LensKit ¶

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.

UnratedItemCandidateSelector is the default and most common implementation of this interface.

abstract 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

abstract 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

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.

abstract __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).

property test¶: Test data for this pair.

property 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, *, n_jobs=None, dask_result=False, **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).
n_jobs (int) –
The number of processes to use for parallel recommendations. Passed as n_jobs to :cls:`joblib.Parallel`. The default, None, will make the process sequential _unless_ called inside the joblib.parallel_backend() context manager.

Note

nprocs is accepted as a deprecated alias.
dask_result (bool) – Whether to return a Dask data frame instead of a Pandas one.

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, *, n_jobs=None, **kwargs)¶

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.
n_jobs (int) –
The number of processes to use for parallel batch prediction. Passed as n_jobs to :cls:`joblib.Parallel`. The default, None, will make the process sequential _unless_ called inside the joblib.parallel_backend() context manager.

Note

nprocs is accepted as a deprecated alias.

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

Scripting Evaluation¶

The MultiEval class is useful to build scripts that evaluate multiple algorithms or algorithm variants, simultaneously, across multiple data sets. It can extract parameters from algorithms and include them in the output, useful for hyperparameter search.

For example:

from lenskit.batch import MultiEval
from lenskit.crossfold import partition_users, SampleN
from lenskit.algorithms import basic, als
from lenskit.util import load_ml_ratings
from lenskit import topn
import pandas as pd

Generate the train-test pairs:

pairs = list(partition_users(load_ml_ratings(), 5, SampleN(5)))

Set up and run the MultiEval experiment:

eval = MultiEval('my-eval', recommend=20)
eval.add_datasets(pairs, name='ML-Small')
eval.add_algorithms(basic.Popular(), name='Pop')
eval.add_algorithms([als.BiasedMF(f) for f in [20, 30, 40, 50]],
                    attrs=['features'], name='ALS')
eval.run()

Now that the experiment is run, we can read its outputs.

First the run metadata:

runs = pd.read_csv('my-eval/runs.csv')
runs.set_index('RunId', inplace=True)
runs.head()

	AlgoClass	AlgoStr	DataSet	Partition	PredTime	RecTime	TrainTime	features	name
RunId
1	Popular	Popular	ML-Small	1	NaN	0.578916	0.278333	NaN	Pop
2	BiasedMF	als.BiasedMF(features=20, regularization=0.1)	ML-Small	1	0.377277	1.324478	5.426510	20.0	ALS
3	BiasedMF	als.BiasedMF(features=30, regularization=0.1)	ML-Small	1	0.326613	1.566073	1.300490	30.0	ALS
4	BiasedMF	als.BiasedMF(features=40, regularization=0.1)	ML-Small	1	0.408973	1.570634	1.904973	40.0	ALS
5	BiasedMF	als.BiasedMF(features=50, regularization=0.1)	ML-Small	1	0.357133	1.700047	2.390314	50.0	ALS

Then the recommendations:

recs = pd.read_parquet('my-eval/recommendations.parquet')
recs.head()

D:Anaconda3libsite-packagespyarrowpandas_compat.py:698: FutureWarning: .labels was deprecated in version 0.24.0. Use .codes instead.
  labels = getattr(columns, 'labels', None) or [
D:Anaconda3libsite-packagespyarrowpandas_compat.py:725: FutureWarning: the 'labels' keyword is deprecated, use 'codes' instead
  return pd.MultiIndex(levels=new_levels, labels=labels, names=columns.names)
D:Anaconda3libsite-packagespyarrowpandas_compat.py:742: FutureWarning: .labels was deprecated in version 0.24.0. Use .codes instead.
  labels, = index.labels

	item	score	user	rank	RunId
0	356	335	6	1	1
1	296	323	6	2	1
2	318	305	6	3	1
3	593	302	6	4	1
4	260	284	6	5	1

In order to evaluate the recommendation list, we need to build a combined set of truth data. Since this is a disjoint partition of users over a single data set, we can just concatenate the individual test frames:

truth = pd.concat((p.test for p in pairs), ignore_index=True)

Now we can set up an analysis and compute the results.

rla = topn.RecListAnalysis()
rla.add_metric(topn.ndcg)
ndcg = rla.compute(recs, truth)
ndcg.head()

Next, we need to combine this with our run data, so that we know what algorithms and configurations we are evaluating:

ndcg = ndcg.join(runs[['AlgoClass', 'features']], on='RunId')
ndcg.head()

		ndcg	AlgoClass	features
user	RunId
1	11	0.0	Popular	NaN
	12	0.0	BiasedMF	20.0
	13	0.0	BiasedMF	30.0
	14	0.0	BiasedMF	40.0
	15	0.0	BiasedMF	50.0

The Popular algorithm has NaN feature count, which groupby doesn’t like; let’s fill those in.

ndcg.loc[ndcg['AlgoClass'] == 'Popular', 'features'] = 0

And finally, we can compute the overall average performance for each algorithm configuration:

ndcg.groupby(['AlgoClass', 'features'])['ndcg'].mean()

AlgoClass  features
BiasedMF   20.0        0.015960
           30.0        0.022558
           40.0        0.025901
           50.0        0.028949
Popular    0.0         0.091814
Name: ndcg, dtype: float64

Multi-Eval Class Reference¶

class lenskit.batch.MultiEval(path, *, predict=True, recommend=100, candidates=None, save_models=False, eval_n_jobs=None, combine=True, **kwargs)¶

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. Any false-y value (None, False, 0) will disable top-n. The literal value True will generate recommendation lists of unlimited size.
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. Pass None to use the default candidate set configured for each algorithm (recommended).
save_models (bool or str) – save individual estimated models to disk. If True, models are pickled to .pkl files; if 'gzip', they are pickled to gzip-compressed .pkl.gz files; if 'joblib', they are pickled with joblib.dump() to uncompressed .jlpkl files.
eval_n_jobs (int or None) – Value to pass to the n_jobs parameter in lenskit.batch.predict() and lenskit.batch.recommend().
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 predictions).

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

Add one or more algorithms to the run.

Parameters

algos (algorithm or list) – the algorithm(s) to add.
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, *, progress=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.
progress – A tqdm.tqdm()-compatible progress function.

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:

from lenskit.datasets import MovieLens
from lenskit.algorithms.basic import Bias
from lenskit.batch import predict
from lenskit.metrics.predict import rmse
ratings = MovieLens('ml-small').ratings.sample(frac=10
test = ratings.iloc[:1000]
train = ratings.iloc[1000:]
algo = Bias()
algo.fit(train)
preds = predict(algo, pairs)
user_rmse = preds.groupby('user').apply(lambda df: rmse(df.prediction, df.rating))
user_rmse.mean()

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.

Warning

Currently, RecListAnalysis will silently drop users who received no recommendations. We are working on an ergonomic API for fixing this problem.

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, *, include_missing=False)¶

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.
include_missing (bool) – True to include users from truth missing from recs. Matches are done via group columns that appear in both recs and truth.

Returns

The results of the analysis.

Return type

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()

Data Set Utilities¶

The lenskit.datasets module provides utilities for reading a variety of commonly-used LensKit data sets. It does not package or automatically download them, but loads them from a local directory where you have unpacked the data set. Each data set class or function takes a path parameter specifying the location of the data set.

The normal mode of operation for these utilities is to provide a class for the data set; this class then exposes the data set’s data as attributes. These attributes are cached internally, so e.g. accessing MovieLens.ratings twice will only load the data file once.

These data files have normalized column names to fit with LensKit’s general conventions. These are the following:

User ID columns are called user.
Item ID columns are called item.
Rating columns are called rating.
Timestamp columns are called timestamp.

Other column names are unchanged. Data tables that provide information about specific things, such as a table of movie titles, are indexed by the relevant ID (e.g. MovieLens.ratings is indexed by item).

MovieLens Data Sets¶

The GroupLens research group provides several data sets extracted from the MovieLens service [ML]. These can be downloaded from https://grouplens.org/datasets/movielens/.

class lenskit.datasets.MovieLens(path='data/ml-20m')¶

Code for reading current MovieLens data sets, including ML-20M, ML-Latest, and ML-Latest-Small.

Parameters: path (str or pathlib.Path) – Path to the directory containing the data set.

property links¶

The movie link table, connecting movie IDs to external identifiers. It is indexed by movie ID.

>>> mlsmall = MovieLens('ml-latest-small')
>>> mlsmall.links
         imdbId  tmdbId
item
1        114709     862
2        113497    8844
3        113228   15602
4        114885   31357
5        113041   11862
...
[9125 rows x 2 columns]

property movies¶

The movie table, with titles and genres. It is indexed by movie ID.

>>> mlsmall = MovieLens('ml-latest-small')
>>> mlsmall.movies
                                                    title                                           genres
item
1                                        Toy Story (1995)      Adventure|Animation|Children|Comedy|Fantasy
2                                          Jumanji (1995)                       Adventure|Children|Fantasy
3                                 Grumpier Old Men (1995)                                   Comedy|Romance
4                                Waiting to Exhale (1995)                             Comedy|Drama|Romance
5                      Father of the Bride Part II (1995)                                           Comedy
...
[9125 rows x 2 columns]

property ratings¶

The rating table.

>>> mlsmall = MovieLens('ml-latest-small')
>>> mlsmall.ratings
        user  item  rating   timestamp
0          1    31     2.5  1260759144
1          1  1029     3.0  1260759179
2          1  1061     3.0  1260759182
3          1  1129     2.0  1260759185
4          1  1172     4.0  1260759205
...
[100004 rows x 4 columns]

property tag_genome¶: The tag genome table, recording inferred item-tag relevance scores. This gets returned as a wide Pandas data frame, with rows indexed by item ID.

property tags¶

The tag application table, recording user-supplied tags for movies.

>>> mlsmall = MovieLens('ml-latest-small')
>>> mlsmall.tags
      user  ...   timestamp
0       15  ...  1138537770
1       15  ...  1193435061
2       15  ...  1170560997
3       15  ...  1170626366
4       15  ...  1141391765
...
[1296 rows x 4 columns]

class lenskit.datasets.ML100K(path='data/ml-100k')¶

The MovieLens 100K data set. This older data set is in a different format from the more current data sets loaded by MovieLens.

property available¶: Query whether the data set exists.

property movies¶

Return the user data (from u.user).

>>> ml = ML100K('ml-100k')
>>> ml.movies               
                                          title      release  ...  War Western
item                                                          ...
1                              Toy Story (1995)  01-Jan-1995  ...    0       0
2                              GoldenEye (1995)  01-Jan-1995  ...    0       0
3                             Four Rooms (1995)  01-Jan-1995  ...    0       0
4                             Get Shorty (1995)  01-Jan-1995  ...    0       0
5                                Copycat (1995)  01-Jan-1995  ...    0       0
...
[1682 rows x 23 columns]

property ratings¶

Return the rating data (from u.data).

>>> ml = ML100K('ml-100k')
>>> ml.ratings              
       user  item  rating  timestamp
0       196   242     3.0  881250949
1       186   302     3.0  891717742
2        22   377     1.0  878887116
3       244    51     2.0  880606923
4       166   346     1.0  886397596
...
[100000 rows x 4 columns]

property users¶

Return the user data (from u.user).

>>> ml = ML100K('ml-100k')
>>> ml.users                
      age gender     occupation     zip
user
1      24      M     technician   85711
2      53      F          other   94043
3      23      M         writer   32067
4      24      M     technician   43537
5      33      F          other   15213
...
[943 rows x 4 columns]

class lenskit.datasets.ML1M(path='data/ml-1m')¶

MovieLens 1M data set.

Note

Some documentation examples use ML-10M100K; that is because this class shares implementation with the 10M data set.

property movies¶

Return the movie data (from movies.dat). Indexed by movie ID.

>>> ml = ML10M()
>>> ml.movies       
                                                    title                                           genres
item
1                                        Toy Story (1995)      Adventure|Animation|Children|Comedy|Fantasy
2                                          Jumanji (1995)                       Adventure|Children|Fantasy
3                                 Grumpier Old Men (1995)                                   Comedy|Romance
4                                Waiting to Exhale (1995)                             Comedy|Drama|Romance
5                      Father of the Bride Part II (1995)                                           Comedy
...
[10681 rows x 2 columns]

property ratings¶

Return the rating data (from ratings.dat).

>>> ml = ML10M()
>>> ml.ratings      
           user  item  rating  timestamp
0             1   122     5.0  838985046
1             1   185     5.0  838983525
2             1   231     5.0  838983392
3             1   292     5.0  838983421
4             1   316     5.0  838983392
...
[10000054 rows x 4 columns]

property users¶

Return the movie data (from users.dat). Indexed by user ID.

>>> ml = ML1M()
>>> ml.users        
     gender  age    zip
user
1         F    1  48067
2         M   56  70072
3         M   25  55117
4         M   45  02460
5         M   25  55455
...
[6040 rows x 3 columns]

class lenskit.datasets.ML10M(path='data/ml-10M100K')¶

MovieLens 10M100K data set.

property movies¶

Return the movie data (from movies.dat). Indexed by movie ID.

>>> ml = ML10M()
>>> ml.movies       
                                                    title                                           genres
item
1                                        Toy Story (1995)      Adventure|Animation|Children|Comedy|Fantasy
2                                          Jumanji (1995)                       Adventure|Children|Fantasy
3                                 Grumpier Old Men (1995)                                   Comedy|Romance
4                                Waiting to Exhale (1995)                             Comedy|Drama|Romance
5                      Father of the Bride Part II (1995)                                           Comedy
...
[10681 rows x 2 columns]

property ratings¶

Return the rating data (from ratings.dat).

>>> ml = ML10M()
>>> ml.ratings      
           user  item  rating  timestamp
0             1   122     5.0  838985046
1             1   185     5.0  838983525
2             1   231     5.0  838983392
3             1   292     5.0  838983421
4             1   316     5.0  838983392
...
[10000054 rows x 4 columns]

ML: F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets: History and Context. ACM Transactions on Interactive Intelligent Systems (TiiS) 5, 4, Article 19 (December 2015), 19 pages. DOI=http://dx.doi.org/10.1145/2827872

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.

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)¶

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(ratings, **kwargs)¶

Train the bias model on some rating data.

Parameters: ratings (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

Most Popular Item Recommendation¶

The Popular algorithm implements most-popular-item recommendation.

class lenskit.algorithms.basic.Popular(selector=None)¶

Bases: lenskit.algorithms.Recommender

Recommend the most popular items.

Parameters: selector (CandidateSelector) – The candidate selector to use. If None, uses a new UnratedItemCandidateSelector.

fit(ratings, **kwargs)¶

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

Parameters

ratings (pandas.DataFrame) – The ratings data.
kwargs – Additional training data the algorithm may require. Algorithms should avoid using the same keyword arguments for different purposes, so that they can be more easily hybridized.

Returns

The algorithm object.

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

Random Item Recommendation¶

The Random algorithm implements random-item recommendation.

class lenskit.algorithms.basic.Random(selector=None, random_state=None)¶

Bases: lenskit.algorithms.Recommender

A random-item recommender.

selector¶

Selects candidate items for recommendation. Default is UnratedItemCandidateSelector.

Type: CandidateSelector

random_state¶

Seed or random state for generating recommendations.

Type: int or numpy.random.RandomState

fit(ratings, **kwargs)¶

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

Parameters

ratings (pandas.DataFrame) – The ratings data.
kwargs – Additional training data the algorithm may require. Algorithms should avoid using the same keyword arguments for different purposes, so that they can be more easily hybridized.

Returns

The algorithm object.

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

Top-N Recommender¶

The TopN class implements a standard top-N recommender that wraps a Predictor and CandidateSelector and returns the top N candidate items by predicted rating. It is the type of recommender returned by Recommender.adapt() if the provided algorithm is not a recommender.

class lenskit.algorithms.basic.TopN(predictor, selector=None)¶

Bases: lenskit.algorithms.Recommender, lenskit.algorithms.Predictor

Basic recommender that implements top-N recommendation using a predictor.

Note

This class does not do anything of its own in fit(). If its predictor and candidate selector are both fit, the top-N recommender does not need to be fit.

Parameters

predictor (Predictor) – The underlying predictor.
selector (CandidateSelector) – The candidate selector. If None, uses UnratedItemCandidateSelector.

fit(ratings, **kwargs)¶

Fit the recommender.

Parameters

ratings (pandas.DataFrame) – The rating or interaction data. Passed changed to the predictor and candidate selector.
kwargs (args,) – Additional arguments for the predictor to use in its training process.

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

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

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

Unrated Item Candidate Selector¶

UnratedItemCandidateSelector is a candidate selector that remembers items users have rated, and returns a candidate set consisting of all unrated items. It is the default candidate selector for TopN.

class lenskit.algorithms.basic.UnratedItemCandidateSelector¶

Bases: lenskit.algorithms.CandidateSelector

CandidateSelector that selects items a user has not rated as candidates. When this selector is fit, it memorizes the rated items.

items_¶

All known items.

Type: pandas.Index

users_¶

All known users.

Type: pandas.Index

user_items_¶

Items rated by each known user, as positions in the items index.

Type: CSR

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.

fit(ratings, **kwargs)¶

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

Parameters

ratings (pandas.DataFrame) – The ratings data.
kwargs – Additional training data the algorithm may require. Algorithms should avoid using the same keyword arguments for different purposes, so that they can be more easily hybridized.

Returns

The algorithm object.

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)¶

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

fit(ratings, **kwargs)¶

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

Parameters

ratings (pandas.DataFrame) – The ratings data.
kwargs – Additional training data the algorithm may require. Algorithms should avoid using the same keyword arguments for different purposes, so that they can be more easily hybridized.

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

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)¶

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.
kwargs – Additional training data the algorithm may require. Algorithms should avoid using the same keyword arguments for different purposes, so that they can be more easily hybridized.

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

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')¶

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.

Parameters

nnbrs (int) – the maximum number of neighbors for scoring each item (None for unlimited)
min_nbrs (int) – the minimum number of neighbors for scoring each item
min_sim (double) – minimum similarity threshold for considering a neighbor
save_nbrs (double) – the number of neighbors to save per item in the trained model (None for unlimited)
center (bool) – whether to normalize (mean-center) rating vectors. Turn this off when working with unary data and other data types that don’t respond well to centering.
aggregate – the type of aggregation to do. Can be weighted-average or sum.

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, **kwargs)¶

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

User-based k-NN¶

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

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.

Parameters

nnbrs (int) – the maximum number of neighbors for scoring each item (None for unlimited)
min_nbrs (int) – the minimum number of neighbors for scoring each item
min_sim (double) – minimum similarity threshold for considering a neighbor
center (bool) – whether to normalize (mean-center) rating vectors. Turn this off when working with unary data and other data types that don’t respond well to centering.
aggregate – the type of aggregation to do. Can be weighted-average or sum.

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, **kwargs)¶

“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

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. These classes, MFPredictor and BiasMFPredictor, define the parameters that are estimated during the Algorithm.fit() process on common 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

property n_features¶: The number of features.

property n_items¶: The number of items.

property 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, method='cd', rand=<built-in method randn of numpy.random.mtrand.RandomState object>, progress=None)¶

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.

It provides two solvers for the optimization step (the method parameter):

'cd' (the default): Coordinate descent [TPT2011], adapted for a separately-trained bias model and to use weighted regularization as in the original ALS paper [ZWSP2008].
'lu': A direct implementation of the original ALS concept [ZWSP2008] using LU-decomposition to solve for the optimized matrices.

See the base class BiasMFPredictor for documentation on the estimated parameters you can extract from a trained model.

ZWSP2008(1,2,3): 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.
TPT2011: Gábor Takács, István Pilászy, and Domonkos Tikk. 2011. Applications of the Conjugate Gradient Method for Implicit Feedback Collaborative Filtering.

Parameters

features (int) – the number of features to train
iterations (int) – the number of iterations to train
reg (float) – the regularization factor; can also be a tuple (ureg, ireg) to specify separate user and item regularization terms.
damping (float) – damping factor for the underlying mean
bias (bool or Bias) – the bias model. If True, fits a Bias with damping damping.
method (str) – the solver to use (see above).
rng (function) – RNG function compatible with :fun:`numpy.random.randn` for initializing matrices.
progress – a tqdm.tqdm()-compatible progress bar function

fit(ratings, **kwargs)¶

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

class lenskit.algorithms.als.ImplicitMF(features, *, iterations=20, reg=0.1, weight=40, method='cg', rand=<built-in method randn of numpy.random.mtrand.RandomState object>, progress=None)¶

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.

'cd' (the default): Conjugate gradient method [TPT2011].
'lu': A direct implementation of the original implicit-feedback ALS concept [HKV2008] using LU-decomposition to solve for the optimized matrices.

See the base class MFPredictor for documentation on the estimated parameters you can extract from a trained model.

HKV2008(1,2,3): 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
TPT2011: Gábor Takács, István Pilászy, and Domonkos Tikk. 2011. Applications of the Conjugate Gradient Method for Implicit Feedback Collaborative Filtering.

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]).
progress – a tqdm.tqdm()-compatible progress bar function

fit(ratings, **kwargs)¶

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

Parameters

ratings (pandas.DataFrame) – The ratings data.
kwargs – Additional training data the algorithm may require. Algorithms should avoid using the same keyword arguments for different purposes, so that they can be more easily hybridized.

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

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. FunkSVD is a regularized biased matrix factorization technique trained with featurewise stochastic gradient descent.

See the base class BiasMFPredictor for documentation on the estimated parameters you can extract from a trained model.

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, **kwargs)¶

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

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, **kwargs)¶

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

Parameters

ratings (pandas.DataFrame) – The ratings data.
kwargs – Additional training data the algorithm may require. Algorithms should avoid using the same keyword arguments for different purposes, so that they can be more easily hybridized.

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