version 1.0.2

`collect()`

on any datasets. When you are using a small dataset, calling `collect()`

and then using Python to get a sense for the data locally (in the driver program) will work fine, but this will not work when you are using a large dataset that doesn't fit in memory on one machine. Solutions that call `collect()`

and do local analysis that could have been done with Spark will likely fail in the autograder and not receive full credit.¶In [2]:

```
import sys
import os
from test_helper import Test
baseDir = os.path.join('data')
inputPath = os.path.join('cs100', 'lab4', 'small')
ratingsFilename = os.path.join(baseDir, inputPath, 'ratings.dat.gz')
moviesFilename = os.path.join(baseDir, inputPath, 'movies.dat')
```

`ratings.dat.gz`

) is formatted as:¶`UserID::MovieID::Rating::Timestamp`

¶`movies.dat`

) dataset is formatted as:¶`MovieID::Title::Genres`

¶`Genres`

field has the format¶`Genres1|Genres2|Genres3|...`

¶`split()`

to parse their lines.¶- #### For each line in the ratings dataset, we create a tuple of (UserID, MovieID, Rating). We drop the timestamp because we do not need it for this exercise.
- #### For each line in the movies dataset, we create a tuple of (MovieID, Title). We drop the Genres because we do not need them for this exercise.

In [3]:

```
numPartitions = 2
rawRatings = sc.textFile(ratingsFilename).repartition(numPartitions)
rawMovies = sc.textFile(moviesFilename)
def get_ratings_tuple(entry):
""" Parse a line in the ratings dataset
Args:
entry (str): a line in the ratings dataset in the form of UserID::MovieID::Rating::Timestamp
Returns:
tuple: (UserID, MovieID, Rating)
"""
items = entry.split('::')
return int(items[0]), int(items[1]), float(items[2])
def get_movie_tuple(entry):
""" Parse a line in the movies dataset
Args:
entry (str): a line in the movies dataset in the form of MovieID::Title::Genres
Returns:
tuple: (MovieID, Title)
"""
items = entry.split('::')
return int(items[0]), items[1]
ratingsRDD = rawRatings.map(get_ratings_tuple).cache()
moviesRDD = rawMovies.map(get_movie_tuple).cache()
ratingsCount = ratingsRDD.count()
moviesCount = moviesRDD.count()
print 'There are %s ratings and %s movies in the datasets' % (ratingsCount, moviesCount)
print 'Ratings: %s' % ratingsRDD.take(3)
print 'Movies: %s' % moviesRDD.take(3)
assert ratingsCount == 487650
assert moviesCount == 3883
assert moviesRDD.filter(lambda (id, title): title == 'Toy Story (1995)').count() == 1
assert (ratingsRDD.takeOrdered(1, key=lambda (user, movie, rating): movie)
== [(1, 1, 5.0)])
```

`sortByKey()`

method. However this choice is problematic, as we can still end up with different results if the key is not unique.¶`unicode`

type instead of the `string`

type as the titles are in unicode characters.¶In [5]:

```
tmp1 = [(1, u'alpha'), (2, u'alpha'), (2, u'beta'), (3, u'alpha'), (1, u'epsilon'), (1, u'delta')]
tmp2 = [(1, u'delta'), (2, u'alpha'), (2, u'beta'), (3, u'alpha'), (1, u'epsilon'), (1, u'alpha')]
oneRDD = sc.parallelize(tmp1)
twoRDD = sc.parallelize(tmp2)
oneSorted = oneRDD.sortByKey(True).collect()
twoSorted = twoRDD.sortByKey(True).collect()
print oneSorted
print twoSorted
assert set(oneSorted) == set(twoSorted) # Note that both lists have the same elements
assert twoSorted[0][0] < twoSorted.pop()[0] # Check that it is sorted by the keys
assert oneSorted[0:2] != twoSorted[0:2] # Note that the subset consisting of the first two elements does not match
```

`take(2)`

), then we would observe different answers - `unicode('%.3f' % key) + ' ' + value`

) before sorting the RDD using sortBy().¶In [6]:

```
def sortFunction(tuple):
""" Construct the sort string (does not perform actual sorting)
Args:
tuple: (rating, MovieName)
Returns:
sortString: the value to sort with, 'rating MovieName'
"""
key = unicode('%.3f' % tuple[0])
value = tuple[1]
return (key + ' ' + value)
print oneRDD.sortBy(sortFunction, True).collect()
print twoRDD.sortBy(sortFunction, True).collect()
```

`sortFunction`

we defined.¶In [7]:

```
oneSorted1 = oneRDD.takeOrdered(oneRDD.count(),key=sortFunction)
twoSorted1 = twoRDD.takeOrdered(twoRDD.count(),key=sortFunction)
print 'one is %s' % oneSorted1
print 'two is %s' % twoSorted1
assert oneSorted1 == twoSorted1
```

`getCountsAndAverages()`

that takes a single tuple of (MovieID, (Rating1, Rating2, Rating3, ...)) and returns a tuple of (MovieID, (number of ratings, averageRating)). For example, given the tuple `(100, (10.0, 20.0, 30.0))`

, your function should return `(100, (3, 20.0))`

¶In [8]:

```
# TODO: Replace <FILL IN> with appropriate code
# First, implement a helper function `getCountsAndAverages` using only Python
def getCountsAndAverages(IDandRatingsTuple):
""" Calculate average rating
Args:
IDandRatingsTuple: a single tuple of (MovieID, (Rating1, Rating2, Rating3, ...))
Returns:
tuple: a tuple of (MovieID, (number of ratings, averageRating))
"""
count = len(IDandRatingsTuple[1])
avg = sum(IDandRatingsTuple[1])/float(count)
return (IDandRatingsTuple[0], (count, avg))
```

In [9]:

```
# TEST Number of Ratings and Average Ratings for a Movie (1a)
Test.assertEquals(getCountsAndAverages((1, (1, 2, 3, 4))), (1, (4, 2.5)),
'incorrect getCountsAndAverages() with integer list')
Test.assertEquals(getCountsAndAverages((100, (10.0, 20.0, 30.0))), (100, (3, 20.0)),
'incorrect getCountsAndAverages() with float list')
Test.assertEquals(getCountsAndAverages((110, xrange(20))), (110, (20, 9.5)),
'incorrect getCountsAndAverages() with xrange')
```

`getCountsAndAverages()`

helper function with Spark to determine movies with highest average ratings.¶- #### Recall that the
`ratingsRDD`

contains tuples of the form (UserID, MovieID, Rating). From`ratingsRDD`

create an RDD with tuples of the form (MovieID, Python iterable of Ratings for that MovieID). This transformation will yield an RDD of the form:`[(1, <pyspark.resultiterable.ResultIterable object at 0x7f16d50e7c90>), (2, <pyspark.resultiterable.ResultIterable object at 0x7f16d50e79d0>), (3, <pyspark.resultiterable.ResultIterable object at 0x7f16d50e7610>)]`

. Note that you will only need to perform two Spark transformations to do this step. - #### Using
`movieIDsWithRatingsRDD`

and your`getCountsAndAverages()`

helper function, compute the number of ratings and average rating for each movie to yield tuples of the form (MovieID, (number of ratings, average rating)). This transformation will yield an RDD of the form:`[(1, (993, 4.145015105740181)), (2, (332, 3.174698795180723)), (3, (299, 3.0468227424749164))]`

. You can do this step with one Spark transformation - #### We want to see movie names, instead of movie IDs. To
`moviesRDD`

, apply RDD transformations that use`movieIDsWithAvgRatingsRDD`

to get the movie names for`movieIDsWithAvgRatingsRDD`

, yielding tuples of the form (average rating, movie name, number of ratings). This set of transformations will yield an RDD of the form:`[(1.0, u'Autopsy (Macchie Solari) (1975)', 1), (1.0, u'Better Living (1998)', 1), (1.0, u'Big Squeeze, The (1996)', 3)]`

. You will need to do two Spark transformations to complete this step: first use the`moviesRDD`

with`movieIDsWithAvgRatingsRDD`

to create a new RDD with Movie names matched to Movie IDs, then convert that RDD into the form of (average rating, movie name, number of ratings). These transformations will yield an RDD that looks like:`[(3.6818181818181817, u'Happiest Millionaire, The (1967)', 22), (3.0468227424749164, u'Grumpier Old Men (1995)', 299), (2.882978723404255, u'Hocus Pocus (1993)', 94)]`

In [53]:

```
# TODO: Replace <FILL IN> with appropriate code
# From ratingsRDD with tuples of (UserID, MovieID, Rating) create an RDD with tuples of
# the (MovieID, iterable of Ratings for that MovieID)
movieIDsWithRatingsRDD = (ratingsRDD
.map(lambda (x, y, z): (y, z))
.groupByKey()
.mapValues(lambda x: list(x)))
print 'movieIDsWithRatingsRDD: %s\n' % movieIDsWithRatingsRDD.take(3)
# Using `movieIDsWithRatingsRDD`, compute the number of ratings and average rating for each movie to
# yield tuples of the form (MovieID, (number of ratings, average rating))
movieIDsWithAvgRatingsRDD = movieIDsWithRatingsRDD.map(getCountsAndAverages)
print 'movieIDsWithAvgRatingsRDD: %s\n' % movieIDsWithAvgRatingsRDD.take(3)
# To `movieIDsWithAvgRatingsRDD`, apply RDD transformations that use `moviesRDD` to get the movie
# names for `movieIDsWithAvgRatingsRDD`, yielding tuples of the form
# (average rating, movie name, number of ratings)
movieNameWithAvgRatingsRDD = (moviesRDD
.join(movieIDsWithAvgRatingsRDD)
.map(lambda (x, (y, z)): (z[1], y, z[0])))
print 'movieNameWithAvgRatingsRDD: %s\n' % movieNameWithAvgRatingsRDD.take(3)
```

In [22]:

```
# TEST Movies with Highest Average Ratings (1b)
Test.assertEquals(movieIDsWithRatingsRDD.count(), 3615,
'incorrect movieIDsWithRatingsRDD.count() (expected 3615)')
movieIDsWithRatingsTakeOrdered = movieIDsWithRatingsRDD.takeOrdered(3)
Test.assertTrue(movieIDsWithRatingsTakeOrdered[0][0] == 1 and
len(list(movieIDsWithRatingsTakeOrdered[0][1])) == 993,
'incorrect count of ratings for movieIDsWithRatingsTakeOrdered[0] (expected 993)')
Test.assertTrue(movieIDsWithRatingsTakeOrdered[1][0] == 2 and
len(list(movieIDsWithRatingsTakeOrdered[1][1])) == 332,
'incorrect count of ratings for movieIDsWithRatingsTakeOrdered[1] (expected 332)')
Test.assertTrue(movieIDsWithRatingsTakeOrdered[2][0] == 3 and
len(list(movieIDsWithRatingsTakeOrdered[2][1])) == 299,
'incorrect count of ratings for movieIDsWithRatingsTakeOrdered[2] (expected 299)')
Test.assertEquals(movieIDsWithAvgRatingsRDD.count(), 3615,
'incorrect movieIDsWithAvgRatingsRDD.count() (expected 3615)')
Test.assertEquals(movieIDsWithAvgRatingsRDD.takeOrdered(3),
[(1, (993, 4.145015105740181)), (2, (332, 3.174698795180723)),
(3, (299, 3.0468227424749164))],
'incorrect movieIDsWithAvgRatingsRDD.takeOrdered(3)')
Test.assertEquals(movieNameWithAvgRatingsRDD.count(), 3615,
'incorrect movieNameWithAvgRatingsRDD.count() (expected 3615)')
Test.assertEquals(movieNameWithAvgRatingsRDD.takeOrdered(3),
[(1.0, u'Autopsy (Macchie Solari) (1975)', 1), (1.0, u'Better Living (1998)', 1),
(1.0, u'Big Squeeze, The (1996)', 3)],
'incorrect movieNameWithAvgRatingsRDD.takeOrdered(3)')
```

`movieNameWithAvgRatingsRDD`

to limit the results to movies with ratings from more than 500 people. We then use the `sortFunction()`

helper function to sort by the average rating to get the movies in order of their rating (highest rating first). You will end up with an RDD of the form: `[(4.5349264705882355, u'Shawshank Redemption, The (1994)', 1088), (4.515798462852263, u"Schindler's List (1993)", 1171), (4.512893982808023, u'Godfather, The (1972)', 1047)]`

¶In [54]:

```
# TODO: Replace <FILL IN> with appropriate code
# Apply an RDD transformation to `movieNameWithAvgRatingsRDD` to limit the results to movies with
# ratings from more than 500 people. We then use the `sortFunction()` helper function to sort by the
# average rating to get the movies in order of their rating (highest rating first)
movieLimitedAndSortedByRatingRDD = (movieNameWithAvgRatingsRDD
.filter(lambda (x, y, z): z > 500)
.sortBy(sortFunction, False))
print 'Movies with highest ratings: %s' % movieLimitedAndSortedByRatingRDD.take(20)
```

In [26]:

```
# TEST Movies with Highest Average Ratings and more than 500 Reviews (1c)
Test.assertEquals(movieLimitedAndSortedByRatingRDD.count(), 194,
'incorrect movieLimitedAndSortedByRatingRDD.count()')
Test.assertEquals(movieLimitedAndSortedByRatingRDD.take(20),
[(4.5349264705882355, u'Shawshank Redemption, The (1994)', 1088),
(4.515798462852263, u"Schindler's List (1993)", 1171),
(4.512893982808023, u'Godfather, The (1972)', 1047),
(4.510460251046025, u'Raiders of the Lost Ark (1981)', 1195),
(4.505415162454874, u'Usual Suspects, The (1995)', 831),
(4.457256461232604, u'Rear Window (1954)', 503),
(4.45468509984639, u'Dr. Strangelove or: How I Learned to Stop Worrying and Love the Bomb (1963)', 651),
(4.43953006219765, u'Star Wars: Episode IV - A New Hope (1977)', 1447),
(4.4, u'Sixth Sense, The (1999)', 1110), (4.394285714285714, u'North by Northwest (1959)', 700),
(4.379506641366224, u'Citizen Kane (1941)', 527), (4.375, u'Casablanca (1942)', 776),
(4.363975155279503, u'Godfather: Part II, The (1974)', 805),
(4.358816276202219, u"One Flew Over the Cuckoo's Nest (1975)", 811),
(4.358173076923077, u'Silence of the Lambs, The (1991)', 1248),
(4.335826477187734, u'Saving Private Ryan (1998)', 1337),
(4.326241134751773, u'Chinatown (1974)', 564),
(4.325383304940375, u'Life Is Beautiful (La Vita \ufffd bella) (1997)', 587),
(4.324110671936759, u'Monty Python and the Holy Grail (1974)', 759),
(4.3096, u'Matrix, The (1999)', 1250)], 'incorrect sortedByRatingRDD.take(20)')
```

`ratingsRDD`

dataset into three pieces:¶- #### A training set (RDD), which we will use to train models
- #### A validation set (RDD), which we will use to choose the best model
- #### A test set (RDD), which we will use for our experiments
#### To randomly split the dataset into the multiple groups, we can use the pySpark randomSplit() transformation.
`randomSplit()`

takes a set of splits and and seed and returns multiple RDDs.

In [27]:

```
trainingRDD, validationRDD, testRDD = ratingsRDD.randomSplit([6, 2, 2], seed=0L)
print 'Training: %s, validation: %s, test: %s\n' % (trainingRDD.count(),
validationRDD.count(),
testRDD.count())
print trainingRDD.take(3)
print validationRDD.take(3)
print testRDD.take(3)
assert trainingRDD.count() == 292716
assert validationRDD.count() == 96902
assert testRDD.count() == 98032
assert trainingRDD.filter(lambda t: t == (1, 914, 3.0)).count() == 1
assert trainingRDD.filter(lambda t: t == (1, 2355, 5.0)).count() == 1
assert trainingRDD.filter(lambda t: t == (1, 595, 5.0)).count() == 1
assert validationRDD.filter(lambda t: t == (1, 1287, 5.0)).count() == 1
assert validationRDD.filter(lambda t: t == (1, 594, 4.0)).count() == 1
assert validationRDD.filter(lambda t: t == (1, 1270, 5.0)).count() == 1
assert testRDD.filter(lambda t: t == (1, 1193, 5.0)).count() == 1
assert testRDD.filter(lambda t: t == (1, 2398, 4.0)).count() == 1
assert testRDD.filter(lambda t: t == (1, 1035, 5.0)).count() == 1
```

`randomSplit()`

transformation.¶`(actual rating - predicted rating)`

for all users and movies for which we have the actual rating. Versions of Spark MLlib beginning with Spark 1.4 include a RegressionMetrics modiule that can be used to compute the RMSE. However, since we are using Spark 1.3.1, we will write our own function.¶`predictedRDD`

and `actualRDD`

RDDs. Both RDDs consist of tuples of the form (UserID, MovieID, Rating)¶- #### Transform
`predictedRDD`

into the tuples of the form ((UserID, MovieID), Rating). For example, tuples like`[((1, 1), 5), ((1, 2), 3), ((1, 3), 4), ((2, 1), 3), ((2, 2), 2), ((2, 3), 4)]`

. You can perform this step with a single Spark transformation. - #### Transform
`actualRDD`

into the tuples of the form ((UserID, MovieID), Rating). For example, tuples like`[((1, 2), 3), ((1, 3), 5), ((2, 1), 5), ((2, 2), 1)]`

. You can perform this step with a single Spark transformation. - #### Using only RDD transformations (you only need to perform two transformations), compute the squared error for each
*matching*entry (i.e., the same (UserID, MovieID) in each RDD) in the reformatted RDDs - do*not*use`collect()`

to perform this step. Note that not every (UserID, MovieID) pair will appear in both RDDs - if a pair does not appear in both RDDs, then it does not contribute to the RMSE. You will end up with an RDD with entries of the form $ (x_i - y_i)^2$ You might want to check out Python's math module to see how to compute these values - #### Using an RDD action (but
**not**`collect()`

), compute the total squared error: $ SE = \sum_{i = 1}^{n} (x_i - y_i)^2 $ - #### Compute
*n*by using an RDD action (but**not**`collect()`

), to count the number of pairs for which you computed the total squared error - #### Using the total squared error and the number of pairs, compute the RSME. Make sure you compute this value as a float.
#### Note: Your solution must only use transformations and actions on RDDs. Do
*not*call`collect()`

on either RDD.

In [36]:

```
# TODO: Replace <FILL IN> with appropriate code
import math
def computeError(predictedRDD, actualRDD):
""" Compute the root mean squared error between predicted and actual
Args:
predictedRDD: predicted ratings for each movie and each user where each entry is in the form
(UserID, MovieID, Rating)
actualRDD: actual ratings where each entry is in the form (UserID, MovieID, Rating)
Returns:
RSME (float): computed RSME value
"""
# Transform predictedRDD into the tuples of the form ((UserID, MovieID), Rating)
predictedReformattedRDD = predictedRDD.map(lambda (x, y, z): ((x, y), z))
# Transform actualRDD into the tuples of the form ((UserID, MovieID), Rating)
actualReformattedRDD = actualRDD.map(lambda (x, y, z): ((x, y), z))
# Compute the squared error for each matching entry (i.e., the same (User ID, Movie ID) in each
# RDD) in the reformatted RDDs using RDD transformtions - do not use collect()
squaredErrorsRDD = (predictedReformattedRDD
.join(actualReformattedRDD)
.map(lambda (x, (y, z)): math.pow(y-z, 2.0)))
# Compute the total squared error - do not use collect()
totalError = squaredErrorsRDD.sum()
# Count the number of entries for which you computed the total squared error
numRatings = squaredErrorsRDD.count()
# Using the total squared error and the number of entries, compute the RSME
return math.sqrt(totalError/float(numRatings))
# sc.parallelize turns a Python list into a Spark RDD.
testPredicted = sc.parallelize([
(1, 1, 5),
(1, 2, 3),
(1, 3, 4),
(2, 1, 3),
(2, 2, 2),
(2, 3, 4)])
testActual = sc.parallelize([
(1, 2, 3),
(1, 3, 5),
(2, 1, 5),
(2, 2, 1)])
testPredicted2 = sc.parallelize([
(2, 2, 5),
(1, 2, 5)])
testError = computeError(testPredicted, testActual)
print 'Error for test dataset (should be 1.22474487139): %s' % testError
testError2 = computeError(testPredicted2, testActual)
print 'Error for test dataset2 (should be 3.16227766017): %s' % testError2
testError3 = computeError(testActual, testActual)
print 'Error for testActual dataset (should be 0.0): %s' % testError3
```

In [37]:

```
# TEST Root Mean Square Error (2b)
Test.assertTrue(abs(testError - 1.22474487139) < 0.00000001,
'incorrect testError (expected 1.22474487139)')
Test.assertTrue(abs(testError2 - 3.16227766017) < 0.00000001,
'incorrect testError2 result (expected 3.16227766017)')
Test.assertTrue(abs(testError3 - 0.0) < 0.00000001,
'incorrect testActual result (expected 0.0)')
```

- #### Pick a set of model parameters. The most important parameter to
`ALS.train()`

is the*rank*, which is the number of rows in the Users matrix (green in the diagram above) or the number of columns in the Movies matrix (blue in the diagram above). (In general, a lower rank will mean higher error on the training dataset, but a high rank may lead to overfitting.) We will train models with ranks of 4, 8, and 12 using the`trainingRDD`

dataset. - #### Create a model using
`ALS.train(trainingRDD, rank, seed=seed, iterations=iterations, lambda_=regularizationParameter)`

with three parameters: an RDD consisting of tuples of the form (UserID, MovieID, rating) used to train the model, an integer rank (4, 8, or 12), a number of iterations to execute (we will use 5 for the`iterations`

parameter), and a regularization coefficient (we will use 0.1 for the`regularizationParameter`

). - #### For the prediction step, create an input RDD,
`validationForPredictRDD`

, consisting of (UserID, MovieID) pairs that you extract from`validationRDD`

. You will end up with an RDD of the form:`[(1, 1287), (1, 594), (1, 1270)]`

- #### Using the model and
`validationForPredictRDD`

, we can predict rating values by calling model.predictAll() with the`validationForPredictRDD`

dataset, where`model`

is the model we generated with ALS.train().`predictAll`

accepts an RDD with each entry in the format (userID, movieID) and outputs an RDD with each entry in the format (userID, movieID, rating). - #### Evaluate the quality of the model by using the
`computeError()`

function you wrote in part (2b) to compute the error between the predicted ratings and the actual ratings in`validationRDD`

. #### Which rank produces the best model, based on the RMSE with the`validationRDD`

dataset? #### Note: It is likely that this operation will take a noticeable amount of time (around a minute in our VM); you can observe its progress on the Spark Web UI. Probably most of the time will be spent running your`computeError()`

function, since, unlike the Spark ALS implementation (and the Spark 1.4 RegressionMetrics module), this does not use a fast linear algebra library and needs to run some Python code for all 100k entries.

In [38]:

```
# TODO: Replace <FILL IN> with appropriate code
from pyspark.mllib.recommendation import ALS
validationForPredictRDD = validationRDD.map(lambda (x, y, z): (x, y))
seed = 5L
iterations = 5
regularizationParameter = 0.1
ranks = [4, 8, 12]
errors = [0, 0, 0]
err = 0
tolerance = 0.03
minError = float('inf')
bestRank = -1
bestIteration = -1
for rank in ranks:
model = ALS.train(trainingRDD, rank, seed=seed, iterations=iterations,
lambda_=regularizationParameter)
predictedRatingsRDD = model.predictAll(validationForPredictRDD)
error = computeError(predictedRatingsRDD, validationRDD)
errors[err] = error
err += 1
print 'For rank %s the RMSE is %s' % (rank, error)
if error < minError:
minError = error
bestRank = rank
print 'The best model was trained with rank %s' % bestRank
```

In [39]:

```
# TEST Using ALS.train (2c)
Test.assertEquals(trainingRDD.getNumPartitions(), 2,
'incorrect number of partitions for trainingRDD (expected 2)')
Test.assertEquals(validationForPredictRDD.count(), 96902,
'incorrect size for validationForPredictRDD (expected 96902)')
Test.assertEquals(validationForPredictRDD.filter(lambda t: t == (1, 1907)).count(), 1,
'incorrect content for validationForPredictRDD')
Test.assertTrue(abs(errors[0] - 0.883710109497) < tolerance, 'incorrect errors[0]')
Test.assertTrue(abs(errors[1] - 0.878486305621) < tolerance, 'incorrect errors[1]')
Test.assertTrue(abs(errors[2] - 0.876832795659) < tolerance, 'incorrect errors[2]')
```

`trainingRDD`

and `validationRDD`

datasets to select the best model. Since we used these two datasets to determine what model is best, we cannot use them to test how good the model is - otherwise we would be very vulnerable to overfitting. To decide how good our model is, we need to use the `testRDD`

dataset. We will use the `bestRank`

you determined in part (2c) to create a model for predicting the ratings for the test dataset and then we will compute the RMSE.¶- #### Train a model, using the
`trainingRDD`

,`bestRank`

from part (2c), and the parameters you used in in part (2c):`seed=seed`

,`iterations=iterations`

, and`lambda_=regularizationParameter`

- make sure you include**all**of the parameters. - #### For the prediction step, create an input RDD,
`testForPredictingRDD`

, consisting of (UserID, MovieID) pairs that you extract from`testRDD`

. You will end up with an RDD of the form:`[(1, 1287), (1, 594), (1, 1270)]`

- #### Use myModel.predictAll() to predict rating values for the test dataset.
- #### For validation, use the
`testRDD`

and your`computeError`

function to compute the RMSE between`testRDD`

and the`predictedTestRDD`

from the model. - #### Evaluate the quality of the model by using the
`computeError()`

function you wrote in part (2b) to compute the error between the predicted ratings and the actual ratings in`testRDD`

.

In [40]:

```
# TODO: Replace <FILL IN> with appropriate code
myModel = ALS.train(trainingRDD, 8, seed=seed, iterations=iterations,
lambda_=regularizationParameter)
testForPredictingRDD = testRDD.map(lambda (x, y, z): (x, y))
predictedTestRDD = myModel.predictAll(testForPredictingRDD)
testRMSE = computeError(testRDD, predictedTestRDD)
print 'The model had a RMSE on the test set of %s' % testRMSE
```

In [41]:

```
# TEST Testing Your Model (2d)
Test.assertTrue(abs(testRMSE - 0.87809838344) < tolerance, 'incorrect testRMSE')
```

- #### Use the
`trainingRDD`

to compute the average rating across all movies in that training dataset. - #### Use the average rating that you just determined and the
`testRDD`

to create an RDD with entries of the form (userID, movieID, average rating). - #### Use your
`computeError`

function to compute the RMSE between the`testRDD`

validation RDD that you just created and the`testForAvgRDD`

.

In [42]:

```
# TODO: Replace <FILL IN> with appropriate code
trainingAvgRating = trainingRDD.map(lambda (x, y, z): z).mean()
print 'The average rating for movies in the training set is %s' % trainingAvgRating
testForAvgRDD = testRDD.map(lambda (x, y, z): (x, y, trainingAvgRating))
testAvgRMSE = computeError(testRDD, testForAvgRDD)
print 'The RMSE on the average set is %s' % testAvgRMSE
```

In [43]:

```
# TEST Comparing Your Model (2e)
Test.assertTrue(abs(trainingAvgRating - 3.57409571052) < 0.000001,
'incorrect trainingAvgRating (expected 3.57409571052)')
Test.assertTrue(abs(testAvgRMSE - 1.12036693569) < 0.000001,
'incorrect testAvgRMSE (expected 1.12036693569)')
```

In [47]:

```
print 'Most rated movies:'
print '(average rating, movie name, number of reviews)'
for ratingsTuple in movieLimitedAndSortedByRatingRDD.take(50):
print ratingsTuple
```

`myUserID`

to 0 for you. Next, create a new RDD `myRatingsRDD`

with your ratings for at least 10 movie ratings. Each entry should be formatted as `(myUserID, movieID, rating)`

(i.e., each entry should be formatted in the same way as `trainingRDD`

). As in the original dataset, ratings should be between 1 and 5 (inclusive). If you have not seen at least 10 of these movies, you can increase the parameter passed to `take()`

in the above cell until there are 10 movies that you have seen (or you can also guess what your rating would be for movies you have not seen).¶In [48]:

```
# TODO: Replace <FILL IN> with appropriate code
myUserID = 0
# Note that the movie IDs are the *last* number on each line. A common error was to use the number of ratings as the movie ID.
myRatedMovies = [
(0, 858, 4.5), (0, 50, 4.5), (0, 260, 4), (0, 2762, 4), (0, 3114, 4), (0, 1, 4), (0, 1240, 3), (0, 1213, 4.5), (0, 2028, 5), (0, 593, 4)
# The format of each line is (myUserID, movie ID, your rating)
# For example, to give the movie "Star Wars: Episode IV - A New Hope (1977)" a five rating, you would add the following line:
# (myUserID, 260, 5),
]
myRatingsRDD = sc.parallelize(myRatedMovies)
print 'My movie ratings: %s' % myRatingsRDD.take(10)
```

`training`

dataset so that the model you train will incorporate your preferences. Spark's union() transformation combines two RDDs; use `union()`

to create a new training dataset that includes your ratings and the data in the original training dataset.¶In [50]:

```
# TODO: Replace <FILL IN> with appropriate code
trainingWithMyRatingsRDD = trainingRDD.union(myRatingsRDD)
print ('The training dataset now has %s more entries than the original training dataset' %
(trainingWithMyRatingsRDD.count() - trainingRDD.count()))
assert (trainingWithMyRatingsRDD.count() - trainingRDD.count()) == myRatingsRDD.count()
```

In [51]:

```
# TODO: Replace <FILL IN> with appropriate code
myRatingsModel = ALS.train(trainingWithMyRatingsRDD, bestRank, seed=seed, iterations=iterations, lambda_=regularizationParameter)
```

- #### For the prediction step, we reuse
`testForPredictingRDD`

, consisting of (UserID, MovieID) pairs that you extracted from`testRDD`

. The RDD has the form:`[(1, 1287), (1, 594), (1, 1270)]`

- #### Use
`myRatingsModel.predictAll()`

to predict rating values for the`testForPredictingRDD`

test dataset, set this as`predictedTestMyRatingsRDD`

- #### For validation, use the
`testRDD`

and your`computeError`

function to compute the RMSE between`testRDD`

and the`predictedTestMyRatingsRDD`

from the model.

In [55]:

```
# TODO: Replace <FILL IN> with appropriate code
predictedTestMyRatingsRDD = myRatingsModel.predictAll(testForPredictingRDD)
testRMSEMyRatings = computeError(testRDD, predictedTestMyRatingsRDD)
print 'The model had a RMSE on the test set of %s' % testRMSEMyRatings
```

`predictAll`

method to compute the error of the model. Here, use the `predictAll`

to predict what ratings you would give to the movies that you did not already provide ratings for.¶- #### Use the Python list
`myRatedMovies`

to transform the`moviesRDD`

into an RDD with entries that are pairs of the form (myUserID, Movie ID) and that does not contain any movies that you have rated. This transformation will yield an RDD of the form:`[(0, 1), (0, 2), (0, 3), (0, 4)]`

. Note that you can do this step with one RDD transformation. - #### For the prediction step, use the input RDD,
`myUnratedMoviesRDD`

, with myRatingsModel.predictAll() to predict your ratings for the movies.

In [58]:

```
# TODO: Replace <FILL IN> with appropriate code
# Use the Python list myRatedMovies to transform the moviesRDD into an RDD with entries that are pairs of the form (myUserID, Movie ID) and that does not contain any movies that you have rated.
myUnratedMoviesRDD = (moviesRDD
.map(lambda (x,y): (myUserID,x)).filter(lambda x: x[1] not in [i[1] for i in myRatedMovies]))
# Use the input RDD, myUnratedMoviesRDD, with myRatingsModel.predictAll() to predict your ratings for the movies
predictedRatingsRDD = myRatingsModel.predictAll(myUnratedMoviesRDD)
```

- #### From Parts (1b) and (1c), we know that we should look at movies with a reasonable number of reviews (e.g., more than 75 reviews). You can experiment with a lower threshold, but fewer ratings for a movie may yield higher prediction errors. Transform
`movieIDsWithAvgRatingsRDD`

from Part (1b), which has the form (MovieID, (number of ratings, average rating)), into an RDD of the form (MovieID, number of ratings):`[(2, 332), (4, 71), (6, 442)]`

- #### We want to see movie names, instead of movie IDs. Transform
`predictedRatingsRDD`

into an RDD with entries that are pairs of the form (Movie ID, Predicted Rating):`[(3456, -0.5501005376936687), (1080, 1.5885892024487962), (320, -3.7952255522487865)]`

- #### Use RDD transformations with
`predictedRDD`

and`movieCountsRDD`

to yield an RDD with tuples of the form (Movie ID, (Predicted Rating, number of ratings)):`[(2050, (0.6694097486155939, 44)), (10, (5.29762541533513, 418)), (2060, (0.5055259373841172, 97))]`

- #### Use RDD transformations with
`predictedWithCountsRDD`

and`moviesRDD`

to yield an RDD with tuples of the form (Predicted Rating, Movie Name, number of ratings),*for movies with more than 75 ratings.*For example:`[(7.983121900375243, u'Under Siege (1992)'), (7.9769201864261285, u'Fifth Element, The (1997)')]`

In [74]:

```
# TODO: Replace <FILL IN> with appropriate code
# Transform movieIDsWithAvgRatingsRDD from part (1b), which has the form (MovieID, (number of ratings, average rating)), into and RDD of the form (MovieID, number of ratings)
movieCountsRDD = movieIDsWithAvgRatingsRDD.map(lambda (x, (y, z)): (x, y))
# Transform predictedRatingsRDD into an RDD with entries that are pairs of the form (Movie ID, Predicted Rating)
predictedRDD = predictedRatingsRDD.map(lambda (x, y, z): (y, z))
# Use RDD transformations with predictedRDD and movieCountsRDD to yield an RDD with tuples of the form (Movie ID, (Predicted Rating, number of ratings))
predictedWithCountsRDD = (predictedRDD
.join(movieCountsRDD))
# Use RDD transformations with PredictedWithCountsRDD and moviesRDD to yield an RDD with tuples of the form (Predicted Rating, Movie Name, number of ratings), for movies with more than 75 ratings
ratingsWithNamesRDD = (predictedWithCountsRDD
.join(moviesRDD)
.map(lambda (x, (y, z)): (y[0], z, y[1]))
.filter(lambda (x, y, z): z > 75))
predictedHighestRatedMovies = ratingsWithNamesRDD.takeOrdered(20, key=lambda x: -x[0])
print ('My highest rated movies as predicted (for movies with more than 75 reviews):\n%s' %
'\n'.join(map(str, predictedHighestRatedMovies)))
```