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SAR+ docstring patch #1648

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Feb 17, 2022
Merged

SAR+ docstring patch #1648

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3984b68
Add type info in docstrings for SARPlus.recommend_k_items()
simonzhaoms Feb 16, 2022
98204df
Correct README
simonzhaoms Feb 16, 2022
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10 changes: 7 additions & 3 deletions contrib/sarplus/README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -77,7 +77,7 @@ train_df = spark.createDataFrame(
# spark dataframe with user/item tuples
test_df = spark.createDataFrame(
[(1, 1, 1), (3, 3, 1)],
["user_id", "item_id', "rating"],
["user_id", "item_id", "rating"],
)

# To use C++ based fast prediction, a local cache directory needs to be
Expand All @@ -91,6 +91,7 @@ test_df = spark.createDataFrame(
# col_item="item_id",
# col_rating="rating",
# col_timestamp="timestamp",
# similarity_type="jaccard",
# cache_path="cache",
# )
# ```
Expand All @@ -104,6 +105,7 @@ test_df = spark.createDataFrame(
# col_item="item_id",
# col_rating="rating",
# col_timestamp="timestamp",
# similarity_type="jaccard",
# cache_path="dbfs:/mnt/sarpluscache/cache",
# )
# ```
Expand All @@ -118,6 +120,7 @@ test_df = spark.createDataFrame(
# col_item="item_id",
# col_rating="rating",
# col_timestamp="timestamp",
# similarity_type="jaccard",
# cache_path=f"synfs:/{job_id}/mnt/sarpluscache/cache",
# )
# ```
Expand All @@ -134,16 +137,17 @@ model = SARPlus(
col_item="item_id",
col_rating="rating",
col_timestamp="timestamp",
similarity_type="jaccard",
)
model.fit(train_df, similarity_type="jaccard")
model.fit(train_df)

# To use C++ based fast prediction, the `use_cache` parameter of
# `SARPlus.recommend_k_items()` also needs to be set to `True`.
#
# ```
# model.recommend_k_items(test_df, top_k=3, use_cache=True).show()
# ```
model.recommend_k_items(test_df, top_k=3).show()
model.recommend_k_items(test_df, top_k=3, remove_seen=False).show()
```

### Jupyter Notebook
Expand Down
14 changes: 9 additions & 5 deletions contrib/sarplus/python/pysarplus/SARPlus.py
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Original file line number Diff line number Diff line change
Expand Up @@ -446,11 +446,15 @@ def recommend_k_items(
"""Recommend top K items for all users which are in the test set.

Args:
test: test Spark dataframe
top_k: top n items to return
remove_seen: remove items test users have already seen in the past from the recommended set
use_cache: use specified local directory stored in self.cache_path as cache for C++ based fast predictions
n_user_prediction_partitions: prediction partitions
test (pyspark.sql.DataFrame): test Spark dataframe.
top_k (int): top n items to return.
remove_seen (bool): remove items test users have already seen in the past from the recommended set.
use_cache (bool): use specified local directory stored in `self.cache_path` as cache for C++ based fast
predictions.
n_user_prediction_partitions (int): prediction partitions.

Returns:
pyspark.sql.DataFrame: Spark dataframe with recommended items
"""
if not use_cache:
return self._recommend_k_items_slow(test, top_k, remove_seen)
Expand Down