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Merged
merged 1 commit into from
Mar 21, 2025
Merged

product association #149

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22 changes: 17 additions & 5 deletions pyretailscience/analysis/product_association.py
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Original file line number Diff line number Diff line change
Expand Up @@ -80,6 +80,8 @@ class ProductAssociation:
- uplift: The ratio of the observed support to the expected support if the products were independent.
"""

_df: pd.DataFrame | None = None

def __init__(
self,
df: pd.DataFrame | ibis.Table,
Expand Down Expand Up @@ -124,7 +126,7 @@ def __init__(
msg = f"The following columns are required but missing: {missing_cols}"
raise ValueError(msg)

self.df = self._calc_association(
self.table = self._calc_association(
df=df,
value_col=value_col,
group_col=group_col,
Expand Down Expand Up @@ -305,10 +307,13 @@ def _calc_association(
result = result[col_order].union(inverse_pairs[col_order])

result = result.filter(result.confidence >= min_confidence)

final_result = result.execute().sort_values(by=["item_1", "item_2"]).reset_index(drop=True)
final_result = final_result.rename(columns={"item_1": f"{value_col}_1", "item_2": f"{value_col}_2"})

final_result = result.order_by(["item_1", "item_2"])
final_result = final_result.rename(
{
f"{value_col}_1": "item_1",
f"{value_col}_2": "item_2",
},
)
return final_result[
[
f"{value_col}_1",
Expand All @@ -321,3 +326,10 @@ def _calc_association(
"uplift",
]
]

@property
def df(self) -> pd.DataFrame:
"""Returns the executed DataFrame."""
if self._df is None:
self._df = self.table.execute().reset_index(drop=True)
return self._df
37 changes: 21 additions & 16 deletions tests/analysis/test_product_association.py
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Original file line number Diff line number Diff line change
Expand Up @@ -106,27 +106,27 @@ def expected_results_pair_items_df(self) -> pd.DataFrame:

def test_calc_association_all_single_items(self, transactions_df, expected_results_single_items_df):
"""Test calculating association rules for a single item versus another of item for all items."""
associations_df = ProductAssociation._calc_association(
associations_df = ProductAssociation(
df=transactions_df,
value_col="product",
group_col=cols.transaction_id,
)

pd.testing.assert_frame_equal(associations_df, expected_results_single_items_df)
result = associations_df.df
pd.testing.assert_frame_equal(result, expected_results_single_items_df)

def test_calc_association_target_single_items(self, transactions_df, expected_results_single_items_df):
"""Test calculating association rules for target single item versus another of item."""
target_item = "bread"

calc_df = ProductAssociation._calc_association(
calc_df = ProductAssociation(
df=transactions_df,
value_col="product",
group_col=cols.transaction_id,
target_item=target_item,
)

result = calc_df.df
pd.testing.assert_frame_equal(
calc_df,
result,
expected_results_single_items_df[expected_results_single_items_df["product_1"] == target_item].reset_index(
drop=True,
),
Expand All @@ -136,15 +136,16 @@ def test_calc_association_min_occurrences(self, transactions_df, expected_result
"""Test calculating association rules with a min occurrences level."""
min_occurrences = 2

calc_df = ProductAssociation._calc_association(
calc_df = ProductAssociation(
df=transactions_df,
value_col="product",
group_col=cols.transaction_id,
min_occurrences=min_occurrences,
)

result = calc_df.df
pd.testing.assert_frame_equal(
calc_df,
result,
expected_results_single_items_df[
(expected_results_single_items_df["occurrences_1"] >= min_occurrences)
& (expected_results_single_items_df["occurrences_2"] >= min_occurrences)
Expand All @@ -155,15 +156,16 @@ def test_calc_association_min_cooccurrences(self, transactions_df, expected_resu
"""Test calculating association rules with a min occurrences level."""
min_cooccurrences = 2

calc_df = ProductAssociation._calc_association(
calc_df = ProductAssociation(
df=transactions_df,
value_col="product",
group_col=cols.transaction_id,
min_cooccurrences=min_cooccurrences,
)

result = calc_df.df
pd.testing.assert_frame_equal(
calc_df,
result,
expected_results_single_items_df[
(expected_results_single_items_df["cooccurrences"] >= min_cooccurrences)
].reset_index(drop=True),
Expand All @@ -173,15 +175,16 @@ def test_calc_association_min_support(self, transactions_df, expected_results_si
"""Test calculating association rules with a min occurrences level."""
min_support = 0.25

calc_df = ProductAssociation._calc_association(
calc_df = ProductAssociation(
df=transactions_df,
value_col="product",
group_col=cols.transaction_id,
min_support=min_support,
)

result = calc_df.df
pd.testing.assert_frame_equal(
calc_df,
result,
expected_results_single_items_df[(expected_results_single_items_df["support"] >= min_support)].reset_index(
drop=True,
),
Expand All @@ -191,15 +194,16 @@ def test_calc_association_min_confidence(self, transactions_df, expected_results
"""Test calculating association rules with a min occurrences level."""
min_confidence = 0.25

calc_df = ProductAssociation._calc_association(
calc_df = ProductAssociation(
df=transactions_df,
value_col="product",
group_col=cols.transaction_id,
min_confidence=min_confidence,
)

result = calc_df.df
pd.testing.assert_frame_equal(
calc_df,
result,
expected_results_single_items_df[
(expected_results_single_items_df["confidence"] >= min_confidence)
].reset_index(drop=True),
Expand All @@ -209,15 +213,16 @@ def test_calc_association_min_uplift(self, transactions_df, expected_results_sin
"""Test calculating association rules with a min occurrences level."""
min_uplift = 1

calc_df = ProductAssociation._calc_association(
calc_df = ProductAssociation(
df=transactions_df,
value_col="product",
group_col=cols.transaction_id,
min_uplift=min_uplift,
)

result = calc_df.df
pd.testing.assert_frame_equal(
calc_df,
result,
expected_results_single_items_df[(expected_results_single_items_df["uplift"] >= min_uplift)].reset_index(
drop=True,
),
Expand Down