{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Homework 3. Bayesian Tomatoes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Due Thursday, October 17, 11:59pm" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
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\n", "\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this assignment, you'll be analyzing movie reviews from [Rotten Tomatoes](http://www.rottentomatoes.com). This assignment will cover:\n", "\n", " * Working with web APIs\n", " * Making and interpreting predictions from a Bayesian perspective\n", " * Using the Naive Bayes algorithm to predict whether a movie review is positive or negative\n", " * Using cross validation to optimize models\n", "\n", "Useful libraries for this assignment\n", "\n", "* [numpy](http://docs.scipy.org/doc/numpy-dev/user/index.html), for arrays\n", "* [scikit-learn](http://scikit-learn.org/stable/), for machine learning\n", "* [json](http://docs.python.org/2/library/json.html) for parsing JSON data from the web.\n", "* [pandas](http://pandas.pydata.org/), for data frames\n", "* [matplotlib](http://matplotlib.org/), for plotting\n", "* [requests](http://docs.python-requests.org/en/latest/), for downloading web content" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline\n", "\n", "import json\n", "\n", "import requests\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "pd.set_option('display.width', 500)\n", "pd.set_option('display.max_columns', 30)\n", "\n", "# set some nicer defaults for matplotlib\n", "from matplotlib import rcParams\n", "\n", "#these colors come from colorbrewer2.org. Each is an RGB triplet\n", "dark2_colors = [(0.10588235294117647, 0.6196078431372549, 0.4666666666666667),\n", " (0.8509803921568627, 0.37254901960784315, 0.00784313725490196),\n", " (0.4588235294117647, 0.4392156862745098, 0.7019607843137254),\n", " (0.9058823529411765, 0.1607843137254902, 0.5411764705882353),\n", " (0.4, 0.6509803921568628, 0.11764705882352941),\n", " (0.9019607843137255, 0.6705882352941176, 0.00784313725490196),\n", " (0.6509803921568628, 0.4627450980392157, 0.11372549019607843),\n", " (0.4, 0.4, 0.4)]\n", "\n", "rcParams['figure.figsize'] = (10, 6)\n", "rcParams['figure.dpi'] = 150\n", "rcParams['axes.color_cycle'] = dark2_colors\n", "rcParams['lines.linewidth'] = 2\n", "rcParams['axes.grid'] = False\n", "rcParams['axes.facecolor'] = 'white'\n", "rcParams['font.size'] = 14\n", "rcParams['patch.edgecolor'] = 'none'\n", "\n", "\n", "def remove_border(axes=None, top=False, right=False, left=True, bottom=True):\n", " \"\"\"\n", " Minimize chartjunk by stripping out unnecessary plot borders and axis ticks\n", " \n", " The top/right/left/bottom keywords toggle whether the corresponding plot border is drawn\n", " \"\"\"\n", " ax = axes or plt.gca()\n", " ax.spines['top'].set_visible(top)\n", " ax.spines['right'].set_visible(right)\n", " ax.spines['left'].set_visible(left)\n", " ax.spines['bottom'].set_visible(bottom)\n", " \n", " #turn off all ticks\n", " ax.yaxis.set_ticks_position('none')\n", " ax.xaxis.set_ticks_position('none')\n", " \n", " #now re-enable visibles\n", " if top:\n", " ax.xaxis.tick_top()\n", " if bottom:\n", " ax.xaxis.tick_bottom()\n", " if left:\n", " ax.yaxis.tick_left()\n", " if right:\n", " ax.yaxis.tick_right()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "from ggplot import *" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction\n", "\n", "Rotten Tomatoes gathers movie reviews from critics. An [entry on the website](http://www.rottentomatoes.com/m/primer/reviews/?type=top_critics) typically consists of a short quote, a link to the full review, and a Fresh/Rotten classification which summarizes whether the critic liked/disliked the movie.\n", "\n", "\n", "When critics give quantitative ratings (say 3/4 stars, Thumbs up, etc.), determining the Fresh/Rotten classification is easy. However, publications like the New York Times don't assign numerical ratings to movies, and thus the Fresh/Rotten classification must be inferred from the text of the review itself.\n", "\n", "This basic task of categorizing text has many applications. All of the following questions boil down to text classification:\n", "\n", " * Is a movie review positive or negative?\n", " * Is an email spam, or not?\n", " * Is a comment on a blog discussion board appropriate, or not?\n", " * Is a tweet about your company positive, or not?\n", " \n", "\n", "Language is incredibly nuanced, and there is an entire field of computer science dedicated to the topic (Natural Language Processing). Nevertheless, we can construct basic language models using fairly straightforward techniques. \n", "\n", "## The Data\n", "\n", "You will be starting with a database of Movies, derived from the MovieLens dataset. This dataset includes information for about 10,000 movies, including the IMDB id for each movie. \n", "\n", "Your first task is to download Rotten Tomatoes reviews from 3000 of these movies, using the Rotten Tomatoes API (Application Programming Interface)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Working with Web APIs\n", "Web APIs are a more convenient way for programs to interact with websites. Rotten Tomatoes has a nice API that gives access to its data in JSON format.\n", "\n", "To use this, you will first need to [register for an API key](http://developer.rottentomatoes.com/member/register). For \"application URL\", you can use anything -- it doesn't matter.\n", "\n", "After you have a key, the [documentation page](http://developer.rottentomatoes.com/iodocs) shows the various data you can fetch from Rotten Tomatoes -- each type of data lives at a different web address. The basic pattern for fetching this data with Python is as follows (compare this to the `Movie Reviews` tab on the documentation page):" ] }, { "cell_type": "code", "collapsed": false, "input": [ "api_key = 'jcquk524tumhccdenfy9pt7v'\n", "movie_id = '770672122' # toy story 3\n", "url = 'http://api.rottentomatoes.com/api/public/v1.0/movies/%s/reviews.json' % movie_id\n", "\n", "#these are \"get parameters\"\n", "options = {'review_type': 'top_critic', 'page_limit': 20, 'page': 1, 'apikey': api_key}\n", "data = requests.get(url, params=options).text\n", "data = json.loads(data) # load a json string into a collection of lists and dicts\n", "\n", "print json.dumps(data['reviews'][0], indent=2) # dump an object into a json string" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "{\n", " \"publication\": \"Village Voice\", \n", " \"links\": {\n", " \"review\": \"http://www.villagevoice.com/2010-06-15/film/toys-are-us-in-toy-story-3/full/\"\n", " }, \n", " \"quote\": \"When teenaged Andy plops down on the grass to share his old toys with a shy little girl, the film spikes with sadness and layered pleasure -- a concise, deeply wise expression of the ephemeral that feels real and yet utterly transporting.\", \n", " \"freshness\": \"fresh\", \n", " \"critic\": \"Eric Hynes\", \n", " \"date\": \"2013-08-04\"\n", "}\n" ] } ], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Part 1: Get the data\n", "Here's a chunk of the MovieLens Dataset:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from io import StringIO \n", "movie_txt = requests.get('https://raw.github.com/cs109/cs109_data/master/movies.dat').text\n", "movie_file = StringIO(movie_txt) # treat a string like a file\n", "movies = pd.read_csv(movie_file, delimiter='\\t')\n", "\n", "#print the first row\n", "movies[['id', 'title', 'imdbID', 'year']].irow(0)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 4, "text": [ "id 1\n", "title Toy story\n", "imdbID 114709\n", "year 1995\n", "Name: 0, dtype: object" ] } ], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### P1.1\n", "\n", "We'd like you to write a function that looks up the first 20 Top Critic Rotten Tomatoes reviews for a movie in the `movies` dataframe. This involves two steps:\n", "\n", "1. Use the `Movie Alias` API to look up the Rotten Tomatoes movie id from the IMDB id\n", "1. Use the `Movie Reviews` API to fetch the first 20 top-critic reviews for this movie\n", "\n", "Not all movies have Rotten Tomatoes IDs. In these cases, your function should return `None`. The detailed spec is below. We are giving you some freedom with how you implement this, but you'll probably want to break this task up into several small functions.\n", "\n", "**Hint**\n", "In some situations, the leading 0s in front of IMDB ids are important. IMDB ids have 7 digits" ] }, { "cell_type": "code", "collapsed": true, "input": [ "\"\"\"\n", "Function\n", "--------\n", "fetch_reviews(movies, row)\n", "\n", "Use the Rotten Tomatoes web API to fetch reviews for a particular movie\n", "\n", "Parameters\n", "----------\n", "movies : DataFrame \n", " The movies data above\n", "row : int\n", " The row of the movies DataFrame to use\n", " \n", "Returns\n", "-------\n", "If you can match the IMDB id to a Rotten Tomatoes ID:\n", " A DataFrame, containing the first 20 Top Critic reviews \n", " for the movie. If a movie has less than 20 total reviews, return them all.\n", " This should have the following columns:\n", " critic : Name of the critic\n", " fresh : 'fresh' or 'rotten'\n", " imdb : IMDB id for the movie\n", " publication: Publication that the critic writes for\n", " quote : string containing the movie review quote\n", " review_data: Date of review\n", " rtid : Rotten Tomatoes ID for the movie\n", " title : Name of the movie\n", " \n", "If you cannot match the IMDB id to a Rotten Tomatoes ID, return None\n", "\n", "Examples\n", "--------\n", ">>> reviews = fetch_reviews(movies, 0)\n", ">>> print len(reviews)\n", "20\n", ">>> print reviews.irow(1)\n", "critic Derek Adams\n", "fresh fresh\n", "imdb 114709\n", "publication Time Out\n", "quote So ingenious in concept, design and execution ...\n", "review_date 2009-10-04\n", "rtid 9559\n", "title Toy story\n", "Name: 1, dtype: object\n", "\"\"\"\n", "#your code here\n", "\n", "\n", "def get_movie_id(imdb_id):\n", " url = 'http://api.rottentomatoes.com/api/public/v1.0/movie_alias.json'\n", " imdb_id = '%.7i' % imdb_id\n", " options = {'apikey': api_key, 'type': 'imdb', 'id': imdb_id}\n", " r = requests.get(url, params=options)\n", " if 'id' in r.json():\n", " return r.json()['id'], r.json()['title']\n", " else:\n", " return None, None\n", " \n", "def get_top_reviews(movie_id, n=20):\n", " url = 'http://api.rottentomatoes.com/api/public/v1.0/movies/%i/reviews.json' % movie_id\n", " options = {'apikey': api_key, 'page_limit': n, 'review_type': 'top_critic'}\n", " r = requests.get(url, params=options)\n", " return r.json()['reviews']\n", " \n", "def fetch_reviews(movies, row, n=20):\n", " imdb_id = movies.ix[row]['imdbID']\n", " rt_id, title = get_movie_id(imdb_id)\n", " if rt_id is not None:\n", " reviews = get_top_reviews(rt_id, n=n)\n", " \n", " df = pd.DataFrame(reviews)\n", " df = df.rename(columns={'freshness': 'fresh'})\n", " df['imdb'] = imdb_id\n", " df['rt_id'] = rt_id\n", " if 'links' in df.columns:\n", " df['review_date'] = df['links'].apply(lambda x: x[u'review'] if 'review' in x else None)\n", " del df['links']\n", " df['title'] = title\n", " return df\n", "\n", "fetch_reviews(movies, 0).irow(1)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 5, "text": [ "critic Derek Adams\n", "date 2009-10-04\n", "fresh fresh\n", "original_score 5/5\n", "publication Time Out\n", "quote So ingenious in concept, design and execution ...\n", "imdb 114709\n", "rt_id 9559\n", "review_date http://www.timeout.com/film/reviews/87745/toy-...\n", "title Toy Story\n", "Name: 1, dtype: object" ] } ], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### P1.2\n", "\n", "Use the function you wrote to retrieve reviews for the first 3,000 movies in the movies dataframe.\n", "\n", "##### Hints\n", "* Rotten Tomatoes limits you to **10,000 API requests a day**. Be careful about this limit! Test your code on smaller inputs before scaling. You are responsible if you hit the limit the day the assignment is due :)\n", "* This will take a while to download. If you don't want to re-run this function every time you restart the notebook, you can save and re-load this data as a CSV file. However, please don't submit this file" ] }, { "cell_type": "code", "collapsed": false, "input": [ "\"\"\"\n", "Function\n", "--------\n", "build_table\n", "\n", "Parameters\n", "----------\n", "movies : DataFrame\n", " The movies data above\n", "rows : int\n", " The number of rows to extract reviews for\n", " \n", "Returns\n", "--------\n", "A dataframe\n", " The data obtained by repeatedly calling `fetch_reviews` on the first `rows`\n", " of `movies`, discarding the `None`s,\n", " and concatenating the results into a single DataFrame\n", "\"\"\"\n", "#your code here\n", "\n", "def build_table(movies, rows):\n", " ans = pd.DataFrame()\n", " for i in range(rows):\n", " new_df = fetch_reviews(movies, i)\n", " ans = pd.concat((ans, new_df), ignore_index=True)\n", " return ans" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "#you can toggle which lines are commented, if you\n", "#want to re-load your results to avoid repeatedly calling this function\n", "\n", "# critics = build_table(movies, 3000)\n", "# critics.to_csv('data/critics.csv', index=False)\n", "critics = pd.read_csv('data/critics.csv')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "#for this assignment, let's drop rows with missing data\n", "critics = critics[~critics.quote.isnull()]\n", "critics = critics[critics.fresh != 'none']\n", "critics = critics[critics.quote.str.len() > 0]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "A quick sanity check that everything looks ok at this point" ] }, { "cell_type": "code", "collapsed": false, "input": [ "assert len(critics) > 10000" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Part 2: Explore\n", "\n", "Before delving into analysis, get a sense of what these data look like. Answer the following questions. Include your code!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**2.1** How many reviews, critics, and movies are in this dataset?\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "critics" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
\n",
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 15610 entries, 0 to 15609\n",
        "Data columns (total 10 columns):\n",
        "critic            14841  non-null values\n",
        "date              15610  non-null values\n",
        "fresh             15610  non-null values\n",
        "imdb              15610  non-null values\n",
        "original_score    7631  non-null values\n",
        "publication       15610  non-null values\n",
        "quote             15610  non-null values\n",
        "review_date       13150  non-null values\n",
        "rt_id             15610  non-null values\n",
        "title             15610  non-null values\n",
        "dtypes: int64(2), object(8)\n",
        "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 10, "text": [ "\n", "Int64Index: 15610 entries, 0 to 15609\n", "Data columns (total 10 columns):\n", "critic 14841 non-null values\n", "date 15610 non-null values\n", "fresh 15610 non-null values\n", "imdb 15610 non-null values\n", "original_score 7631 non-null values\n", "publication 15610 non-null values\n", "quote 15610 non-null values\n", "review_date 13150 non-null values\n", "rt_id 15610 non-null values\n", "title 15610 non-null values\n", "dtypes: int64(2), object(8)" ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "#your code here\n", "print 'Reviews', len(critics)\n", "print 'Critics', len(critics.critic.dropna().drop_duplicates())\n", "print 'Moviews', len(critics.title.dropna().drop_duplicates())" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Reviews 15610\n", "Critics 621\n", "Moviews 1931\n" ] } ], "prompt_number": 11 }, { "cell_type": "markdown", "metadata": {}, "source": [ "**2.2** What does the distribution of number of reviews per reviewer look like? Make a histogram" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#Your code here\n", "critics.groupby(by='critic').critic.count().hist(bins=range(20))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 13, "text": [ "" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "/Users/danielfrg/anaconda/envs/harvard-ds/lib/python2.7/site-packages/matplotlib/font_manager.py:1236: UserWarning: findfont: Font family ['serif'] not found. Falling back to Bitstream Vera Sans\n", " (prop.get_family(), self.defaultFamily[fontext]))\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 13 }, { "cell_type": "markdown", "metadata": {}, "source": [ "**2.3** List the 5 critics with the most reviews, along with the publication they write for" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#Your code here\n", "top_critics = critics.groupby(by=['critic', 'publication']).critic.count()\n", "top_critics.sort(ascending=False)\n", "top_critics.head(5)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 14, "text": [ "critic publication \n", "Roger Ebert Chicago Sun-Times 1078\n", "James Berardinelli ReelViews 806\n", "Janet Maslin New York Times 519\n", "Variety Staff Variety 434\n", "Jonathan Rosenbaum Chicago Reader 414\n", "dtype: int64" ] } ], "prompt_number": 14 }, { "cell_type": "markdown", "metadata": {}, "source": [ "**2.4** Of the critics with > 100 reviews, plot the distribution of average \"freshness\" rating per critic" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#Your code here\n", "critics['fresh_int'] = critics.fresh == 'fresh'\n", "gb_critic = critics.groupby(by='critic')\n", "n_reviews = gb_critic.critic.count()\n", "means = gb_critic.fresh_int.mean() \n", "\n", "means[n_reviews > 100].hist(bins=10)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 15, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 15 }, { "cell_type": "markdown", "metadata": {}, "source": [ "**2.5**\n", "Using the original `movies` dataframe, plot the rotten tomatoes Top Critics Rating as a function of year. Overplot the average for each year, ignoring the score=0 examples (some of these are missing data). Comment on the result -- is there a trend? What do you think it means?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#Your code here\n", "data = movies[['year', 'rtTopCriticsRating']]\n", "data = data.convert_objects(convert_numeric=True)\n", "\n", "ggplot(aes(x='year', y='rtTopCriticsRating'), data=data[data.rtTopCriticsRating > 0].sort('year')) + \\\n", " geom_point() + stat_smooth(color='red', se=True) " ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 16, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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49VcYnJ2hCg+Hqndv5s/wAPyo2qOvv/4aqampzLKDgwPefvttpjfRycnpobw1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PHHHzOJCYIg8Oijj3KCB71ej5UrV7K4jh07duT4oTe231KplOFIUxYLjKdOwef33+H1+++wKBTQ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WloZff/0V8+fPx+nTp7Fo0SJ06tQJrVu3Rnx8PAYNGgRLS0veGeU4DnZ2drCzs+OdR5FIBGtr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\nkIqNje3O5hERERFRN5L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"text": [ "" ] } ], "prompt_number": 16 }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Your Comment Here*\n", "\n", "New movies suck :P" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Part 3: Sentiment Analysis\n", "\n", "You will now use a [Naive Bayes classifier](https://en.wikipedia.org/wiki/Naive_Bayes_classifier) to build a prediction model for whether a review is fresh or rotten, depending on the text of the review. See Lecture 9 for a discussion of Naive Bayes.\n", "\n", "Most models work with numerical data, so we need to convert the textual collection of reviews to something numerical. A common strategy for text classification is to represent each review as a \"bag of words\" vector -- a long vector\n", "of numbers encoding how many times a particular word appears in a blurb.\n", "\n", "Scikit-learn has an object called a `CountVectorizer` that turns text into a bag of words. Here's a quick tutorial:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.feature_extraction.text import CountVectorizer\n", "\n", "text = ['Hop on pop', 'Hop off pop', 'Hop Hop hop']\n", "print \"Original text is\\n\", '\\n'.join(text)\n", "\n", "vectorizer = CountVectorizer(min_df=0)\n", "\n", "# call `fit` to build the vocabulary\n", "vectorizer.fit(text)\n", "\n", "# call `transform` to convert text to a bag of words\n", "x = vectorizer.transform(text)\n", "\n", "# CountVectorizer uses a sparse array to save memory, but it's easier in this assignment to \n", "# convert back to a \"normal\" numpy array\n", "x = x.toarray()\n", "\n", "print\n", "print \"Transformed text vector is \\n\", x\n", "\n", "# `get_feature_names` tracks which word is associated with each column of the transformed x\n", "print\n", "print \"Words for each feature:\"\n", "print vectorizer.get_feature_names()\n", "\n", "# Notice that the bag of words treatment doesn't preserve information about the *order* of words, \n", "# just their frequency" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Original text is\n", "Hop on pop\n", "Hop off pop\n", "Hop Hop hop\n", "\n", "Transformed text vector is \n", "[[1 0 1 1]\n", " [1 1 0 1]\n", " [3 0 0 0]]\n", "\n", "Words for each feature:\n", "[u'hop', u'off', u'on', u'pop']\n" ] } ], "prompt_number": 17 }, { "cell_type": "markdown", "metadata": {}, "source": [ "**3.1**\n", "\n", "Using the `critics` dataframe, compute a pair of numerical X, Y arrays where:\n", " \n", " * X is a `(nreview, nwords)` array. Each row corresponds to a bag-of-words representation for a single review. This will be the *input* to your model.\n", " * Y is a `nreview`-element 1/0 array, encoding whether a review is Fresh (1) or Rotten (0). This is the desired *output* from your model.\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#hint: Consult the scikit-learn documentation to\n", "# learn about what these classes do do\n", "from sklearn.cross_validation import train_test_split\n", "from sklearn.naive_bayes import MultinomialNB\n", "\n", "\"\"\"\n", "Function\n", "--------\n", "make_xy\n", "\n", "Build a bag-of-words training set for the review data\n", "\n", "Parameters\n", "-----------\n", "critics : Pandas DataFrame\n", " The review data from above\n", " \n", "vectorizer : CountVectorizer object (optional)\n", " A CountVectorizer object to use. If None,\n", " then create and fit a new CountVectorizer.\n", " Otherwise, re-fit the provided CountVectorizer\n", " using the critics data\n", " \n", "Returns\n", "-------\n", "X : numpy array (dims: nreview, nwords)\n", " Bag-of-words representation for each review.\n", "Y : numpy array (dims: nreview)\n", " 1/0 array. 1 = fresh review, 0 = rotten review\n", "\n", "Examples\n", "--------\n", "X, Y = make_xy(critics)\n", "\"\"\"\n", "def make_xy(critics, vectorizer=None):\n", " #Your code here \n", " if vectorizer is None:\n", " vectorizer = CountVectorizer()\n", " X = vectorizer.fit_transform(critics.quote)\n", " Y = (critics.fresh == 'fresh').values.astype(np.int)\n", " return X, Y" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 38 }, { "cell_type": "code", "collapsed": false, "input": [ "X, y = make_xy(critics)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 42 }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "**3.2** Next, randomly split the data into two groups: a\n", "training set and a validation set. \n", "\n", "Use the training set to train a `MultinomialNB` classifier,\n", "and print the accuracy of this model on the validation set\n", "\n", "**Hint**\n", "You can use [`train_test_split`](http://scikit-learn.org/stable/modules/generated/sklearn.cross_validation.train_test_split.html) to split up the training data" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#Your code here\n", "from sklearn.cross_validation import train_test_split\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1234)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 43 }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.naive_bayes import MultinomialNB\n", "clf = MultinomialNB()\n", "clf.fit(X_train, y_train)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 44, "text": [ "MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)" ] } ], "prompt_number": 44 }, { "cell_type": "code", "collapsed": false, "input": [ "clf.score(X_test, y_test)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 45, "text": [ "0.77770659833440103" ] } ], "prompt_number": 45 }, { "cell_type": "markdown", "metadata": {}, "source": [ "**3.3:**\n", "\n", "We say a model is **overfit** if it performs better on the training data than on the test data. Is this model overfit? If so, how much more accurate is the model on the training data compared to the test data?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Your code here. Print the accuracy on the test and training dataset\n", "clf.score(X_train, y_train)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 46, "text": [ "0.9204722247643452" ] } ], "prompt_number": 46 }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Interpret these numbers in a few sentences here*\n", "\n", "Yes, the model is overfitting because the accuracy on the trainig set is considerably higher than on the validation set." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**3.4: Model Calibration**\n", "\n", "Bayesian models like the Naive Bayes classifier have the nice property that they compute probabilities of a particular classification -- the `predict_proba` and `predict_log_proba` methods of `MultinomialNB` compute these probabilities. \n", "\n", "Being the respectable Bayesian that you are, you should always assess whether these probabilities are **calibrated** -- that is, whether a prediction made with a confidence of `x%` is correct approximately `x%` of the time. We care about calibration because it tells us whether we can trust the probabilities computed by a model. If we can trust model probabilities, we can make better decisions using them (for example, we can calculate how much we should bet or invest in a given prediction).\n", "\n", "Let's make a plot to assess model calibration. Schematically, we want something like this:\n", "\n", "\n", "\n", "In words, we want to:\n", "\n", "* Take a collection of examples, and compute the freshness probability for each using `clf.predict_proba`\n", "* Gather examples into bins of similar freshness probability (the diagram shows 5 groups -- you should use something closer to 20)\n", "* For each bin, count the number of examples in that bin, and compute the fraction of examples in the bin which are fresh\n", "* In the upper plot, graph the expected P(Fresh) (x axis) and observed freshness fraction (Y axis). Estimate the uncertainty in observed freshness fraction $F$ via the [equation](http://en.wikipedia.org/wiki/Binomial_proportion_confidence_interval) $\\sigma = \\sqrt{F (1-F) / N}$\n", "* Overplot the line y=x. This is the trend we would expect if the model is calibrated\n", "* In the lower plot, show the number of examples in each bin\n", "\n", "**Hints**\n", "\n", "The output of `clf.predict_proba(X)` is a `(N example, 2)` array. The first column gives the probability $P(Y=0)$ or $P(Rotten)$, and the second gives $P(Y=1)$ or $P(Fresh)$.\n", "\n", "The above image is just a guideline -- feel free to explore other options!" ] }, { "cell_type": "code", "collapsed": false, "input": [ "\"\"\"\n", "Function\n", "--------\n", "calibration_plot\n", "\n", "Builds a plot like the one above, from a classifier and review data\n", "\n", "Inputs\n", "-------\n", "clf : Classifier object\n", " A MultinomialNB classifier\n", "X : (Nexample, Nfeature) array\n", " The bag-of-words data\n", "Y : (Nexample) integer array\n", " 1 if a review is Fresh\n", "\"\"\" \n", "#your code here\n", "\n", "def calibration_plot(clf, X, y):\n", " proba = clf.predict_proba(X)[:, 1]\n", " data = pd.DataFrame({'proba': proba, 'fresh': y})\n", " \n", " bins = np.linspace(0, 1, 20)\n", " bin_center = (bins[:-1] + bins[1:]) / 2\n", " cuts = pd.cut(proba, bins)\n", " \n", " calibration = data.groupby(cuts).fresh.agg(['mean', 'count'])\n", " calibration['bin_center'] = bin_center\n", " calibration['std'] = np.sqrt(calibration.bin_center * (1 - calibration.bin_center) / calibration['count'])\n", "\n", " print ggplot(aes(x='bin_center', y='mean'), data=calibration) + \\\n", " geom_point() + xlim(0, 1) + ylim(0, 1) + stat_smooth(color='red', se=True, span=0.25)\n", " \n", " print ggplot(aes(x='proba'), data=data) + geom_histogram(binwidth=0.02) + xlim(0, 1)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 115 }, { "cell_type": "code", "collapsed": false, "input": [ "calibration_plot(clf, X_test, y_test)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\n", "" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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vMWTIkNwfTNPgmj0bnn/8Ax1TpiB29tm5P0aFUBQFJpMJXq+XE59yqDI/BYiI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\nO3fuRGVlJUaPHt2T8ShJEjnPx48fx9GjRzFq1KhUxaMkSuRcNzU14cs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"text": [ "" ] } ], "prompt_number": 117 }, { "cell_type": "markdown", "metadata": {}, "source": [ "**3.5** We might say a model is *over-confident* if the freshness fraction is usually closer to 0.5 than expected (that is, there is more uncertainty than the model predicted). Likewise, a model is *under-confident* if the probabilities are usually further away from 0.5. Is this model generally over- or under-confident? " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Your Answer Here*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Cross Validation\n", "\n", "Our classifier has a few free parameters. The two most important are:\n", "\n", " 1. The `min_df` keyword in `CountVectorizer`, which will ignore words which appear in fewer than `min_df` fraction of reviews. Words that appear only once or twice can lead to overfitting, since words which occur only a few times might correlate very well with Fresh/Rotten reviews by chance in the training dataset.\n", " \n", " 2. The [`alpha` keyword](http://scikit-learn.org/stable/modules/naive_bayes.html#multinomial-naive-bayes) in the Bayesian classifier is a \"smoothing parameter\" -- increasing the value decreases the sensitivity to any single feature, and tends to pull prediction probabilities closer to 50%. \n", "\n", "As discussed in lecture and HW2, a common technique for choosing appropriate values for these parameters is **cross-validation**. Let's choose good parameters by maximizing the cross-validated log-likelihood.\n", "\n", "**3.6** Using `clf.predict_log_proba`, write a function that computes the log-likelihood of a dataset" ] }, { "cell_type": "code", "collapsed": false, "input": [ "\"\"\"\n", "Function\n", "--------\n", "log_likelihood\n", "\n", "Compute the log likelihood of a dataset according to a bayesian classifier. \n", "The Log Likelihood is defined by\n", "\n", "L = Sum_fresh(logP(fresh)) + Sum_rotten(logP(rotten))\n", "\n", "Where Sum_fresh indicates a sum over all fresh reviews, \n", "and Sum_rotten indicates a sum over rotten reviews\n", " \n", "Parameters\n", "----------\n", "clf : Bayesian classifier\n", "x : (nexample, nfeature) array\n", " The input data\n", "y : (nexample) integer array\n", " Whether each review is Fresh\n", "\"\"\"\n", "#your code here\n", "\n", "def log_likelihood(clf, x, y):\n", " prob = clf.predict_log_proba(x)\n", " fresh = y == 1\n", " return prob[fresh, 1].sum() + prob[~fresh, 0].sum()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 120 }, { "cell_type": "code", "collapsed": false, "input": [ "log_likelihood(clf, X_test, y_test)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 121, "text": [ "-2582.9773078870385" ] } ], "prompt_number": 121 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here's a function to estimate the cross-validated value of a scoring function, given a classifier and data" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.cross_validation import KFold\n", "\n", "def cv_score(clf, x, y, score_func):\n", " \"\"\"\n", " Uses 5-fold cross validation to estimate a score of a classifier\n", " \n", " Inputs\n", " ------\n", " clf : Classifier object\n", " x : Input feature vector\n", " y : Input class labels\n", " score_func : Function like log_likelihood, that takes (clf, x, y) as input,\n", " and returns a score\n", " \n", " Returns\n", " -------\n", " The average score obtained by randomly splitting (x, y) into training and \n", " test sets, fitting on the training set, and evaluating score_func on the test set\n", " \n", " Examples\n", " cv_score(clf, x, y, log_likelihood)\n", " \"\"\"\n", " result = 0\n", " nfold = 5\n", " for train, test in KFold(y.size, nfold): # split data into train/test groups, 5 times\n", " clf.fit(x[train], y[train]) # fit\n", " result += score_func(clf, x[test], y[test]) # evaluate score function on held-out data\n", " return result / nfold # average\n", "\n", "# as a side note, this function is builtin to the newest version of sklearn. We could just write\n", "# sklearn.cross_validation.cross_val_score(clf, x, y, scorer=log_likelihood)." ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 122 }, { "cell_type": "markdown", "metadata": {}, "source": [ "**3.7**\n", "\n", "Fill in the remaining code in this block, to loop over many values of `alpha` and `min_df` to determine\n", "which settings are \"best\" in the sense of maximizing the cross-validated log-likelihood" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#the grid of parameters to search over\n", "alphas = [0, .1, 1, 5, 10, 50]\n", "min_dfs = [1e-5, 1e-4, 1e-3, 1e-2, 1e-1]\n", "\n", "#Find the best value for alpha and min_df, and the best classifier\n", "best_alpha = None\n", "best_min_df = None\n", "max_loglike = -np.inf\n", "\n", "for alpha in alphas:\n", " for min_df in min_dfs: \n", " vectorizer = CountVectorizer(min_df = min_df) \n", " X, Y = make_xy(critics, vectorizer)\n", " \n", " #your code here\n", " clf = MultinomialNB(alpha=alpha)\n", " loglike = cv_score(clf, X, Y, log_likelihood)\n", "\n", " if loglike > max_loglike:\n", " max_loglike = loglike\n", " best_alpha, best_min_df = alpha, min_df" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 123 }, { "cell_type": "code", "collapsed": false, "input": [ "print \"alpha: %f\" % best_alpha\n", "print \"min_df: %f\" % best_min_df" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "alpha: 5.000000\n", "min_df: 0.001000\n" ] } ], "prompt_number": 124 }, { "cell_type": "markdown", "metadata": {}, "source": [ "**3.8** Now that you've determined values for alpha and min_df that optimize the cross-validated log-likelihood, repeat the steps in 3.1, 3.2, and 3.4 to train a final classifier with these parameters, re-evaluate the accuracy, and draw a new calibration plot." ] }, { "cell_type": "code", "collapsed": false, "input": [ "#Your code here\n", "\n", "vectorizer = CountVectorizer(min_df=best_min_df)\n", "X, y = make_xy(critics, vectorizer)\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1234)\n", "\n", "clf = MultinomialNB(alpha=best_alpha).fit(X_train, y_train)\n", "\n", "calibration_plot(clf, X_test, y_test)\n", "\n", "training_accuracy = clf.score(X_train, y_train)\n", "test_accuracy = clf.score(X_test, y_test)\n", "\n", "training_accuracy, test_accuracy" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\n", "" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 126, "text": [ "(0.78255696897593119, 0.74781123211616485)" ] }, { "metadata": {}, "output_type": "display_data", "png": 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Ezj9f7YooBzCcEhERHYdYLIZgMMitSNtJH42i8pFHYKqtxe6FCyF266Z2SZQjshpO4/E4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\noV5++WXddtttbdccPu2/fv16rV27Vq+88or+8Ic/6OOPP9Ytt9zSoY9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"text": [ "" ] } ], "prompt_number": 126 }, { "cell_type": "markdown", "metadata": {}, "source": [ "**3.9** Discuss the various ways in which Cross-Validation has affected the model. Is the new model more or less accurate? Is overfitting better or worse? Is the model more or less calibrated?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Your Answer Here*\n", "\n", "The grid search using crossvalidation gave better parameters and the overfitting was reduced a lot.\n", "\n", "The calibration is also better." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*To think about/play with, but not to hand in: What would happen if you tried this again using a function besides the log-likelihood -- for example, the classification accuracy?*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Part 4: Interpretation. What words best predict a fresh or rotten review?\n", "\n", "**4.1**\n", "Using your classifier and the `vectorizer.get_feature_names` method, determine which words best predict a positive or negative review. Print the 10 words\n", "that best predict a \"fresh\" review, and the 10 words that best predict a \"rotten\" review. For each word, what is the model's probability of freshness if the word appears one time?\n", "\n", "#### Hints\n", "\n", "* Try computing the classification probability for a feature vector which consists of all 0s, except for a single 1. What does this probability refer to?\n", "\n", "* `np.eye` generates a matrix where the ith row is all 0s, except for the ith column which is 1." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Your code here\n", "\n", "all_words = np.array(vectorizer.get_feature_names())\n", "\n", "x = np.eye(X_test.shape[1])\n", "probas = clf.predict_log_proba(x)[:, 0]\n", "idx = np.argsort(probas)\n", "\n", "good_words = pd.Series(1 - np.exp(probas[idx[:10]]), index=all_words[idx[:10]])\n", "bad_words = pd.Series(1 - np.exp(probas[idx[-10:]]), index=all_words[idx[-10:]])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 148 }, { "cell_type": "code", "collapsed": false, "input": [ "good_words" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 149, "text": [ "delight 0.902645\n", "masterpiece 0.881520\n", "touching 0.873689\n", "entertaining 0.866424\n", "finest 0.863998\n", "witty 0.860746\n", "remarkable 0.857335\n", "superb 0.849986\n", "moving 0.848029\n", "captures 0.847631\n", "dtype: float64" ] } ], "prompt_number": 149 }, { "cell_type": "code", "collapsed": false, "input": [ "bad_words" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 150, "text": [ "dull 0.229352\n", "tedious 0.227405\n", "disappointment 0.227405\n", "unfunny 0.227405\n", "problem 0.215512\n", "bland 0.199145\n", "uninspired 0.189715\n", "pointless 0.170839\n", "unfortunately 0.163110\n", "lame 0.142484\n", "dtype: float64" ] } ], "prompt_number": 150 }, { "cell_type": "markdown", "metadata": {}, "source": [ "**4.2**\n", "\n", "One of the best sources for inspiration when trying to improve a model is to look at examples where the model performs poorly. \n", "\n", "Find 5 fresh and rotten reviews where your model performs particularly poorly. Print each review." ] }, { "cell_type": "code", "collapsed": false, "input": [ "#Your code here\n", "X, y = make_xy(critics, vectorizer)\n", "\n", "probas = clf.predict_proba(X)[:, 1]\n", "predict = clf.predict(X)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 190 }, { "cell_type": "code", "collapsed": false, "input": [ "bad_fresh = np.argsort(prob)[(y == 1) & (predict == 0)]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 218 }, { "cell_type": "code", "collapsed": false, "input": [ "data = critics[['title', 'fresh', 'quote']]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 239 }, { "cell_type": "code", "collapsed": false, "input": [ "data['prediction'] = predict\n", "data['probas'] = probas" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 240 }, { "cell_type": "code", "collapsed": false, "input": [ "bad_fresh = data.sort('probas')[(data.fresh == 'fresh') & (data.prediction == 0)]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 243 }, { "cell_type": "code", "collapsed": false, "input": [ "for index, row in bad_fresh[:5].iterrows():\n", " print \"Movie: %s | Correct: %s | Prediction: %i(%f)\" % (row['title'], row['fresh'], row['prediction'], row['probas'])\n", " print row['quote']\n", " print" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Movie: Young Frankenstein | Correct: fresh | Prediction: 0(0.001063)\n", "Some of the gags don't work, but fewer than in any previous Brooks film that I've seen, and when the jokes are meant to be bad, they are riotously poor. What more can one ask of Mel Brooks?\n", "\n", "Movie: The Fugitive | Correct: fresh | Prediction: 0(0.001567)\n", "Though it's a good half hour too long, this overblown 1993 spin-off of the 60s TV show otherwise adds up to a pretty good suspense thriller.\n", "\n", "Movie: Little Big Man | Correct: fresh | Prediction: 0(0.002598)\n", "Might it be a serious attempt to right some unretrievable wrong via gallows humor which avoids the polemics? This seems to be the course taken; the attempt at least can be respected in theory.\n", "\n", "Movie: Charlotte's Web | Correct: fresh | Prediction: 0(0.003695)\n", "There's too much talent and too strong a story to mess it up. There was potential for more here, but this incarnation is nothing to be ashamed of, and some of the actors answer the bell.\n", "\n", "Movie: Bad Boys | Correct: fresh | Prediction: 0(0.009323)\n", "A good half-hour's worth of nonsense in the middle keeps Bad Boys from being little better than a break- even proposition.\n", "\n" ] } ], "prompt_number": 245 }, { "cell_type": "code", "collapsed": false, "input": [ "bad_rotten = data.sort('probas')[(data.fresh == 'rotten') & (data.prediction == 1)]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 249 }, { "cell_type": "code", "collapsed": false, "input": [ "for index, row in bad_rotten[:5].iterrows():\n", " print \"Movie: %s | Correct: %s | Prediction: %i(%f)\" % (row['title'], row['fresh'], row['prediction'], row['probas'])\n", " print row['quote']\n", " print" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Movie: Valkyrie | Correct: rotten | Prediction: 1(0.500475)\n", "What you miss in both Defiance and Valkyrie is inner conflict. Their protagonists have not an instant of self-doubt. They're figures in historical pageants, not characters in a drama.\n", "\n", "Movie: The Adventures of Buckaroo Banzai Across the 8th Dimension | Correct: rotten | Prediction: 1(0.500613)\n", "It violates every rule of storytelling and narrative structure in creating a self-contained world of its own.\n", "\n", "Movie: The Horse Whisperer | Correct: rotten | Prediction: 1(0.500899)\n", "This has loads of craft and honor but never quite takes off.\n", "\n", "Movie: Home for the Holidays | Correct: rotten | Prediction: 1(0.501135)\n", "With many of the conversations going on simultaneously, it's difficult -- sometimes even impossible -- to know who is saying what and to whom.\n", "\n", "Movie: Star Wars: Episode VI - Return of the Jedi | Correct: rotten | Prediction: 1(0.501448)\n", "Let's not pretend we're watching art!\n", "\n" ] } ], "prompt_number": 250 }, { "cell_type": "markdown", "metadata": {}, "source": [ "**4.3** What do you notice about these mis-predictions? Naive Bayes classifiers assume that every word affects the probability independently of other words. In what way is this a bad assumption? In your answer, report your classifier's Freshness probability for the review \"This movie is not remarkable, touching, or superb in any way\"." ] }, { "cell_type": "code", "collapsed": false, "input": [ "clf.predict_proba(vectorizer.transform(['This movie is not remarkable, touching, or superb in any way']))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 248, "text": [ "array([[ 0.01948186, 0.98051814]])" ] } ], "prompt_number": 248 }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Your answer here*\n", "\n", "Words like 'but' are used to negate the next words but the classifier does not pick those the negation only single words.\n", "The previous example shows this." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**4.4**\n", "If this was your final project, what are 3 things you would try in order to build a more effective review classifier? What other exploratory or explanatory visualizations do you think might be helpful?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Your answer here*\n", "\n", "Nice visualization: A tag cloud with the tod words to predict fress vs rotten.\n", "\n", "Improvements:\n", "\n", "- Use N-grams to take into account interaction between words.\n", "- Use more features besides only the quote." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### How to Submit\n", "\n", "Restart and run your notebook one last time, to make sure the output from each cell is up to date. To submit your homework, create a folder named lastname_firstinitial_hw3 and place your solutions in the folder. Double check that the file is still called HW3.ipynb, and that it contains your code. Please do **not** include the critics.csv data file, if you created one. Compress the folder (please use .zip compression) and submit to the CS109 dropbox in the appropriate folder. If we cannot access your work because these directions are not followed correctly, we will not grade your work!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "*css tweaks in this cell*\n", "" ] } ], "metadata": {} } ] }