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datamodel.py
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datamodel.py
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"""Datamodel of pyjedai.
"""
import pandas as pd
from pandas import DataFrame
import re
import csv
import nltk
from nltk.corpus import stopwords
from abc import ABC, abstractmethod
from collections import defaultdict
from ordered_set import OrderedSet
from tqdm import tqdm
from shapely.geometry import shape
from shapely.wkt import loads
class PYJEDAIFeature(ABC):
_method_name: str
_method_info: str
_method_short_name: str
def __init__(self) -> None:
super().__init__()
self._progress_bar: tqdm
self.execution_time: float
self.tqdm_disable: bool
self.data: Data
@abstractmethod
def _configuration(self) -> dict:
pass
@abstractmethod
def evaluate(self,
prediction=None,
export_to_df: bool = False,
export_to_dict: bool = False,
with_classification_report: bool = False,
verbose: bool = True) -> any:
pass
def method_configuration(self) -> dict:
"""Returns configuration details
"""
return {
"name" : self._method_name,
"parameters" : self._configuration(),
"runtime": self.execution_time
}
def report(self) -> None:
"""Prints Block Building method configuration
"""
parameters = ("\n" + ''.join(['\t{0}: {1}\n'.format(k, v) for k, v in self._configuration().items()])) \
if len(self._configuration().items()) != 0 else ' None'
print(
"Method name: " + self._method_name +
"\nMethod info: " + self._method_info +
"\nParameters: " + parameters +
"\nRuntime: {:2.4f} seconds".format(self.execution_time)
)
@abstractmethod
def stats(self) -> None:
pass
class Data:
"""The corpus of the dataset that will be processed with pyjedai. \
Contains all the information of the dataset and will be passed to each step \
of the ER workflow.
"""
def __init__(
self,
dataset_1: DataFrame,
id_column_name_1: str,
attributes_1: list = None,
dataset_name_1: str = None,
dataset_2: DataFrame = None,
attributes_2: list = None,
id_column_name_2: str = None,
dataset_name_2: str = None,
ground_truth: DataFrame = None,
skip_ground_truth_processing: bool = False
) -> None:
# Original Datasets as pd.DataFrame
if isinstance(dataset_1, pd.DataFrame):
self.dataset_1 = dataset_1
else:
raise AttributeError("Dataset 1 must be a pandas DataFrame")
if dataset_2 is not None:
if id_column_name_2 is None:
raise AttributeError("Must provide datasets 2 id column")
if isinstance(dataset_2, pd.DataFrame):
self.dataset_2 = dataset_2
else:
raise AttributeError("Dataset 2 must be a pandas DataFrame")
# Processed dataframes to lists (all attribute columns)
# Tranformed to list for optimization (list)
self.entities_d1: list
self.entities_d2: list = None
# D1 and D2 dataframes concatenated
self.entities: DataFrame
# Datasets specs
self.is_dirty_er = dataset_2 is None
self.dataset_limit = self.num_of_entities_1 = len(dataset_1)
self.num_of_entities_2: int = len(dataset_2) if dataset_2 is not None else 0
self.num_of_entities: int = self.num_of_entities_1 + self.num_of_entities_2
self.id_column_name_1 = id_column_name_1
self.id_column_name_2 = id_column_name_2
self.dataset_name_1 = dataset_name_1
self.dataset_name_2 = dataset_name_2
# Fill NaN values with empty string
self.dataset_1 = self.dataset_1.astype(str)
self.dataset_1.fillna("", inplace=True)
if not self.is_dirty_er:
self.dataset_2 = self.dataset_2.astype(str)
self.dataset_2.fillna("", inplace=True)
# Attributes
if attributes_1 is None:
if dataset_1.columns.values.tolist():
self.attributes_1 = dataset_1.columns.values.tolist()
if self.id_column_name_1 in self.attributes_1:
self.attributes_1.remove(self.id_column_name_1)
else:
raise AttributeError(
"Dataset 1 must contain column names if attributes_1 is empty.")
else:
self.attributes_1: list = attributes_1
if dataset_2 is not None:
if attributes_2 is None:
if dataset_2.columns.values.tolist():
self.attributes_2 = dataset_2.columns.values.tolist()
if self.id_column_name_2 in self.attributes_2:
self.attributes_2.remove(self.id_column_name_2)
else:
raise AttributeError("Dataset 2 must contain column names if attributes_2 is empty.")
else:
self.attributes_2: list = attributes_2
# Ground truth data
self.skip_ground_truth_processing = skip_ground_truth_processing
if ground_truth is not None and not skip_ground_truth_processing:
self.ground_truth = ground_truth.astype(str)
self.ground_truth.drop_duplicates(inplace=True)
self._ids_mapping_1: dict
self._gt_to_ids_reversed_1: dict
self._ids_mapping_2: dict
self._gt_to_ids_reversed_2: dict
else:
self.ground_truth = None
self.entities = self.dataset_1 = self.dataset_1.astype(str)
# Concatenated columns into new dataframe
self.entities_d1 = self.dataset_1[self.attributes_1]
if not self.is_dirty_er:
self.dataset_2 = self.dataset_2.astype(str)
self.entities_d2 = self.dataset_2[self.attributes_2]
self.entities = pd.concat([self.dataset_1, self.dataset_2],
ignore_index=True)
# if not skip_ground_truth_processing:
self._create_gt_mapping()
if ground_truth is not None:
if skip_ground_truth_processing:
self.ground_truth = ground_truth
else:
self._store_pairs()
else:
self.ground_truth = None
def _store_pairs(self) -> None:
"""Creates a mapping:
- pairs_of : ids of first dataset to ids of true matches from second dataset"""
self.duplicate_of = defaultdict(set)
for _, row in self.ground_truth.iterrows():
id1, id2 = (row.iloc[0], row.iloc[1])
if id1 in self.duplicate_of: self.duplicate_of[id1].add(id2)
else: self.duplicate_of[id1] = {id2}
def _are_true_positives(self, id1 : int, id2 : int):
"""Checks if given pair of identifiers represents a duplicate.
Identifiers must be inorder, first one belonging to the first and the second to the second dataset
Args:
id1 (int, optional): Identifier from the first dataframe.
id2 (int, optional): Identifier from the second dataframe.
Returns:
_type_: _description_
"""
return id1 in self.duplicate_of and id2 in self.duplicate_of[id1]
def _create_gt_mapping(self) -> None:
"""Creates two mappings:
- _ids_mapping_X: ids from initial dataset to index
- _gt_to_ids_reversed_X (inversed _ids_mapping_X): index number \
from range to initial dataset id
"""
if self.ground_truth is not None:
self.ground_truth = self.ground_truth.astype(str)
# else:
# return
self._ids_mapping_1 = dict(
zip(
self.dataset_1[self.id_column_name_1].tolist(),
range(0, self.num_of_entities_1)
)
)
self._gt_to_ids_reversed_1 = dict(
zip(
self._ids_mapping_1.values(),
self._ids_mapping_1.keys()
)
)
if not self.is_dirty_er:
self._ids_mapping_2 = dict(
zip(
self.dataset_2[self.id_column_name_2].tolist(),
range(self.num_of_entities_1, self.num_of_entities_1+self.num_of_entities_2)
)
)
self._gt_to_ids_reversed_2 = dict(
zip(
self._ids_mapping_2.values(),
self._ids_mapping_2.keys()
)
)
def get_pyjedai_id_of(self, dataset_id: any) -> int:
pass
def get_real_id_of(self, pyjedai_id: int) -> any:
if pyjedai_id < self.dataset_limit:
return self._gt_to_ids_reversed_1[pyjedai_id]
else:
return self._gt_to_ids_reversed_2[pyjedai_id]
def print_specs(self) -> None:
"""Dataset report.
"""
def calculate_memory_usage_of_pandas(dataframe: pd.DataFrame) -> float:
memory_usage = dataframe.memory_usage(deep=True).sum()
if memory_usage > 1024**4:
memory_usage /= (1024**4)
unit = "TB"
elif memory_usage > 1024**3:
memory_usage /= (1024**3)
unit = "GB"
elif memory_usage > 1024**2:
memory_usage /= (1024**2)
unit = "MB"
elif memory_usage > 1024:
memory_usage /= (1024)
unit = "KB"
else:
unit = "B"
return memory_usage, unit
print('*' * 123)
print(' ' * 50, 'Data Report')
print('*' * 123)
print("Type of Entity Resolution: ", "Dirty" if self.is_dirty_er else "Clean-Clean" )
name1 = self.dataset_name_1 if self.dataset_name_1 is not None else "D1"
print("Dataset 1 (" + name1 + "):")
print("\tNumber of entities: ", self.num_of_entities_1)
print("\tNumber of NaN values: ", self.dataset_1.isnull().sum().sum())
memory_usage, unit = calculate_memory_usage_of_pandas(self.dataset_1)
print("\tMemory usage [" + unit + "]: ", "{:.2f}".format(memory_usage))
print("\tAttributes:")
for attr in self.attributes_1:
print("\t\t", attr)
if not self.is_dirty_er:
name2 = self.dataset_name_2 if self.dataset_name_2 is not None else "D2"
print("Dataset 2 (" + name2 + "):")
print("\tNumber of entities: ", self.num_of_entities_2)
print("\tNumber of NaN values: ", self.dataset_2.isnull().sum().sum())
memory_usage, unit = calculate_memory_usage_of_pandas(self.dataset_2)
print("\tMemory usage [" + unit + "]: ", "{:.2f}".format(memory_usage))
print("\tAttributes:")
for attr in self.attributes_2:
print("\t\t", attr)
print("\nTotal number of entities: ", self.num_of_entities)
if self.ground_truth is not None:
print("Number of matching pairs in ground-truth: ", len(self.ground_truth))
print(u'\u2500' * 123)
# Functions that removes stopwords, punctuation, uni-codes, numbers from the dataset
def clean_dataset(self,
remove_stopwords: bool = True,
remove_punctuation: bool = True,
remove_numbers:bool = True,
remove_unicodes: bool = True) -> None:
"""Removes stopwords, punctuation, uni-codes, numbers from the dataset.
"""
nltk.download('stopwords')
# Make self.dataset_1 and self.dataset_2 lowercase
self.dataset_1 = self.dataset_1.applymap(lambda x: x.lower())
if not self.is_dirty_er:
self.dataset_2 = self.dataset_2.applymap(lambda x: x.lower())
if remove_numbers:
self.dataset_1 = self.dataset_1.applymap(lambda x: re.sub(r'\d+', '', x))
if not self.is_dirty_er:
self.dataset_2 = self.dataset_2.applymap(lambda x: re.sub(r'\d+', '', x))
if remove_unicodes:
self.dataset_1 = self.dataset_1.applymap(lambda x: re.sub(r'[^\x00-\x7F]+', '', x))
if not self.is_dirty_er:
self.dataset_2 = self.dataset_2.applymap(lambda x: re.sub(r'[^\x00-\x7F]+', '', x))
if remove_punctuation:
self.dataset_1 = self.dataset_1.applymap(lambda x: re.sub(r'[^\w\s]','',x))
if not self.is_dirty_er:
self.dataset_2 = self.dataset_2.applymap(lambda x: re.sub(r'[^\w\s]','',x))
if remove_stopwords:
self.dataset_1 = self.dataset_1.applymap(lambda x: ' '.join([word for word in x.split() if word not in (stopwords.words('english'))]))
if not self.is_dirty_er:
self.dataset_2 = self.dataset_2.applymap(lambda x: ' '.join([word for word in x.split() if word not in (stopwords.words('english'))]))
self.entities = self.dataset_1 = self.dataset_1.astype(str)
# Concatenated columns into new dataframe
self.entities_d1 = self.dataset_1[self.attributes_1]
if not self.is_dirty_er:
self.dataset_2 = self.dataset_2.astype(str)
self.entities_d2 = self.dataset_2[self.attributes_2]
self.entities = pd.concat([self.dataset_1, self.dataset_2],
ignore_index=True)
def stats_about_data(self) -> None:
stats_df = pd.DataFrame(columns=['word_count_1', 'word_count_2'])
# Calculate the average number of words per line
stats_df['word_count_1'] = self.dataset_1.apply(lambda row: len(row.str.split()), axis=1)
print(stats_df['word_count_1'])
average_words_per_line_1 = stats_df['word_count_1'].mean()
print(average_words_per_line_1)
if not self.is_dirty_er:
stats_df['word_count_2'] = self.dataset_2.apply(lambda row: len(row.str.split()), axis=1)
average_words_per_line_2 = stats_df['word_count_2'].mean()
print(average_words_per_line_2)
return stats_df
class SpatialData:
def __init__(
self,
source_reader: csv.reader,
source_delimiter: str,
target_reader: csv.reader,
target_delimiter: str,
skip_header: bool=False
) -> None:
self.source_geometriesSize = 0
self.source_reader = source_reader
self.source_delimiter = source_delimiter
self.targetGeometriesSize = 0
self.target_reader = target_reader
self.target_delimiter = target_delimiter
self.skip_header = skip_header
self.source_geometries = []
self.targetGeometries = []
self.readSourceGeometries()
self.readTargetGeometries()
return
def readSourceGeometries(self) -> list:
geometries_loaded = 0
geometries_failed = 0
geoCollections = 0
if(self.skip_header == True):
next(self.source_reader)
for geometry in self.source_reader:
try:
geometry, *information = [s.split(self.source_delimiter)[0] for s in geometry]
geometry = shape(loads(geometry))
except:
geometries_failed += 1
continue
if geometry.geom_type == "GeometryCollection":
geoCollections += 1
else:
self.source_geometries.append(geometry)
geometries_loaded += 1
# print("SpatialData initialized:","\n Geometries loaded:", geometries_loaded, "\n Geometries failed:", geometries_failed, "\n GeoCollections found:", geoCollections,"\n")
self.source_geometries_size = geometries_loaded
return
def readTargetGeometries(self) -> list:
geometries_loaded = 0
geometries_failed = 0
geoCollections = 0
if(self.skip_header == True):
next(self.target_reader)
for geometry in self.target_reader:
try:
geometry, *information = [s.split(self.target_delimiter)[0] for s in geometry]
geometry = shape(loads(geometry))
except:
geometries_failed += 1
continue
if geometry.geom_type == "GeometryCollection":
geoCollections += 1
else:
self.targetGeometries.append(geometry)
geometries_loaded += 1
# print("SpatialData initialized:","\n Geometries loaded: ", geometries_loaded, "\n Geometries failed: ", geometries_failed, "\n GeoCollections found: ", geoCollections)
self.targetGeometriesSize = geometries_loaded
return
class SchemaData:
"""Data module for schema matching tasks. Valentine-based structure.
"""
def __init__(
self,
dataset_1: DataFrame,
attributes_1: list,
dataset_2: DataFrame,
attributes_2: list,
dataset_name_1: str = None,
dataset_name_2: str = None,
ground_truth: DataFrame = None,
) -> None:
# Original Datasets as pd.DataFrame
if isinstance(dataset_1, pd.DataFrame):
self.dataset_1 = dataset_1
else:
raise AttributeError("Dataset 1 must be a pandas DataFrame")
if dataset_2 is not None:
if isinstance(dataset_2, pd.DataFrame):
self.dataset_2 = dataset_2
else:
raise AttributeError("Dataset 2 must be a pandas DataFrame")
if ground_truth is not None:
self.ground_truth = ground_truth.to_records(index=False).tolist()
class Block:
"""The main module used for storing entities in the blocking steps of pyjedai module. \
Consists of 2 sets of profile entities 1 for Dirty ER and 2 for Clean-Clean ER.
"""
def __init__(self) -> None:
self.entities_D1: set = OrderedSet()
self.entities_D2: set = OrderedSet()
def get_cardinality(self, is_dirty_er) -> int:
"""Returns block cardinality.
Args:
is_dirty_er (bool): Dirty or Clean-Clean ER.
Returns:
int: Cardinality
"""
if is_dirty_er:
return len(self.entities_D1)*(len(self.entities_D1)-1)/2
return len(self.entities_D1) * len(self.entities_D2)
def get_size(self) -> int:
"""Returns block size.
Returns:
int: Block size
"""
return len(self.entities_D1) + len(self.entities_D2)
def verbose(self, key: any, is_dirty_er: bool) -> None:
"""Prints contents of a block
Args:
key (any): Block key
is_dirty_er (bool): Dirty or Clean-Clean ER.
"""
print("\nBlock ", "\033[1;32m"+key+"\033[0m", " has cardinality ", str(self.get_cardinality(is_dirty_er)) ," and contains entities with ids: ")
if is_dirty_er:
print("Dirty dataset: " + "[\033[1;34m" + \
str(len(self.entities_D1)) + " entities\033[0m]")
print(self.entities_D1)
else:
print("Clean dataset 1: " + "[\033[1;34m" + \
str(len(self.entities_D1)) + " entities\033[0m]")
print(self.entities_D1)
print("Clean dataset 2: " + "[\033[1;34m" + str(len(self.entities_D2)) + \
" entities\033[0m]")
print(self.entities_D2)