refactor: Rename package 'best' to 'one2one'

bdikit/api.py

@@ -20,8 +20,8 @@
 from IPython.display import display, Markdown
 from bdikit.utils import get_gdc_data, get_gdc_metadata
 
-from bdikit.schema_matching.best.base import BaseSchemaMatcher
-from bdikit.schema_matching.best.matcher_factory import SchemaMatchers
+from bdikit.schema_matching.one2one.base import BaseSchemaMatcher
+from bdikit.schema_matching.one2one.matcher_factory import SchemaMatchers
 from bdikit.schema_matching.topk.base import BaseTopkSchemaMatcher
 from bdikit.schema_matching.topk.matcher_factory import TopkMatchers
 from bdikit.value_matching.base import BaseValueMatcher, ValueMatch, ValueMatchingResult

bdikit/schema_matching/best/contrastivelearning.py → bdikit/schema_matching/one2one/contrastivelearning.py

@@ -1,5 +1,5 @@
 import pandas as pd
-from bdikit.schema_matching.best.base import BaseSchemaMatcher
+from bdikit.schema_matching.one2one.base import BaseSchemaMatcher
 from bdikit.models.contrastive_learning.cl_api import DEFAULT_CL_MODEL
 from bdikit.schema_matching.topk.contrastivelearning import CLTopkSchemaMatcher
 

bdikit/schema_matching/best/gpt.py → bdikit/schema_matching/one2one/gpt.py

@@ -1,6 +1,6 @@
 import pandas as pd
 from openai import OpenAI
-from bdikit.schema_matching.best.base import BaseSchemaMatcher
+from bdikit.schema_matching.one2one.base import BaseSchemaMatcher
 
 
 class GPTSchemaMatcher(BaseSchemaMatcher):

bdikit/schema_matching/best/matcher_factory.py → bdikit/schema_matching/one2one/matcher_factory.py

@@ -1,42 +1,42 @@
 import importlib
 from enum import Enum
 from typing import Mapping, Any
-from bdikit.schema_matching.best.base import BaseSchemaMatcher
+from bdikit.schema_matching.one2one.base import BaseSchemaMatcher
 
 
 class SchemaMatchers(Enum):
     SIMFLOOD = (
         "similarity_flooding",
-        "bdikit.schema_matching.best.valentine.SimFloodSchemaMatcher",
+        "bdikit.schema_matching.one2one.valentine.SimFloodSchemaMatcher",
     )
     COMA = (
         "coma",
-        "bdikit.schema_matching.best.valentine.ComaSchemaMatcher",
+        "bdikit.schema_matching.one2one.valentine.ComaSchemaMatcher",
     )
     CUPID = (
         "cupid",
-        "bdikit.schema_matching.best.valentine.CupidSchemaMatcher",
+        "bdikit.schema_matching.one2one.valentine.CupidSchemaMatcher",
     )
     DISTRIBUTION_BASED = (
         "distribution_based",
-        "bdikit.schema_matching.best.valentine.DistributionBasedSchemaMatcher",
+        "bdikit.schema_matching.one2one.valentine.DistributionBasedSchemaMatcher",
     )
     JACCARD_DISTANCE = (
         "jaccard_distance",
-        "bdikit.schema_matching.best.valentine.JaccardDistanceSchemaMatcher",
+        "bdikit.schema_matching.one2one.valentine.JaccardDistanceSchemaMatcher",
     )
-    GPT = ("gpt", "bdikit.schema_matching.best.gpt.GPTSchemaMatcher")
+    GPT = ("gpt", "bdikit.schema_matching.one2one.gpt.GPTSchemaMatcher")
     CT_LEARNING = (
         "ct_learning",
-        "bdikit.schema_matching.best.contrastivelearning.ContrastiveLearningSchemaMatcher",
+        "bdikit.schema_matching.one2one.contrastivelearning.ContrastiveLearningSchemaMatcher",
     )
     TWO_PHASE = (
         "two_phase",
-        "bdikit.schema_matching.best.twophase.TwoPhaseSchemaMatcher",
+        "bdikit.schema_matching.one2one.twophase.TwoPhaseSchemaMatcher",
     )
     MAX_VAL_SIM = (
         "max_val_sim",
-        "bdikit.schema_matching.best.maxvalsim.MaxValSimSchemaMatcher",
+        "bdikit.schema_matching.one2one.maxvalsim.MaxValSimSchemaMatcher",
     )
 
     def __init__(self, matcher_name: str, matcher_path: str):

bdikit/schema_matching/best/maxvalsim.py → bdikit/schema_matching/one2one/maxvalsim.py

@@ -1,6 +1,6 @@
 import pandas as pd
 from typing import Optional
-from bdikit.schema_matching.best.base import BaseSchemaMatcher
+from bdikit.schema_matching.one2one.base import BaseSchemaMatcher
 from bdikit.models.contrastive_learning.cl_api import DEFAULT_CL_MODEL
 from bdikit.schema_matching.topk.base import BaseTopkSchemaMatcher
 from bdikit.schema_matching.topk.contrastivelearning import CLTopkSchemaMatcher

bdikit/schema_matching/best/twophase.py → bdikit/schema_matching/one2one/twophase.py

@@ -1,7 +1,7 @@
 import pandas as pd
 from typing import Optional
-from bdikit.schema_matching.best.base import BaseSchemaMatcher
-from bdikit.schema_matching.best.valentine import SimFloodSchemaMatcher
+from bdikit.schema_matching.one2one.base import BaseSchemaMatcher
+from bdikit.schema_matching.one2one.valentine import SimFloodSchemaMatcher
 from bdikit.models.contrastive_learning.cl_api import DEFAULT_CL_MODEL
 from bdikit.schema_matching.topk.base import BaseTopkSchemaMatcher
 from bdikit.schema_matching.topk.contrastivelearning import CLTopkSchemaMatcher

bdikit/schema_matching/best/valentine.py → bdikit/schema_matching/one2one/valentine.py

@@ -1,6 +1,6 @@
 import pandas as pd
 from typing import Dict, Callable
-from bdikit.schema_matching.best.base import BaseSchemaMatcher
+from bdikit.schema_matching.one2one.base import BaseSchemaMatcher
 from valentine import valentine_match
 from valentine.algorithms.matcher_results import MatcherResults
 from valentine.algorithms.jaccard_distance import StringDistanceFunction

bdikit/value_matching/polyfuzz.py

@@ -41,7 +41,7 @@ def match(
                 target = top_matches[index]
                 similarity = top_matches[index + 1]
                 if similarity >= self.threshold:
-                    matches.append((source, target, similarity))
+                    matches.append(ValueMatch(source, target, similarity))
 
         return matches
 

docs/source/api.rst

@@ -4,4 +4,4 @@
 
 .. automodule:: bdikit.api
     :members:
-    :exclude-members: SchemaMatchers, ValueMatchers, TopkMatchers, ValueMatchingResult, ColumnMappingSpec
+    :exclude-members: ColumnMappingSpec

docs/source/schema-matching.rst

@@ -16,13 +16,13 @@ To see how to use these methods, please refer to the documentation of :py:func:`
       - Class
       - Description
     * - ``ct_learning``
-      - :class:`~bdikit.mapping_algorithms.column_mapping.algorithms.ContrastiveLearningSchemaMatcher`
+      - :class:`~bdikit.schema_matching.one2one.contrastivelearning.ContrastiveLearningSchemaMatcher`
       - | Uses a contrastive (CT) learning model to learn embeddings for columns and retrieves the best match most similar columns using the cosine similarity between the column embeddings.
     * - ``two_phase``
-      - :class:`~bdikit.mapping_algorithms.column_mapping.algorithms.TwoPhaseSchemaMatcher`
+      - :class:`~bdikit.schema_matching.one2one.twophase.TwoPhaseSchemaMatcher`
       - | The two-phase schema matching method first uses a a top-k column matcher (e.g., `ct_learning`) to prune the search space (keeping only the top-k most likely matches), and then uses another column matcher to choose the best match from the pruned search space.
     * - ``gpt``
-      - :class:`~bdikit.mapping_algorithms.column_mapping.algorithms.GPTSchemaMatcher`
+      - :class:`~bdikit.schema_matching.one2one.gpt.GPTSchemaMatcher`
       - | This method uses the `ct_learning` to prune the search space and then uses a large language model (GPT4) to choose the best column match, given a set of top-k most likely candidates retrieved using the `ct_learning` method in the first phase.
 
 .. list-table:: Methods from other libraries
@@ -32,17 +32,17 @@ To see how to use these methods, please refer to the documentation of :py:func:`
       - Class
       - Description
     * - ``similarity_flooding``
-      - :class:`~bdikit.mapping_algorithms.column_mapping.algorithms.SimFloodSchemaMatcher`
+      - :class:`~bdikit.schema_matching.one2one.valentine.SimFloodSchemaMatcher`
       - | Similarity Flooding transforms schemas into directed graphs and merges them into a propagation graph. The algorithm iteratively propagates similarity scores to neighboring nodes until convergence. This algorithm was proposed by Sergey Melnik, Hector Garcia-Molina, and Erhard Rahm in "Similarity Flooding: A Versatile Graph Matching Algorithm and Its Application to Schema Matching" (ICDE, 2002).
     * - ``coma``
-      - :class:`~bdikit.mapping_algorithms.column_mapping.algorithms.ComaSchemaMatcher`
+      - :class:`~bdikit.schema_matching.one2one.valentine.ComaSchemaMatcher`
       - | COMA is a matcher that combines multiple schema-based matchers, representing schemas as rooted directed acyclic graphs. This algorithm was proposed by Do, Hong-Hai, and Erhard Rahm in "COMA — a system for flexible combination of schema matching approaches." (VLDB 2002). *This algorithm requires Java to be installed on the system.*
     * - ``cupid``
-      - :class:`~bdikit.mapping_algorithms.column_mapping.algorithms.CupidSchemaMatcher`
+      - :class:`~bdikit.schema_matching.one2one.valentine.CupidSchemaMatcher`
       - | Cupid is a schema-based approach that translates schemas into tree structures. It calculates overall similarity using linguistic and structural similarities, with tree transformations helping to compute context-based similarity. This algorithm was proposed by Madhavan et al. in "Generic Schema Matching with Cupid" (VLDB, 2001)​.
     * - ``distribution_based``
-      - :class:`~bdikit.mapping_algorithms.column_mapping.algorithms.DistributionBasedSchemaMatcher`
+      - :class:`~bdikit.schema_matching.one2one.valentine.DistributionBasedSchemaMatcher`
       - | Distribution-based Matching compares the distribution of data values in columns using the Earth Mover's Distance. It clusters relational attributes based on these comparisons. This algorithm was proposed by Zhang et al. in "Automatic discovery of attributes in relational databases" (SIGMOD 2011).
     * - ``jaccard_distance``
-      - :class:`~bdikit.mapping_algorithms.column_mapping.algorithms.JaccardSchemaMatcher`
+      - :class:`~bdikit.schema_matching.one2one.valentine.JaccardSchemaMatcher`
       - | This algorithm computes pairwise column similarities using Jaccard similarity, treating values as identical if their Levenshtein distance is below a threshold. The algorithm was proposed by Koutras et al. in "Valentine: Evaluating matching techniques for dataset discovery" (ICDE 2021).

requirements.txt

@@ -12,4 +12,4 @@ matplotlib<3.9
 panel!=1.4.3
 Levenshtein
 autofj
-nltk>=3.9.1
+nltk>=3.9.1

tests/test_schema_matching.py

@@ -1,13 +1,13 @@
 import pandas as pd
-from bdikit.schema_matching.best.valentine import (
+from bdikit.schema_matching.one2one.valentine import (
     SimFloodSchemaMatcher,
     JaccardSchemaMatcher,
     DistributionBasedSchemaMatcher,
     ComaSchemaMatcher,
     CupidSchemaMatcher,
 )
-from bdikit.schema_matching.best.twophase import TwoPhaseSchemaMatcher
-from bdikit.schema_matching.best.contrastivelearning import ContrastiveLearningSchemaMatcher
+from bdikit.schema_matching.one2one.twophase import TwoPhaseSchemaMatcher
+from bdikit.schema_matching.one2one.contrastivelearning import ContrastiveLearningSchemaMatcher
 
 
 def test_basic_column_mapping_algorithms():