 to 233406\n", "Data columns (total 34 columns):\n", "approveDate int64\n", "articleID object\n", "articleWordCount float64\n", "commentBody object\n", "commentID float64\n", "commentSequence float64\n", "commentTitle object\n", "commentType object\n", "createDate float64\n", "depth float64\n", "editorsSelection int64\n", "inReplyTo float64\n", "newDesk object\n", "parentID float64\n", "parentUserDisplayName object\n", "permID object\n", "picURL object\n", "printPage float64\n", "recommendations float64\n", "recommendedFlag float64\n", "replyCount float64\n", "reportAbuseFlag float64\n", "sectionName object\n", "sharing int64\n", "status object\n", "timespeople float64\n", "trusted float64\n", "typeOfMaterial object\n", "updateDate int64\n", "userDisplayName object\n", "userID float64\n", "userLocation object\n", "userTitle object\n", "userURL object\n", "dtypes: float64(15), int64(4), object(15)\n", "memory usage: 581.2+ MB\n", "None\n", "(2086862, 40)\n" ] } ], "source": [ "# read in the Comments files (large-ish, 500M+)\n", "df_comments = read_files('Comments')\n", "print(df_comments.info())\n", "# join the two by articleID; drop the comments that are not associated with articles\n", "df = pd.merge(df_comments, df_articles, how='inner',\n", " on='articleID')\n", "# drop duplicate comments\n", "df.drop_duplicates(subset='commentID', inplace=True)\n", "print(df.shape)\n", "df.reset_index(drop=True, inplace=True)\n" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "Count missing values and drop the features if there are too many (> 1 million) missing. We can come back later and try to extract useful information from them." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "approveDate 0\n", "articleID 0\n", "articleWordCount 0\n", "commentBody 0\n", "commentID 0\n", "commentSequence 0\n", "commentTitle 73979\n", "commentType 0\n", "createDate 0\n", "depth 0\n", "editorsSelection 0\n", "inReplyTo 0\n", "newDesk 0\n", "parentID 0\n", "parentUserDisplayName 1528830\n", "permID 22\n", "picURL 0\n", "printPage 0\n", "recommendations 0\n", "recommendedFlag 2086862\n", "replyCount 0\n", "reportAbuseFlag 2086862\n", "sectionName 149613\n", "sharing 0\n", "status 0\n", "timespeople 0\n", "trusted 0\n", "typeOfMaterial 0\n", "updateDate 0\n", "userDisplayName 641\n", "userID 0\n", "userLocation 480\n", "userTitle 2086552\n", "userURL 2086841\n", "byline 0\n", "headline 0\n", "keywords 0\n", "pubDate 0\n", "snippet 0\n", "documentType 0\n", "dtype: int64\n", "(2086862, 35)\n" ] } ], "source": [ "print(df.isnull().sum())\n", "\n", "# several features are missing for most or all cases, drop them\n", "df = df.loc[:, df.isnull().sum() < 10**6]\n", "print(df.shape)" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "Discretizing the target variable \n", "\n", "Next, look at the target (**Recommendations**) and turn it into four categories for classification. The original variable is a count heavily slanted toward zero:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Target is recommendations, heavily slanted distribution\n", "sns.distplot(df.recommendations.loc[df.recommendations < 30], kde = False)" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "Take a look at the bottom of the distribution, dominated by zeros:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.0 385013\n", "1.0 278544\n", "2.0 209162\n", "3.0 162014\n", "4.0 127792\n", "5.0 103044\n", "6.0 84626\n", "7.0 71178\n", "8.0 60258\n", "9.0 51066\n", "Name: recommendations, dtype: int64\n" ] } ], "source": [ "print(df.recommendations.value_counts().head(10))" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "and the top:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "text/plain": [ "504511 7709.0\n", "77679 7938.0\n", "696412 8124.0\n", "1027939 8125.0\n", "1987659 8160.0\n", "696432 8514.0\n", "2062146 8639.0\n", "1738646 8713.0\n", "915605 9279.0\n", "2062163 10472.0\n", "Name: recommendations, dtype: float64" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.recommendations.sort_values().tail(10)" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "So the vast majority have fewer than 15 upvotes and there are only a handful with over 6000. Let's slice the variable into four roughly equal sized categories, and rename it to **recs**. We also change it to an _int_ that ranges 1 to 4 in order to make throwing it into a variety of models a bit easier (including multinomial logistic regression). (The alternative is a labeled categorical variable)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[3, 9) 0.292\n", "[9, 100000) 0.290\n", "[1, 3) 0.234\n", "[0, 1) 0.184\n", "Name: reclabels, dtype: float64\n" ] } ], "source": [ "# cut the interval into 4 bins\n", "df['reclabels'] = pd.cut(df.recommendations, bins=(0, 1, 3, 9, 100000),\n", " include_lowest=True, right=False)\n", "print(df.reclabels.value_counts(normalize=True).round(3))\n", "# change it to int\n", "df['recs'] = df.reclabels.cat.codes\n" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "The distribution of the new categorical variable looks much more balanced:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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TXV7Purye9cznWvY8XCLiB4DPAe/PzOcj4uVt5flIo5ftv5o2xsfH22+8+7FaXRp6Ha9Ttx7ZMv9zjIga13OqQj9GxXyv5/S9uyv1ZPh1cy3bBVBPZ4tFxEk0g+XTmXlvKT9dhrQoX/eX+j5g2azDl5Zap/rSFvVObUiS+qBn4VJmf90BPJqZH521aRuwviyvB+6bVV8XEWMRsRp4rgxtTQNrImJReZC/Bpgu256PiNWlrXVzztWqDUlSH/RyWOxtwLuAv46Ib5Tah4CNwNaIuAp4Ari0bJuiOQ15F82pyFcCZOaBiPgwsLPsd1NmHijL1/DKVOT7y4cObUiS+qBn4ZKZfwmMtdl8fov9G8C1bc61Gdjcov4QsLJF/ZlWbUiS+sPf0JckVWe4SJKqM1wkSdUZLpKk6gwXSVJ1hoskqTrDRZJUneEiSarOcJEkVWe4SJKqM1wkSdUZLpKk6gwXSVJ1hoskqTrDRZJUneEiSarOcJEkVWe4SJKqM1wkSdUZLpKk6gwXSVJ1hoskqTrDRZJUneEiSarOcJEkVXdir04cEZuBi4D9mbmy1F4P3A2cDjwOXJqZByNiDNgEXAi8AFyRmQ+XY9YDN5bT3pyZk6U+DtwJnAJMAddlZqNdG736PiVJ/1Ev71zuBCbm1G4AHsjM5cADZR3gAmB5+bwbuA1eDqMNwCrgXGBDRCwqx9wGXD3ruIkjtCFJ6pOehUtmfhU4MKe8Fpgsy5PAxbPqWzKzkZk7gIURcRrwDmB7Zh4odx/bgYmy7dTM3JGZDWDLnHO1akOS1Cc9GxZrY3FmPlmWnwIWl+UlwJ5Z++0ttU71vS3qndo4opmZmW53Pa55neryetbl9axnPtey3+HysvJ8pHEstTE+Pt5+492P1ejSSOh4nbr1yJb5n2NE1LieUxX6MSrmez2n791dqSfDr5tr2S6A+j1b7OkypEX5ur/U9wHLZu23tNQ61Ze2qHdqQ5LUJ/0Ol23A+rK8HrhvVn1dRIxFxGrguTK0NQ2siYhF5UH+GmC6bHs+IlaXmWbr5pyrVRuSpD7p5VTkzwBvB94YEXtpzvraCGyNiKuAJ4BLy+5TNKch76I5FflKgMw8EBEfBnaW/W7KzMOTBK7hlanI95cPHdqQJPVJz8IlMy9vs+n8Fvs2gGvbnGczsLlF/SFgZYv6M63akCT1j7+hL0mqznCRJFVnuEiSqjNcJEnVGS6SpOoMF0lSdYaLJKk6w0WSVJ3hIkmqznCRJFVnuEiSqjNcJEnVGS6SpOoMF0lSdYaLJKk6w0WSVJ3hIkmqznCRJFVnuEiSqjNcJEnVGS6SpOoMF0lSdYaLJKk6w0WSVJ3hIkmq7sRBd6BXImIC2AQsAG7PzI0D7pIkHTdG8s4lIhYAtwIXACuAyyNixWB7JUnHj5EMF+BcYFdm7s7MF4G7gLUD7pMkHTfGGo3GoPtQXURcAkxk5n8v6+8CVmXme9odMzMzM3oXQpL6YHx8fGxubWSfubxarS6OJOnojOqw2D5g2az1paUmSeqDUb1z2Qksj4gzaIbKZcAvD7ZLknT8GMk7l8w8BLwHmAYeBbZm5jcH2ytJOn6M5AN9SdJgjeSdiyRpsAwXSVJ1o/pAf+T4Opt6ImIzcBGwPzNXDro/wy4ilgFbgMVAA/hkZm4abK+GU0S8FvgqcDLN/z7fk5kbBturo+OdyxDwdTbV3QlMDLoTI+QQ8BuZuQJYDVzrv8+j9j3gvMw8EzgLmIiI1QPu01ExXIaDr7OpKDO/ChwYdD9GRWY+mZkPl+Vv05yhuWSwvRpOmdnIzO+U1ZPKZyhnXTksNhyWAHtmre8FVg2oL1JbEXE68FbgwQF3ZWiVkYoZ4M3ArZk5lNfSOxdJVUTEDwCfA96fmc8Puj/DKjNfysyzaL5Z5NyIGMrngobLcPB1NjqmRcRJNIPl05l576D7Mwoy81ngKwzp80HDZTi8/DqbiHgNzdfZbBtwnyQAImIMuAN4NDM/Ouj+DLOI+KGIWFiWTwF+DnhssL06Ov6G/pCIiAuBj9Ocirw5M28ZcJeGVkR8Bng78EbgaWBDZt4x0E4NsYj4aeD/AH8N/FspfygzpwbXq+EUET8BTNL8OT+B5qurbhpsr46O4SJJqs5hMUlSdYaLJKk6w0WSVJ3hIkmqznCRJFVnuEiSqjNcpGNARPieP40U/0FLAxIRDeB/AT8PfBH4rYi4HvhvNH829wFXZ+ZT5c0MH6H5KpCXgN2Z+YsR8Z+AP6T5P4onATdn5mf6/91I/553LtJg/Wtm/mRm/lZE/CrwJmB1Zp4NTAG/X/b7IPCjwNnlb31cXerXA79bXnS4Eri/v92XWvPORRqsyVnLvwCcAzwcEdD8+XyubLuI5h/kehEgM/+51L8C3BgRbwK2D+vr2TV6DBdpsL4za3mM5rDW5m4PzsyPR8SfAj8L/EFEfCkzb6zdSenVclhMOnZsA66JiEUAEXFyRJxZtv0Z8P7y7IWIeGP5+mOZ+feZ+UfAJpp/tVQaOO9cpGNEZv5xCY2/KMNiJwCfAP4K2Aj8b+AbEfEisAu4BHhfRPwM8CLNv7/+3kH0XZrLtyJLkqpzWEySVJ3hIkmqznCRJFVnuEiSqjNcJEnVGS6SpOoMF0lSdf8fe+AUm7GC4N4AAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.countplot(df.recs)\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "Turn categorical to dummies \n", "\n", "We'll do it using a wrapper around the useful Pandas function **get_dummies**. (There is an analogous function in Scikit-learn.) Note the fact that we drop one (usually the first) category in order to prevent colinearity issues (at least in the case of Logistic Regression for example). Look at non-numeric features:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 2086862 entries, 0 to 2086861\n", "Data columns (total 18 columns):\n", "articleID object\n", "commentBody object\n", "commentTitle object\n", "commentType object\n", "newDesk object\n", "permID object\n", "picURL object\n", "sectionName object\n", "status object\n", "typeOfMaterial object\n", "userDisplayName object\n", "userLocation object\n", "byline object\n", "headline object\n", "keywords object\n", "pubDate object\n", "snippet object\n", "documentType object\n", "dtypes: object(18)\n", "memory usage: 286.6+ MB\n" ] } ], "source": [ "df.select_dtypes(include='object').info()" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "Of these we'll pick the potentially useful ones and leave the raw text features (**byline**, **headline**, **keywords**, and **commentBody**) for now. One of the categorical variables in the dataset (whether or not the comment has been recommended by the NYTimes staff, **editorsSelection**) is really coded as _int_, so we include it here. We'll also ignore several features (such as **userLocation** and **userDisplayName**) for now, which contain quite a bit of noise, though they could be utilized later." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(2086862, 121)\n", "\n", "RangeIndex: 2086862 entries, 0 to 2086861\n", "Columns: 121 entries, editorsSelection_1 to documentType_blogpost\n", "dtypes: uint8(121)\n", "memory usage: 240.8 MB\n", "None\n" ] } ], "source": [ "# one hot encoding function for categorical\n", "def OneHotEncoding(list_of_variables, data=df):\n", " '''One-hot-encodes a list of string (categorical) variables from a dataframe\n", " into binary features for modeling. It drops the last category to prevent colinearity problems (reference category).\n", " Outputs a dataframe the same number of rows as original df.'''\n", " new_df = []\n", " for var in list_of_variables:\n", " new_df.append(pd.get_dummies(\n", " data[var],\n", " drop_first=True,\n", " prefix=(var)))\n", " return pd.concat(new_df, axis=1)\n", "\n", "# \n", "list_of_categorical = ['editorsSelection',\n", " 'newDesk',\n", " 'sectionName',\n", " 'typeOfMaterial',\n", " 'commentType',\n", " 'documentType'\n", "]\n", "\n", "\n", "df_temp = OneHotEncoding(list_of_categorical, data=df)\n", "print(df_temp.shape)\n", "print(df_temp.info())\n" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "And let's grab the numeric features which could be meaningful and combine into a new dataframe. These are most if not all of the numerical features which could be related to the target: length measured in words of the article, page of the newspaper it appeared on, whether the author of the comments is a member of the NYTimes staff and so on. Again, we will not include the number of responses a comment has received as that should be a consequence, rather than a predictor of upvotes." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(2086862, 129)\n" ] } ], "source": [ "list_of_numeric = ['approveDate',\n", " 'articleWordCount',\n", " 'depth',\n", " 'printPage',\n", " 'timespeople',\n", " 'trusted',\n", " 'sharing',\n", " 'recs'\n", "]\n", "# combine the two types of features\n", "X_full = pd.concat([df_temp, df[list_of_numeric]], axis=1)\n", "X_full.reset_index(drop=True, inplace=True)\n", "del df_temp\n", "print(X_full.shape)\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "One last housekeeping chore: the raw text of the comment bodies, which we will use later, contain some html markup which we should remove (as suggested by the original author of the dataset). We do that with a simple wrapper:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [], "source": [ "# This list of artefacts to remove is from the original dataset exploration post by Aashita Kesarwani https://www.kaggle.com/aashita/exploratory-data-analysis-of-comments-on-nyt/code\n", "replacements = {\"(
)\": \"\",\n", " \"(
)\": \"\",\n", " '().*()': '',\n", " '(&)': '',\n", " '(>)': '',\n", " '(>)': '',\n", " '(<)': '',\n", " '(\\xa0)': ' ',\n", " }\n", "\n", "\n", "def preprocess(commentBody):\n", " '''Function to clean up the body of comments by removing artefacts'''\n", " for pattern, replacement in replacements.items():\n", " commentBody = commentBody.str.replace(pattern, replacement)\n", " return commentBody\n", "\n", "\n", "# clean up the comments\n", "df['commentBody'] = preprocess(df.commentBody)\n" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "To wrap up the data loading, let's create a smaller dev set (only 5% or about 100K comments) that we can play with without waiting for long executions. We'll keep the index so that we may pull more features from the full dataset later (not run here)." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Int64Index: 104343 entries, 454762 to 846424\n", "Columns: 129 entries, editorsSelection_1 to recs\n", "dtypes: float64(5), int64(2), int8(1), uint8(121)\n", "memory usage: 18.5 MB\n", "None\n", "(104343, 129)\n" ] } ], "source": [ "dev_set_size = .05\n", "dev_set_index = X_full.sample(frac=dev_set_size, random_state=12).index\n", "X = X_full.loc[dev_set_index, :]\n", "print(X.info())\n", "print(X.shape)\n" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "Initial prediction \n", "\n", "First, let's load the libraries we will use for this and try to fit a simple multinomial logistic model. To fit the logistic regression we will need to remove features that have no variance (rare categories that end up with no examples because of our reduced sample, for example) or are extremely highly correlated with other features. Both of these pose problems for the simple logistic model (extreme or perfect multicolinearity) and are generally not going to contribute much to the prediction anyway.\n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [], "source": [ "from scipy import stats\n", "from sklearn.linear_model import LogisticRegression, RidgeClassifier\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.preprocessing import StandardScaler, label_binarize\n", "from sklearn.pipeline import Pipeline, make_pipeline, FeatureUnion\n", "from sklearn.decomposition import PCA\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix, roc_auc_score\n", "import statsmodels.api as st\n", "from xgboost.sklearn import XGBClassifier\n", "import xgboost\n", "scaler_standard = StandardScaler()\n", "################################################################################\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "Next, let's create a simple function that reports the highest correlations within a dataset. We need it in order to eliminate features that are highly correlated and we'll be using it during the feature creation phase." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "text/plain": [ "depth commentType_userReply 0.989546\n", "sectionName_Book Review newDesk_BookReview 0.984232\n", "typeOfMaterial_Op-Ed newDesk_OpEd 0.969793\n", "typeOfMaterial_Letter newDesk_Letters 0.951944\n", "typeOfMaterial_Editorial newDesk_Editorial 0.945840\n", "typeOfMaterial_Op-Ed typeOfMaterial_News -0.812191\n", "typeOfMaterial_News newDesk_OpEd -0.806177\n", "sectionName_The Daily newDesk_Podcasts 0.774582\n", "sectionName_Retirement newDesk_SpecialSections 0.752679\n", "documentType_blogpost newDesk_Unknown 0.698441\n", "typeOfMaterial_Blog newDesk_Unknown 0.698441\n", "typeOfMaterial_Obituary (... newDesk_Obits 0.674114\n", "sectionName_Television newDesk_Culture 0.574249\n", "articleWordCount newDesk_Magazine 0.567521\n", "sectionName_Unknown sectionName_Politics -0.560913\n", "newDesk_Well sectionName_Family 0.552346\n", "sectionName_Politics newDesk_National 0.530280\n", "newDesk_Washington sectionName_Politics 0.528095\n", "sectionName_Europe newDesk_Foreign 0.479791\n", "typeOfMaterial_News sectionName_Politics 0.468780\n", "dtype: float64" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def top_correlations(df, n=20, abbreviate=True):\n", " '''Display top n correlations less than 1 from a pd.DataFrame'''\n", " original_names = df.columns\n", " if abbreviate:\n", " df.columns = [str(x)[:25] + '...' if len(x) >\n", " 25 else x for x in original_names]\n", " corrs = df.corr().unstack()\n", " corrs = corrs.reindex(corrs.abs().sort_values(ascending=False).index)\n", " corrs = corrs[corrs != 1]\n", " df.columns = original_names\n", " return corrs.iloc[::2].head(n)\n", "\n", "top_correlations(X)" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "Evidently many of the section and news desk categories largely overlap. Also, the feature **depth** which measures whether a comment is a reply to another comment or a reply to a reply, etc (level 1 is an original comment, level 2 is a response and so on) unsurprisingly overlaps with the 'userReply' value of **commentType**. For now we'll solve this be eliminating one of each pair, preferring to keep the more informative feature **depth** and eliminating all **newDesk** features, as the original variable has more missing values." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "text/plain": [ "sectionName_Unknown sectionName_Politics -0.560913\n", "sectionName_Politics typeOfMaterial_News 0.468780\n", "typeOfMaterial_News printPage -0.446288\n", " sectionName_Unknown -0.396765\n", "printPage sectionName_Unknown 0.388485\n", "recs depth -0.378531\n", "sectionName_Australia typeOfMaterial_Biography 0.377921\n", "printPage typeOfMaterial_Editorial 0.303589\n", "typeOfMaterial_Editorial typeOfMaterial_News -0.303015\n", "typeOfMaterial_News articleWordCount 0.299464\n", "typeOfMaterial_Blog sectionName_Room For Deba... 0.299045\n", "documentType_blogpost sectionName_Room For Deba... 0.299045\n", "typeOfMaterial_Review sectionName_Television 0.298029\n", "typeOfMaterial_News sectionName_Sunday Review -0.291467\n", "printPage sectionName_Politics -0.284387\n", "sectionName_Sunday Review sectionName_Unknown -0.275152\n", "typeOfMaterial_Review sectionName_Book Review 0.270038\n", "sectionName_Book Review typeOfMaterial_Interview 0.237462\n", "timespeople approveDate -0.215452\n", "sectionName_Unknown typeOfMaterial_Editorial 0.181141\n", "dtype: float64" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# do not use inplace here, we want a copy! \n", "X = X.drop(['commentType_userReply',\n", " 'typeOfMaterial_Op-Ed'], \n", " axis=1)\n", "X = X.drop(list(X.filter(regex='newDesk')), axis=1)\n", "top_correlations(X)" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "First fit, Multinomial Logistic: " ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "We separate the target and predictors, split into a train and test set, which we'll consider a hold-out set and not train on, and remove all features with zero variance within a category (otherwise our logistic regression optimization will not converge with the default settings)." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [], "source": [ "# separate out the target\n", "predictors = X.columns[X.columns != 'recs']\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(\n", " X[predictors], X['recs'], test_size=0.25, random_state=12)\n", "\n", "# check for no variance within category of the target (problem for fitting regression)\n", "variance_per_category = X_train.groupby(y_train).var() == 0\n", "novariance = variance_per_category.sum() > 0\n", "X_train.drop(X_train.columns[novariance],\n", " axis=1,\n", " inplace=True)\n", "X_test.drop(X_test.columns[novariance],\n", " axis=1,\n", " inplace=True)\n", "\n" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Optimization terminated successfully.\n", " Current function value: 1.261488\n", " Iterations 7\n", " precision recall f1-score support\n", "\n", " 0 0.41 0.39 0.40 4893\n", " 1 0.35 0.15 0.21 6025\n", " 2 0.33 0.33 0.33 7506\n", " 3 0.43 0.64 0.51 7662\n", "\n", "avg / total 0.38 0.39 0.37 26086\n", "\n", "MLogit accuracy is about 0.39\n" ] } ], "source": [ "# fit \n", "# This is equivalent to using SKlearn's MLogit with 'newton-cg' solver\n", "# LogisticRegression(multi_class='multinomial', solver='newton-cg'),\n", "X_logit = st.add_constant(X_train, prepend=False)\n", "MLogit = st.MNLogit(y_train, X_logit)\n", "logit_fit = MLogit.fit()\n", "# this is necessary for the summary:\n", "stats.chisqprob = lambda chisq, df: stats.chi2.sf(chisq, df)\n", "# print(logit_fit.summary())\n", "logit_y_hat = logit_fit.predict(exog=st.add_constant(X_test, prepend=False))\n", "# need to convert it to actual predictions as these are probabilities\n", "logit_y_hat = logit_y_hat.idxmax(axis=1)\n", "print(classification_report(y_test, logit_y_hat))\n", "print('MLogit accuracy is about %.2f' % accuracy_score(y_test, logit_y_hat))\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "OK, now we have our first (simple) fit and the first accuracy score - the model classified about 39% of cases correctly, which is an improvement of about 10% over the baseline (constant) model. The classification report shows that the model has the greatest amount of trouble guessing the second category (One or two upvotes), and is considerably better at guessing the fourth (most popular) category (recall column reports the share of cases of each category the classifier was able to correctly identify). Of all of the comments which received between one and two upvotes, our logistic regression classifier was able to recall only about 15%, in contrast to almost 70% for the fourth category (more than eight upvotes).\n", " \n", "Without delving too deeply in the interpretation of the coefficients (uncomment if you want to take a look, long output...), it seems like the numeric features generally had the greatest impact. We'll say more about them in the next section, but these are features such as the length of the original article, the **depth** of the comment, whether the comment was recommended by the NYTimes staff (**editorSelection**), posted by NYTimes staff members or trusted users. While the various categories derived from the **sectionName** variable generally weren't that important, there were a few very popular categories there - for example 'Family' and 'Sunday Review.' Of course, all of the coefficients here are to be interpreted relative to the omitted reference category, but our focus here is on prediction so we won't dwell on the coefficients." ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "A bag of classifiers \n", "\n", "Next, let's create a bag of classifiers. We are somewhat agnostic as to the most appropriate one at this stage, so we'll include several. In addition to logistic regression, we'll do Ridge regression (essentially a penalized regression), nearest neighbors and two ensemble models (Random Forest and Extreme Gradient Boost). Feel free to drop in your favorite classifier in the mix. I like XGB both for speed (run on a GPU it is quite fast), which allows more experiments, and for the fact that it can report feature importance - which features were present in more trees and how much gain in coverage did each provide. We won't do much model tuning at this stage, relying on the rather conservative default parameter values baked into Scikit-learn." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [], "source": [ "models = [('MLogit', LogisticRegression(n_jobs=-1)),\n", " ('Ridge', RidgeClassifier()),\n", " ('KNN', KNeighborsClassifier(n_jobs=-1)),\n", " ('RandomForest', RandomForestClassifier(n_jobs=-1)),\n", " ('XGB', XGBClassifier(tree_method='gpu_hist',\n", " n_estimators=50)),\n", " ]" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "Let's put together a pipeline to report the scores on each of our these classifiers. It will allow us to compare classification performance after the feature engineering stage. To be on the safe side, we re-scale the data with Scikit-learn's standard scaler (center at zero and divide by standard deviation), although given the classifier families we chose this is not strictly necessary." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "Fitting MLogit\n", "Accuracy of MLogit: 0.3893\n", "\n", "Classification report:\n", "\n", " precision recall f1-score support\n", "\n", " None 0.41 0.40 0.40 4893\n", " 1 or 2 0.35 0.14 0.20 6025\n", " 3 to 8 0.34 0.30 0.32 7506\n", " 9 or more 0.42 0.67 0.51 7662\n", "\n", "avg / total 0.38 0.39 0.36 26086\n", "\n", "ROC_AUC_score: 0.5650\n", "\n", "\n", "Fitting Ridge\n", "Accuracy of Ridge: 0.3823\n", "\n", "Classification report:\n", "\n", " precision recall f1-score support\n", "\n", " None 0.40 0.43 0.42 4893\n", " 1 or 2 0.36 0.10 0.16 6025\n", " 3 to 8 0.34 0.18 0.23 7506\n", " 9 or more 0.39 0.77 0.52 7662\n", "\n", "avg / total 0.37 0.38 0.33 26086\n", "\n", "ROC_AUC_score: 0.5622\n", "\n", "\n", "Fitting KNN\n", "Accuracy of KNN: 0.4086\n", "\n", "Classification report:\n", "\n", " precision recall f1-score support\n", "\n", " None 0.41 0.49 0.44 4893\n", " 1 or 2 0.31 0.34 0.32 6025\n", " 3 to 8 0.36 0.35 0.35 7506\n", " 9 or more 0.58 0.47 0.52 7662\n", "\n", "avg / total 0.42 0.41 0.41 26086\n", "\n", "ROC_AUC_score: 0.5879\n", "\n", "\n", "Fitting RandomForest\n", "Accuracy of RandomForest: 0.4270\n", "\n", "Classification report:\n", "\n", " precision recall f1-score support\n", "\n", " None 0.42 0.44 0.43 4893\n", " 1 or 2 0.32 0.34 0.33 6025\n", " 3 to 8 0.37 0.37 0.37 7506\n", " 9 or more 0.59 0.54 0.56 7662\n", "\n", "avg / total 0.43 0.43 0.43 26086\n", "\n", "ROC_AUC_score: 0.5905\n", "\n", "\n", "Fitting XGB\n", "Accuracy of XGB: 0.3948\n", "\n", "Classification report:\n", "\n", " precision recall f1-score support\n", "\n", " None 0.44 0.40 0.42 4893\n", " 1 or 2 0.36 0.18 0.24 6025\n", " 3 to 8 0.33 0.20 0.25 7506\n", " 9 or more 0.41 0.75 0.53 7662\n", "\n", "avg / total 0.38 0.39 0.36 26086\n", "\n", "ROC_AUC_score: 0.5675\n" ] } ], "source": [ "def predict_recs(X_train=X_train, \n", " y_train=y_train,\n", " X_test=X_test,\n", " y_test=y_test,\n", " models=models,\n", " target_names=['None', '1 or 2', '3 to 8', '9 or more']):\n", " '''Fits and scores models'''\n", " Accuracy = {}\n", " for name, model in models:\n", " pipe = make_pipeline(scaler_standard, model)\n", " print()\n", " print('\\nFitting ' + name)\n", " pipe.fit(X_train, y_train)\n", " y_hat = pipe.predict(X_test)\n", " Accuracy[name] = np.round(accuracy_score(\n", " y_test, y_hat), 4)\n", " print('Accuracy of %s: %.4f' % (name, Accuracy[name]))\n", " print('\\nClassification report:\\n')\n", " print(classification_report(\n", " y_test, y_hat, target_names=target_names))\n", " y_hat_bin = label_binarize(y_hat, range(3))\n", " y_true_bin = label_binarize(y_test, range(3))\n", " ROC_AUC_score = roc_auc_score(y_true_bin, y_hat_bin)\n", " print('ROC_AUC_score: %.4f' % ROC_AUC_score)\n", " # print(confusion_matrix(\n", " # y_test, y_hat))\n", " return Accuracy\n", "\n", "\n", "# predict with only existing features\n", "minimal_features = predict_recs(X_train, y_train)\n" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "Some of our models did a little better than others, notably Random Forest seems to give the best predictions, at least at this stage. All of them had trouble with the second category, perhaps because it is difficult in principle to identify comments which received one-to-two upvotes as opposed to zero or more than two. " ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "Feature Engineering \n", "\n", "Time to create some meaningful features! We'll start with the numeric features that are already in the dataset and which showed much promise in the simple logistic model.\n", "\n", "We will create features only on the dev set for now, which would take quite a bit less time. Eventually we would like to utilize the full data that we have. In order to speed things up a bit, let's use some multiprocessing (ie all of the cores we have available). I wrote a little function wrapper that spreads the task over as many cores as the system makes available and displays a nice progress bar so we know how long it will take. The overhead involved with multiprocessing usually makes it less time efficient to bother, but with a dataset of 2 million rows, it will tend to be faster. (We won't run it )\n" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [], "source": [ "from multiprocessing import cpu_count, Pool\n", "from tqdm import tqdm_notebook\n", "\n", "def multip(func, iterable):\n", " '''Simple wrapper for multiprocessing a function over an iterable. Preserves order.'''\n", " with Pool(cpu_count()) as pool:\n", " out = list(tqdm_notebook(pool.imap(func, iterable),\n", " total=len(iterable),\n", " mininterval=1))\n", " return out\n" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "Features based on the original variables \n", "\n", "The original numeric features in the data contain information on some potentially important attributes of both comments and articles. We care about the article features simply because some articles are much more likely to garner more attention. Consequently, the comments on these articles will tend to get more upvotes.\n" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "reply \n", "\n", "**inReplyTo** - an original variable which simply tells us which comment the current comment is responding to (based on the ID of the comment) or holds a value of zero if it is not a response.\n", "One way to extract a useful feature from this would be to create a variable which takes a value of the number of recommendations/upvotes which the original comments obtained and a value of zero if this is not a reply at all. The expectation is responding to a popular comment should get you more visibility and, by extension, more upvotes. We can think of this as the Reddit top-comment hijacking strategy, which will be familiar to anyone who has used Reddit, though the visibility of the responses is nowhere near as prominent as it is on Reddit." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a31311dbcc0444968b45e8d77f7aff94", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, max=104343), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "count 104343.000000\n", "mean 30.265471\n", "std 175.474726\n", "min 0.000000\n", "25% 0.000000\n", "50% 0.000000\n", "75% 1.000000\n", "max 8160.000000\n", "Name: reply, dtype: float64\n" ] } ], "source": [ "# feature is zero if not a reply and number of upvotes of original comment if it is a reply\n", "def replycounter(x):\n", " if x == 0:\n", " return 0\n", " elif x != 0:\n", " return df.recommendations.at[int(np.where(df.commentID == x)[0])]\n", " else:\n", " return np.nan\n", "\n", "# run only on the dev set for speed\n", "X['reply'] = multip(replycounter, df.inReplyTo[dev_set_index])\n", "\n", "# uncomment this for full set\n", "# X_full['reply'] = multip(replycounter, df.inReplyTo)\n", "\n", "print(X.reply.describe())\n" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "Another 'object'-type variable which we could utilize is **byline**, which reports the authors of an article. \n", "Here's a quick look:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "text/plain": [ "By THE EDITORIAL BOARD 140540\n", "By PAUL KRUGMAN 66631\n", "By DAVID BROOKS 55968\n", "By CHARLES M. BLOW 50041\n", "By FRANK BRUNI 44710\n", "By NICHOLAS KRISTOF 40030\n", "By ROSS DOUTHAT 36244\n", "By GAIL COLLINS 32452\n", "By DAVID LEONHARDT 25004\n", "By ROGER COHEN 23732\n", "By THE LEARNING NETWORK 20692\n", "By THOMAS L. FRIEDMAN 20481\n", "By PETER BAKER 19019\n", "By DEB AMLEN 18411\n", "By JULIE HIRSCHFELD DAVIS 17310\n", "By MICHELLE GOLDBERG 17250\n", "By MAUREEN DOWD 17225\n", "By BRET STEPHENS 16457\n", "By THOMAS B. EDSALL 15487\n", "By MICHAEL D. SHEAR 14170\n", "Name: byline, dtype: int64" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.byline.value_counts().head(20)" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "Unsurprisingly, some authors tend to generate many more comments than others (the top being the Editorial Board of course, followed by many of the op-ed contributors). One quick and easy way to make use of this is to simply get dummies for each of the top authors, in our case we can do it for the top 20, although obviously one could go for more." ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "30e27d4dd4dd44a8a6664dc2229a8d6e", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, max=2086862), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "# get column names of top 20 authors\n", "top_authors = list(df.byline.value_counts().head(20).index)\n", "\n", "# deliberately picked 'Another Author' as the replacement string as it should end up being removed as the first category\n", "def author_pick(x):\n", " if x in top_authors:\n", " return x\n", " else:\n", " return 'Another Author'\n", "\n", "\n", "series_temp = multip(author_pick, df.byline)\n", "df_temp = OneHotEncoding(['author'], pd.DataFrame(\n", " series_temp, columns=['author']))\n", "\n", "# add them to the full set\n", "X_full = pd.concat([X_full, df_temp], axis=1)\n", "# and place them in the dev set \n", "X = pd.concat([X, df_temp.loc[dev_set_index, :]], axis=1)\n", "del df_temp\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "Next, I noticed in the MLogit output that one of the time variables seemed to have a large impact - **approveDate**. This variable effectively report the exact timing of a comment being approved, and therefore appearing online - kind of like the publication time of a comment. Let's take a peek at what it looks like:" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "text/plain": [ "0 1491245186\n", "1 1491188619\n", "2 1491188617\n", "3 1491167820\n", "4 1491167815\n", "Name: approveDate, dtype: int64" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.approveDate.head()" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "Ooh, that's ugly - and meaningless at first glance. After some digging around we figure out that this variable is in Posix format: it reports seconds since 1970-01-01. Let's create a few features that can be meaningful for us: \n", "\n", "1. **approveHour** - Hour of the day in which a comment was published. We expect, for example, that people are reading and responding to online content more during the day than at night. Categorical.\n", "\n", "2. **approveDay** - Same for day of the week, perhaps weekends are more procrastination-prone than weekdays. Categorical.\n", "\n", "2. **hoursAfterArticle** - The number of hours elapsed between an article's publication and the corresponding comments' approval (and therefore publication). We can use the **pubDate** original variable from the articles dataset for the former. That one is coded as a string, so we'll have to convert it to a time format as well. We expect that if too much time passes after an article is published, it will simply get too few eyeballs. Continuous.\n", " " ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 2017-04-01 00:15:41\n", "1 2017-04-01 00:15:41\n", "2 2017-04-01 00:15:41\n", "3 2017-04-01 00:15:41\n", "4 2017-04-01 00:15:41\n", "Name: pubDate, dtype: object\n", "0 2017-04-01 00:15:41\n", "1 2017-04-01 00:15:41\n", "2 2017-04-01 00:15:41\n", "3 2017-04-01 00:15:41\n", "4 2017-04-01 00:15:41\n", "Name: pubDate, dtype: datetime64[ns]\n" ] } ], "source": [ "# fix article publication date\n", "print(df.pubDate.head())\n", "df.pubDate = pd.to_datetime(df.pubDate)\n", "print(df.pubDate.head())" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 1491245186\n", "1 1491188619\n", "2 1491188617\n", "3 1491167820\n", "4 1491167815\n", "Name: approveDate, dtype: int64\n", "0 2017-04-03 18:46:26\n", "1 2017-04-03 03:03:39\n", "2 2017-04-03 03:03:37\n", "3 2017-04-02 21:17:00\n", "4 2017-04-02 21:16:55\n", "Name: approveTime, dtype: datetime64[ns]\n", "count 2086862\n", "unique 1233915\n", "top 2017-04-26 15:53:49\n", "freq 42\n", "first 2017-01-02 00:51:07\n", "last 2018-05-02 03:50:35\n", "Name: approveTime, dtype: object\n" ] } ], "source": [ "# convert all time columns from posix to regular format (we'll only use one of them)\n", "print(df.approveDate.head())\n", "timecolumns = ['approveDate', 'createDate', 'updateDate']\n", "df[['approveTime', 'createTime', 'updateTime']] = df[timecolumns].apply(\n", " lambda t: pd.to_datetime(t, unit='s'))\n", "print(df.approveTime.head())\n", "print(df.approveTime.describe())" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 6PM\n", "1 3AM\n", "2 3AM\n", "3 9PM\n", "4 9PM\n", "Name: approveHour, dtype: object\n", "(104343, 133)\n" ] } ], "source": [ "# change the index to approveTime\n", "df.index = df.approveTime\n", "# sample by hour, (to the next hour)\n", "df['approveHour'] = [\n", " str(x) + 'AM' if x < 12 else str(x - 12) + 'PM' for x in df.index.hour]\n", "# sample by day\n", "df['approveDay'] = df.index.weekday_name\n", "df.reset_index(drop=True, inplace=True)\n", "print(df.approveHour.head())\n", "# add dummies for both to df using the function we built earlier\n", "df_temp = OneHotEncoding(['approveHour',\n", " 'approveDay'])\n", "# combine the two types of features and remove the old one\n", "X_full = pd.concat([X_full, df_temp], axis=1)\n", "del X_full['approveDate']\n", "# and place them in the dev set as well\n", "X = pd.concat([X, df_temp.loc[dev_set_index, :]], axis=1)\n", "del X['approveDate']\n", "print(X.shape)\n", "del df_temp\n" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 66.0\n", "1 50.0\n", "2 50.0\n", "3 45.0\n", "4 45.0\n", "Name: hoursAfterArticle, dtype: float64\n", "count 2.086862e+06\n", "mean 2.956882e+01\n", "std 2.166681e+02\n", "min -8.000000e+00\n", "25% 7.000000e+00\n", "50% 1.300000e+01\n", "75% 2.000000e+01\n", "max 1.030800e+04\n", "Name: hoursAfterArticle, dtype: float64\n" ] } ], "source": [ "# create a feature for time of posting of comment since article published\n", "df['timeDelta'] = df.approveTime - df.pubDate\n", "# convert this timedelta object to hours (downsample)\n", "df['hoursAfterArticle'] = df.timeDelta.astype(\n", " 'timedelta64[h]')\n", "\n", "print(df.hoursAfterArticle.head())\n", "print(df.hoursAfterArticle.describe())" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "Ooops! Negative hours... Hmm, the minimum value of the new variable is -8. Since we haven't yet discovered time travel, this means that either we are experiencing issues with time zones or there's simply some errors in the data - some articles appeared before their publication time or some comments' approval time was misrecorded. Either way, the issue affects very few cases, so we'll simply set all of the negative values to zero, noting the fact that we may be introducing some noise and even bias here. \n" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1373\n", "count 104343.000000\n", "mean 30.577068\n", "std 230.122268\n", "min 0.000000\n", "25% 7.000000\n", "50% 13.000000\n", "75% 20.000000\n", "max 10182.000000\n", "Name: hoursAfterArticle, dtype: float64\n" ] } ], "source": [ "print(df[df.hoursAfterArticle < 0].shape[0])\n", "df.loc[df.hoursAfterArticle < 0, 'hoursAfterArticle'] = 0\n", "# And assign to the large DataFrame:\n", "X_full['hoursAfterArticle'] = df['hoursAfterArticle']\n", "# and the small dev set\n", "X['hoursAfterArticle'] = X_full.loc[dev_set_index, 'hoursAfterArticle']\n", "print(X.hoursAfterArticle.describe())" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "Features based on raw text data " ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "OK, here comes the messy raw text manipulation. We have so far not utilized the actual body of comments as well as some of the raw text data we possess about the articles. So let's do that.\n", "My intuition about comment upvotes is that generally comments will be more popular when they read like their author put in time and effort to write them, made them more informative, polished and balanced. This could mean quite a few different things, but here's one way to approach the quantification problem. We expect that popular comments will not be too short (low effort!), will use more sophisticated language (ie more rare words, longer words, longer sentences, etc.), will have no or few spelling mistakes (no excuses now that most browsers offer spell-check out of the box), and will not be too negative-sounding (positive or neutral sentiment). These are of course little more than hunches at this stage.\n" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "A NYTimes vocabulary \n", "\n", "To get measures of all of these, we'll use a few external libraries and one more dataset. I would like to measure the extent to which comments use _the language of the NYTimes_. To that effect, it makes sense to create a vocabulary based on the text of NYTimes articles. I found another wonderful dataset on Kaggle - available \n", "[here](https://www.kaggle.com/nzalake52/new-york-times-articles/data). It contains over 8,000 articles, and while not very well documented, should more than suffice for our purposes here.\n", "\n", "For both speed and ease of use, we can use the built-in tokenizer in Scikit-learn, tuning it a bit in order to get only words. We tokenize the raw text files of the articles' bodies immediately upon reading them, append them to a list and transform it into a Pandas Series (for convenience). Then, we fit the tokenizer to them to learn the frequency of each word's usage. Frequency is important here, as we want to get a sense whether a comment uses rare English words." ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2846 years revival glass menagerie return broadway starring time academy award winner sally field ama...\n", "dtype: object\n" ] }, { "data": { "text/plain": [ "CountVectorizer(analyzer='word', binary=False, decode_error='strict',\n", " dtype=, encoding='utf-8', input='content',\n", " lowercase=True, max_df=1.0, max_features=None, min_df=1,\n", " ngram_range=(1, 1), preprocessor=None, stop_words='english',\n", " strip_accents='unicode', token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b',\n", " tokenizer=None, vocabulary=None)" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Get some NYTimes articles to train on\n", "counter = CountVectorizer(stop_words='english',\n", " strip_accents='unicode',\n", " analyzer='word',\n", " min_df=1)\n", "# first get the tokenizer tool from CountVectorizer\n", "tokenize = counter.build_analyzer()\n", "docs = []\n", "doc = ''\n", "\n", "with open('./Data/nytimes_news_articles.txt', encoding='utf-8') as file:\n", " for line in file:\n", " if line.startswith('URL'):\n", " if doc != '':\n", " docs.append(' '.join(tokenize(doc)))\n", " doc = ''\n", " else:\n", " doc += line\n", "\n", "docs = pd.Series(docs)\n", "print(docs.sample())\n", "# fit the CountVectorizer to the data\n", "counter.fit(docs)\n" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "Whenever we tokenize it is a good idea to take a look at the beginning and end of the token lists, remembering that raw text is messy business. A useful little built-in function allows us to explore the dictionary we are building:" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['힘들어지고', '힘을', '힘이', '发烧一次', '富二代', '百毒', '素质', '聪明一次', '贤二机器僧', '高洪波']\n" ] } ], "source": [ "token_names = counter.get_feature_names()\n", "# last 10 tokens\n", "print(token_names[-10:])\n" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "Uh-oh! This doesn't look like english!\n", "After some digging around, the culprit is several articles in Asian characters. \n", "Let's identify and remove all of the articles in which a large portion of the words are not in English:" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "6109 12.66\n", "8606 13.01\n", "3632 13.01\n", "4550 13.17\n", "2671 13.53\n", "7998 13.62\n", "1097 13.84\n", "7590 14.94\n", "8506 99.68\n", "7693 99.85\n", "dtype: float64\n" ] } ], "source": [ "def score_English(article):\n", " '''Report the percent of words in a document which are not English (ASCI)'''\n", " if not article:\n", " return np.nan\n", " else:\n", " n_english = 0\n", " for token in article:\n", " try:\n", " token.encode('ascii')\n", " except UnicodeEncodeError:\n", " continue\n", " else:\n", " n_english += 1\n", " return np.round(n_english / len(article) * 100, 2)\n", "\n", "\n", "percent_english = docs.apply(score_English)\n", "print(percent_english.sort_values().head(10))" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "We remove the non-English articles and refit:" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['zuther', 'zuwarah', 'zuyu', 'zuyus', 'zverev', 'zvi', 'zvolen', 'zwd', 'zweden', 'zweibel', 'zweig', 'zwick', 'zwicker', 'zwilling', 'zwirner', 'zwolski', 'zyaratgah', 'zych', 'zydeco', 'zyklon', 'zylinski', 'zynga', 'zytiga', 'zytkow', 'zywicki', 'zz', '发烧一次', '百毒', '聪明一次', '贤二机器僧']\n" ] } ], "source": [ "# remove them:\n", "docs = docs[percent_english > 20]\n", "# and try again:\n", "counter.fit(docs)\n", "# get the token names again\n", "feature_names = counter.get_feature_names()\n", "# the end looks good now:\n", "print(feature_names[-30:])\n" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "And the beginning of the list is full of numbers, let's get rid of those as well:" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['00', '000', '0000', '0000364334', '0001', '000s', '000th', '001', '0017', '002']\n", "['____________________________________________________', '_idkmatilda', 'a1', 'a10', 'a16', 'a1c', 'a2', 'a20', 'a24', 'a2z']\n" ] } ], "source": [ "print(feature_names[:10])\n", "vocab = feature_names[2330:]\n", "print(vocab[:10])" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "Obviously we are not quite done yet - the tokenizer picked up quite a few non-words, let's run each by a proper English dictionary and eliminate the non-words." ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['ab', 'aback', 'abacus', 'abandon', 'abandoned', 'abandoning', 'abandonment', 'abandons', 'abasement', 'abashed'] ['zoological', 'zoologist', 'zoology', 'zoom', 'zoomed', 'zooming', 'zooms', 'zoos', 'zucchini', 'zydeco']\n" ] } ], "source": [ "import enchant\n", "us_english = enchant.Dict(\"en_US\")\n", "# create a clean copy of the dictionary only with recognized English words\n", "\n", "vocab = [word for word in vocab if us_english.check(word)]\n", "print(vocab[:10], vocab[-10:])" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "OK, this looks much better, we have a proper english vocabulary based on the NYTimes. This is why did all of the work: using an IDF counter we can get the frequency of each recognized word and use that to build several features based on the diversity of a comment's vocabulary.\n", "\n", "All of this will be built on a measure commonly used in document classification in Natural Language Processing tasks called 'IDF' or 'inverse document frequency.' It measures how infrequently a word is used in a body of documents (how rare the word is), which is quite useful for the typical document classification task, usually normalized by multiplying by term frequency, get 'TFIDF.' This is not what we need here though - we simply want IDF, so here's what it looks like and how to get it.\n", "First, we train a a tokenizer to the docs using only our cleaned up vocabulary:" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "text/plain": [ "TfidfVectorizer(analyzer='word', binary=False, decode_error='strict',\n", " dtype=, encoding='utf-8', input='content',\n", " lowercase=True, max_df=1.0, max_features=None, min_df=1,\n", " ngram_range=(1, 1), norm=None, preprocessor=None, smooth_idf=True,\n", " stop_words=None, strip_accents=None, sublinear_tf=False,\n", " token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b', tokenizer=None, use_idf=True,\n", " vocabulary=['ab', 'aback', 'abacus', 'abandon', 'abandoned', 'abandoning', 'abandonment', 'abandons', 'abasement', 'abashed', 'abate', 'abated', 'abatement', 'abatements', 'abating', 'abbey', 'abbot', 'abbreviated', 'abbreviation', 'abbreviations', 'abdicate', 'abdicated', 'abdicating', 'abdication'...ogical', 'zoologist', 'zoology', 'zoom', 'zoomed', 'zooming', 'zooms', 'zoos', 'zucchini', 'zydeco'])" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tfidf = TfidfVectorizer(norm=None,\n", " vocabulary=vocab,\n", " min_df=1,\n", " )\n", "\n", "tfidf.fit(docs)\n" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "Now, let's see if it is behaving as expected. Here's a very simple example that shows us the IDF value for a word that appears in a given percentage of documents, using the conventional formula for IDF ([wiki](https://en.wikipedia.org/wiki/Tf-idf)):" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "A term which appears in 0.01% of documents has an IDF of 9.52.\n", "\n", "A term which appears in 0.05% of documents has an IDF of 8.42.\n", "\n", "A term which appears in 0.10% of documents has an IDF of 7.81.\n", "\n", "A term which appears in 0.50% of documents has an IDF of 6.28.\n", "\n", "A term which appears in 1.00% of documents has an IDF of 5.60.\n", "\n", "A term which appears in 5.00% of documents has an IDF of 3.99.\n", "\n", "A term which appears in 10.00% of documents has an IDF of 3.30.\n", "\n", "A term which appears in 50.00% of documents has an IDF of 1.69.\n", "\n", "A term which appears in 100.00% of documents has an IDF of 1.00.\n" ] } ], "source": [ "# check an individual word's idf:\n", "num_docs = 10000\n", "doc_freq = [1, 5, 10, 50, 100, 500, 1000, 5000, 10000]\n", "idf = [np.log((1 + num_docs)/(1 + x)) + 1 for x in doc_freq]\n", "for x, y in zip(doc_freq, idf):\n", " print('\\nA term which appears in %.2f%% of documents has an IDF of %.2f.' %\n", " (x/num_docs*100, y))" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "OK, now let's see run some English words, in decreasing order of frequency, by our trained vocabulary:" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "'zealot' has an IDF of 8.70.\n", "\n", "'edifice' has an IDF of 7.89.\n", "\n", "'prolific' has an IDF of 5.86.\n", "\n", "'simple' has an IDF of 3.87.\n", "\n", "'good' has an IDF of 2.23.\n", "\n", "'like' has an IDF of 1.53.\n" ] } ], "source": [ "def word_idf(word, tfidf=tfidf):\n", " idf = np.round(tfidf.idf_[tfidf.vocabulary_[word]], 2)\n", " print('\\n\\'' + word + '\\' has an IDF of %.2f.' % idf)\n", "\n", "\n", "word_idf('zealot')\n", "word_idf('edifice')\n", "word_idf('prolific')\n", "word_idf('simple')\n", "word_idf('good')\n", "word_idf('like')\n" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "That will do. \n", "Let's get back to the feature engineering.\n", "\n", "Using our trained IDF counter, we can transform all comments into a matrix of IDF scores (each row is an array of the IDF scores of the the tokens present in a comment). Then we can use that matrix (and some additional wizardry) to create a bin count - a count of each IDF integer. Think about this as the vocabulary rarity profile of a comment. While we could just rely on the mean IDF (or one of the other basic stats), doing a bin count allows us to probe a little deeper - two comments might have a very different IDF profile yet have the same mean IDF.\n", "For additional convenience, we create a simple summarizer function which reports a count, minimum, mean, standard deviation and maximum value given an array. In case a comment contains no recognizable words, we simply assign a value of zero to all measures. A safer choice here would be to exclude the comment altogether - but very few comments fall in this category so are are not worried about bias too much (especially given the size of the full dataset)." ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [], "source": [ "# grab only the dev set comments:\n", "comments = df.commentBody[dev_set_index]\n", "# uncomment next line for full set:\n", "# comments = df.commentBody\n", "\n", "def summarizer(array):\n", " '''Simple summary from an array: count, min, mean, stand dev, max'''\n", " return np.array([array.size,\n", " np.min(array),\n", " np.mean(array),\n", " np.std(array),\n", " np.max(array)])\n", "\n", "\n", "def comment_idf_profile(row):\n", " '''Summarize idf scores for each recognized token in a comment and provide a bin count percent of idf scores.'''\n", " tokens = comment_tf[row, :].data\n", " if tokens.size != 0:\n", " idfs = comment_matrix[row, :].data / tokens\n", " out_summary = summarizer(idfs)\n", " out_bin = np.bincount(idfs.astype(int), minlength=10)[1:]\n", " out_bin_percent = out_bin / len(tokens)\n", " output = np.hstack([out_summary, out_bin_percent])\n", " else:\n", " output = np.zeros(14)\n", " return output\n", "\n", "comment_matrix = tfidf.transform(comments)\n", "comment_matrix.sort_indices() # it's inplace\n", "counter = CountVectorizer(vocabulary=vocab,\n", " min_df=1)\n", "comment_tf = counter.transform(comments)\n", "# comment_tokens = list(map(tokenize, comments))\n" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "7399dadc02b0434a950b0ff4b34877b4", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, max=104343), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "RangeIndex: 104343 entries, 0 to 104342\n", "Data columns (total 14 columns):\n", "recognizedWordCount 104343 non-null float64\n", "MinIdf 104343 non-null float64\n", "MeanIdf 104343 non-null float64\n", "StdDevIdf 104343 non-null float64\n", "MaxIdf 104343 non-null float64\n", "Idf1Percent 104343 non-null float64\n", "Idf2Percent 104343 non-null float64\n", "Idf3Percent 104343 non-null float64\n", "Idf4Percent 104343 non-null float64\n", "Idf5Percent 104343 non-null float64\n", "Idf6Percent 104343 non-null float64\n", "Idf7Percent 104343 non-null float64\n", "Idf8Percent 104343 non-null float64\n", "Idf9Percent 104343 non-null float64\n", "dtypes: float64(14)\n", "memory usage: 11.1 MB\n", "None\n", " recognizedWordCount MinIdf MeanIdf StdDevIdf \\\n", "count 104343.000000 104343.000000 104343.000000 104343.000000 \n", "mean 26.594597 2.036913 4.319403 1.524829 \n", "std 22.324581 0.733856 0.640760 0.422875 \n", "min 0.000000 0.000000 0.000000 0.000000 \n", "25% 10.000000 1.533347 3.992844 1.339523 \n", "50% 20.000000 1.861595 4.313917 1.575367 \n", "75% 37.000000 2.278323 4.635546 1.777224 \n", "max 130.000000 9.397959 9.397959 4.083889 \n", "\n", " MaxIdf Idf1Percent Idf2Percent Idf3Percent \\\n", "count 104343.000000 104343.000000 104343.000000 104343.000000 \n", "mean 7.645723 0.043301 0.180081 0.268976 \n", "std 1.482213 0.063563 0.122437 0.138241 \n", "min 0.000000 0.000000 0.000000 0.000000 \n", "25% 6.833010 0.000000 0.111111 0.200000 \n", "50% 7.893882 0.027778 0.166667 0.263158 \n", "75% 8.704812 0.063158 0.238095 0.333333 \n", "max 9.397959 1.000000 1.000000 1.000000 \n", "\n", " Idf4Percent Idf5Percent Idf6Percent Idf7Percent \\\n", "count 104343.000000 104343.000000 104343.000000 104343.000000 \n", "mean 0.199408 0.135141 0.082615 0.050508 \n", "std 0.122862 0.107708 0.086615 0.070343 \n", "min 0.000000 0.000000 0.000000 0.000000 \n", "25% 0.133333 0.071429 0.000000 0.000000 \n", "50% 0.200000 0.125000 0.071429 0.034483 \n", "75% 0.255814 0.181818 0.119403 0.076923 \n", "max 1.000000 1.000000 1.000000 1.000000 \n", "\n", " Idf8Percent Idf9Percent \n", "count 104343.000000 104343.000000 \n", "mean 0.030566 0.006184 \n", "std 0.058250 0.024845 \n", "min 0.000000 0.000000 \n", "25% 0.000000 0.000000 \n", "50% 0.000000 0.000000 \n", "75% 0.045455 0.000000 \n", "max 1.000000 1.000000 \n" ] } ], "source": [ "scored_df = multip(comment_idf_profile, range(comment_matrix.shape[0]))\n", "scored_df = pd.DataFrame(scored_df)\n", "idf_labs_percent = ['Idf' + str(x) + 'Percent' for x in range(1, 10)]\n", "scored_df.columns = ['recognizedWordCount',\n", " 'MinIdf',\n", " 'MeanIdf',\n", " 'StdDevIdf',\n", " 'MaxIdf'] + idf_labs_percent\n", "# quick sanity check\n", "print(scored_df.info())\n", "print(scored_df.describe())" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "All in all, we ended up creating 14 new features, we won't worry about the correlations between them just yet, but we'll take a look at those before prediction. " ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "Time to throw in the kitchen sink! \n", "\n", "Let's create more features based on more of the indicators we are looking for. Relying on two NLP libraries (TextBlob and the conventional NLTK), we obtain a host of features: basic stats on words and word length, basic stats on sentences and sentence length, sentiment polarity (positive-negative) and subjectivity (subjective-objective) scores, spelling (percent of correctly spelled words), and even part-of-speech breakdown (how many nouns, adjectives, adverbs and so on as a percentage of all words.) The full list of parts-of-speech tags is available \n", "[here](https://www.clips.uantwerpen.be/pages/mbsp-tags).\n", "\n", "For most of these features, our expectation is that generally comments should fall in an ideal range - not too short, nor too long in terms of sentence length and comment length; not too many adjectives but also not too few and so on. Of course, in terms of spelling we expect lower error rate to be better.\n", "\n", "A few additional small considerations. We put all of these indicators in one large function simply to economize on the overhead associated with multiprocessing. Also, there is a little regular expression magic at the beginning of the function to fix a very common error - no space after end of sentence punctuation (.?!). We fix that because it leads to more noise in all of our features. We spell-check all words which are not 'proper nouns', meaning names, as that should generate more accurate counts. Finally, comments shorter that three characters are coded as all zeroes - they are simply too short to get much information. Again, one could also eliminate them out of the sample if worried about bias." ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "03ac8ac072b0416194ee7a7b715847cc", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, max=104343), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "# get all the POS tags to create an array\n", "from nltk.data import load\n", "from nltk import FreqDist\n", "from nltk.tokenize import WhitespaceTokenizer\n", "from enchant.checker import SpellChecker\n", "import re\n", "from textblob import TextBlob\n", "spell_checker = SpellChecker(us_english)\n", "universal_tags = load('help/tagsets/upenn_tagset.pickle')\n", "tags = universal_tags.keys()\n", "tag_labels_percent = [label + '_Percent' for label in tags]\n", "\n", "\n", "def comment_stats(comment):\n", " '''Report stats for a comment. Stats on sentences, words, part-of-speech tags as portion of the total, spelling and sentiment scores. Comments shorter than 3 characters output zeroes.'''\n", " if len(comment) < 3:\n", " return np.zeros(58)\n", " ################################################################################\n", " # insert space after period or comma if there isn't one already (common error)\n", " comment = re.sub(r'(?<=[.,?!])(?=[^\\s])', r' ', comment)\n", " ################################################################################\n", " # sentence stats\n", " blob = TextBlob(comment)\n", " sentences = blob.sentences\n", " tokenized_sents = [sentence.words for sentence in sentences]\n", " sent_lengths = np.array([len(sentence)\n", " for sentence in tokenized_sents])\n", " out_sent = summarizer(sent_lengths)\n", " ################################################################################\n", " # word stats\n", " word_lengths = np.array([len(word) for word in blob.words])\n", " if word_lengths.size == 0:\n", " return np.zeros(58)\n", " out_word = summarizer(word_lengths)\n", " ################################################################################\n", " # sentiment analysis: polarity and objectivity\n", " out_polarity = list(blob.sentiment)\n", " ################################################################################\n", " # spellcheck stats\n", " # replace most punctuation by space for whitespacetokenizer, leave apostrophes for contractions\n", " comment_nopunct = re.sub(r'[,.?!#():-=\"“”%$]', ' ', comment)\n", " blob_nopunct = TextBlob(comment_nopunct, tokenizer=WhitespaceTokenizer())\n", " non_NNP = [word for word, tag in blob_nopunct.tags if tag !=\n", " 'NNP']\n", " if not non_NNP:\n", " out_spell = np.zeros(1)\n", " else:\n", " spell_errors = 0\n", " for word in non_NNP:\n", " try:\n", " spell_errors += not spell_checker.check(word.strip('\\'\\`'))\n", " except:\n", " spell_errors += 1\n", " out_spell = np.array(spell_errors / len(non_NNP))\n", " ################################################################################\n", " # part of speech stats\n", " fd = FreqDist(tag for (word, tag) in blob.tags)\n", " ordered_freqs = np.array(\n", " [fd[key] if key in fd.keys() else 0 for key in tags])\n", " out_pos_percent = np.squeeze(ordered_freqs / len(word_lengths))\n", " ################################################################################\n", " return np.hstack([out_word, out_sent, out_polarity, out_spell,\n", " out_pos_percent])\n", "\n", "\n", "comment_stats_df = multip(comment_stats, comments)\n" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 104343 entries, 0 to 104342\n", "Data columns (total 58 columns):\n", "WordCount 104343 non-null float64\n", "minWordLength 104343 non-null float64\n", "meanWordLength 104343 non-null float64\n", "stdDevWordLength 104343 non-null float64\n", "maxWordLength 104343 non-null float64\n", "SentCount 104343 non-null float64\n", "minSentLength 104343 non-null float64\n", "meanSentLength 104343 non-null float64\n", "stdDevSentLength 104343 non-null float64\n", "maxSentLength 104343 non-null float64\n", "commentPolarity 104343 non-null float64\n", "commentObjectivity 104343 non-null float64\n", "commentSpellErrorsPercent 104343 non-null float64\n", "LS_Percent 104343 non-null float64\n", "TO_Percent 104343 non-null float64\n", "VBN_Percent 104343 non-null float64\n", "''_Percent 104343 non-null float64\n", "WP_Percent 104343 non-null float64\n", "UH_Percent 104343 non-null float64\n", "VBG_Percent 104343 non-null float64\n", "JJ_Percent 104343 non-null float64\n", "VBZ_Percent 104343 non-null float64\n", "--_Percent 104343 non-null float64\n", "VBP_Percent 104343 non-null float64\n", "NN_Percent 104343 non-null float64\n", "DT_Percent 104343 non-null float64\n", "PRP_Percent 104343 non-null float64\n", ":_Percent 104343 non-null float64\n", "WP$_Percent 104343 non-null float64\n", "NNPS_Percent 104343 non-null float64\n", "PRP$_Percent 104343 non-null float64\n", "WDT_Percent 104343 non-null float64\n", "(_Percent 104343 non-null float64\n", ")_Percent 104343 non-null float64\n", "._Percent 104343 non-null float64\n", ",_Percent 104343 non-null float64\n", "``_Percent 104343 non-null float64\n", "$_Percent 104343 non-null float64\n", "RB_Percent 104343 non-null float64\n", "RBR_Percent 104343 non-null float64\n", "RBS_Percent 104343 non-null float64\n", "VBD_Percent 104343 non-null float64\n", "IN_Percent 104343 non-null float64\n", "FW_Percent 104343 non-null float64\n", "RP_Percent 104343 non-null float64\n", "JJR_Percent 104343 non-null float64\n", "JJS_Percent 104343 non-null float64\n", "PDT_Percent 104343 non-null float64\n", "MD_Percent 104343 non-null float64\n", "VB_Percent 104343 non-null float64\n", "WRB_Percent 104343 non-null float64\n", "NNP_Percent 104343 non-null float64\n", "EX_Percent 104343 non-null float64\n", "NNS_Percent 104343 non-null float64\n", "SYM_Percent 104343 non-null float64\n", "CC_Percent 104343 non-null float64\n", "CD_Percent 104343 non-null float64\n", "POS_Percent 104343 non-null float64\n", "dtypes: float64(58)\n", "memory usage: 46.2 MB\n" ] } ], "source": [ "comment_stats_df = pd.DataFrame(comment_stats_df)\n", "comment_stats_df.columns = ['WordCount',\n", " 'minWordLength',\n", " 'meanWordLength',\n", " 'stdDevWordLength',\n", " 'maxWordLength',\n", " 'SentCount',\n", " 'minSentLength',\n", " 'meanSentLength',\n", " 'stdDevSentLength',\n", " 'maxSentLength',\n", " 'commentPolarity',\n", " 'commentObjectivity',\n", " 'commentSpellErrorsPercent'] + tag_labels_percent\n", "# quick sanity check\n", "comment_stats_df.describe()\n", "comment_stats_df.info()\n" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [], "source": [ "# add the both sets of new features to the dev set\n", "X = pd.concat([X.reset_index(drop=True),\n", " scored_df,\n", " comment_stats_df], axis=1)\n", "\n", "# uncomment for full set\n", "# X_full = pd.concat([X_full,\n", "# scored_df,\n", "# comment_stats_df], axis=1)\n" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "OK, before we continue with the feature engineering, let's do a small unit test: let's look at the body of a comment and see if the features we just created match." ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "On top of the confusion and conflict, boys (and girls) must contend with bullying, and more importantly a tacit acceptance of bullying, porn viewing, isolation that comes with technology and dubious nutrition.Band-aid proposals until these kids do the work for us and get the AR-15s out of the classroom could be requirements to attend 1-2 hours a week (on a rotation basis or as elective) in middle and high school dedicated to:Drama exercises/public speaking/verbal articulationCivics (no brainer)Group talks/debates and outward bound/team building exercises where boys and girls are separated (transgenders get to choose which group)Meditation or a martial art\n", "\n", "reply 0.000000\n", "author_By BRET STEPHENS 0.000000\n", "author_By CHARLES M. BLOW 0.000000\n", "author_By DAVID BROOKS 0.000000\n", "author_By DAVID LEONHARDT 0.000000\n", "author_By DEB AMLEN 0.000000\n", "author_By FRANK BRUNI 0.000000\n", "author_By GAIL COLLINS 0.000000\n", "author_By JULIE HIRSCHFELD DAVIS 0.000000\n", "author_By MAUREEN DOWD 0.000000\n", "author_By MICHAEL D. SHEAR 0.000000\n", "author_By MICHELLE GOLDBERG 0.000000\n", "author_By NICHOLAS KRISTOF 0.000000\n", "author_By PAUL KRUGMAN 0.000000\n", "author_By PETER BAKER 0.000000\n", "author_By ROGER COHEN 0.000000\n", "author_By ROSS DOUTHAT 0.000000\n", "author_By THE EDITORIAL BOARD 0.000000\n", "author_By THE LEARNING NETWORK 0.000000\n", "author_By THOMAS B. EDSALL 0.000000\n", "author_By THOMAS L. FRIEDMAN 0.000000\n", "approveHour_0PM 0.000000\n", "approveHour_10AM 0.000000\n", "approveHour_10PM 0.000000\n", "approveHour_11AM 0.000000\n", "approveHour_11PM 0.000000\n", "approveHour_1AM 0.000000\n", "approveHour_1PM 0.000000\n", "approveHour_2AM 0.000000\n", "approveHour_2PM 0.000000\n", "approveHour_3AM 0.000000\n", "approveHour_3PM 0.000000\n", "approveHour_4AM 0.000000\n", "approveHour_4PM 0.000000\n", "approveHour_5AM 0.000000\n", "approveHour_5PM 1.000000\n", "approveHour_6AM 0.000000\n", "approveHour_6PM 0.000000\n", "approveHour_7AM 0.000000\n", "approveHour_7PM 0.000000\n", "approveHour_8AM 0.000000\n", "approveHour_8PM 0.000000\n", "approveHour_9AM 0.000000\n", "approveHour_9PM 0.000000\n", "approveDay_Monday 0.000000\n", "approveDay_Saturday 0.000000\n", "approveDay_Sunday 0.000000\n", "approveDay_Thursday 1.000000\n", "approveDay_Tuesday 0.000000\n", "approveDay_Wednesday 0.000000\n", "hoursAfterArticle 19.000000\n", "recognizedWordCount 50.000000\n", "MinIdf 2.127298\n", "MeanIdf 4.758959\n", "StdDevIdf 1.507268\n", "MaxIdf 8.145196\n", "Idf1Percent 0.000000\n", "Idf2Percent 0.160000\n", "Idf3Percent 0.120000\n", "Idf4Percent 0.280000\n", "Name: 88888, dtype: float64\n", "Idf5Percent 0.180000\n", "Idf6Percent 0.220000\n", "Idf7Percent 0.020000\n", "Idf8Percent 0.020000\n", "Idf9Percent 0.000000\n", "WordCount 103.000000\n", "minWordLength 1.000000\n", "meanWordLength 5.349515\n", "stdDevWordLength 3.406757\n", "maxWordLength 18.000000\n", "SentCount 2.000000\n", "minSentLength 32.000000\n", "meanSentLength 51.500000\n", "stdDevSentLength 19.500000\n", "maxSentLength 71.000000\n", "commentPolarity 0.260000\n", "commentObjectivity 0.423333\n", "commentSpellErrorsPercent 0.071429\n", "LS_Percent 0.000000\n", "TO_Percent 0.029126\n", "VBN_Percent 0.019417\n", "''_Percent 0.000000\n", "WP_Percent 0.000000\n", "UH_Percent 0.000000\n", "VBG_Percent 0.000000\n", "JJ_Percent 0.087379\n", "VBZ_Percent 0.038835\n", "--_Percent 0.000000\n", "VBP_Percent 0.029126\n", "NN_Percent 0.223301\n", " ... \n", "WP$_Percent 0.000000\n", "NNPS_Percent 0.000000\n", "PRP$_Percent 0.000000\n", "WDT_Percent 0.009709\n", "(_Percent 0.000000\n", ")_Percent 0.000000\n", "._Percent 0.000000\n", ",_Percent 0.000000\n", "``_Percent 0.000000\n", "$_Percent 0.000000\n", "RB_Percent 0.009709\n", "RBR_Percent 0.009709\n", "RBS_Percent 0.000000\n", "VBD_Percent 0.000000\n", "IN_Percent 0.126214\n", "FW_Percent 0.000000\n", "RP_Percent 0.000000\n", "JJR_Percent 0.000000\n", "JJS_Percent 0.000000\n", "PDT_Percent 0.000000\n", "MD_Percent 0.019417\n", "VB_Percent 0.048544\n", "WRB_Percent 0.009709\n", "NNP_Percent 0.038835\n", "EX_Percent 0.000000\n", "NNS_Percent 0.097087\n", "SYM_Percent 0.000000\n", "CC_Percent 0.097087\n", "CD_Percent 0.000000\n", "POS_Percent 0.000000\n", "Name: 88888, Length: 63, dtype: float64\n" ] } ], "source": [ "print(comments.iloc[88888])\n", "print()\n", "print(X.iloc[88888, 83:143])\n", "print(X.iloc[88888, 143:])\n" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "Great, it's a match: number of words, sentences, spelling, etc.\n", "\n", "Final step in the feature engineering: Token features \n", "\n", "Basically, these are simply binary features of whether a certain token (usually word) is included or not. To economize on time and memory, we'll limit ourselves to the top (most frequent) 50 tokens. Obviously, we could easily increase the count or eliminate the limitation altogether, but we would end up with many more features.\n", "The first one will be based on the list of keywords that came with each article, **keywords**. Quite simply, our expectation here is that some keywords describing articles may be associated with more comments and upvotes in general (hot topics).\n", "This is what they look like:" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "text/plain": [ "54478 ['Biological and Chemical Warfare', 'Reality Television', 'Trump, Donald J', 'Syria', 'Russia']\n", "2077425 ['United States Politics and Government', 'Federal Budget (US)', 'Ryan, Paul D Jr', 'Trump, Dona...\n", "1648858 ['Gun Control', 'Demonstrations, Protests and Riots', 'Voting and Voters', 'Parkland (Fla)', 'Na...\n", "1651376 ['United States Politics and Government', 'Veterans', 'Appointments and Executive Changes', 'Vet...\n", "1347153 ['Democracy (Theory and Philosophy)', 'Books and Literature', 'Berry, Wendell']\n", "Name: keywords, dtype: object" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.keywords.sample(5)" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "We simply tokenize the keyword lists, this time tuning the tokenizer to remove the artefacts of the lists and split on commas. Essentially, we are building a word list of the most commonly occurring keywords and record whether an article (and therefore all the comments attached to it) contains them in the description or not. We fit (train) on the full set but transform (count) only the dev set. The result is a sparse matrix, which we will convert to dense before the prediction. " ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['Keyword:\"\"', 'Keyword:\"appointments and executive changes\"', 'Keyword:\"blacks\"', 'Keyword:\"china\"', 'Keyword:\"classified information and state secrets\"', 'Keyword:\"clinton, hillary rodham\"', 'Keyword:\"comey, james b\"', 'Keyword:\"cyberwarfare and defense\"', 'Keyword:\"democratic party\"', 'Keyword:\"demonstrations, protests and riots\"', 'Keyword:\"discrimination\"', 'Keyword:\"espionage and intelligence services\"', 'Keyword:\"executive orders and memorandums\"', 'Keyword:\"federal budget (us)\"', 'Keyword:\"federal bureau of investigation\"', 'Keyword:\"global warming\"', 'Keyword:\"gun control\"', 'Keyword:\"health insurance and managed care\"', 'Keyword:\"house of representatives\"', 'Keyword:\"illegal immigration\"', 'Keyword:\"immigration and emigration\"', 'Keyword:\"international trade and world market\"', 'Keyword:\"justice department\"', 'Keyword:\"labor and jobs\"', 'Keyword:\"law and legislation\"', 'Keyword:\"mueller, robert s iii\"', 'Keyword:\"news and news media\"', 'Keyword:\"obama, barack\"', 'Keyword:\"parkland, fla, shooting (2018)\"', 'Keyword:\"patient protection and affordable care act (2010)\"', 'Keyword:\"politics and government\"', 'Keyword:\"presidential election of 2016\"', 'Keyword:\"putin, vladimir v\"', 'Keyword:\"refugees and displaced persons\"', 'Keyword:\"republican party\"', 'Keyword:\"russia\"', 'Keyword:\"russian interference in 2016 us elections and ties to trump associates\"', 'Keyword:\"ryan, paul d jr\"', 'Keyword:\"school shootings and armed attacks\"', 'Keyword:\"senate\"', 'Keyword:\"special prosecutors (independent counsel)\"', 'Keyword:\"supreme court (us)\"', 'Keyword:\"syria\"', 'Keyword:\"trump, donald j\"', 'Keyword:\"united states\"', 'Keyword:\"united states defense and military forces\"', 'Keyword:\"united states economy\"', 'Keyword:\"united states international relations\"', 'Keyword:\"united states politics and government\"', 'Keyword:\"women and girls\"']\n" ] } ], "source": [ "keywords = df.keywords.iloc[dev_set_index]\n", "\n", "# # uncomment for full set\n", "# keywords = df.keywords\n", "\n", "def keyword_tokenizer(keyword):\n", " '''Split into keywords (phrases and remove all quotes and brackets)'''\n", " return [phrase.strip('\\'\\[\\] ') for phrase in keyword.split('\\',')]\n", "\n", "\n", "counter_keywords = CountVectorizer(stop_words='english',\n", " analyzer='word',\n", " tokenizer=keyword_tokenizer,\n", " max_features=50)\n", "counter_keywords.fit(df.keywords)\n", "common_keywords = counter_keywords.transform(keywords)\n", "keyword_features = counter_keywords.get_feature_names()\n", "keyword_features = ['Keyword:\"' + feature +\n", " '\"' for feature in keyword_features]\n", "print(keyword_features)\n" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "Yep, this does seem like a list of hot topics in the news for the two periods the articles covered." ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "Finally, the last feature to be engineered is the same counter for most commonly occuring words, but this time applied to the comments themselves. How many times does a comment include the name 'Trump' for example? We'll be a little more generous and allow the top 100 such tokens to be counted (and again you could easily increase the number of tokens here). We will also exclude extremely common tokens - ones that appear in more than 30% of the comments and look for either single tokens or bigrams of tokens. Warning: this will take a few minutes... " ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['CommentWord:\"actually\"', 'CommentWord:\"administration\"', 'CommentWord:\"america\"', 'CommentWord:\"american\"', 'CommentWord:\"americans\"', 'CommentWord:\"article\"', 'CommentWord:\"believe\"', 'CommentWord:\"best\"', 'CommentWord:\"better\"', 'CommentWord:\"care\"', 'CommentWord:\"change\"', 'CommentWord:\"children\"', 'CommentWord:\"come\"', 'CommentWord:\"congress\"', 'CommentWord:\"country\"', 'CommentWord:\"day\"', 'CommentWord:\"democrats\"', 'CommentWord:\"did\"', 'CommentWord:\"didn\"', 'CommentWord:\"does\"', 'CommentWord:\"doesn\"', 'CommentWord:\"doing\"', 'CommentWord:\"don\"', 'CommentWord:\"donald\"', 'CommentWord:\"election\"', 'CommentWord:\"fact\"', 'CommentWord:\"far\"', 'CommentWord:\"going\"', 'CommentWord:\"good\"', 'CommentWord:\"gop\"', 'CommentWord:\"government\"', 'CommentWord:\"great\"', 'CommentWord:\"health\"', 'CommentWord:\"help\"', 'CommentWord:\"hope\"', 'CommentWord:\"house\"', 'CommentWord:\"just\"', 'CommentWord:\"know\"', 'CommentWord:\"law\"', 'CommentWord:\"left\"', 'CommentWord:\"let\"', 'CommentWord:\"life\"', 'CommentWord:\"like\"', 'CommentWord:\"little\"', 'CommentWord:\"long\"', 'CommentWord:\"look\"', 'CommentWord:\"make\"', 'CommentWord:\"man\"', 'CommentWord:\"maybe\"', 'CommentWord:\"media\"', 'CommentWord:\"money\"', 'CommentWord:\"mr\"', 'CommentWord:\"need\"', 'CommentWord:\"new\"', 'CommentWord:\"news\"', 'CommentWord:\"obama\"', 'CommentWord:\"office\"', 'CommentWord:\"party\"', 'CommentWord:\"pay\"', 'CommentWord:\"people\"', 'CommentWord:\"person\"', 'CommentWord:\"point\"', 'CommentWord:\"political\"', 'CommentWord:\"power\"', 'CommentWord:\"president\"', 'CommentWord:\"problem\"', 'CommentWord:\"public\"', 'CommentWord:\"read\"', 'CommentWord:\"real\"', 'CommentWord:\"really\"', 'CommentWord:\"republican\"', 'CommentWord:\"republicans\"', 'CommentWord:\"right\"', 'CommentWord:\"russia\"', 'CommentWord:\"said\"', 'CommentWord:\"say\"', 'CommentWord:\"school\"', 'CommentWord:\"state\"', 'CommentWord:\"states\"', 'CommentWord:\"support\"', 'CommentWord:\"tax\"', 'CommentWord:\"thing\"', 'CommentWord:\"things\"', 'CommentWord:\"think\"', 'CommentWord:\"time\"', 'CommentWord:\"times\"', 'CommentWord:\"trump\"', 'CommentWord:\"use\"', 'CommentWord:\"ve\"', 'CommentWord:\"vote\"', 'CommentWord:\"want\"', 'CommentWord:\"war\"', 'CommentWord:\"way\"', 'CommentWord:\"white\"', 'CommentWord:\"women\"', 'CommentWord:\"won\"', 'CommentWord:\"work\"', 'CommentWord:\"world\"', 'CommentWord:\"year\"', 'CommentWord:\"years\"']\n" ] } ], "source": [ "\n", "# exclude very common words (appear in more than half the comments)\n", "counter = CountVectorizer(stop_words='english',\n", " analyzer='word',\n", " ngram_range=(1, 2),\n", " max_df=.3,\n", " max_features=100,\n", " )\n", "\n", "counter.fit(df.commentBody)\n", "common_tokens = counter.transform(comments)\n", "token_names = counter.get_feature_names()\n", "token_names = ['CommentWord:\"' + feature + '\"' for feature in token_names]\n", "print(token_names)\n" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "Add them to the dev set. We also downgrade a few of the data types to save on memory (this is not strictly necessary, but it is useful with the full set which easily runs over memory limits)." ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 104343 entries, 0 to 104342\n", "Columns: 356 entries, editorsSelection_1 to CommentWord:\"years\"\n", "dtypes: float64(79), int64(151), int8(1), uint8(125)\n", "memory usage: 195.6 MB\n", "None\n", "\n", "RangeIndex: 104343 entries, 0 to 104342\n", "Columns: 356 entries, editorsSelection_1 to CommentWord:\"years\"\n", "dtypes: float32(79), int8(152), uint8(125)\n", "memory usage: 59.0 MB\n", "None\n" ] } ], "source": [ "X = pd.concat([X,\n", " pd.DataFrame(common_keywords.toarray(),\n", " columns=keyword_features),\n", " pd.DataFrame(common_tokens.toarray(),\n", " columns=token_names)],\n", " axis=1)\n", "\n", "# # uncomment for full set\n", "# X_full = pd.concat([X_full.reset_index(drop=True),\n", "# pd.DataFrame(common_keywords.toarray(),\n", "# columns=keyword_features),\n", "# pd.DataFrame(comment_tokens.toarray(),\n", "# columns=token_names)],\n", "# axis=1)\n", "\n", "# Memory saving type conversion\n", "# this is dangerous when storing very large numbers, but we don't have those here\n", "print(X.info())\n", "# ints\n", "for column in X.columns[X.dtypes.eq('int')]:\n", " X[column] = X[column].astype('int8')\n", "# floats \n", "for column in X.columns[X.dtypes.eq('float')]:\n", " X[column] = X[column].astype('float32')\n", "print(X.info())\n" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "Before putting our newly created features to good use, let's make sure the correlations between them are not too high. We can do it manually, we can do it using a dimension-reduction technique such as Principal Component Analysis, and we can write a function to eliminate the one feature in a highly correlated pair that has a lower correlation to the target. Why don't we try all three:" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "text/plain": [ "recognizedWordCount WordCount 0.975403\n", "author_By THE EDITORIAL B... typeOfMaterial_Editorial 0.973378\n", "stdDevSentLength maxSentLength 0.814365\n", "MaxIdf StdDevIdf 0.790523\n", "meanSentLength minSentLength 0.761150\n", "WordCount SentCount 0.757449\n", "Keyword:\"health insurance... Keyword:\"patient protecti... 0.756432\n", "stdDevWordLength maxWordLength 0.741531\n", "meanSentLength maxSentLength 0.739466\n", "recognizedWordCount SentCount 0.733457\n", "Keyword:\"executive orders... Keyword:\"refugees and dis... 0.699262\n", "Keyword:\"\" author_By THE LEARNING NE... 0.690874\n", "Keyword:\"parkland, fla, s... Keyword:\"school shootings... 0.689578\n", "Keyword:\"school shootings... Keyword:\"gun control\" 0.686446\n", "Keyword:\"comey, james b\" Keyword:\"federal bureau o... 0.672945\n", "Keyword:\"special prosecut... Keyword:\"mueller, robert ... 0.671814\n", "recognizedWordCount maxSentLength 0.670206\n", "WordCount maxSentLength 0.669039\n", "Keyword:\"trump, donald j\" Keyword:\"united states po... 0.629903\n", "Keyword:\"gun control\" Keyword:\"parkland, fla, s... 0.579318\n", "dtype: float64" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "top_correlations(X)" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "OK, several issues here. It seems our two number-of-words counters basically report the same measure, even though they use different tokenizers. Same for editorials - author and type of material. Additionally, too many of our standard deviation and maximum features are highly correlated (which makes a lot of sense given how a standard deviation is calculated.) Let's remove the worst offenders for now using a small function, setting a rather arbitrary limit of Pearson's r less than 0.7 (or greater than -0.7):" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "List of dropped: \n", "['WordCount', 'author_By THE EDITORIAL BOARD', 'stdDevSentLength', 'StdDevIdf', 'minSentLength', 'Keyword:\"patient protection and affordable care act (2010)\"', 'stdDevWordLength', 'meanSentLength', 'SentCount']\n", "(104343, 347)\n" ] }, { "data": { "text/plain": [ "Keyword:\"refugees and dis... Keyword:\"executive orders... 0.699262\n", "Keyword:\"\" author_By THE LEARNING NE... 0.690874\n", "Keyword:\"parkland, fla, s... Keyword:\"school shootings... 0.689578\n", "Keyword:\"school shootings... Keyword:\"gun control\" 0.686446\n", "Keyword:\"comey, james b\" Keyword:\"federal bureau o... 0.672945\n", "Keyword:\"special prosecut... Keyword:\"mueller, robert ... 0.671814\n", "maxSentLength recognizedWordCount 0.670206\n", "Keyword:\"united states po... Keyword:\"trump, donald j\" 0.629903\n", "Keyword:\"parkland, fla, s... Keyword:\"gun control\" 0.579318\n", "MeanIdf MaxIdf 0.576268\n", "CommentWord:\"health\" CommentWord:\"care\" 0.572974\n", "sectionName_Politics sectionName_Unknown -0.560913\n", "Keyword:\"refugees and dis... Keyword:\"immigration and ... 0.548510\n", "Keyword:\"mueller, robert ... Keyword:\"russian interfer... 0.546594\n", "approveDay_Sunday sectionName_Sunday Review 0.533464\n", "Keyword:\"house of represe... Keyword:\"senate\" 0.530111\n", "MaxIdf recognizedWordCount 0.520249\n", "recognizedWordCount maxWordLength 0.511606\n", "Keyword:\"cyberwarfare and... Keyword:\"russia\" 0.498118\n", "VB_Percent MD_Percent 0.496314\n", "dtype: float64" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def feature_pruner(df=X, y='recs', max_corr=.7):\n", " '''Removes features with higher corrcoef than max_corr. Picks based on corr with y (higher absolute is better). Returns DataFrame'''\n", " top = top_correlations(df, n=df.shape[1] ** 2, abbreviate=False)\n", " top = top[np.abs(top) >= max_corr]\n", " if top.size == 0:\n", " return df\n", " left = top.index.get_level_values(0)\n", " right = top.index.get_level_values(1)\n", " pairs = [[left, right] for left, right in zip(left, right)]\n", " to_drop = []\n", " for a, b in pairs:\n", " if a in to_drop or b in to_drop:\n", " continue\n", " if np.abs(df[y].corr(df[a])) >= np.abs(df[y].corr(df[b])):\n", " to_drop.append(b)\n", " else:\n", " to_drop.append(a)\n", " print('List of dropped: ')\n", " print(to_drop)\n", " df = df.drop(columns=to_drop)\n", " return df\n", "\n", "\n", "X_pruned = feature_pruner(X)\n", "print(X_pruned.shape)\n", "top_correlations(X_pruned)" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "A quick look at the correlations with the target. Obviously this can only tell us about linear relationships, but it is still a good sanity check before modeling:" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "text/plain": [ "recs 1.000000\n", "depth -0.378531\n", "Keyword:\"\" -0.184677\n", "editorsSelection_1 0.154576\n", "author_By THE LEARNING NETWORK -0.152353\n", "recognizedWordCount 0.130134\n", "MaxIdf 0.112667\n", "reply -0.107110\n", "maxSentLength 0.104036\n", "maxWordLength 0.098088\n", "trusted 0.088921\n", "CommentWord:\"trump\" 0.082718\n", "MinIdf -0.082413\n", "minWordLength -0.081297\n", "hoursAfterArticle -0.078762\n", "author_By DEB AMLEN -0.054650\n", "sectionName_Unknown -0.054377\n", "timespeople 0.054288\n", "commentObjectivity 0.052938\n", "approveHour_10AM 0.047911\n", "dtype: float64" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "correlations = X_pruned.corrwith(X_pruned.recs)\n", "correlations.reindex(correlations.abs().sort_values(\n", " ascending=False).index).head(20)\n" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "We'll have to take a closer look at the empty keyword feature as well as the The Learning Network as author if they show up again, something funny is going on with them (perhaps commenting was disabled for them for some types of articles?) The rest are more or less to be expected, quite a few of our engineered features are coming up, we'll see if the classifier we use for feature importance uses them." ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "Training & Re-evaluation \n", "At last, time to see whether the feature engineering paid off. \n", "As usual, we split the data into training and test and remove zero-variance features within a category of the target." ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [], "source": [ "# separate out the target\n", "predictors = X_pruned.columns[X_pruned.columns != 'recs']\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(\n", " X_pruned[predictors], X_pruned['recs'], test_size=0.25, random_state=12)\n", "\n", "# check for no variance within category of the target (problem for fitting regression)\n", "variance_per_category = X_train.groupby(y_train).var() == 0\n", "novariance = variance_per_category.sum() > 0\n", "X_train.drop(X_train.columns[novariance],\n", " axis=1,\n", " inplace=True)\n", "X_test.drop(X_test.columns[novariance],\n", " axis=1,\n", " inplace=True)\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "And fit the logistic regression classifier again: " ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Optimization terminated successfully.\n", " Current function value: 1.198865\n", " Iterations 9\n", "\n", " precision recall f1-score support\n", "\n", " 0 0.48 0.40 0.44 4893\n", " 1 0.35 0.22 0.27 6025\n", " 2 0.35 0.41 0.38 7506\n", " 3 0.50 0.62 0.55 7662\n", "\n", "avg / total 0.42 0.43 0.42 26086\n", "\n", "MLogit accuracy is about 0.43\n" ] } ], "source": [ "# MLogit fit\n", "X_logit = st.add_constant(X_train, prepend=False)\n", "MLogit = st.MNLogit(y_train, X_logit)\n", "logit_fit = MLogit.fit()\n", "# print(logit_fit.summary())\n", "logit_y_hat = logit_fit.predict(exog=st.add_constant(X_test, prepend=False))\n", "# need to convert it to actual predictions as these are probabilities\n", "logit_y_hat = logit_y_hat.idxmax(axis=1)\n", "print()\n", "print(classification_report(y_test, logit_y_hat))\n", "print('MLogit accuracy is about %.2f' % accuracy_score(y_test, logit_y_hat))\n" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [], "source": [ "# print(logit_fit.summary())" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "Yay! Accuracy went up by four percent relative to the minimal feature set. And the classifier correctly recalls the second class 22% of the time, a 7% improvement. Uncomment the summary command to see the significance of individual features (long output...).\n", "\n", "Next, let's use our bag of classifiers: \n" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "Fitting MLogit\n", "Accuracy of MLogit: 0.4233\n", "\n", "Classification report:\n", "\n", " precision recall f1-score support\n", "\n", " None 0.47 0.41 0.44 4893\n", " 1 or 2 0.35 0.20 0.25 6025\n", " 3 to 8 0.35 0.40 0.37 7506\n", " 9 or more 0.49 0.63 0.55 7662\n", "\n", "avg / total 0.41 0.42 0.41 26086\n", "\n", "ROC_AUC_score: 0.5821\n", "\n", "\n", "Fitting Ridge\n", "Accuracy of Ridge: 0.4173\n", "\n", "Classification report:\n", "\n", " precision recall f1-score support\n", "\n", " None 0.45 0.42 0.44 4893\n", " 1 or 2 0.35 0.18 0.23 6025\n", " 3 to 8 0.35 0.37 0.36 7506\n", " 9 or more 0.47 0.65 0.55 7662\n", "\n", "avg / total 0.40 0.42 0.40 26086\n", "\n", "ROC_AUC_score: 0.5796\n", "\n", "\n", "Fitting KNN\n", "Accuracy of KNN: 0.3211\n", "\n", "Classification report:\n", "\n", " precision recall f1-score support\n", "\n", " None 0.31 0.33 0.32 4893\n", " 1 or 2 0.26 0.31 0.28 6025\n", " 3 to 8 0.31 0.35 0.33 7506\n", " 9 or more 0.43 0.29 0.35 7662\n", "\n", "avg / total 0.34 0.32 0.32 26086\n", "\n", "ROC_AUC_score: 0.5410\n", "\n", "\n", "Fitting RandomForest\n", "Accuracy of RandomForest: 0.3681\n", "\n", "Classification report:\n", "\n", " precision recall f1-score support\n", "\n", " None 0.38 0.38 0.38 4893\n", " 1 or 2 0.28 0.28 0.28 6025\n", " 3 to 8 0.32 0.34 0.33 7506\n", " 9 or more 0.47 0.46 0.47 7662\n", "\n", "avg / total 0.37 0.37 0.37 26086\n", "\n", "ROC_AUC_score: 0.5602\n", "\n", "\n", "Fitting XGB\n", "Accuracy of XGB: 0.4241\n", "\n", "Classification report:\n", "\n", " precision recall f1-score support\n", "\n", " None 0.49 0.40 0.44 4893\n", " 1 or 2 0.35 0.19 0.25 6025\n", " 3 to 8 0.35 0.41 0.38 7506\n", " 9 or more 0.48 0.63 0.55 7662\n", "\n", "avg / total 0.42 0.42 0.41 26086\n", "\n", "ROC_AUC_score: 0.5831\n" ] }, { "data": { "text/plain": [ "{'MLogit': 0.4233,\n", " 'Ridge': 0.4173,\n", " 'KNN': 0.3211,\n", " 'RandomForest': 0.3681,\n", " 'XGB': 0.4241}" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "predict_recs(X_train, y_train, X_test, y_test)" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "So increasing the number of predictors posed a problem for some of our classifiers, let's increase their capacity to handle more features by upgrading the key parameter value for each one that can be tuned quickly." ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "Fitting MLogit\n", "Accuracy of MLogit: 0.4233\n", "\n", "Classification report:\n", "\n", " precision recall f1-score support\n", "\n", " None 0.47 0.41 0.44 4893\n", " 1 or 2 0.35 0.20 0.25 6025\n", " 3 to 8 0.35 0.40 0.37 7506\n", " 9 or more 0.49 0.63 0.55 7662\n", "\n", "avg / total 0.41 0.42 0.41 26086\n", "\n", "ROC_AUC_score: 0.5821\n", "\n", "\n", "Fitting Ridge\n", "Accuracy of Ridge: 0.4173\n", "\n", "Classification report:\n", "\n", " precision recall f1-score support\n", "\n", " None 0.45 0.42 0.44 4893\n", " 1 or 2 0.35 0.18 0.23 6025\n", " 3 to 8 0.35 0.37 0.36 7506\n", " 9 or more 0.47 0.65 0.55 7662\n", "\n", "avg / total 0.40 0.42 0.40 26086\n", "\n", "ROC_AUC_score: 0.5796\n", "\n", "\n", "Fitting KNN\n", "Accuracy of KNN: 0.3570\n", "\n", "Classification report:\n", "\n", " precision recall f1-score support\n", "\n", " None 0.46 0.20 0.28 4893\n", " 1 or 2 0.30 0.22 0.25 6025\n", " 3 to 8 0.31 0.50 0.39 7506\n", " 9 or more 0.43 0.42 0.42 7662\n", "\n", "avg / total 0.37 0.36 0.35 26086\n", "\n", "ROC_AUC_score: 0.5459\n", "\n", "\n", "Fitting RandomForest\n", "Accuracy of RandomForest: 0.4257\n", "\n", "Classification report:\n", "\n", " precision recall f1-score support\n", "\n", " None 0.48 0.39 0.43 4893\n", " 1 or 2 0.35 0.21 0.27 6025\n", " 3 to 8 0.35 0.39 0.37 7506\n", " 9 or more 0.49 0.65 0.56 7662\n", "\n", "avg / total 0.42 0.43 0.41 26086\n", "\n", "ROC_AUC_score: 0.5815\n", "\n", "\n", "Fitting XGB\n", "Accuracy of XGB: 0.4431\n", "\n", "Classification report:\n", "\n", " precision recall f1-score support\n", "\n", " None 0.52 0.41 0.46 4893\n", " 1 or 2 0.36 0.22 0.27 6025\n", " 3 to 8 0.36 0.46 0.41 7506\n", " 9 or more 0.53 0.62 0.57 7662\n", "\n", "avg / total 0.44 0.44 0.43 26086\n", "\n", "ROC_AUC_score: 0.5932\n" ] } ], "source": [ "models = [('MLogit', LogisticRegression(n_jobs=-1)),\n", " ('Ridge', RidgeClassifier()),\n", " ('KNN', KNeighborsClassifier(n_neighbors=50, n_jobs=-1)),\n", " ('RandomForest', RandomForestClassifier(n_estimators=200, n_jobs=-1)),\n", " ('XGB', XGBClassifier(tree_method='gpu_hist',\n", " n_estimators=300)),\n", " ]\n", "all_features = predict_recs(X_train, y_train, X_test, y_test, models)" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "All of the classifiers that can easily be tuned did better with some tuning. In the end, we will use XGB with a cross-validated number of trees, so we should get good performance of the full set.\n", "An alternative and popular method of feature reduction is using PCA. Since some classifiers tend to do better with a reduced number of dimensions, we could also try it. Obviously we could grid-search the optimal number of components to reduce down to, but let's take a quick look at what happens to our predictive accuracy with 100." ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(78257, 100)\n", "(26086, 100)\n", "\n", "\n", "Fitting MLogit\n", "Accuracy of MLogit: 0.3983\n", "\n", "Classification report:\n", "\n", " precision recall f1-score support\n", "\n", " None 0.42 0.37 0.39 4893\n", " 1 or 2 0.35 0.17 0.23 6025\n", " 3 to 8 0.34 0.24 0.28 7506\n", " 9 or more 0.43 0.75 0.54 7662\n", "\n", "avg / total 0.38 0.40 0.37 26086\n", "\n", "ROC_AUC_score: 0.5629\n", "\n", "\n", "Fitting Ridge\n", "Accuracy of Ridge: 0.3914\n", "\n", "Classification report:\n", "\n", " precision recall f1-score support\n", "\n", " None 0.41 0.39 0.40 4893\n", " 1 or 2 0.35 0.14 0.20 6025\n", " 3 to 8 0.34 0.21 0.26 7506\n", " 9 or more 0.41 0.77 0.53 7662\n", "\n", "avg / total 0.38 0.39 0.35 26086\n", "\n", "ROC_AUC_score: 0.5611\n", "\n", "\n", "Fitting KNN\n", "Accuracy of KNN: 0.3577\n", "\n", "Classification report:\n", "\n", " precision recall f1-score support\n", "\n", " None 0.42 0.24 0.30 4893\n", " 1 or 2 0.32 0.23 0.26 6025\n", " 3 to 8 0.31 0.40 0.35 7506\n", " 9 or more 0.41 0.50 0.45 7662\n", "\n", "avg / total 0.36 0.36 0.35 26086\n", "\n", "ROC_AUC_score: 0.5470\n", "\n", "\n", "Fitting RandomForest\n", "Accuracy of RandomForest: 0.4115\n", "\n", "Classification report:\n", "\n", " precision recall f1-score support\n", "\n", " None 0.46 0.40 0.43 4893\n", " 1 or 2 0.34 0.20 0.25 6025\n", " 3 to 8 0.35 0.35 0.35 7506\n", " 9 or more 0.46 0.65 0.54 7662\n", "\n", "avg / total 0.40 0.41 0.40 26086\n", "\n", "ROC_AUC_score: 0.5765\n", "\n", "\n", "Fitting XGB\n", "Accuracy of XGB: 0.4217\n", "\n", "Classification report:\n", "\n", " precision recall f1-score support\n", "\n", " None 0.48 0.41 0.44 4893\n", " 1 or 2 0.34 0.20 0.25 6025\n", " 3 to 8 0.35 0.38 0.36 7506\n", " 9 or more 0.48 0.65 0.55 7662\n", "\n", "avg / total 0.41 0.42 0.41 26086\n", "\n", "ROC_AUC_score: 0.5811\n" ] } ], "source": [ "dim_reduce = PCA(n_components=100)\n", "X_train_reduced = dim_reduce.fit_transform(X_train)\n", "X_test_reduced = dim_reduce.transform(X_test)\n", "print(X_train_reduced.shape)\n", "print(X_test_reduced.shape)\n", "\n", "reduced_features = predict_recs(X_train_reduced, y_train, X_test_reduced, y_test, models)" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "That didn't really do much for us (though perhaps it would on the full set). For the two regression classifiers extra features are not really a problem. In principle the same should be true for the ensemble methods (Random Forest and XGB), however it often is not, especially if two features related to the target and highly correlated with each other are run in a model without a sufficiently high number of cases relative to the number of features. In this case, however, we ended up losing useful data and the ensembles classifiers ended up doing worse with the reduced set. The only model that did benefit is KNN - in this case the reduction in variance helped (remember we also increased the parameter K, the number of neighbors a case is compared to). Even that improvement is marginal, however.\n", "\n", "Let's fine-tune the Extreme Gradient Boost using the original data a little bit - there is a way to find the optimal number of trees (estimators) by iteratively adding and comparing accuracy on an evaluation set. Using the non-reduced data also allows us to examine feature importance, which we are interested in. We'll allow the number to go up to 2000." ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Optimal number of trees: 171\n", "Accuracy of score: 0.4464\n", " precision recall f1-score support\n", "\n", " None 0.53 0.41 0.46 4893\n", " 1 or 2 0.36 0.24 0.28 6025\n", " 3 to 8 0.36 0.45 0.40 7506\n", " 9 or more 0.54 0.63 0.58 7662\n", "\n", "avg / total 0.44 0.45 0.44 26086\n", "\n" ] } ], "source": [ "XGB = XGBClassifier(tree_method='gpu_hist',\n", " max_bin=512,\n", " max_depth=7,\n", " n_estimators=2000)\n", "\n", "trained = XGB.fit(X_train,\n", " y_train,\n", " eval_set=[(X_test, y_test)],\n", " early_stopping_rounds=200,\n", " verbose=False)\n", "\n", "optimal_n_trees = trained.best_ntree_limit\n", "print('Optimal number of trees: %d' % (optimal_n_trees))\n", "XGB.set_params(n_estimators=optimal_n_trees)\n", "XGB.fit(X_train, y_train)\n", "y_hat = XGB.predict(X_test)\n", "print('Accuracy of score: %.4f' % (accuracy_score(y_test, y_hat)))\n", "print(classification_report(y_test, y_hat, \n", " target_names = ['None', '1 or 2', '3 to 8', '9 or more']))\n", "\n", "# this is how we can get the feature importance attributes saved by the model. Let's use the ones from the full set, not the dev set \n", "# df_features = pd.DataFrame({'Weight': XGB.get_booster().get_score(importance_type='weight'),\n", "# 'Gain': XGB.get_booster().get_score(importance_type='gain'),\n", "# 'Cover': XGB.get_booster().get_score(importance_type='cover')},\n", "# index=X_train.columns)\n" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "This increased the accuracy of XGB up to 45%. Now that we have a decently performing classifier, why don't we can try to train it on the full set. I ran this separately on my DIY machine learning server at home (with a GTX1080TI that runs XGB fast), as it would take too long in this notebook and requires a few tricks to fit in memory with its over 2 mln cases and a high number of trees. \n", "\n", "At the end, it reached an accuracy of almost 49% on the hold-out set, which is pretty good - an improvement of 20% over the constant baseline model and of 11% of the XGB model with no feature engineering. It also managed to recall 29% of the the problematic second category, which is a solid improvement of more than 11% over the initial run.\n", "Let's take a look at the feature importance attributes of the model: " ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [], "source": [ "df_features = pd.read_pickle('./Data/Feature_importance_full.pkl')" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "Here we are using the feature importance functionality to see what features displayed the highest gain (allowed us to classify cases better when included) and had the greatest weight (were present in greatest number of trees, which means they mattered when classifying a large number of cases). " ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "text/html": [ "
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Keyword:\"global warming\"633.023.019341177123.471271
Keyword:\"school shootings and armed attacks\"505.022.711046119801.236389
Keyword:\"gun control\"615.021.96987980282.095568
approveHour_5AM434.021.439328155686.510859
approveHour_4AM474.021.305978152005.843738
approveDay_Saturday2021.019.98463225385.068124
sectionName_Sunday Review967.019.30637459144.668091
Keyword:\"russia\"896.018.88355224850.877874
sectionName_Live273.018.882208185213.265113
approveHour_3PM1613.018.55180650622.438727
sectionName_Politics1636.017.78170336545.528892
Keyword:\"united states international relations\"1127.017.76637127295.655350
Keyword:\"comey, james b\"879.017.759434142679.586790
sectionName_Baseball288.017.516210363076.870441
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" ], "text/plain": [ " Weight Gain \\\n", "Keyword:\"\" 497.0 682.196571 \n", "depth 3042.0 412.020744 \n", "editorsSelection_1 716.0 341.794156 \n", "author_By THE LEARNING NETWORK 430.0 112.460529 \n", "trusted 1154.0 66.849172 \n", "typeOfMaterial_News 2553.0 57.146109 \n", "author_By DEB AMLEN 626.0 56.199554 \n", "author_By GAIL COLLINS 500.0 44.423127 \n", "Keyword:\"syria\" 661.0 34.882420 \n", "sectionName_Family 428.0 32.341839 \n", "author_By DAVID BROOKS 683.0 31.381635 \n", "approveHour_3AM 603.0 27.642708 \n", "approveDay_Sunday 1440.0 25.434009 \n", "approveHour_6AM 302.0 25.242134 \n", "hoursAfterArticle 28067.0 24.095876 \n", "author_By ROSS DOUTHAT 581.0 24.012026 \n", "Keyword:\"global warming\" 633.0 23.019341 \n", "Keyword:\"school shootings and armed attacks\" 505.0 22.711046 \n", "Keyword:\"gun control\" 615.0 21.969879 \n", "approveHour_5AM 434.0 21.439328 \n", "approveHour_4AM 474.0 21.305978 \n", "approveDay_Saturday 2021.0 19.984632 \n", "sectionName_Sunday Review 967.0 19.306374 \n", "Keyword:\"russia\" 896.0 18.883552 \n", "sectionName_Live 273.0 18.882208 \n", "approveHour_3PM 1613.0 18.551806 \n", "sectionName_Politics 1636.0 17.781703 \n", "Keyword:\"united states international relations\" 1127.0 17.766371 \n", "Keyword:\"comey, james b\" 879.0 17.759434 \n", "sectionName_Baseball 288.0 17.516210 \n", "\n", " Cover \n", "Keyword:\"\" 151838.109084 \n", "depth 136746.635392 \n", "editorsSelection_1 316714.079275 \n", "author_By THE LEARNING NETWORK 224883.973088 \n", "trusted 84256.032967 \n", "typeOfMaterial_News 38005.193426 \n", "author_By DEB AMLEN 185494.032612 \n", "author_By GAIL COLLINS 112118.262682 \n", "Keyword:\"syria\" 134829.897287 \n", "sectionName_Family 212454.863198 \n", "author_By DAVID BROOKS 224897.240937 \n", "approveHour_3AM 108925.055124 \n", "approveDay_Sunday 39614.574964 \n", "approveHour_6AM 234501.792347 \n", "hoursAfterArticle 29920.784922 \n", "author_By ROSS DOUTHAT 212154.869081 \n", "Keyword:\"global warming\" 177123.471271 \n", "Keyword:\"school shootings and armed attacks\" 119801.236389 \n", "Keyword:\"gun control\" 80282.095568 \n", "approveHour_5AM 155686.510859 \n", "approveHour_4AM 152005.843738 \n", "approveDay_Saturday 25385.068124 \n", "sectionName_Sunday Review 59144.668091 \n", "Keyword:\"russia\" 24850.877874 \n", "sectionName_Live 185213.265113 \n", "approveHour_3PM 50622.438727 \n", "sectionName_Politics 36545.528892 \n", "Keyword:\"united states international relations\" 27295.655350 \n", "Keyword:\"comey, james b\" 142679.586790 \n", "sectionName_Baseball 363076.870441 " ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_features.sort_values('Gain', ascending=False).head(30)" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "OK, some of these (empty keyword, the Learning Network or others as authors) are what we could call 'structural' factors - meaning they pertain to the article rather than the comments themselves. Others are about the comments (depth, selected by editor, written by a trusted user, approved at certain hours of the day or posted a certain number of hours after the original article) and will be of greater interest to us.\n", "\n", "As the simple averages of empty article keyword and Learning Network as the authors indicate, the comment upvotes for those were extremely low - perhaps upvoting was disabled for a portion of time on them? Additional detective work would be necessary to ascertain what happened here." ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "author_By THE LEARNING NETWORK\n", "0 19.933151\n", "1 0.034280\n", "Name: recommendations, dtype: float64\n", "Keyword:\"\"\n", "0 20.098355\n", "1 0.720330\n", "Name: recommendations, dtype: float64\n" ] } ], "source": [ "# bring in the original unmodified recommendations variable\n", "recommendations = df.loc[dev_set_index, 'recommendations']\n", "X['recommendations'] = recommendations.reset_index(drop=True)\n", "\n", "print(X.groupby('author_By THE LEARNING NETWORK').recommendations.mean())\n", "\n", "print(X.groupby('Keyword:\"\"').recommendations.mean())\n" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "If we sort our important features by 'Weight' (in how many trees do they appear), we see quite a few surprising factors - hours after article and articleWordCount as well as the percentage of names. I'll say more about them in the next section - there is plenty of intriguing information here." ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "text/html": [ "
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WeightGainCover
hoursAfterArticle28067.024.09587629920.784922
articleWordCount20748.011.75355227152.405073
printPage12070.015.04509230600.598564
reply9793.09.45721720839.824937
commentPolarity8195.03.45919711807.220214
meanWordLength8133.03.35857613323.139771
NNP_Percent8077.04.15344110649.774066
NN_Percent7708.03.34862514371.441559
DT_Percent7424.03.3845029474.644469
RB_Percent7282.03.40215511412.144736
commentObjectivity7256.03.36313011736.138772
IN_Percent7185.03.38232213826.117528
Idf3Percent7053.03.3405059648.121062
JJ_Percent7042.03.33851910733.782276
MeanIdf6835.03.5444449056.971679
Idf4Percent6774.03.2701827689.664120
maxSentLength6728.03.97515916023.769561
PRP_Percent6636.04.14371125704.084771
NNS_Percent6531.03.47585011027.535167
CC_Percent6466.03.4232828732.754822
Idf5Percent6464.03.3008319638.631578
recognizedWordCount6333.08.57369813550.883542
VB_Percent6332.03.63928411144.892127
Idf2Percent6153.03.29226212727.000114
VBZ_Percent6100.03.44471114305.933591
VBP_Percent5917.03.41599014272.319094
TO_Percent5890.03.37231513012.840049
VBN_Percent5560.03.33065211091.542413
VBG_Percent5473.03.42843610130.894194
MinIdf5468.03.21039516043.595959
Idf6Percent5438.03.4211146735.352357
MaxIdf5348.03.50503710320.106724
VBD_Percent5342.03.93010610834.886340
MD_Percent5189.04.56300325085.857509
PRP$_Percent5157.06.18486820498.521030
Idf7Percent5072.03.62108213139.742682
maxWordLength4701.03.50034720085.722387
commentSpellErrorsPercent4466.04.37388923221.690001
CD_Percent4242.03.66140910338.213885
Idf1Percent4220.03.4252748179.586358
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" ], "text/plain": [ " Weight Gain Cover\n", "hoursAfterArticle 28067.0 24.095876 29920.784922\n", "articleWordCount 20748.0 11.753552 27152.405073\n", "printPage 12070.0 15.045092 30600.598564\n", "reply 9793.0 9.457217 20839.824937\n", "commentPolarity 8195.0 3.459197 11807.220214\n", "meanWordLength 8133.0 3.358576 13323.139771\n", "NNP_Percent 8077.0 4.153441 10649.774066\n", "NN_Percent 7708.0 3.348625 14371.441559\n", "DT_Percent 7424.0 3.384502 9474.644469\n", "RB_Percent 7282.0 3.402155 11412.144736\n", "commentObjectivity 7256.0 3.363130 11736.138772\n", "IN_Percent 7185.0 3.382322 13826.117528\n", "Idf3Percent 7053.0 3.340505 9648.121062\n", "JJ_Percent 7042.0 3.338519 10733.782276\n", "MeanIdf 6835.0 3.544444 9056.971679\n", "Idf4Percent 6774.0 3.270182 7689.664120\n", "maxSentLength 6728.0 3.975159 16023.769561\n", "PRP_Percent 6636.0 4.143711 25704.084771\n", "NNS_Percent 6531.0 3.475850 11027.535167\n", "CC_Percent 6466.0 3.423282 8732.754822\n", "Idf5Percent 6464.0 3.300831 9638.631578\n", "recognizedWordCount 6333.0 8.573698 13550.883542\n", "VB_Percent 6332.0 3.639284 11144.892127\n", "Idf2Percent 6153.0 3.292262 12727.000114\n", "VBZ_Percent 6100.0 3.444711 14305.933591\n", "VBP_Percent 5917.0 3.415990 14272.319094\n", "TO_Percent 5890.0 3.372315 13012.840049\n", "VBN_Percent 5560.0 3.330652 11091.542413\n", "VBG_Percent 5473.0 3.428436 10130.894194\n", "MinIdf 5468.0 3.210395 16043.595959\n", "Idf6Percent 5438.0 3.421114 6735.352357\n", "MaxIdf 5348.0 3.505037 10320.106724\n", "VBD_Percent 5342.0 3.930106 10834.886340\n", "MD_Percent 5189.0 4.563003 25085.857509\n", "PRP$_Percent 5157.0 6.184868 20498.521030\n", "Idf7Percent 5072.0 3.621082 13139.742682\n", "maxWordLength 4701.0 3.500347 20085.722387\n", "commentSpellErrorsPercent 4466.0 4.373889 23221.690001\n", "CD_Percent 4242.0 3.661409 10338.213885\n", "Idf1Percent 4220.0 3.425274 8179.586358" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_features.sort_values('Weight', ascending=False).head(40)" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "#### Takeaways \n", "So what are the most important factors in predicting why some comments are upvoted and others are not? If we take a look at the top features from our full set model and examine them using the output from the full MLogit model, we get an idea of which features mattered and how.\n", "Let's start with the most surprising finding:\n", "\n", "**Time is of the essence!**\n", " \n", "Commenting on an article within a few hours after it is published is one of the most important factors in predicting popularity. Early comments have a much greater chance of snowballing in upvotes and the window of opportunity closes rather quickly with every passing hour. This is the solution to the puzzle posed in the intro: comment A appeared within an hour following the publication of the article, while comment B clocked in at 24 hours. If you wish for your comment to go the distance, better post early! \n", "Here is a visual of the percentage breakdown of the four categories across hours after article split in 20 equal-sized (meaning equal number of comments in each) bins:" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# function to plot the categories across a quantized X variable\n", "def stacked_bar_plotter(x, y='recs', q=20, df=X):\n", " '''Create a percent-stacked-bars plot of y over the quantized x variable'''\n", " plt.style.use('seaborn-white')\n", " df_s = df[[x, y]]\n", " label = x + ' split in ' + str(q) + ' quantiles'\n", " df_s[label] = pd.qcut(df[x], q, duplicates='drop')\n", " # prepare percent categories for each bar\n", " grouped = df_s.groupby(label).recs.value_counts(normalize=True)\n", " df_s = grouped.unstack()\n", " catlabels = ['None', '1 or 2', '3 - 8', '9 or more']\n", " df_s.columns = catlabels\n", " df_s['labels'] = df_s.index\n", " df_s['xticks'] = df_s.labels.cat.codes\n", " fig = plt.figure()\n", " ax = plt.subplot(111)\n", " for i in range(4):\n", " bot = df_s.iloc[:, 0:i].sum(axis=1)\n", " ax.bar(df_s.xticks, df_s.iloc[:, i], bottom=bot)\n", " box = ax.get_position()\n", " ax.set_position([box.x0, box.y0 + box.height * .1,\n", " box.width, box.height * .9])\n", " plt.xticks(df_s.xticks, df_s.labels, rotation=30)\n", " plt.yticks(np.linspace(0, 1, 11),\n", " [str(x) + '%' for x in range(0, 101, 10)])\n", " plt.xlabel(label)\n", " plt.ylabel('Percent of cases')\n", " ax.legend(loc='upper center', bbox_to_anchor=(0.5, 1.1),\n", " labels=catlabels, ncol=4, fontsize='large',\n", " markerscale=1.5)\n", "\n", "stacked_bar_plotter('hoursAfterArticle')" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "More than half the comments submitted in the first two hours ended up in the highest category with 9 or more upvotes! This is in contrast to less than a quarter for the ones submitted after just 15 hours. Here are the means for the original variable, **recommendations** as well:" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [], "source": [ "# quantile plotter \n", "def quantile_plotter(x, y='recs', q=20, df=X):\n", " '''Create a pointplot of y over the quantized x variable'''\n", " df_small = df[[x, y]]\n", " label = x + ' split in ' + str(q) + ' quantiles'\n", " df_small[label] = pd.qcut(df[x], q, duplicates='drop')\n", " sns.pointplot(x=label,\n", " y=y,\n", " data=df_small,\n", " join=False)\n", " plt.xticks(rotation=30)\n", " \n" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "quantile_plotter('hoursAfterArticle', y='recommendations')" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "It depicts the average number of upvotes (with error bars) per unit of time after the publication of an article. Each unit of time here is based on 5% of the original observations of hours after the article was published before the comment appeared. The trend is clear - the sooner, the better. \n", "The highest average category (ie highest number of upvotes) happens within the first two hours and then total upvotes gradually decline. There is an uptick around X hours - this is due to the fact that most articles appear online slightly before midnight US Eastern Time, while most people wake up and read articles about 9-11am, so it simply reflects peak readership volume, occuring 9-10 hours later. There is not much point in posting comments roughly two days after the publication of an article." ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "**Commenting on comments doesn't bring in the upvotes**\n", "The next one is about replying to other users' comments and it also surprised me. Unlike Reddit, where replying to the top comment is often the only way to be noticed, at the Times reply comments rarely attract many upvotes. The feature about replying that we used, **depth** ('1' is original comment, '2' is a response to a comment, '3' is a response to a response and so on) is the strongest predictor of a comment's popularity category. We even tried adding a feature based on the number of upvotes of the original comment, guessing that replying to popular comments should result in more upvotes, but it didn't really make any difference. A simple mean conditional on the level of depth speaks volumes here: original comments have a mean of about 25 upvotes, while level 4 responses are at zero:" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "text/plain": [ "depth\n", "1.0 25.883516\n", "2.0 3.008920\n", "3.0 0.675810\n", "4.0 0.142857\n", "5.0 0.000000\n", "Name: recommendations, dtype: float64" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X.groupby('depth').recommendations.mean()" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "quantile_plotter('reply', 'recommendations')" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "And a simple linear correlation between the number of upvotes of the original comment and the reply comment shows us there's nothing there (certainly no linear relationship):" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "text/plain": [ "-0.03" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X.reply.corr(X.recommendations).round(2)" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "**There is an optimal article length for getting upvoted comments - at about 800 words.** \n", "Apparently the ideal zone between too short and too long for most readers is somewhere around 800 words. We should keep in mind, though, that this is where most articles are in terms of length anyway. Looking at the plot below, clearly articles below 500 words are just too short. I am surprised there is such a thing as too short an article, but anything less than 500 simply doesn't do it for many readers.\n", "\n", "There are several interpretations here. One is that readers who read longer articles tend to be more likely to comment and respond to comments - perhaps they have more time to spend on the site; the 'newspaper junkies'. Another is that articles below 500 words are simply too short to be worthy of much of a response - there isn't all that much to say about them. Finally, it is possible that longer articles simply attract more readers which translates into higher overall upvote counts. I would not have guessed that in general about online articles, but this is the NYTimes, so perhaps the attention span of the average reader is longer than usual. \n", "\n", "Also surprising is the fact that on the opposite end of the spectrum, comments made to articles over 2000 words rake in higher counts on average than anything in between. There is still an audience for the long newspaper article and it does not shy from participating in the communal conversation." ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "quantile_plotter('articleWordCount', 'recommendations', df=df)" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "And a jointplot to show the histogram of each along with the joint distribution. The darker the color inside the plot, the more cases that occur there. The outside plots simply show the histogram of each of the variables. There is a large number of articles at about 700-800 words, which is why we see the spike (dark vertical column) in the hex plot. " ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 72, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X_plot = X[X.recommendations < 20]\n", "sns.jointplot(x='articleWordCount',\n", " y='recommendations',\n", " xlim=(0, 2500),\n", " ylim=(-1, 20),\n", " kind=\"hex\",\n", " data=X_plot,\n", " extent=[0, 2500, 0, 20],\n", " stat_func=None)\n" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "**Print page - most of the important big-headline articles appear in the first 30 pages of the paper, where they attract more readers and more comments.**\n", "\n", "This is why the vast majority of comments are to articles in the first 30 pages. Generally, more important articles appear in the first few pages on the paper version of the newspaper, so naturally we expect upvotes to go down with every page. It is not quite so simple though - the very first few pages including the front page are a mixed bag and there is a peak around pages 19-25 perhaps because of the editorials and columnist sections. " ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "stacked_bar_plotter('printPage', 'recs')" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "**Getting the coveted 'NYTimes Pick' helps a lot. So does being a trusted user.** \n", "\n", "Again, nothing unusual - the greater visibility that comes with a NYTimes Pick icon and being placed on top of a short list of comments in the NYTimes staff tab obviously results in more comments. Simple comparison of means (even without a t-test which given the sample size would only confirm the obivous) would lead us to conclude that on average getting picked by the staff brings about 250 upvotes!" ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "text/plain": [ "editorsSelection_1\n", "0 14.282758\n", "1 301.788889\n", "Name: recommendations, dtype: float64" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X.groupby('editorsSelection_1').recommendations.mean()" ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "text/plain": [ "trusted\n", "0.0 17.691285\n", "1.0 75.878780\n", "Name: recommendations, dtype: float64" ] }, "execution_count": 76, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X.groupby('trusted').recommendations.mean()\n" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "**Hot button issues bring about reactions, but not always upvotes** \n", " \n", "Articles about things like 'school shootings', 'global warming', 'russia', are consistently associated with comments that receive _fewer_ upvotes on average. This could be simply because too many people react by writing comments, but I don't think it is that simple. It might be something about the emotional reaction the articles evoke or about a selection effect - who reads them and whether they tend to upvote. Intriguingly some article keywords and even comment words (words included in a comment) are associated with higher upvotes - for example: 'women' and 'illegal immigration.' This might also reflect the period of time the articles covered and might be, in some sense, over-fitting to one or a few articles, but more probing would be necessary to make sense of these features. " ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Keyword:\"global warming\"\n", "0 19.799027\n", "1 16.575217\n", "Name: recommendations, dtype: float64\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print(X.groupby('Keyword:\"global warming\"').recommendations.mean())\n", "sns.barplot(x='Keyword:\"global warming\"',\n", " y='recommendations',\n", " data=X)\n", "\n" ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 78, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.barplot(x='Keyword:\"women and girls\"',\n", " y='recommendations',\n", " data=X)\n" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "**Effort pays off: say more; use a rich vocabulary; refer to people, places and organizations sparingly; spell-check!**\n", "I was really curious whether comment length, or the use of sophisticated vocabulary (measured by IDF and word length), grammar complexity (max sentence length), spelling error rates, or the use of particular parts of speech more often leads to higher upvote counts. The classification model provides some evidence that all of these things matter. Starting with the easiest one, here is a look at comment word length:\n" ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "image/png": 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\n", 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "stacked_bar_plotter('recognizedWordCount', 'recs')" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "The relationship between comment size and upvotes is almost monotonic - and the highest length of over 77 words displays the highest average upvote totals." ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "Next, let's look at one of the features that reflect vocabulary sophistication - the average inverse document frequency for a comment. There seems to be an ideal range in the middle here, but I am affraid it might be influenced by the presense of overly short comments - that's a sure way of getting very low or very high average IDF." ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "quantile_plotter('MeanIdf', 'recommendations')" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "If we limit the comments to only those longer than 10 words, the range does shrink, so size was a confounding factor here. The only thing we can conclude from the plot is that low IDF does bring a comment down, but a higher average infrequency of words does not really help." ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "quantile_plotter('MeanIdf', 'recommendations', df=X.loc[X.recognizedWordCount > 10, :])" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "The other measure of vocabulary complexity that showed up in the top features of the model is very simple - average word length. It behaves in much the same way - too low is a problem, but beyond a certain threshold, it makes little difference." ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "image/png": 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\n", 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "quantile_plotter('meanWordLength', 'recommendations', df=X.loc[X.recognizedWordCount > 10, :])" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "Let's finish the IDF measures with max. The idea is that if a comment contains one very rare word, it is more likely to get upvotes. Yep, that does seem to be true and ditto for max word length and even our measure of grammar complexity (very crude) - max sentence length:" ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "image/png": 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\n", 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "quantile_plotter('maxWordLength', 'recommendations')" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "Spelling next: generally the higher the number of spelling mistakes, the lower the upvote count. The number of comments with no spelling erorrs is very high, which is why the value at zero is both lower and has a much smaller error bar. " ] }, { "cell_type": "code", "execution_count": 88, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "quantile_plotter('commentSpellErrorsPercent', 'recommendations')" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "Finally, among the parts-of-speech tags there is a notable leader - 'NNP,' which stands for names of people, organizations or places (proper nouns). Too many names or too few leads to lower upvote totals. Naturally, this could be a function of the types of articles that include many names - politics & news. It is not due to very short comments, however." ] }, { "cell_type": "code", "execution_count": 89, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "quantile_plotter('NNP_Percent', 'recommendations')" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "**Don't be too negative or too positive, be slightly positive.**\n", " \n", "Finally, let's look at the tone of the comments - polarity measures positive or negative affect, essentially what is the ratio of negative to positive words, on a scale from -1 to 1. It seems that overly critical or overly effusive sounding comments don't fare well with the audience - there is an optimum close to neutrality, erring slightly on the side of positive. The dip at zero is simply due to the large number of cases there." ] }, { "cell_type": "code", "execution_count": 90, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "quantile_plotter('commentPolarity', 'recommendations')" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "OK, one last question: does the time of day and day of the week matter and if so how? Some of the dummies based on these features appear to give the gradient boost sizeable gain." ] }, { "cell_type": "code", "execution_count": 91, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 91, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "categories = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n", "df['approveDayCat'] = df.approveDay.astype('category',\n", " ordered=True,\n", " categories=categories)\n", "sns.pointplot(x = 'approveDayCat',\n", " y = 'recommendations',\n", " data=df,\n", " join=False\n", ")" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "Hmm, I didn't expect this - apparently there is a build-up in terms of comments (and probably reading of articles) from Thursday to Saturday; Sunday is the worst day to comment and then the first three work days see a gradual descent to the weekly nadir on Wednesday. The differences between the days of the week are not large, but it is rather intriguing that they exist." ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "Finally, these are the hours of the day at which comments get the highest upvote counts: clear spike around 9AM, while most comments are posted in the early afternoon. Does this mean that the early bird gets the upvote worm or is simply a function of NYTimes reporters commenting in those hours?" ] }, { "cell_type": "code", "execution_count": 92, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "text/plain": [ "(array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,\n", " 17, 18, 19, 20, 21, 22, 23]), )" ] }, "execution_count": 92, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df['approveHourCat'] = df.approveTime.dt.hour\n", "sns.pointplot(x = 'approveHourCat',\n", " y = 'recommendations',\n", " data=df,\n", " join=False)\n", " \n", "plt.xticks(rotation=30)" ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 93, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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AxMREpk+fzsWLFwkODiYkJASAiIgIYmNjsdlszJw5Ew+PWs+uL/KT0pBnkJUbT52FSefOnUlOTq7S/t5771VpCwsLIywsrFKbp6ena4LJK7Vr146UlJQq7Q6HA4fDYaHHIiLyQ+nru4iIWKYwERERyxQmIiJimcJEREQsU5iIiIhlChMREbFMYSIiIpYpTERExDKFiYiIWKYwERERy+psOhUREakfP4V53DQyERERyxQmIiJimcJEREQsU5iIiIhlChMREbFMYSIiIpYpTERExDKFiYiIWKYwERERyxQmIiJimcJEREQsU5iIiIhlChMREbFMYSIiIpYpTERExDKFiYiIWKabY/0AP4Ub0YiI/JRoZCIiIpZpZCIicoP6MY+yaGQiIiKWKUxERMQyhYmIiFh2Q58zefPNN2ncuLHp5XVlloiIezd0mNQ3XVIsIj9XOswlIiKWKUxERMQyhYmIiFimMBEREcvq9QS80+kkPj6e9u3bA9ChQweefvpppkyZQkVFBQEBAbz22mvY7XbS09NZtWoVHh4eREREEB4eTnl5OQkJCRw7dgxPT0+SkpJo3bo1Bw4cYObMmQB07NixVie6RUTEunofmTzwwAMkJyeTnJzMtGnTWLRoEdHR0aSkpNCmTRvS0tI4d+4cS5cuZeXKlSQnJ7Nq1SqKior44IMP8PPz409/+hNjx45lwYIFAMyePZvExERSU1MpLi5m+/bt9f2wRERuaNf9MJfT6WTgwIEA9O/fn8zMTLKzswkKCsLX1xcfHx+6du1KVlYWmZmZDB48GICQkBCysrIoKyvj6NGjdOnSpdI6RESk/tT770xyc3MZO3Ysp0+fJi4ujvPnz2O32wFo1qwZBQUFFBYW4u/v76rx9/ev0u7h4YHNZqOwsBA/Pz/XspfXISIi9adew+SOO+4gLi6Ohx9+mMOHD/PEE09QUVHh+rthGG7ratNe3bIiIlJ36vUwV/PmzRkyZAg2m43bb7+dW2+9ldOnT1NaWgpAXl4egYGBBAYGUlhY6KrLz893tV8edZSXl2MYBgEBARQVFbmWvbwOERGpP/U6MklPT6egoIAxY8ZQUFDAyZMnGTFiBFu2bGH48OFs3bqV0NBQgoODmTp1KmfOnMHT05OsrCwSExMpLi5m8+bNhIaGkpGRQY8ePfD29qZt27bs3r2b7t27s3XrVhwOR30+LJFr0jQ6ciOo1zAZMGAAkydP5pNPPqG8vJyZM2fSqVMnnn/+edatW0fLli159NFH8fb2ZtKkSYwZMwabzcb48ePx9fVlyJAh7Ny5k6ioKOx2O3PmzAEgMTGR6dOnc/HiRYKDgwkJCanPh1Xn9GEkIj919RomjRs35p133qnS/t5771VpCwsLIywsrFLb5d+WXK1du3akpKT8eB0VEZFaue6XBouISMOnMBEREcsUJiIiYpnCRERELFOYiIiIZQoTERGxTGEiIiKWKUxERMQyhYmIiFimMBEREcsUJiIiYpnCRERELFOYiIiIZQoTERGxTGEiIiKW1ev9TKR+6aZaIlJfNDIRERHLFCYiImKZDnOJmKTDhiLV08hEREQsU5iIiIhlChMREbFMYSIiIpbpBLxUoRPNIlJbGpmIiIhlChMREbFMYSIiIpYpTERExDKFiYiIWKarueSGo6vVRH58GpmIiIhlGpnIj0bf+EVuXBqZiIiIZRqZSIOlkZDIT4dGJiIiYplGJnLdaYQh0vBpZCIiIpYpTERExDKFiYiIWKYwERERy35WJ+BfffVVsrOzsdlsJCYm0qVLl+vdJRGRG8LPJkz+67/+i2+//ZZ169Zx6NAhEhMTWbdu3fXulojIDeFnEyaZmZkMGjQIgF/96lecPn2a4uJiGjduXGXZiooKAM6dO1erbRw5cgSA4uLiWteoTnWqs15Xm5qfe931eA1OnDgB/P9n6JVshmEYptf2EzZt2jT69evnCpTo6Ghmz57NnXfeWWXZ3bt3ExMTU99dFBH5WVi7di3du3ev1PazGZlc7VoZ2blzZ9auXUtAQACenp712CsRkYaroqKCgoICOnfuXOVvP5swCQwMpLCw0PX//Px8AgIC3C7r4+NTJVVFRKRmbdq0cdv+s7k0uHfv3mzZsgWAffv2ERgY6PZ8iYiI/Ph+NiOTrl27cs899xAZGYnNZtMcTiIi9ehncwJeRESun5/NYS4REbl+FCYiImKZwuQKr776KqNGjSIyMpKvvvqqVrUHDx5k0KBBrFmzplZ18+bNY9SoUTz++ONs3bq1xuXPnz9PfHw8sbGxhIeHk5GRUavtlZaWMmjQIDZu3GhqeafTSc+ePXE4HDgcDmbNmmV6W+np6QwbNowRI0awbds2UzUbNmxwbcvhcHDfffeZqispKSEuLg6Hw0FkZCSff/65qbqLFy8ybdo0IiMjcTgcHDp0qMaaq1/r48eP43A4iI6OJj4+nrKyshprAFavXs0999xDSUlJrbY1evRoYmNjGT16NAUFBabq/vu//5uoqCgcDgdjxozhn//8p6m6yz7//HM6duxoup8JCQkMHTrU9TpW9/pfXVdeXs6kSZMYOXIkTz75JKdPnzZV9+yzz7q2NXToUKZNm2aq7ssvv3Q9L7///e9Nb+/QoUPExMQQGxvL1KlTuXDhgtu6q9/fZvYVd3Vgbn9xtz0z+8vVdWb3l0oMMQzDMJxOp/G73/3OMAzDyM3NNSIiIkzXlpSUGLGxscbUqVON5ORk03WZmZnG008/bRiGYfzzn/80+vXrV2PNhx9+aLz77ruGYRjGkSNHjAcffND09gzDMF5//XVjxIgRxvvvv29q+V27dhkTJkyo1TYM49LjefDBB42zZ88aeXl5xtSpU2u9DqfTacycOdPUssnJycb8+fMNwzCMEydOGA899JCpuq1btxrx8fGGYRjGt99+69oHquPutU5ISDA2bdpkGIZhLFiwwFi7dm2NNf/5n/9pvP7668avf/1ro7i42PS2pkyZYnz44YeGYRjGmjVrjLlz55qqmzBhgvHdd98ZhmEYixcvNt5++21TdYZhGKWlpUZsbKzRu3dv0/18/vnnjU8//dTt8teqW7NmjTFr1izDMAwjNTXV+Pjjj03387KEhAQjOzvbVN1jjz1mHDp0yDAMw3j77beNZcuWmaobO3assW3bNsMwDGPJkiVGenp6lTp37++a9pXq6szsL+7qzOwv7urM7C9X08jkX6qbjsUMu93O8uXLCQwMrNU277//ft58800A/Pz8OH/+vNtpCq40ZMgQ/u3f/g249C21efPmprd36NAhcnNz+fWvf12rfv4QmZmZ9OrVi8aNGxMYGFirEc1lS5cuZdy4caaW/cUvfkFRUREAZ86c4Re/+IWpuv/93/91TQh6++23c+zYsWu+Bu5ea6fTycCBAwHo378/mZmZNdYMGjSIiRMnYrPZarWtGTNm8NBDD1V5zDXVLVq0iNatW2MYBnl5ebRo0cJUHcA777xDdHQ0drvddD/NcFeXkZHBsGHDABg1apTreTW7vW+++YazZ8+6neTVXd2Vz+Hp06fd7jfu6r799lvXNkJDQ/niiy+q1Ll7f9e0r1RXN3DgwBr3F3d1ZvYXd3VvvPFGjfvL1RQm/1JYWFhpR/L39692SHg1Ly8vfHx8ar1NT09PGjVqBEBaWhp9+/Y1/Yv8yMhIJk+eTGJiountzZ07l4SEhFr3Mzc3l7FjxxIVFeX2TePOkSNHKC0tZezYsURHR7t901zLV199xW233VbtD0+v9pvf/IZjx44xePBgYmNjef75503VdejQgR07dlBRUcE333zD4cOHOXXqVLXLu3utz58/7/qgbdasWZX9xl2Nmd9Auatr1KgRnp6eVFRUkJKSwtChQ03VAXz22WeEhYVRWFjo+sCuqe4f//gHBw4c4OGHH65VPwHWrFnDE088wcSJE90eJnFXd/ToUT777DMcDgcTJ050++F3rffb6tWriY2NNd3PxMRExo8fz0MPPcSePXt47LHHTNV16NCB7du3A5cOAV75g+nL3L2/a9pXqqvz9fV1+5hqqjOzv1T3OVTT/nI1hUk1jHq8Yvrjjz8mLS2N6dOnm65JTU3l7bff5g9/+IOpvv75z3/m3nvvpXXr1rXq2x133EFcXBxvv/02c+fO5cUXX6z2OO/VioqKWLJkCXPmzOGFF16o1XOalpbm9o1dnb/85S+0bNmSv/71r6xatYqXX37ZVF2/fv0ICgoiJiaGVatW0bZtW0uvfX3sNxUVFUyZMoWePXvSq1cv03V9+/Zl8+bNtG3blnfffddUTVJSEi+88EKt+zh8+HAmT57M6tWr6dSpE0uWLDFVZxgGd955J8nJybRv355ly5aZ3mZZWRl79uyhZ8+epmtmzZrFkiVL2LJlC926dSMlJcVU3fPPP89HH33EE088gWEY13zdq3t/17Sv/JDPBXd1ZveXq+tqu78oTP6lNtOx/Jg+//xz3nnnHZYvX27q20dOTg7Hjx8HoFOnTlRUVJg6ObZt2zY++eQTIiIi2LBhA2+99RY7d+6ssa558+YMGTIEm83G7bffzq233kpeXl6Ndc2aNeO+++7Dy8uL22+/nVtuucXcSbx/cTqdpk++A2RlZdGnTx8A7rrrLvLz82s8ZHjZxIkTSU1N5aWXXuLMmTM0a9bM9Hbh0mihtLQUgLy8vFof7qmtF154gTZt2hAXF2e65q9//SsANpvN9S28Jnl5eXzzzTdMnjyZiIgI8vPzq/3Wf7VevXrRqVMnAAYMGMDBgwdN1d16663cf//9APTp04fc3FxTdXDpZHpt72H097//nW7dugEQEhJCTk6OqbrbbruNZcuWsXr1aoKDg2nVqpXb5a5+f5vdV2r7uXCtOjP7y9V1P2R/UZj8y/WYjuXs2bPMmzePZcuW0bRpU1M1u3fv5o9//CNw6dDcuXPnTJ0fWLhwIe+//z7r168nPDyccePGERISUmNdeno6//Ef/wFAQUEBJ0+eNHWepk+fPuzatYuLFy9y6tQp0/2ES2+yW265pdpj9O60adOG7Oxs4NKhkltuucXUIcMDBw64vnl/9tln3H333Xh41O5tERIS4tp3tm7dSmhoaK3qayM9PR1vb2+effbZWtUtXryY//mf/wEgOzvb7WzaV2vevDkff/wx69evZ/369QQGBpq+WnHChAkcPnwYuPTFoH379qbq+vbt67oSb9++fab6ednevXu56667TC8Pl8LrcmDt3bu32nmnrrZo0SLXFWobN25kwIABVZZx9/42s6/8kM+F6urM7C/u6n7I/qJfwF9h/vz57N692zUdi9kdMycnh7lz53L06FG8vLxo3rw5ixcvrnFHWLduHYsXL670Qs2dO5eWLVtWW1NaWsqLL77I8ePHKS0tJS4uzu2OfC2LFy+mVatWjBgxosZli4uLmTx5MmfOnKG8vJy4uDj69etnajupqamkpaUB8Mwzz7g9mepOTk4OCxcuZMWKFaaWh0uXBicmJnLy5EkuXLhAfHy8qUNAFy9eJDExkdzcXG666Sbmz5/Pbbfdds2+Xf1az58/n4SEBL7//ntatmxJUlIS3t7e16wJCQlh586d/O1vfyMoKIh7772XKVOm1LitkydPctNNN7m+6PzqV79i5syZNdb94Q9/4NVXX8XT0xMfHx/mzZtXZQRW0348YMAAPv30U1PPSWxsLO+++y4333wzjRo1IikpydT25s+fz+zZsykoKKBRo0bMnTuXW2+91VQ/Fy9eTLdu3RgyZIjp127ixInMmzcPb29vmjRpwquvvoqfn1+NdZMnT2bWrFkYhkH37t3dHgp09/6eM2cOU6dOrXZfqa6uR48eOJ3Oa+4v7uqOHTuGn5/fNfcXd3XPPvssCxYsuOb+cjWFiYiIWKbDXCIiYpnCRERELFOYiIiIZQoTERGxTGEiIiKWKUxEfkKcTidRUVGV2o4cOULfvn1/tG189dVXjB49mhEjRhAeHs4zzzzj+k1Idc6fP29qVmu5cSlMRG4gBQUFxMXFER8fz8aNG9mwYQNDhgzh6aefrnYadYD9+/crTOSafjb3gBcx6+LFi8yYMYNvvvmGsrIygoODGT16NKNHj6Zv374cOHAAgDepptuwAAAEs0lEQVTeeIPmzZtz9913M27cOJxOJyUlJcyZM4cOHTowYMAAHn74YQ4fPsyiRYtIS0sjNTWVm2++mWbNmvHKK6+wdOlSmjRpwtixYwF46623KCkpIT4+npdffplvv/2WkpISHnnkEZ566qka+37u3DmmTZvGiRMnuHDhAsOHDyc6OpqNGzeyc+dO5s+fD4DD4eCZZ57B09OTt956i5tuuonBgwdz5MgRhg0bVmmqmqFDh9K3b1+8vLw4dOgQM2bMwNPTk+LiYp577jnuv/9+XnzxRc6cOcO8efOq/FhOBDQykRvQ6dOn6dixI2vXrmXDhg3s2LGDc+fOcfjwYUaMGEFKSgoPPPCAa9qaiooK2rdvT3JyMlFRUSxatMi1rjvuuINFixZx7NgxFi9ezMqVK0lOTua2225j5cqVDBs2zDV9BsBHH33E8OHDWb16NYGBgSQnJ7NhwwY+/PBDV4hdS3JyMn5+fqxdu5ZVq1axYsWKGg9R5eTkMG/ePMLDw8nNzSUoKKjKMk2aNAEuTdETHx/PqlWrmDp1Km+88QY+Pj787ne/IyQkREEi1dLIRG44fn5+HD9+nFGjRmG32ykoKCAnJ4emTZvSuXNnALp27cqqVatcNZcnkezatatrrjLA9Q1///793HPPPa5pKx544AFSU1OJi4ujrKyMw4cP8/333+Pp6UmHDh147bXXOHHiBF9++SVwacbb7777jiZNmnDw4EEcDodrG99//73r39nZ2a5pcHx8fOjcuTP79u275uO98847XVOiXJ6OvDoBAQHMmzePN954g/LycrdTwIu4ozCRG86HH37I3r17Wbt2LV5eXq4P5ytnFjIMo9KNiK7825XtV8+r5K7+kUceYfPmzZw/f951Xwi73c748eMJCwurVOd0OunQoQPJycmutiNHjhAdHV1l21du5+r28vJyt33s0KEDWVlZVeavys7OpkuXLsyaNYvf/OY3jBw5koMHD7oOz4nURIe55IZz8uRJ7rzzTry8vMjJyeG7776jrKyM06dPs3//fuDSlPZX3vN8165dAOzZs8ftvdAvjxAu351z586dBAcHA5fCJCMjg4yMDB555BEAunXrxkcffQRcOoeTlJRkahQQHBzsmlX33Llz7Nu3zzUiOnHihOvxff31127ro6Oj2bx5s+vxAGzatIkXX3yR8vJyCgsLXTP8btq0yXXvGg8Pj2ueoBdRmMgNJywsjL/97W/ExsaydetWnnrqKV555RVuvvlmNm7cyBNPPEFWVhajR4921ezfv58xY8awfv16JkyYUGWdLVq0ID4+nt/+9rfExMRw6tQpnnzySQBat26NzWbD39/fdf+KmJgYGjVqxKhRo4iIiMDX19fUdOMOh4OSkhJiYmJ48sknGTduHL/85S/p3bs3Fy5cICIigvnz51d7Lxh/f3/WrFnD8uXLeeyxx4iKiuLTTz9l5cqV2O12nnrqKaZMmcKYMWPo1q0bTZo0Yc6cOQQFBbF79+4fdKMsuTFo1mAR/v9Q0meffVblbx07dmTfvn14eemosEh1NDIRERHLNDIRERHLNDIRERHLFCYiImKZwkRERCxTmIiIiGUKExERsez/AD8EKKSacxvIAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.countplot('approveHourCat', data=df, color='gray')" ] }, { "cell_type": "markdown", "metadata": { "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "source": [ "### This is all, hope you enjoyed it :)\n", "\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.2" }, "name": "NYTCommentsNotebook.ipynb" }, "nbformat": 4, "nbformat_minor": 2 }