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Chapter 4
We will sample our dataset to form a training dataset. This also helps reduce
the size of the dataset that will be searched, making the Apriori algorithm run faster.
We obtain all reviews from the first 200 users:
ratings = all_ratings[all_ratings['UserID'].isin(range(200))]
Next, we can create a dataset of only the favorable reviews in our sample:
favorable_ratings = ratings[ratings["Favorable"]]
We will be searching the user's favorable reviews for our itemsets. So, the next thing
we need is the movies which each user has given a favorable. We can compute this
by grouping the dataset by the User ID and iterating over the movies in each group:
favorable_reviews_by_users = dict((k, frozenset(v.values))
for k, v in favorable_ratings
groupby("UserID")["MovieID"])
In the preceding code, we stored the values as a frozenset, allowing us to quickly
check if a movie has been rated by a user. Sets are much faster than lists for this type
of operation, and we will use them in a later code.
Finally, we can create a DataFrame that tells us how frequently each movie has been
given a favorable review:
num_favorable_by_movie = ratings[["MovieID", "Favorable"]].
groupby("MovieID").sum()
We can see the top five movies by running the following code:
num_favorable_by_movie.sort("Favorable", ascending=False)[:5]
Let's see the top five movies list:
MovieID Favorable
50 100
100 89
258 83
181 79
174 74
[ 67 ]