LensKit¶

LensKit is a set of Python tools for experimenting with and studying recommender systems. It provides support for training, running, and evaluating recommender algorithms in a flexible fashion suitable for research and education.

LensKit for Python (also known as LKPY) is the successor to the Java-based LensKit project.

Installation¶

To install the current release with Anaconda (recommended):

conda install -c lenskit lenskit

Or you can use pip:

pip install lenskit

To use the latest development version, install directly from GitHub:

pip install git+https://github.com/lenskit/lkpy

Then see Getting Started.

Resources¶

Getting Started¶

This notebook gets you started with a brief nDCG evaluation with LensKit for Python.

Setup¶

We first import the LensKit components we need:

[1]:

from lenskit import batch, topn, util
from lenskit import crossfold as xf
from lenskit.algorithms import Recommender, als, item_knn as knn
from lenskit import topn

And Pandas is very useful:

[2]:

import pandas as pd

[3]:

%matplotlib inline

Loading Data¶

We’re going to use the ML-100K data set:

[4]:

ratings = pd.read_csv('ml-100k/u.data', sep='\t',
                      names=['user', 'item', 'rating', 'timestamp'])
ratings.head()

[4]:

	user	item	rating	timestamp
0	196	242	3	881250949
1	186	302	3	891717742
2	22	377	1	878887116
3	244	51	2	880606923
4	166	346	1	886397596

Defining Algorithms¶

Let’s set up two algorithms:

[5]:

algo_ii = knn.ItemItem(20)
algo_als = als.BiasedMF(50)

Running the Evaluation¶

In LensKit, our evaluation proceeds in 2 steps:

Generate recommendations
Measure them

If memory is a concern, we can measure while generating, but we will not do that for now.

We will first define a function to generate recommendations from one algorithm over a single partition of the data set. It will take an algorithm, a train set, and a test set, and return the recommendations.

Note: before fitting the algorithm, we clone it. Some algorithms misbehave when fit multiple times.

Note 2: our algorithms do not necessarily implement the Recommend interface, so we adapt them. This fills in a default candidate selector.

The code function looks like this:

[6]:

def eval(aname, algo, train, test):
    fittable = util.clone(algo)
    fittable = Recommender.adapt(fittable)
    fittable.fit(train)
    users = test.user.unique()
    # now we run the recommender
    recs = batch.recommend(fittable, users, 100)
    # add the algorithm name for analyzability
    recs['Algorithm'] = aname
    return recs

Now, we will loop over the data and the algorithms, and generate recommendations:

[7]:

all_recs = []
test_data = []
for train, test in xf.partition_users(ratings[['user', 'item', 'rating']], 5, xf.SampleFrac(0.2)):
    test_data.append(test)
    all_recs.append(eval('ItemItem', algo_ii, train, test))
    all_recs.append(eval('ALS', algo_als, train, test))

With the results in place, we can concatenate them into a single data frame:

[8]:

all_recs = pd.concat(all_recs, ignore_index=True)
all_recs.head()

[8]:

	item	score	user	rank	Algorithm
0	285	4.543364	5	1	ItemItem
1	1449	4.532999	5	2	ItemItem
2	1251	4.494639	5	3	ItemItem
3	114	4.479512	5	4	ItemItem
4	166	4.399639	5	5	ItemItem

To compute our analysis, we also need to concatenate the test data into a single frame:

[9]:

test_data = pd.concat(test_data, ignore_index=True)

We analyze our recommendation lists with a RecListAnalysis. It takes care of the hard work of making sure that the truth data (our test data) and the recoommendations line up properly.

We do assume here that each user only appears once per algorithm. Since our crossfold method partitions users, this is fine.

[10]:

rla = topn.RecListAnalysis()
rla.add_metric(topn.ndcg)
results = rla.compute(all_recs, test_data)
results.head()

/home/MICHAELEKSTRAND/anaconda3/envs/lkpy-dev/lib/python3.7/site-packages/pandas/core/indexing.py:1494: PerformanceWarning: indexing past lexsort depth may impact performance.
  return self._getitem_tuple(key)

[10]:

		ndcg
user	Algorithm
1	ALS	0.265268
1	ItemItem	0.259708
2	ALS	0.148335
2	ItemItem	0.081890
3	ALS	0.026615

Now we have nDCG values!

[11]:

results.groupby('Algorithm').ndcg.mean()

[11]:

Algorithm
ALS         0.139689
ItemItem    0.102075
Name: ndcg, dtype: float64

[12]:

results.groupby('Algorithm').ndcg.mean().plot.bar()

[12]:

<matplotlib.axes._subplots.AxesSubplot at 0x7f03842f8860>

[ ]:

Algorithm Interfaces¶

LKPY’s batch routines and utility support for managing algorithms expect algorithms to implement consistent interfaces. This page describes those interfaces.

The interfaces are realized as abstract base classes with the Python abc module. Implementations must be registered with their interfaces, either by subclassing the interface or by calling abc.ABCMeta.register().

Base Algorithm¶

Algorithms follow the SciKit fit-predict paradigm for estimators, except they know natively how to work with Pandas objects.

The Algorithm interface defines common methods.

class lenskit.algorithms.Algorithm¶

Base class for LensKit algorithms. These algorithms follow the SciKit design pattern for estimators.

fit(ratings, *args, **kwargs)¶

Train a model using the specified ratings (or similar) data.

Parameters:	ratings (pandas.DataFrame) – The ratings data. args – Additional training data the algorithm may require. kwargs – Additional training data the algorithm may require.
Returns:	The algorithm object.

get_params(deep=True)¶

Get the parameters for this algorithm (as in scikit-learn). Algorithm parameters should match constructor argument names.

The default implementation returns all attributes that match a constructor parameter name. It should be compatible with scikit.base.BaseEstimator.get_params() method so that LensKit alogrithms can be cloned with scikit.base.clone() as well as lenskit.util.clone().

Returns:	the algorithm parameters.
Return type:	dict

Recommendation¶

The Recommender interface provides an interface to generating recommendations. Not all algorithms implement it; call Recommender.adapt() on an algorithm to get a recommender for any algorithm that at least implements Predictor. For example:

pred = Bias(damping=5)
rec = Recommender.adapt(pred)

Note

We are rethinking the ergonomics of this interface, and it may change in LensKit 0.6. We expect keep compatibility in the lenskit.batch.recommend() API, though.

class lenskit.algorithms.Recommender¶

Recommends lists of items for users.

classmethod adapt(algo)¶

Ensure that an algorithm is a Recommender. If it is not a recommender, it is wrapped in a lenskit.basic.TopN with a default candidate selector.

Note

Since 0.6.0, since algorithms are fit directly, you should call this method before calling Algorithm.fit(), unless you will always be passing explicit candidate sets to recommend().

Parameters:	algo (Predictor) – the underlying rating predictor.

recommend(user, n=None, candidates=None, ratings=None)¶

Compute recommendations for a user.

Parameters:	user – the user ID n (int) – the number of recommendations to produce (`None` for unlimited) candidates (array-like) – The set of valid candidate items; if `None`, a default set will be used. For many algorithms, this is their `CandidateSelector`. ratings (pandas.Series) – the user’s ratings (indexed by item id); if provided, they may be used to override or augment the model’s notion of a user’s preferences.
Returns:	a frame with an `item` column; if the recommender also produces scores, they will be in a `score` column.
Return type:	pandas.DataFrame

Candidate Selection¶

Some recommenders use a candidate selector to identify possible items to recommend. These are also treated as algorithms, mainly so that they can memorize users’ prior ratings to exclude them from recommendation.

class lenskit.algorithms.CandidateSelector¶

Select candidates for recommendation for a user, possibly with some additional ratings.

candidates(user, ratings=None)¶

Select candidates for the user.

Parameters:	user – The user key or ID. ratings (pandas.Series or array-like) – Ratings or items to use instead of whatever ratings were memorized for this user. If a `pandas.Series`, the series index is used; if it is another array-like it is assumed to be an array of items.

static rated_items(ratings)¶: Utility function for converting a series or array into an array of item IDs. Useful in implementations of candidates().

Rating Prediction¶

class lenskit.algorithms.Predictor¶

Predicts user ratings of items. Predictions are really estimates of the user’s like or dislike, and the Predictor interface makes no guarantees about their scale or granularity.

predict(pairs, ratings=None)¶

Compute predictions for user-item pairs. This method is designed to be compatible with the general SciKit paradigm; applications typically want to use predict_for_user().

Parameters:	pairs (pandas.DataFrame) – The user-item pairs, as `user` and `item` columns. ratings (pandas.DataFrame) – user-item rating data to replace memorized data.
Returns:	The predicted scores for each user-item pair.
Return type:	pandas.Series

predict_for_user(user, items, ratings=None)¶

Compute predictions for a user and items.

Parameters:	user – the user ID items (array-like) – the items to predict ratings (pandas.Series) – the user’s ratings (indexed by item id); if provided, they may be used to override or augment the model’s notion of a user’s preferences.
Returns:	scores for the items, indexed by item id.
Return type:	pandas.Series

Crossfold preparation¶

The LKPY crossfold module provides support for preparing data sets for cross-validation. Crossfold methods are implemented as functions that operate on data frames and return generators of (train, test) pairs (lenskit.crossfold.TTPair objects). The train and test objects in each pair are also data frames, suitable for evaluation or writing out to a file.

Crossfold methods make minimal assumptions about their input data frames, so the frames can be ratings, purchases, or whatever. They do assume that each row represents a single data point for the purpose of splitting and sampling.

Experiment code should generally use these functions to prepare train-test files for training and evaluating algorithms. For example, the following will perform a user-based 5-fold cross-validation as was the default in the old LensKit:

import pandas as pd
import lenskit.crossfold as xf
ratings = pd.read_csv('ml-20m/ratings.csv')
ratings = ratings.rename(columns={'userId': 'user', 'movieId': 'item'})
for i, tp in enumerate(xf.partition_users(ratings, 5, xf.SampleN(5))):
    tp.train.to_csv('ml-20m.exp/train-%d.csv' % (i,))
    tp.train.to_parquet('ml-20m.exp/train-%d.parquet % (i,))
    tp.test.to_csv('ml-20m.exp/test-%d.csv' % (i,))
    tp.test.to_parquet('ml-20m.exp/test-%d.parquet % (i,))

Row-based splitting¶

The simplest preparation methods sample or partition the rows in the input frame. A 5-fold partition_rows() split will result in 5 splits, each of which extracts 20% of the rows for testing and leaves 80% for training.

lenskit.crossfold.partition_rows(data, partitions)¶

Partition a frame of ratings or other datainto train-test partitions. This function does not care what kind of data is in data, so long as it is a Pandas DataFrame (or equivalent).

Parameters:	data (`pandas.DataFrame` or equivalent) – a data frame containing ratings or other data you wish to partition. partitions (integer) – the number of partitions to produce
Return type:	iterator
Returns:	an iterator of train-test pairs

lenskit.crossfold.sample_rows(data, partitions, size, disjoint=True)¶

Sample train-test a frame of ratings into train-test partitions. This function does not care what kind of data is in data, so long as it is a Pandas DataFrame (or equivalent).

We can loop over a sequence of train-test pairs:

>>> ratings = util.load_ml_ratings()
>>> for train, test in sample_rows(ratings, 5, 1000):
...     print(len(test))
1000
1000
1000
1000
1000

Sometimes for testing, it is useful to just get a single pair:

>>> train, test = sample_rows(ratings, None, 1000)
>>> len(test)
1000
>>> len(test) + len(train) - len(ratings)
0

Parameters:	data (pandas.DataFrame) – Data frame containing ratings or other data to partition. partitions (int or None) – The number of partitions to produce. If `None`, produce a _single_ train-test pair instead of an iterator or list. size (int) – The size of each sample. disjoint (bool) – If `True`, force samples to be disjoint.
Returns:	An iterator of train-test pairs.
Return type:	iterator

User-based splitting¶

It’s often desirable to use users, instead of raw rows, as the basis for splitting data. This allows you to control the experimental conditions on a user-by-user basis, e.g. by making sure each user is tested with the same number of ratings. These methods require that the input data frame have a user column with the user names or identifiers.

The algorithm used by each is as follows:

Sample or partition the set of user IDs into n sets of test users.
For each set of test users, select a set of that user’s rows to be test rows.
Create a training set for each test set consisting of the non-selected rows from each

of that set’s test users, along with all rows from each non-test user.

lenskit.crossfold.partition_users(data, partitions: int, method: lenskit.crossfold.PartitionMethod)¶

Partition a frame of ratings or other data into train-test partitions user-by-user. This function does not care what kind of data is in data, so long as it is a Pandas DataFrame (or equivalent) and has a user column.

Parameters:	data (`pandas.DataFrame` or equivalent) – a data frame containing ratings or other data you wish to partition. partitions (integer) – the number of partitions to produce method – The method for selecting test rows for each user.
Return type:	iterator
Returns:	an iterator of train-test pairs

lenskit.crossfold.sample_users(data, partitions: int, size: int, method: lenskit.crossfold.PartitionMethod, disjoint=True)¶

Create train-test partitions by sampling users. This function does not care what kind of data is in data, so long as it is a Pandas DataFrame (or equivalent) and has a user column.

Parameters:	data (pandas.DataFrame) – Data frame containing ratings or other data you wish to partition. partitions (int) – The number of partitions. size (int) – The sample size. method (PartitionMethod) – The method for obtaining user test ratings.
Returns:	An iterator of train-test pairs (as `TTPair` objects).
Return type:	iterator

Selecting user test rows¶

These functions each take a method to decide how select each user’s test rows. The method is a function that takes a data frame (containing just the user’s rows) and returns the test rows. This function is expected to preserve the index of the input data frame (which happens by default with common means of implementing samples).

We provide several partition method factories:

lenskit.crossfold.SampleN(n)¶

Randomly select a fixed number of test rows per user/item.

Parameters:	n – The number of test items to select.

lenskit.crossfold.SampleFrac(frac)¶

Randomly select a fraction of test rows per user/item.

Parameters:	frac – the fraction of items to select for testing.

lenskit.crossfold.LastN(n, col='timestamp')¶

Select a fixed number of test rows per user/item, based on ordering by a column.

Parameters:	n – The number of test items to select. col – The column to sort by.

lenskit.crossfold.LastFrac(frac, col='timestamp')¶

Select a fraction of test rows per user/item.

Parameters:	frac – the fraction of items to select for testing. col – The column to sort by.

Utility Classes¶

class lenskit.crossfold.PartitionMethod¶

Partition methods select test rows for a user or item. Partition methods are callable; when called with a data frame, they return the test rows.

__call__(udf)¶

Subset a data frame.

Parameters:	udf – The input data frame of rows for a user or item.
Returns:	The data frame of test rows, a subset of udf.

class lenskit.crossfold.TTPair¶

Train-test pair (named tuple).

test¶: Test data for this pair.

train¶: Train data for this pair.

Batch-Running Recommenders¶

The functions in lenskit.batch enable you to generate many recommendations or predictions at the same time, useful for evaluations and experiments.

Recommendation¶

lenskit.batch.recommend(algo, users, n, candidates=None, *, nprocs=None, **kwargs)¶

Batch-recommend for multiple users. The provided algorithm should be a algorithms.Recommender.

Parameters:

algo – the algorithm
users (array-like) – the users to recommend for
n (int) – the number of recommendations to generate (None for unlimited)
candidates – the users’ candidate sets. This can be a function, in which case it will be passed each user ID; it can also be a dictionary, in which case user IDs will be looked up in it. Pass None to use the recommender’s built-in candidate selector (usually recommended).
nprocs (int) – The number of processes to use for parallel recommendations.

Returns:

A frame with at least the columns user, rank, and item; possibly also score, and any other columns returned by the recommender.

Rating Prediction¶

lenskit.batch.predict(algo, pairs, *, nprocs=None)¶

Generate predictions for user-item pairs. The provided algorithm should be a algorithms.Predictor or a function of two arguments: the user ID and a list of item IDs. It should return a dictionary or a pandas.Series mapping item IDs to predictions.

To use this function, provide a pre-fit algorithm:

>>> from lenskit.algorithms.basic import Bias
>>> from lenskit.metrics.predict import rmse
>>> ratings = util.load_ml_ratings()
>>> bias = Bias()
>>> bias.fit(ratings[:-1000])
<lenskit.algorithms.basic.Bias object at ...>
>>> preds = predict(bias, ratings[-1000:])
>>> preds.head()
       user  item  rating   timestamp  prediction
99004   664  8361     3.0  1393891425    3.288286
99005   664  8528     3.5  1393891047    3.559119
99006   664  8529     4.0  1393891173    3.573008
99007   664  8636     4.0  1393891175    3.846268
99008   664  8641     4.5  1393890852    3.710635
>>> rmse(preds['prediction'], preds['rating'])
0.8326992222...

Parameters:	algo (lenskit.algorithms.Predictor) – A rating predictor function or algorithm. pairs (pandas.DataFrame) – A data frame of (`user`, `item`) pairs to predict for. If this frame also contains a `rating` column, it will be included in the result. nprocs (int) – The number of processes to use for parallel batch prediction.
Returns:	a frame with columns `user`, `item`, and `prediction` containing the prediction results. If `pairs` contains a rating column, this result will also contain a rating column.
Return type:	pandas.DataFrame

Scripting Evaluation¶

class lenskit.batch.MultiEval(path, predict=True, recommend=100, candidates=<class 'lenskit.topn.UnratedCandidates'>, nprocs=None, combine=True)¶

A runner for carrying out multiple evaluations, such as parameter sweeps.

Parameters:

path (str or pathlib.Path) – the working directory for this evaluation. It will be created if it does not exist.
predict (bool) – whether to generate rating predictions.
recommend (int) – the number of recommendations to generate per user (None to disable top-N).
candidates (function) – the default candidate set generator for recommendations. It should take the training data and return a candidate generator, itself a function mapping user IDs to candidate sets.
combine (bool) – whether to combine output; if False, output will be left in separate files, if True, it will be in a single set of files (runs, recommendations, and preditions).

add_algorithms(algos, parallel=False, attrs=[], **kwargs)¶

Add one or more algorithms to the run.

Parameters:	algos (algorithm or list) – the algorithm(s) to add. parallel (bool) – if `True`, allow this algorithm to be trained in parallel with others. attrs (list of str) – a list of attributes to extract from the algorithm objects and include in the run descriptions. kwargs – additional attributes to include in the run descriptions.

add_datasets(data, name=None, candidates=None, **kwargs)¶

Add one or more datasets to the run.

Parameters:

data –
The input data set(s) to run. Can be one of the following:
- A tuple of (train, test) data.
- An iterable of (train, test) pairs, in which case the iterable is not consumed until it is needed.
- A function yielding either of the above, to defer data load until it is needed.
Data can be either data frames or paths; paths are loaded after detection using util.read_df_detect().
kwargs – additional attributes pertaining to these data sets.

collect_results()¶: Collect the results from non-combined runs into combined output files.

persist_data()¶: Persist the data for an experiment, replacing in-memory data sets with file names. Once this has been called, the sweep can be pickled.

run(runs=None)¶

Run the evaluation.

Parameters:	runs (int or set-like) – If provided, a specific set of runs to run. Useful for splitting an experiment into individual runs. This is a set of 1-based run IDs, not 0-based indexes.

run_count()¶: Get the number of runs in this evaluation.

Evaluating Recommender Output¶

LensKit’s evaluation support is based on post-processing the output of recommenders and predictors. The batch utilities provide support for generating these outputs.

We generally recommend using Jupyter notebooks for evaluation.

Prediction Accuracy Metrics¶

The lenskit.metrics.predict module contains prediction accuracy metrics. These are intended to be used as a part of a Pandas split-apply-combine operation on a data frame that contains both predictions and ratings; for convenience, the lenskit.batch.predict() function will include ratings in the prediction frame when its input user-item pairs contains ratings. So you can perform the following to compute per-user RMSE over some predictions:

preds = predict(algo, pairs)
user_rmse = preds.groupby('user').apply(lambda df: rmse(df.prediction, df.rating))

Metric Functions¶

Prediction metric functions take two series, predictions and truth.

lenskit.metrics.predict.rmse(predictions, truth, missing='error')¶

Compute RMSE (root mean squared error).

Parameters:	predictions (pandas.Series) – the predictions truth (pandas.Series) – the ground truth ratings from data missing (string) – how to handle predictions without truth. Can be one of `'error'` or `'ignore'`.
Returns:	the root mean squared approximation error
Return type:	double

lenskit.metrics.predict.mae(predictions, truth, missing='error')¶

Compute MAE (mean absolute error).

Parameters:	predictions (pandas.Series) – the predictions truth (pandas.Series) – the ground truth ratings from data missing (string) – how to handle predictions without truth. Can be one of `'error'` or `'ignore'`.
Returns:	the mean absolute approximation error
Return type:	double

Working with Missing Data¶

LensKit rating predictors do not report predictions when their core model is unable to predict. For example, a nearest-neighbor recommender will not score an item if it cannot find any suitable neighbors. Following the Pandas convention, these items are given a score of NaN (when Pandas implements better missing data handling, it will use that, so use pandas.Series.isna()/pandas.Series.notna(), not the isnan versions.

However, this causes problems when computing predictive accuracy: recommenders are not being tested on the same set of items. If a recommender only scores the easy items, for example, it could do much better than a recommender that is willing to attempt more difficult items.

A good solution to this is to use a fallback predictor so that every item has a prediction. In LensKit, lenskit.algorithms.basic.Fallback implements this functionality; it wraps a sequence of recommenders, and for each item, uses the first one that generates a score.

You set it up like this:

cf = ItemItem(20)
base = Bias(damping=5)
algo = Fallback(cf, base)

Top-N Evaluation¶

LensKit’s support for top-N evaluation is in two parts, because there are some subtle complexities that make it more dfficult to get the right data in the right place for computing metrics correctly.

Top-N Analysis¶

The lenskit.topn module contains the utilities for carrying out top-N analysis, in conjucntion with lenskit.batch.recommend() and its wrapper in lenskit.batch.MultiEval.

The entry point to this is RecListAnalysis. This class encapsulates an analysis with one or more metrics, and can apply it to data frames of recommendations. An analysis requires two data frames: the recommendation frame contains the recommendations themselves, and the truth frame contains the ground truth data for the users. The analysis is flexible with regards to the columns that identify individual recommendation lists; usually these will consist of a user ID, data set identifier, and algorithm identifier(s), but the analysis is configurable and its defaults make minimal assumptions. The recommendation frame does need an item column with the recommended item IDs, and it should be in order within a single recommendation list.

The truth frame should contain (a subset of) the columns identifying recommendation lists, along with item and, if available, rating (if no rating is provided, the metrics that need a rating value will assume a rating of 1 for every item present). It can contain other items that custom metrics may find useful as well.

For example, a recommendation frame may contain:

DataSet
Partition
Algorithm
user
item
rank
score

And the truth frame:

DataSet
user
item
rating

The analysis will use this truth as the relevant item data for measuring the accuracy of the roecommendation lists. Recommendations will be matched to test ratings by data set, user, and item, using RecListAnalysis defaults.

class lenskit.topn.RecListAnalysis(group_cols=None)¶

Compute one or more top-N metrics over recommendation lists.

This method groups the recommendations by the specified columns, and computes the metric over each group. The default set of grouping columns is all columns except the following:

item
rank
score
rating

The truth frame, truth, is expected to match over (a subset of) the grouping columns, and contain at least an item column. If it also contains a rating column, that is used as the users’ rating for metrics that require it; otherwise, a rating value of 1 is assumed.

Parameters:	group_cols (list) – The columns to group by, or `None` to use the default.

add_metric(metric, *, name=None, **kwargs)¶

Add a metric to the analysis.

A metric is a function of two arguments: the a single group of the recommendation frame, and the corresponding truth frame. The truth frame will be indexed by item ID. Many metrics are defined in lenskit.metrics.topn; they are re-exported from lenskit.topn for convenience.

Parameters:	metric – The metric to compute. name – The name to assign the metric. If not provided, the function name is used. **kwargs – Additional arguments to pass to the metric.

compute(recs, truth)¶

Run the analysis. Neither data frame should be meaningfully indexed.

Parameters:	recs (pandas.DataFrame) – A data frame of recommendations. truth (pandas.DataFrame) – A data frame of ground truth (test) data.
Returns:	The results of the analysis.
Return type:	pandas.DataFrame

Metrics¶

The lenskit.metrics.topn module contains metrics for evaluating top-N recommendation lists.

Classification Metrics¶

These metrics treat the recommendation list as a classification of relevant items.

lenskit.metrics.topn.precision(recs, truth)¶: Compute recommendation precision.

lenskit.metrics.topn.recall(recs, truth)¶: Compute recommendation recall.

Ranked List Metrics¶

These metrics treat the recommendation list as a ranked list of items that may or may not be relevant.

lenskit.metrics.topn.recip_rank(recs, truth)¶

Compute the reciprocal rank of the first relevant item in a list of recommendations.

If no elements are relevant, the reciprocal rank is 0.

Utility Metrics¶

The NDCG function estimates a utility score for a ranked list of recommendations.

lenskit.metrics.topn.ndcg(recs, truth, discount=<ufunc 'log2'>)¶

Compute the normalized discounted cumulative gain.

Discounted cumultative gain is computed as:

\[\begin{align*} \mathrm{DCG}(L,u) & = \sum_{i=1}^{|L|} \frac{r_{ui}}{d(i)} \end{align*}\]

This is then normalized as follows:

\[\begin{align*} \mathrm{nDCG}(L, u) & = \frac{\mathrm{DCG}(L,u)}{\mathrm{DCG}(L_{\mathrm{ideal}}, u)} \end{align*}\]

Parameters:	recs – The recommendation list. truth – The user’s test data. discount (ufunc) – The rank discount function. Each item’s score will be divided the discount of its rank, if the discount is greater than 1.

We also expose the internal DCG computation directly.

lenskit.metrics.topn._dcg(scores, discount=<ufunc 'log2'>)¶

Compute the Discounted Cumulative Gain of a series of recommended items with rating scores. These should be relevance scores; they can be \({0,1}\) for binary relevance data.

This is not a true top-N metric, but is a utility function for other metrics.

Parameters:	scores (array-like) – The utility scores of a list of recommendations, in recommendation order. discount (ufunc) – the rank discount function. Each item’s score will be divided the discount of its rank, if the discount is greater than 1.
Returns:	the DCG of the scored items.
Return type:	double

Loading Outputs¶

We typically store the output of recommendation runs in LensKit experiments in CSV or Parquet files. The lenskit.batch.MultiEval class arranges to run a set of algorithms over a set of data sets, and store the results in a collection of Parquet files in a specified output directory.

There are several files:

runs.parquet: The _runs_, algorithm-dataset combinations. This file contains the names & any associated properties of each algorithm and data set run, such as a feature count.
recommendations.parquet: The recommendations, with columns RunId, user, rank, item, and rating.
predictions.parquet: The rating predictions, if the test data includes ratings.

For example, if you want to examine nDCG by neighborhood count for a set of runs on a single data set, you can do:

import pandas as pd
from lenskit.metrics import topn as lm

runs = pd.read_parquet('eval-dir/runs.parquet')
recs = pd.read_parquet('eval-dir/recs.parquet')
meta = runs.loc[:, ['RunId', 'max_neighbors']]

# compute each user's nDCG
user_ndcg = recs.groupby(['RunId', 'user']).rating.apply(lm.ndcg)
user_ndcg = user_ndcg.reset_index(name='nDCG')
# combine with metadata for feature count
user_ndcg = pd.merge(user_ndcg, meta)
# group and aggregate
nbr_ndcg = user_ndcg.groupby('max_neighbors').nDCG.mean()
nbr_ndcg.plot()

Errors and Diagnostics¶

Logging¶

LensKit algorithms and evaluation routines report diagnostic data using the standard Python logging framework. Loggers are named after the corresponding Python module, and all live under the lenskit namespace.

Algorithms usually report erroneous or anomalous conditions using Python exceptions and warnings. Evaluation code, such as that in lenskit.batch, typically reports such conditions using the logger, as the common use case is to be running them in a script.

Warnings¶

In addition to Python standard warning types such as warnings.DeprecationWarning, LensKit uses the following warning classes to report anomalous problems in use of LensKit.

class lenskit.DataWarning¶: Warning raised for detectable problems with input data.

Algorithms¶

LKPY provides general algorithmic concepts, along with implementations of several algorithms. These algorithm interfaces are based on the SciKit design patterns [SKAPI], adapted for Pandas-based data structures.

[SKAPI]

Lars Buitinck, Gilles Louppe, Mathieu Blondel, Fabian Pedregosa, Andreas Mueller, Olivier Grisel, Vlad Niculae, Peter Prettenhofer, Alexandre Gramfort, Jaques Grobler, Robert Layton, Jake Vanderplas, Arnaud Joly, Brian Holt, and Gaël Varoquaux. 2013. API design for machine learning software: experiences from the scikit-learn project. arXiv:1309.0238 [cs.LG].

Basic and Utility Algorithms¶

The lenskit.algorithms.basic module contains baseline and utility algorithms for nonpersonalized recommendation and testing.

Personalized Mean Rating Prediction¶

class lenskit.algorithms.basic.Bias(items=True, users=True, damping=0.0)¶

Bases: lenskit.algorithms.Predictor

A user-item bias rating prediction algorithm. This implements the following predictor algorithm:

\[s(u,i) = \mu + b_i + b_u\]

where \(\mu\) is the global mean rating, \(b_i\) is item bias, and \(b_u\) is the user bias. With the provided damping values \(\beta_{\mathrm{u}}\) and \(\beta_{\mathrm{i}}\), they are computed as follows:

\[\begin{align*} \mu & = \frac{\sum_{r_{ui} \in R} r_{ui}}{|R|} & b_i & = \frac{\sum_{r_{ui} \in R_i} (r_{ui} - \mu)}{|R_i| + \beta_{\mathrm{i}}} & b_u & = \frac{\sum_{r_{ui} \in R_u} (r_{ui} - \mu - b_i)}{|R_u| + \beta_{\mathrm{u}}} \end{align*}\]

The damping values can be interpreted as the number of default (mean) ratings to assume a priori for each user or item, damping low-information users and items towards a mean instead of permitting them to take on extreme values based on few ratings.

Parameters:	items – whether to compute item biases users – whether to compute user biases damping (number or tuple) – Bayesian damping to apply to computed biases. Either a number, to damp both user and item biases the same amount, or a (user,item) tuple providing separate damping values.

mean_¶

The global mean rating.

Type:	double

item_offsets_¶

The item offsets (\(b_i\) values)

Type:	pandas.Series

user_offsets_¶

The item offsets (\(b_u\) values)

Type:	pandas.Series

fit(data)¶

Train the bias model on some rating data.

Parameters:	data (DataFrame) – a data frame of ratings. Must have at least user, item, and rating columns.
Returns:	the fit bias object.
Return type:	Bias

predict_for_user(user, items, ratings=None)¶

Compute predictions for a user and items. Unknown users and items are assumed to have zero bias.

Parameters:	user – the user ID items (array-like) – the items to predict ratings (pandas.Series) – the user’s ratings (indexed by item id); if provided, will be used to recompute the user’s bias at prediction time.
Returns:	scores for the items, indexed by item id.
Return type:	pandas.Series

Fallback Predictor¶

The Fallback rating predictor is a simple hybrid that takes a list of composite algorithms, and uses the first one to return a result to predict the rating for each item.

A common case is to fill in with Bias when a primary predictor cannot score an item.

class lenskit.algorithms.basic.Fallback(algorithms, *others)¶

Bases: lenskit.algorithms.Predictor

The Fallback algorithm predicts with its first component, uses the second to fill in missing values, and so forth.

fit(ratings, *args, **kwargs)¶

Train a model using the specified ratings (or similar) data.

Parameters:	ratings (pandas.DataFrame) – The ratings data. args – Additional training data the algorithm may require. kwargs – Additional training data the algorithm may require.
Returns:	The algorithm object.

predict_for_user(user, items, ratings=None)¶

Compute predictions for a user and items.

Parameters:	user – the user ID items (array-like) – the items to predict ratings (pandas.Series) – the user’s ratings (indexed by item id); if provided, they may be used to override or augment the model’s notion of a user’s preferences.
Returns:	scores for the items, indexed by item id.
Return type:	pandas.Series

Memorized Predictor¶

The Memorized recommender is primarily useful for test cases. It memorizes a set of rating predictions and returns them.

class lenskit.algorithms.basic.Memorized(scores)¶

Bases: lenskit.algorithms.Predictor

The memorized algorithm memorizes socres provided at construction time.

fit(*args, **kwargs)¶

Train a model using the specified ratings (or similar) data.

Parameters:	ratings (pandas.DataFrame) – The ratings data. args – Additional training data the algorithm may require. kwargs – Additional training data the algorithm may require.
Returns:	The algorithm object.

predict_for_user(user, items, ratings=None)¶

Compute predictions for a user and items.

Parameters:	user – the user ID items (array-like) – the items to predict ratings (pandas.Series) – the user’s ratings (indexed by item id); if provided, they may be used to override or augment the model’s notion of a user’s preferences.
Returns:	scores for the items, indexed by item id.
Return type:	pandas.Series

k-NN Collaborative Filtering¶

LKPY provides user- and item-based classical k-NN collaborative Filtering implementations. These lightly-configurable implementations are intended to capture the behavior of the Java-based LensKit implementations to provide a good upgrade path and enable basic experiments out of the box.

Item-based k-NN¶

class lenskit.algorithms.item_knn.ItemItem(nnbrs, min_nbrs=1, min_sim=1e-06, save_nbrs=None, center=True, aggregate='weighted-average')¶

Bases: lenskit.algorithms.Predictor

Item-item nearest-neighbor collaborative filtering with ratings. This item-item implementation is not terribly configurable; it hard-codes design decisions found to work well in the previous Java-based LensKit code.

item_index_¶

the index of item IDs.

Type:	pandas.Index

item_means_¶

the mean rating for each known item.

Type:	numpy.ndarray

item_counts_¶

the number of saved neighbors for each item.

Type:	numpy.ndarray

sim_matrix_¶

the similarity matrix.

Type:	matrix.CSR

user_index_¶

the index of known user IDs for the rating matrix.

Type:	pandas.Index

rating_matrix_¶

the user-item rating matrix for looking up users’ ratings.

Type:	matrix.CSR

fit(ratings)¶

Train a model.

The model-training process depends on save_nbrs and min_sim, but not on other algorithm parameters.

Parameters:	ratings (pandas.DataFrame) – (user,item,rating) data for computing item similarities.

predict_for_user(user, items, ratings=None)¶

Compute predictions for a user and items.

Parameters:	user – the user ID items (array-like) – the items to predict ratings (pandas.Series) – the user’s ratings (indexed by item id); if provided, they may be used to override or augment the model’s notion of a user’s preferences.
Returns:	scores for the items, indexed by item id.
Return type:	pandas.Series

User-based k-NN¶

class lenskit.algorithms.user_knn.UserUser(nnbrs, min_nbrs=1, min_sim=0, center=True, aggregate='weighted-average')¶

Bases: lenskit.algorithms.Predictor

User-user nearest-neighbor collaborative filtering with ratings. This user-user implementation is not terribly configurable; it hard-codes design decisions found to work well in the previous Java-based LensKit code.

user_index_¶

User index.

Type:	pandas.Index

item_index_¶

Item index.

Type:	pandas.Index

user_means_¶

User mean ratings.

Type:	numpy.ndarray

rating_matrix_¶

Normalized user-item rating matrix.

Type:	matrix.CSR

transpose_matrix_¶

Transposed un-normalized rating matrix.

Type:	matrix.CSR

fit(ratings)¶

“Train” a user-user CF model. This memorizes the rating data in a format that is usable for future computations.

Parameters:	ratings (pandas.DataFrame) – (user, item, rating) data for collaborative filtering.
Returns:	a memorized model for efficient user-based CF computation.
Return type:	UUModel

predict_for_user(user, items, ratings=None)¶

Compute predictions for a user and items.

Parameters:	user – the user ID items (array-like) – the items to predict ratings (pandas.Series) – the user’s ratings (indexed by item id); if provided, will be used to recompute the user’s bias at prediction time.
Returns:	scores for the items, indexed by item id.
Return type:	pandas.Series

Classic Matrix Factorization¶

LKPY provides classical matrix factorization implementations.

Common Support
Alternating Least Squares
FunkSVD

Common Support ¶

The mf_common module contains common support code for matrix factorization algorithms.

class lenskit.algorithms.mf_common.MFPredictor¶

Common predictor for matrix factorization.

user_index_¶

Users in the model (length=:math:m).

Type:	pandas.Index

item_index_¶

Items in the model (length=:math:n).

Type:	pandas.Index

user_features_¶

The \(m \times k\) user-feature matrix.

Type:	numpy.ndarray

item_features_¶

The \(n \times k\) item-feature matrix.

Type:	numpy.ndarray

lookup_items(items)¶

Look up the indices for a set of items.

Parameters:	items (array-like) – the item IDs to look up.
Returns:	the item indices. Unknown items will have negative indices.
Return type:	numpy.ndarray

lookup_user(user)¶

Look up the index for a user.

Parameters:	user – the user ID to look up
Returns:	the user index.
Return type:	int

n_features¶: The number of features.

n_items¶: The number of items.

n_users¶: The number of users.

score(user, items)¶

Score a set of items for a user. User and item parameters must be indices into the matrices.

Parameters:	user (int) – the user index items (array-like of int) – the item indices raw (bool) – if `True`, do return raw scores without biases added back.
Returns:	the scores for the items.
Return type:	numpy.ndarray

class lenskit.algorithms.mf_common.BiasMFPredictor¶

Common model for biased matrix factorization.

user_index_¶

Users in the model (length=:math:m).

Type:	pandas.Index

item_index_¶

Items in the model (length=:math:n).

Type:	pandas.Index

global_bias_¶

The global bias term.

Type:	double

user_bias_¶

The user bias terms.

Type:	numpy.ndarray

item_bias_¶

The item bias terms.

Type:	numpy.ndarray

user_features_¶

The \(m \times k\) user-feature matrix.

Type:	numpy.ndarray

item_features_¶

The \(n \times k\) item-feature matrix.

Type:	numpy.ndarray

score(user, items, raw=False)¶

Score a set of items for a user. User and item parameters must be indices into the matrices.

Parameters:	user (int) – the user index items (array-like of int) – the item indices raw (bool) – if `True`, do return raw scores without biases added back.
Returns:	the scores for the items.
Return type:	numpy.ndarray

Alternating Least Squares ¶

LensKit provides alternating least squares implementations of matrix factorization suitable for explicit feedback data. These implementations are parallelized with Numba, and perform best with the MKL from Conda.

class lenskit.algorithms.als.BiasedMF(features, *, iterations=20, reg=0.1, damping=5, bias=True)¶

Bases: lenskit.algorithms.mf_common.BiasMFPredictor

Biased matrix factorization trained with alternating least squares [ZWSP2008]. This is a prediction-oriented algorithm suitable for explicit feedback data.

[ZWSP2008]

Yunhong Zhou, Dennis Wilkinson, Robert Schreiber, and Rong Pan. 2008. Large-Scale Parallel Collaborative Filtering for the Netflix Prize. In +Algorithmic Aspects in Information and Management_, LNCS 5034, 337–348. DOI 10.1007/978-3-540-68880-8_32.

Parameters:	features (int) – the number of features to train iterations (int) – the number of iterations to train reg (double) – the regularization factor damping (double) – damping factor for the underlying mean

fit(ratings)¶

Run ALS to train a model.

Parameters:	ratings – the ratings data frame.
Returns:	The algorithm (for chaining).

predict_for_user(user, items, ratings=None)¶

Compute predictions for a user and items.

Parameters:	user – the user ID items (array-like) – the items to predict ratings (pandas.Series) – the user’s ratings (indexed by item id); if provided, they may be used to override or augment the model’s notion of a user’s preferences.
Returns:	scores for the items, indexed by item id.
Return type:	pandas.Series

class lenskit.algorithms.als.ImplicitMF(features, *, iterations=20, reg=0.1, weight=40)¶

Bases: lenskit.algorithms.mf_common.MFPredictor

Implicit matrix factorization trained with alternating least squares [HKV2008]. This algorithm outputs ‘predictions’, but they are not on a meaningful scale. If its input data contains rating values, these will be used as the ‘confidence’ values; otherwise, confidence will be 1 for every rated item.

[HKV2008]

(1, 2) Y. Hu, Y. Koren, and C. Volinsky. 2008. Collaborative Filtering for Implicit Feedback Datasets. In _Proceedings of the 2008 Eighth IEEE International Conference on Data Mining_, 263–272. DOI 10.1109/ICDM.2008.22

Parameters:	features (int) – the number of features to train iterations (int) – the number of iterations to train reg (double) – the regularization factor weight (double) – the scaling weight for positive samples (\(\alpha\) in [HKV2008]).

fit(ratings)¶

Train a model using the specified ratings (or similar) data.

Parameters:	ratings (pandas.DataFrame) – The ratings data. args – Additional training data the algorithm may require. kwargs – Additional training data the algorithm may require.
Returns:	The algorithm object.

predict_for_user(user, items, ratings=None)¶

Compute predictions for a user and items.

Parameters:	user – the user ID items (array-like) – the items to predict ratings (pandas.Series) – the user’s ratings (indexed by item id); if provided, they may be used to override or augment the model’s notion of a user’s preferences.
Returns:	scores for the items, indexed by item id.
Return type:	pandas.Series

FunkSVD ¶

FunkSVD is an SVD-like matrix factorization that uses stochastic gradient descent, configured much like coordinate descent, to train the user-feature and item-feature matrices.

class lenskit.algorithms.funksvd.FunkSVD(features, iterations=100, *, lrate=0.001, reg=0.015, damping=5, range=None, bias=True)¶

Bases: lenskit.algorithms.mf_common.BiasMFPredictor

Algorithm class implementing FunkSVD matrix factorization.

Parameters:

features (int) – the number of features to train
iterations (int) – the number of iterations to train each feature
lrate (double) – the learning rate
reg (double) – the regularization factor
damping (double) – damping factor for the underlying mean
bias (Predictor) – the underlying bias model to fit. If True, then a basic.Bias model is fit with damping.
range (tuple) – the (min, max) rating values to clamp ratings, or None to leave predictions unclamped.

fit(ratings)¶

Train a FunkSVD model.

Parameters:	ratings – the ratings data frame.

predict_for_user(user, items, ratings=None)¶

Compute predictions for a user and items.

Parameters:	user – the user ID items (array-like) – the items to predict ratings (pandas.Series) – the user’s ratings (indexed by item id); if provided, they may be used to override or augment the model’s notion of a user’s preferences.
Returns:	scores for the items, indexed by item id.
Return type:	pandas.Series

Hierarchical Poisson Factorization¶

This module provides a LensKit bridge to the hpfrec library implementing hierarchical Poisson factorization [GHB2013].

[GHB2013]

Prem Gopalan, Jake M. Hofman, and David M. Blei. 2013. Scalable Recommendation with Poisson Factorization. arXiv:1311.1704 [cs, stat] (November 2013). Retrieved February 9, 2017 from http://arxiv.org/abs/1311.1704.

class lenskit.algorithms.hpf.HPF(features, **kwargs)¶

Hierarchical Poisson factorization, provided by hpfrec.

Parameters:	features (int) – the number of features **kwargs – arguments passed to `hpfrec.HPF`.

fit(ratings)¶

Train a model using the specified ratings (or similar) data.

Parameters:	ratings (pandas.DataFrame) – The ratings data. args – Additional training data the algorithm may require. kwargs – Additional training data the algorithm may require.
Returns:	The algorithm object.

predict_for_user(user, items, ratings=None)¶

Compute predictions for a user and items.

Parameters:	user – the user ID items (array-like) – the items to predict ratings (pandas.Series) – the user’s ratings (indexed by item id); if provided, they may be used to override or augment the model’s notion of a user’s preferences.
Returns:	scores for the items, indexed by item id.
Return type:	pandas.Series

Implicit¶

This module provides a LensKit bridge to Ben Frederickson’s implicit library implementing some implicit-feedback recommender algorithms, with an emphasis on matrix factorization.

class lenskit.algorithms.implicit.ALS(*args, **kwargs)¶: LensKit interface to implicit.als.

class lenskit.algorithms.implicit.BPR(*args, **kwargs)¶: LensKit interface to implicit.bpr.

Utility Functions¶

Matrix Utilities¶

We have some matrix-related utilities, since matrices are used so heavily in recommendation algorithms.

Building Ratings Matrices¶

lenskit.matrix.sparse_ratings(ratings, scipy=False)¶

Convert a rating table to a sparse matrix of ratings.

Parameters:	ratings (pandas.DataFrame) – a data table of (user, item, rating) triples. scipy – if `True`, return a SciPy matrix instead of `CSR`.
Returns:	a named tuple containing the sparse matrix, user index, and item index.
Return type:	RatingMatrix

class lenskit.matrix.RatingMatrix¶

A rating matrix with associated indices.

matrix¶

The rating matrix, with users on rows and items on columns.

Type:	CSR or scipy.sparse.csr_matrix

users¶

mapping from user IDs to row numbers.

Type:	pandas.Index

items¶

mapping from item IDs to column numbers.

Type:	pandas.Index

Compressed Sparse Row Matrices¶

We use CSR-format sparse matrices in quite a few places. Since SciPy’s sparse matrices are not directly usable from Numba, we have implemented a Numba-compiled CSR representation that can be used from accelerated algorithm implementations.

class lenskit.matrix.CSR(nrows=None, ncols=None, nnz=None, ptrs=None, inds=None, vals=None, N=None)¶

Simple compressed sparse row matrix. This is like scipy.sparse.csr_matrix, with a couple of useful differences:

It is backed by a Numba jitclass, so it can be directly used from Numba-optimized functions.
The value array is optional, for cases in which only the matrix structure is required.
The value array, if present, is always double-precision.

You generally don’t want to create this class yourself with the constructor. Instead, use one of its class methods.

If you need to pass an instance off to a Numba-compiled function, use N:

_some_numba_fun(csr.N)

We use the indirection between this and the Numba jitclass so that the main CSR implementation can be pickled, and so that we can have class and instance methods that are not compatible with jitclass but which are useful from interpreted code.

N¶

the Numba jitclass backing (has the same attributes and most methods).

Type:	_CSR

nrows¶

the number of rows.

Type:	int

ncols¶

the number of columns.

Type:	int

nnz¶

the number of entries.

Type:	int

rowptrs¶

the row pointers.

Type:	numpy.ndarray

colinds¶

the column indices.

Type:	numpy.ndarray

values¶

the values

Type:	numpy.ndarray

classmethod from_coo(rows, cols, vals, shape=None)¶

Create a CSR matrix from data in COO format.

Parameters:	rows (array-like) – the row indices. cols (array-like) – the column indices. vals (array-like) – the data values; can be `None`. shape (tuple) – the array shape, or `None` to infer from row & column indices.

classmethod from_scipy(mat, copy=True)¶

Convert a scipy sparse matrix to an internal CSR.

Parameters:	mat (scipy.sparse.spmatrix) – a SciPy sparse matrix. copy (bool) – if `False`, reuse the SciPy storage if possible.
Returns:	a CSR matrix.
Return type:	CSR

row(row)¶

Return a row of this matrix as a dense ndarray.

Parameters:	row (int) – the row index.
Returns:	the row, with 0s in the place of missing values.
Return type:	numpy.ndarray

row_cs(row)¶: Get the column indcies for the stored values of a row.

row_extent(row)¶

Get the extent of a row in the underlying column index and value arrays.

Parameters:	row (int) – the row index.
Returns:	`(s, e)`, where the row occupies positions \([s, e)\) in the CSR data.
Return type:	tuple

row_nnzs()¶

Get a vector of the number of nonzero entries in each row.

Note

This method is not available from Numba.

Returns:	the number of nonzero entries in each row.
Return type:	numpy.ndarray

row_vs(row)¶: Get the stored values of a row.

rowinds() → numpy.ndarray¶: Get the row indices from this array. Combined with colinds and values, this can form a COO-format sparse matrix.

Note

This method is not available from Numba.

sort_values()¶: Sort CSR rows in nonincreasing order by value.

Note

This method is not available from Numba.

to_scipy()¶

Convert a CSR matrix to a SciPy scipy.sparse.csr_matrix.

Parameters:	self (CSR) – A CSR matrix.
Returns:	A SciPy sparse matrix with the same data.
Return type:	scipy.sparse.csr_matrix

transpose(values=True)¶

Transpose a CSR matrix.

Note

This method is not available from Numba.

Parameters:	values (bool) – whether to include the values in the transpose.
Returns:	the transpose of this matrix (or, equivalently, this matrix in CSC format).
Return type:	CSR

class lenskit.matrix._CSR(nrows, ncols, nnz, ptrs, inds, vals)¶: Internal implementation class for CSR. If you work with CSRs from Numba, you will use this.

Math utilities¶

Solvers¶

lenskit.math.solve.dposv(A, b, lower=False)¶: Interface to the BLAS dposv function. A Numba-accessible verison without error checking is exposed as _dposv().

lenskit.math.solve.solve_tri(A, b, transpose=False, lower=True)¶

Solve the system \(Ax = b\), where \(A\) is triangular. This is equivalent to scipy.linalg.solve_triangular(), but does not check for non-singularity. It is a thin wrapper around the BLAS dtrsv function.

Parameters:	A (ndarray) – the matrix. b (ndarray) – the taget vector. transpose (bool) – whether to solve \(Ax = b\) or \(A^T x = b\). lower (bool) – whether \(A\) is lower- or upper-triangular.

Numba-accessible internals¶

lenskit.math.solve._dposv()¶

lenskit.math.solve._dtrsv()¶

Miscellaneous¶

Miscellaneous utility functions.

lenskit.util.clone(algo)¶

Clone an algorithm, but not its fitted data. This is like scikit.base.clone(), but may not work on arbitrary SciKit estimators. LensKit algorithms are compatible with SciKit clone, however, so feel free to use that if you need more general capabilities.

This function is somewhat derived from the SciKit one.

>>> from lenskit.algorithms.basic import Bias
>>> orig = Bias()
>>> copy = clone(orig)
>>> copy is orig
False
>>> copy.damping == orig.damping
True

lenskit.util.fspath(path)¶: Backport of os.fspath() function for Python 3.5.

lenskit.util.load_ml_ratings(path='ml-latest-small')¶

Load the ratings from a modern MovieLens data set (ML-20M or one of the ‘latest’ data sets).

>>> load_ml_ratings().head()
    user item rating  timestamp
 1      31    2.5 1260759144
 1    1029    3.0 1260759179
 1    1061    3.0 1260759182
 1    1129    2.0 1260759185
 1    1172    4.0 1260759205

Parameters:	path – The path where the MovieLens data is unpacked.
Returns:	The rating data, with user and item columns named properly for LensKit.
Return type:	pandas.DataFrame

lenskit.util.read_df_detect(path)¶

Read a Pandas data frame, auto-detecting the file format based on filename suffix. The following file types are supported:

CSV: File has suffix .csv, read with pandas.read_csv().
Parquet: File has suffix .parquet, .parq, or .pq, read with pandas.read_parquet().

lenskit.util.write_parquet(path, frame, append=False)¶

Write a Parquet file.

Parameters:	path (pathlib.Path) – The path of the Parquet file to write. frame (pandas.DataFrame) – The data to write. append (bool) – Whether to append to the file or overwrite it.

Release Notes¶

0.6.0¶

See the GitHub milestone for a summary of what’s happening!

The save and load methods on algorithms have been removed. Just pickle fitted models to save their data. This is what SciKit does, we see no need to deviate.
The APIs and model structures for top-N recommendation is reworked to enable algorithms to produce recommendations more automatically. The Recommender interfaces now take a CandidateSelector to determine default candidates, so client code does not need to compute candidates on their own. One effect of this is that the batch.recommend function no longer requires a candidate selector, and there can be problems if you call Recommender.adapt before fitting a model.
Top-N evaluation has been completely revamped to make it easier to correctly implement and run evaluation metrics. Batch recommend no longer attaches ratings to recommendations. See Top-N evaluation for details.
Batch recommend & predict functions now take nprocs as a keyword-only argument.
Several bug fixes and testing improvements.

Internal Changes¶

These changes should not affect you if you are only consuming LensKit’s algorithm and evaluation capabilities.

Rewrite the CSR class to be more ergonomic from Python, at the expense of making the NumPy jitclass indirect. It is available in the .N attribute. Big improvement: it is now picklable.

0.5.0¶

LensKit 0.5.0 modifies the algorithm APIs to follow the SciKit design patterns instead of our previous custom patterns. Highlights of this change:

Algorithms are trained in-place — we no longer have distinct model objects.
Model data is stored as attributes on the algorithm object that end in _.
Instead of writing model = algo.train_model(ratings), call algo.fit(ratings).

We also have some new capabilities:

Ben Frederickson’s Implicit library

0.3.0¶

A number of improvements, including replacing Cython/OpenMP with Numba and adding ALS.

LensKit¶

Installation¶

Resources¶

Getting Started¶

Setup¶

Loading Data¶

Defining Algorithms¶

Running the Evaluation¶

Algorithm Interfaces¶

Base Algorithm¶

Recommendation¶

Candidate Selection¶

Rating Prediction¶

Crossfold preparation¶

Row-based splitting¶

User-based splitting¶

Selecting user test rows¶

Utility Classes¶

Batch-Running Recommenders¶

Recommendation¶

Rating Prediction¶

Scripting Evaluation¶

Evaluating Recommender Output¶

Prediction Accuracy Metrics¶

Metric Functions¶

Working with Missing Data¶

Top-N Evaluation¶

Top-N Analysis¶

Metrics¶

Classification Metrics¶

Ranked List Metrics¶

Utility Metrics¶

Loading Outputs¶

Errors and Diagnostics¶

Logging¶

Warnings¶

Algorithms¶

Basic and Utility Algorithms¶

Personalized Mean Rating Prediction¶

Fallback Predictor¶

Memorized Predictor¶

k-NN Collaborative Filtering¶

Item-based k-NN¶

User-based k-NN¶

Classic Matrix Factorization¶

Common Support¶

Alternating Least Squares¶

FunkSVD¶

Hierarchical Poisson Factorization¶

Implicit¶

Utility Functions¶

Matrix Utilities¶

Building Ratings Matrices¶

Compressed Sparse Row Matrices¶

Math utilities¶

Solvers¶

Numba-accessible internals¶

Miscellaneous¶

Release Notes¶

0.6.0¶

Internal Changes¶

0.5.0¶

0.3.0¶

0.2.0¶

0.1.0¶

Indices and tables¶

Common Support ¶

Alternating Least Squares ¶

FunkSVD ¶