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Staging to main: Fix issue with SciPy and prepare for release 1.2.0 (#…
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…2094)

* Update SETUP.md

Remove left-over reference to conda script that has been deleted.

* remove cast to array in similarity functions

Signed-off-by: miguelgfierro <miguelgfierro@users.noreply.github.com>

* remove scipy limitation

Signed-off-by: miguelgfierro <miguelgfierro@users.noreply.github.com>

* flattening matrix so dataframe can be built correctly

Signed-off-by: Scott Graham <5720537+gramhagen@users.noreply.github.com>

* Adding toarray() suggested by @gramhagen

Signed-off-by: miguelgfierro <miguelgfierro@users.noreply.github.com>

* Added r-precision

Signed-off-by: David Davó <david@ddavo.me>

* Added r-precision tests

Signed-off-by: David Davó <david@ddavo.me>

* trying to substitude geta1 with ravel

Signed-off-by: miguelgfierro <miguelgfierro@users.noreply.github.com>

* Changed metric "result" to "result.toarray()"

Signed-off-by: Simon Zhao <simonyansenzhao@gmail.com>

* Revert recommenders/models/sar/sar_singlenode.py

Signed-off-by: Simon Zhao <simonyansenzhao@gmail.com>

* Use cooccurrence.toarray()

Signed-off-by: Simon Zhao <simonyansenzhao@gmail.com>

* return result if isinstance(result, np.ndarray) else result.toarray()

Signed-off-by: Simon Zhao <simonyansenzhao@gmail.com>

* Return numpy array instead of numpy matrix

Signed-off-by: Simon Zhao <simonyansenzhao@gmail.com>

* Prepare for Release Recommenders 1.2.0 (#2092)

* New release Recommenders 1.2.0 💥💥

* updated news

Signed-off-by: miguelgfierro <miguelgfierro@users.noreply.github.com>

---------

Signed-off-by: miguelgfierro <miguelgfierro@users.noreply.github.com>
Co-authored-by: miguelgfierro <miguelgfierro@users.noreply.github.com>

* Add reference to scenarios to README.md

There is no link to the scenarios page from the front page, so it is hard to find. Added reference.

---------

Signed-off-by: miguelgfierro <miguelgfierro@users.noreply.github.com>
Signed-off-by: Scott Graham <5720537+gramhagen@users.noreply.github.com>
Signed-off-by: David Davó <david@ddavo.me>
Signed-off-by: Simon Zhao <simonyansenzhao@gmail.com>
Co-authored-by: Andreas Argyriou <anargyri@users.noreply.github.com>
Co-authored-by: miguelgfierro <miguelgfierro@users.noreply.github.com>
Co-authored-by: Scott Graham <5720537+gramhagen@users.noreply.github.com>
Co-authored-by: David Davó <david@ddavo.me>
Co-authored-by: Simon Zhao <simonyansenzhao@gmail.com>
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9 changes: 9 additions & 0 deletions NEWS.md
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Expand Up @@ -5,12 +5,21 @@ Licensed under the MIT License.

# What's New

## Update May 2, 2024

We have a new release [Recommenders 1.2.0](https://github.com/microsoft/recommenders/releases/tag/1.2.0)!

So many changes since our last release. We have full tests on Python 3.8 to 3.11 (around 1800 tests), upgraded performance in many algorithms, reviewed notebooks, and many more improvements.


## Update October 10, 2023

We are pleased to announce that this repository (formerly known as Microsoft Recommenders, https://github.com/microsoft/recommenders), has joined the [Linux Foundation of AI and Data](https://lfaidata.foundation/) (LF AI & Data)! The new organization, `recommenders-team`, reflects this change.

We hope this move makes it easy for anyone to contribute! Our objective continues to be building an ecosystem and a community to sustain open source innovations and collaborations in recommendation systems.

Now to access the repo, instead of going to https://github.com/microsoft/recommenders, you need to go to https://github.com/recommenders-team/recommenders. The old URL will still resolve to the new one, but we recommend that you update your bookmarks.

## Update August 18, 2023

We moved to a new organization! Now to access the repo, instead of going to https://github.com/microsoft/recommenders, you need to go to https://github.com/recommenders-team/recommenders. The old URL will still resolve to the new one, but we recommend you to update your bookmarks.
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10 changes: 5 additions & 5 deletions README.md
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Expand Up @@ -9,13 +9,11 @@ Licensed under the MIT License.

<img src="https://raw.githubusercontent.com/recommenders-team/artwork/main/color/recommenders_color.svg" width="800">

## What's New (October, 2023)
## What's New (May, 2024)

We are pleased to announce that this repository (formerly known as Microsoft Recommenders, https://github.com/microsoft/recommenders), has joined the [Linux Foundation of AI and Data](https://lfaidata.foundation/) (LF AI & Data)! The new organization, `recommenders-team`, reflects this change.
We have a new release [Recommenders 1.2.0](https://github.com/microsoft/recommenders/releases/tag/1.2.0)!

We hope this move makes it easy for anyone to contribute! Our objective continues to be building an ecosystem and a community to sustain open source innovations and collaborations in recommendation systems.

Now to access the repo, instead of going to https://github.com/microsoft/recommenders, you need to go to https://github.com/recommenders-team/recommenders. The old URL will still resolve to the new one, but we recommend that you update your bookmarks.
So many changes since our last release. We have full tests on Python 3.8 to 3.11 (around 1800 tests), upgraded performance in many algorithms, reviewed notebooks, and many more improvements.

## Introduction

Expand All @@ -35,6 +33,8 @@ Several utilities are provided in [recommenders](recommenders) to support common

For a more detailed overview of the repository, please see the documents on the [wiki page](https://github.com/microsoft/recommenders/wiki/Documents-and-Presentations).

For some of the practical scenarios where recommendation systems have been applied, see [scenarios](scenarios).

## Getting Started

We recommend [conda](https://docs.conda.io/projects/conda/en/latest/glossary.html?highlight=environment#conda-environment) for environment management, and [VS Code](https://code.visualstudio.com/) for development. To install the recommenders package and run an example notebook on Linux/WSL:
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2 changes: 0 additions & 2 deletions SETUP.md
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Expand Up @@ -150,8 +150,6 @@ Currently, tests are done on **Python CPU** (the base environment), **Python GPU

Another way is to build a docker image and use the functions inside a [docker container](#setup-guide-for-docker).

Another alternative is to run all the recommender utilities directly from a local copy of the source code. This requires installing all the necessary dependencies from Anaconda and PyPI. For instructions on how to do this, see [this guide](conda.md).

## Setup for Making a Release

The process of making a new release and publishing it to [PyPI](https://pypi.org/project/recommenders/) is as follows:
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2 changes: 1 addition & 1 deletion recommenders/__init__.py
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Expand Up @@ -2,7 +2,7 @@
# Licensed under the MIT License.

__title__ = "Recommenders"
__version__ = "1.1.1"
__version__ = "1.2.0"
__author__ = "Recommenders contributors"
__license__ = "MIT"
__copyright__ = "Copyright 2018-present Recommenders contributors."
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58 changes: 58 additions & 0 deletions recommenders/evaluation/python_evaluation.py
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Expand Up @@ -541,6 +541,63 @@ def recall_at_k(
return (df_hit_count["hit"] / df_hit_count["actual"]).sum() / n_users


def r_precision_at_k(
rating_true,
rating_pred,
col_user=DEFAULT_USER_COL,
col_item=DEFAULT_ITEM_COL,
col_prediction=DEFAULT_PREDICTION_COL,
relevancy_method="top_k",
k=DEFAULT_K,
threshold=DEFAULT_THRESHOLD,
**_,
):
"""R-precision at K.
R-precision can be defined as the precision@R for each user, where R is the
numer of relevant items for the query. Its also equivalent to the recall at
the R-th position.
Note:
As R can be high, in this case, the k indicates the maximum possible R.
If every user has more than k true items, then r-precision@k is equal to
precision@k. You might need to raise the k value to get meaningful results.
Args:
rating_true (pandas.DataFrame): True DataFrame
rating_pred (pandas.DataFrame): Predicted DataFrame
col_user (str): column name for user
col_item (str): column name for item
col_prediction (str): column name for prediction
relevancy_method (str): method for determining relevancy ['top_k', 'by_threshold', None]. None means that the
top k items are directly provided, so there is no need to compute the relevancy operation.
k (int): number of top k items per user
threshold (float): threshold of top items per user (optional)
Returns:
float: recall at k (min=0, max=1). The maximum value is 1 even when fewer than
k items exist for a user in rating_true.
"""
df_hit, df_hit_count, n_users = merge_ranking_true_pred(
rating_true=rating_true,
rating_pred=rating_pred,
col_user=col_user,
col_item=col_item,
col_prediction=col_prediction,
relevancy_method=relevancy_method,
k=k,
threshold=threshold,
)

if df_hit.shape[0] == 0:
return 0.0

df_merged = df_hit.merge(df_hit_count[[col_user, 'actual']])
df_merged = df_merged[df_merged['rank'] <= df_merged['actual']]

return (df_merged.groupby(col_user).size() / df_hit_count.set_index(col_user)['actual']).mean()


def ndcg_at_k(
rating_true,
rating_pred,
Expand Down Expand Up @@ -824,6 +881,7 @@ def get_top_k_items(
exp_var.__name__: exp_var,
precision_at_k.__name__: precision_at_k,
recall_at_k.__name__: recall_at_k,
r_precision_at_k.__name__: r_precision_at_k,
ndcg_at_k.__name__: ndcg_at_k,
map_at_k.__name__: map_at_k,
map.__name__: map,
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12 changes: 6 additions & 6 deletions recommenders/utils/python_utils.py
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Expand Up @@ -62,7 +62,7 @@ def jaccard(cooccurrence):
with np.errstate(invalid="ignore", divide="ignore"):
result = cooccurrence / (diag_rows + diag_cols - cooccurrence)

return np.array(result)
return np.array(result) if isinstance(result, np.ndarray) else result.toarray()


def lift(cooccurrence):
Expand All @@ -85,7 +85,7 @@ def lift(cooccurrence):
with np.errstate(invalid="ignore", divide="ignore"):
result = cooccurrence / (diag_rows * diag_cols)

return np.array(result)
return np.array(result) if isinstance(result, np.ndarray) else result.toarray()


def mutual_information(cooccurrence):
Expand All @@ -106,7 +106,7 @@ def mutual_information(cooccurrence):
with np.errstate(invalid="ignore", divide="ignore"):
result = np.log2(cooccurrence.shape[0] * lift(cooccurrence))

return np.array(result)
return np.array(result) if isinstance(result, np.ndarray) else result.toarray()


def lexicographers_mutual_information(cooccurrence):
Expand All @@ -128,7 +128,7 @@ def lexicographers_mutual_information(cooccurrence):
with np.errstate(invalid="ignore", divide="ignore"):
result = cooccurrence * mutual_information(cooccurrence)

return np.array(result)
return np.array(result) if isinstance(result, np.ndarray) else result.toarray()


def cosine_similarity(cooccurrence):
Expand All @@ -151,7 +151,7 @@ def cosine_similarity(cooccurrence):
with np.errstate(invalid="ignore", divide="ignore"):
result = cooccurrence / np.sqrt(diag_rows * diag_cols)

return np.array(result)
return np.array(result) if isinstance(result, np.ndarray) else result.toarray()


def inclusion_index(cooccurrence):
Expand All @@ -173,7 +173,7 @@ def inclusion_index(cooccurrence):
with np.errstate(invalid="ignore", divide="ignore"):
result = cooccurrence / np.minimum(diag_rows, diag_cols)

return np.array(result)
return np.array(result) if isinstance(result, np.ndarray) else result.toarray()


def get_top_k_scored_items(scores, top_k, sort_top_k=False):
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2 changes: 1 addition & 1 deletion setup.py
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Expand Up @@ -43,7 +43,7 @@
"retrying>=1.3.4,<2",
"scikit-learn>=1.2.0,<2", # requires scipy, and introduce breaking change affects feature_extraction.text.TfidfVectorizer.min_df
"scikit-surprise>=1.1.3",
"scipy>=1.10.1,<1.11.0", # FIXME: We limit <1.11.0 until #1954 is fixed
"scipy>=1.10.1",
"seaborn>=0.13.0,<1", # requires matplotlib, packaging
"transformers>=4.27.0,<5", # requires packaging, pyyaml, requests, tqdm
]
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15 changes: 15 additions & 0 deletions tests/unit/recommenders/evaluation/test_python_evaluation.py
Original file line number Diff line number Diff line change
Expand Up @@ -25,6 +25,7 @@
exp_var,
get_top_k_items,
precision_at_k,
r_precision_at_k,
recall_at_k,
ndcg_at_k,
map_at_k,
Expand Down Expand Up @@ -366,6 +367,20 @@ def test_python_recall_at_k(rating_true, rating_pred, rating_nohit):
assert recall_at_k(rating_true, rating_pred, k=10) == pytest.approx(0.37777, TOL)


def test_python_r_precision(rating_true, rating_pred, rating_nohit):
assert r_precision_at_k(
rating_true=rating_true,
rating_pred=rating_true,
col_prediction=DEFAULT_RATING_COL,
k=10,
) == pytest.approx(1, TOL)
assert r_precision_at_k(rating_true, rating_nohit, k=5) == 0.0
assert r_precision_at_k(rating_true, rating_pred, k=3) == pytest.approx(0.21111, TOL)
assert r_precision_at_k(rating_true, rating_pred, k=5) == pytest.approx(0.24444, TOL)
# Equivalent to precision
assert r_precision_at_k(rating_true, rating_pred, k=10) == pytest.approx(0.37777, TOL)


def test_python_auc(rating_true_binary, rating_pred_binary):
assert auc(
rating_true=rating_true_binary,
Expand Down

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