def test_sample_table_valid_2(self): A = read_csv_metadata(path_a) B = read_csv_metadata(path_b, key='ID') C = read_csv_metadata(path_c, ltable=A, rtable=B) D = sample_table(C, 10, True) self.assertEqual(id(cm.get_ltable(D)), id(cm.get_ltable(C))) self.assertEqual(id(cm.get_rtable(D)), id(cm.get_rtable(C))) self.assertEqual(cm.get_fk_ltable(D), cm.get_fk_ltable(C)) self.assertEqual(cm.get_fk_rtable(D), cm.get_fk_rtable(C)) self.assertEqual(len(D), 10)
def test_sample_table_valid_2(self): A = read_csv_metadata(path_a) B = read_csv_metadata(path_b, key='ID') C = read_csv_metadata(path_c, ltable=A, rtable=B) D = sample_table(C, 10, True) self.assertEqual(id(cm.get_ltable(D)), id(cm.get_ltable(C))) self.assertEqual(id(cm.get_rtable(D)), id(cm.get_rtable(C))) self.assertEqual(cm.get_fk_ltable(D), cm.get_fk_ltable(C)) self.assertEqual(cm.get_fk_rtable(D), cm.get_fk_rtable(C)) self.assertEqual(len(D), 10)
def _validate_lr_tables(blocker_output_list): """ Validate the input blocker output list. """ ltable_ids = [] rtable_ids = [] fk_ltable_list = [] fk_rtable_list = [] # Iterate through the DataFrame list and keep track the following # # 1) Validate whether the input objects are all DataFrames # # 2) Update the ltable, rtable (ids), fk_ltable and fk_rtable. for data_frame in blocker_output_list: py_entitymatching.utils.validation_helper.validate_object_type(data_frame, pd.DataFrame) ltable_ids.append(id(cm.get_ltable(data_frame))) rtable_ids.append(id(cm.get_rtable(data_frame))) fk_ltable_list.append(cm.get_fk_ltable(data_frame)) fk_rtable_list.append(cm.get_fk_rtable(data_frame)) # Check whether all the ltables in the catalog are same for the input # DataFrame list ltable_ids = set(ltable_ids) # # We expect all the ltable ids are same (i.e the len. of set should be 1) if not len(ltable_ids) == 1: logger.error('Candidate set list contains different left tables') raise AssertionError('Candidate set list contains different ' 'left tables') # Check whether all the rtables in the catalog are same for the input # DataFrame list rtable_ids = set(rtable_ids) # # We expect all the ltable ids are same (i.e the len. of set should be 1) if not len(rtable_ids) == 1: logger.error('Candidate set list contains different right tables') raise AssertionError('Candidate set list contains different ' 'right tables') # Check whether all the fk_ltable values in the catalog are same for the # input DataFrame list fk_ltable_set = set(fk_ltable_list) if not len(fk_ltable_set) == 1: logger.error('Candidate set list contains different foreign key ' 'for left tables') raise AssertionError('Candidate set list contains different ' 'foreign key for left tables') # Check whether all the fk_rtable values in the catalog are same for the # input DataFrame list fk_rtable_set = set(fk_rtable_list) if not len(fk_rtable_set) == 1: logger.error('Candidate set list contains different foreign key ' 'for right tables') raise AssertionError('Candidate set list contains different ' 'foreign key for right tables') # If everything looks fine then return True return True
def test_copy_properties_valid_2(self): A = read_csv_metadata(path_a) B = read_csv_metadata(path_b) C = read_csv_metadata(path_c, ltable=A, rtable=B) C1 = pd.read_csv(path_c) cm.copy_properties(C, C1) self.assertEqual(cm.is_dfinfo_present(C1), True) p = cm.get_all_properties(C1) p1 = cm.get_all_properties(C1) self.assertEqual(p, p1) self.assertEqual(cm.get_key(C1), cm.get_key(C)) self.assertEqual(cm.get_ltable(C1).equals(A), True) self.assertEqual(cm.get_rtable(C1).equals(B), True) self.assertEqual(cm.get_fk_ltable(C1), cm.get_fk_ltable(C)) self.assertEqual(cm.get_fk_rtable(C1), cm.get_fk_rtable(C))
def __init__(self, matcher, matcher_type, exclude_attrs_or_feature_table, dictionary, table, fp_dataframe, fn_dataframe): super(MainWindowManager, self).__init__() # Set the parameters as attributes to the class. self.matcher = matcher self.matcher_type = matcher_type self.exclude_attrs_or_feature_table = exclude_attrs_or_feature_table self.dictionary = dictionary self.table = table self.fp_dataframe = fp_dataframe self.fn_dataframe = fn_dataframe # Get the instance for QtGui em._viewapp = QtGui.QApplication.instance() if em._viewapp is None: em._viewapp = QtGui.QApplication([]) app = em._viewapp ltable = cm.get_ltable(self.table) rtable = cm.get_rtable(self.table) # Get the copy of ltable and rtable l_df = ltable.copy() r_df = rtable.copy() # Set it as dataframes in the class self.l_df = l_df.set_index(cm.get_key(ltable), drop=False) self.r_df = r_df.set_index(cm.get_key(rtable), drop=False) # Set the metric widget, dataframe widget , combo box and the # dataframe correctly. self.metric_widget = None self.dataframe_widget = None self.combo_box = None self.current_combo_text = 'False Positives' self.current_dataframe = self.fp_dataframe self.setup_gui() width = min((40 + 1) * 105, app.desktop().screenGeometry().width() - 50) height = min((50 + 1) * 41, app.desktop().screenGeometry().width() - 100) # set the size of height and width corrrectly. self.resize(width, height)
def __init__(self, matcher, matcher_type, exclude_attrs_or_feature_table, dictionary, table, fp_dataframe, fn_dataframe): super(MainWindowManager, self).__init__() # Set the parameters as attributes to the class. self.matcher = matcher self.matcher_type = matcher_type self.exclude_attrs_or_feature_table = exclude_attrs_or_feature_table self.dictionary = dictionary self.table = table self.fp_dataframe = fp_dataframe self.fn_dataframe = fn_dataframe # Get the instance for QtWidgets em._viewapp = QtWidgets.QApplication.instance() if em._viewapp is None: em._viewapp = QtWidgets.QApplication([]) app = em._viewapp ltable = cm.get_ltable(self.table) rtable = cm.get_rtable(self.table) # Get the copy of ltable and rtable l_df = ltable.copy() r_df = rtable.copy() # Set it as dataframes in the class self.l_df = l_df.set_index(cm.get_key(ltable), drop=False) self.r_df = r_df.set_index(cm.get_key(rtable), drop=False) # Set the metric widget, dataframe widget , combo box and the # dataframe correctly. self.metric_widget = None self.dataframe_widget = None self.combo_box = None self.current_combo_text = 'False Positives' self.current_dataframe = self.fp_dataframe self.setup_gui() width = min((40 + 1) * 105, app.desktop().screenGeometry().width() - 50) height = min((50 + 1) * 41, app.desktop().screenGeometry().width() - 100) # set the size of height and width corrrectly. self.resize(width, height)
def combine_blocker_outputs_via_union(blocker_output_list, l_prefix='ltable_', r_prefix='rtable_', verbose=False): """ Combines multiple blocker outputs by doing a union of their tuple pair ids (foreign key ltable, foreign key rtable). Specifically, this function takes in a list of DataFrames (candidate sets, typically the output from blockers) and returns a consolidated DataFrame. The output DataFrame contains the union of tuple pair ids (foreign key ltable, foreign key rtable) and other attributes from the input list of DataFrames. This function makes some assumptions about the input DataFrames. First, each DataFrame is expected to contain the following metadata in the catalog: key, fk_ltable, fk_rtable, ltable, and rtable. Second, all the DataFrames must be a result of blocking from the same underlying tables. Concretely the ltable and rtable properties must refer to the same DataFrame across all the input tables. Third, all the input DataFrames must have the same fk_ltable and fk_rtable properties. Finally, in each input DataFrame, for the attributes included from the ltable or rtable, the attribute names must be prefixed with the given l_prefix and r_prefix in the function. The input DataFrames may contain different attribute lists and it demands the question of how to combine them. Currently py_entitymatching takes an union of attribute names that has prefix l_prefix or r_prefix across input tables. After taking the union, for each tuple id pair included in output, the attribute values (for union-ed attribute names) are probed from ltable/rtable and included in the output. A subtle point to note here is, if an input DataFrame has a column added by user (say label for some reason), then that column will not be present in the output. The reason is, the same column may not be present in other candidate sets so it is not clear about how to combine them. One possibility is to include label in output for all tuple id pairs, but set as NaN for the values not present. Currently py_entitymatching does not include such columns and addressing it will be part of future work. Args: blocker_output_list (list of DataFrames): The list of DataFrames that should be combined. l_prefix (string): The prefix given to the attributes from the ltable. r_prefix (string): The prefix given to the attributes from the rtable. verbose (boolean): A flag to indicate whether more detailed information about the execution steps should be printed out (default value is False). Returns: A new DataFrame with the combined tuple pairs and other attributes from all the blocker lists. Raises: AssertionError: If `l_prefix` is not of type string. AssertionError: If `r_prefix` is not of type string. AssertionError: If the length of the input DataFrame list is 0. AssertionError: If `blocker_output_list` is not a list of DataFrames. AssertionError: If the ltables are different across the input list of DataFrames. AssertionError: If the rtables are different across the input list of DataFrames. AssertionError: If the `fk_ltable` values are different across the input list of DataFrames. AssertionError: If the `fk_rtable` values are different across the input list of DataFrames. """ # validate input parameters # The l_prefix is expected to be of type string if not isinstance(l_prefix, six.string_types): logger.error('l_prefix is not of type string') raise AssertionError('l_prefix is not of type string') # The r_prefix is expected to be of type string if not isinstance(r_prefix, six.string_types): logger.error('r_prefix is not of type string') raise AssertionError('r_prefix is not of type string') # We cannot combine empty DataFrame list if not len(blocker_output_list) > 0: logger.error('There no DataFrames to combine') raise AssertionError('There are no DataFrames to combine') # Validate the assumptions about the input tables. # # 1) All the input object must be DataFrames # # 2) All the input DataFrames must have the metadata as that of a # candidate set # # 3) All the input DataFrames must have the same fk_ltable and fk_rtable _validate_lr_tables(blocker_output_list) # # Get the ltable and rtable. We take it from the first DataFrame as all # the DataFrames contain the same ltables and rtables ltable = cm.get_ltable(blocker_output_list[0]) rtable = cm.get_rtable(blocker_output_list[0]) # # Get the fk_ltable and fk_rtable. We take it from the first DataFrame as # all the DataFrames contain the same ltables and rtables fk_ltable = cm.get_fk_ltable(blocker_output_list[0]) fk_rtable = cm.get_fk_rtable(blocker_output_list[0]) # Retrieve the keys for the ltable and rtables. l_key = cm.get_key(ltable) r_key = cm.get_key(rtable) # Check if the fk_ltable is starting with the given prefix, if not its # not an error. Just raise a warning. if fk_ltable.startswith(l_prefix) is False: logger.warning( 'Foreign key for ltable is not starting with the given prefix (' '%s)', l_prefix) # Check if the fk_rtable is starting with the given prefix, if not its # not an error. Just raise a warning. if fk_rtable.startswith(r_prefix) is False: logger.warning( 'Foreign key for rtable is not starting with the given prefix (' '%s)', r_prefix) # Initialize lists # # keep track of projected tuple pair ids tuple_pair_ids = [] # # keep track of output attributes from the left table l_output_attrs = [] # # keep track of output attributes from the right table r_output_attrs = [] # for each DataFrame in the given list, project out tuple pair ids, get the # attributes from the ltable and rtable for data_frame in blocker_output_list: # Project out the tuple pair ids. A tuple pair id is a fk_ltable, # fk_rtable pair projected_tuple_pair_ids = data_frame[[fk_ltable, fk_rtable]] # Update the list that tracks tuple pair ids tuple_pair_ids.append(projected_tuple_pair_ids) # Get the columns, which should be segregated into the attributes # from the ltable and table col_set = (gh.list_diff(list(data_frame.columns), [fk_ltable, fk_rtable, cm.get_key(data_frame)])) # Segregate the columns as attributes from the ltable and rtable l_attrs, r_attrs = _lr_cols(col_set, l_prefix, r_prefix) # Update the l_output_attrs, r_output_attrs l_output_attrs.extend(l_attrs) # the reason we use extend because l_attrs a list r_output_attrs.extend(r_attrs) ch.log_info( logger, 'Concatenating the tuple pair ids across given ' 'blockers ...', verbose) # concatenate the tuple pair ids from the list of input DataFrames concatenated_tuple_pair_ids = pd.concat(tuple_pair_ids) ch.log_info(logger, 'Concatenating the tuple pair ids ... DONE', verbose) ch.log_info(logger, 'Deduplicating the tuple pair ids ...', verbose) # Deduplicate the DataFrame. Now the returned DataFrame will contain # unique tuple pair ids. # noinspection PyUnresolvedReferences deduplicated_tuple_pair_ids = concatenated_tuple_pair_ids.drop_duplicates() ch.log_info(logger, 'Deduplicating the tuple pair ids ... DONE', verbose) # Construct output table # # Get unique list of attributes across different tables l_output_attrs = gh.list_drop_duplicates(l_output_attrs) r_output_attrs = gh.list_drop_duplicates(r_output_attrs) # Reset the index that might have lingered from concatenation. deduplicated_tuple_pair_ids.reset_index(inplace=True, drop=True) # Add the output attribtues from the ltable and rtable. # NOTE: This approach may be inefficient as it probes the ltable, rtable # to get the attribute values. A better way would be to fill the # attribute values from the input list of DataFrames. This attribute values # could be harvested (at the expense of some space) while we iterate the # input blocker output list for the first time. # noinspection PyProtectedMember consolidated_data_frame = gh._add_output_attributes( deduplicated_tuple_pair_ids, fk_ltable, fk_rtable, ltable, rtable, l_key, r_key, l_output_attrs, r_output_attrs, l_prefix, r_prefix, validate=False) # Sort the DataFrame ordered by fk_ltable and fk_rtable. # The function "sort" will be depreciated in the newer versions of # pandas DataFrame, and it will replaced by 'sort_values' function. So we # will first try to use sort_values if this fails we will use sort. try: consolidated_data_frame.sort_values([fk_ltable, fk_rtable], inplace=True) except AttributeError: consolidated_data_frame.sort([fk_ltable, fk_rtable], inplace=True) # update the catalog for the consolidated DataFrame # First get a column name for the key key = ch.get_name_for_key(consolidated_data_frame.columns) # Second, add the column name as the key consolidated_data_frame = ch.add_key_column(consolidated_data_frame, key) # Third, reset the index to remove any out of order index values from # the sort. consolidated_data_frame.reset_index(inplace=True, drop=True) # Finally, set the properties for the consolidated DataFrame in the catalog cm.set_candset_properties(consolidated_data_frame, key, fk_ltable, fk_rtable, ltable, rtable) # Return the consolidated DataFrame return consolidated_data_frame
def combine_blocker_outputs_via_union( blocker_output_list, l_prefix='ltable_', r_prefix='rtable_', verbose=False): """ Combines multiple blocker outputs by doing a union of their tuple pair ids (foreign key ltable, foreign key rtable). Specifically, this function takes in a list of DataFrames (candidate sets, typically the output from blockers) and returns a consolidated DataFrame. The output DataFrame contains the union of tuple pair ids (foreign key ltable, foreign key rtable) and other attributes from the input list of DataFrames. This function makes some assumptions about the input DataFrames. First, each DataFrame is expected to contain the following metadata in the catalog: key, fk_ltable, fk_rtable, ltable, and rtable. Second, all the DataFrames must be a result of blocking from the same underlying tables. Concretely the ltable and rtable properties must refer to the same DataFrame across all the input tables. Third, all the input DataFrames must have the same fk_ltable and fk_rtable properties. Finally, in each input DataFrame, for the attributes included from the ltable or rtable, the attribute names must be prefixed with the given l_prefix and r_prefix in the function. The input DataFrames may contain different attribute lists and it demands the question of how to combine them. Currently py_entitymatching takes an union of attribute names that has prefix l_prefix or r_prefix across input tables. After taking the union, for each tuple id pair included in output, the attribute values (for union-ed attribute names) are probed from ltable/rtable and included in the output. A subtle point to note here is, if an input DataFrame has a column added by user (say label for some reason), then that column will not be present in the output. The reason is, the same column may not be present in other candidate sets so it is not clear about how to combine them. One possibility is to include label in output for all tuple id pairs, but set as NaN for the values not present. Currently py_entitymatching does not include such columns and addressing it will be part of future work. Args: blocker_output_list (list of DataFrames): The list of DataFrames that should be combined. l_prefix (string): The prefix given to the attributes from the ltable. r_prefix (string): The prefix given to the attributes from the rtable. verbose (boolean): A flag to indicate whether more detailed information about the execution steps should be printed out (default value is False). Returns: A new DataFrame with the combined tuple pairs and other attributes from all the blocker lists. Raises: AssertionError: If `l_prefix` is not of type string. AssertionError: If `r_prefix` is not of type string. AssertionError: If the length of the input DataFrame list is 0. AssertionError: If `blocker_output_list` is not a list of DataFrames. AssertionError: If the ltables are different across the input list of DataFrames. AssertionError: If the rtables are different across the input list of DataFrames. AssertionError: If the `fk_ltable` values are different across the input list of DataFrames. AssertionError: If the `fk_rtable` values are different across the input list of DataFrames. Examples: >>> import py_entitymatching as em >>> ab = em.AttrEquivalenceBlocker() >>> C = ab.block_tables(A, B, 'zipcode', 'zipcode') >>> ob = em.OverlapBlocker() >>> D = ob.block_candset(C, 'address', 'address') >>> block_f = em.get_features_for_blocking(A, B) >>> rb = em.RuleBasedBlocker() >>> rule = ['address_address_lev(ltuple, rtuple) > 6'] >>> rb.add_rule(rule, block_f) >>> E = rb.block_tables(A, B) >>> F = em.combine_blocker_outputs_via_union([C, E]) """ # validate input parameters # The l_prefix is expected to be of type string py_entitymatching.utils.validation_helper.validate_object_type(l_prefix, six.string_types, 'l_prefix') # The r_prefix is expected to be of type string py_entitymatching.utils.validation_helper.validate_object_type(r_prefix, six.string_types, 'r_prefix') # We cannot combine empty DataFrame list if not len(blocker_output_list) > 0: logger.error('There no DataFrames to combine') raise AssertionError('There are no DataFrames to combine') # Validate the assumptions about the input tables. # # 1) All the input object must be DataFrames # # 2) All the input DataFrames must have the metadata as that of a # candidate set # # 3) All the input DataFrames must have the same fk_ltable and fk_rtable _validate_lr_tables(blocker_output_list) # # Get the ltable and rtable. We take it from the first DataFrame as all # the DataFrames contain the same ltables and rtables ltable = cm.get_ltable(blocker_output_list[0]) rtable = cm.get_rtable(blocker_output_list[0]) # # Get the fk_ltable and fk_rtable. We take it from the first DataFrame as # all the DataFrames contain the same ltables and rtables fk_ltable = cm.get_fk_ltable(blocker_output_list[0]) fk_rtable = cm.get_fk_rtable(blocker_output_list[0]) # Retrieve the keys for the ltable and rtables. l_key = cm.get_key(ltable) r_key = cm.get_key(rtable) # Check if the fk_ltable is starting with the given prefix, if not its # not an error. Just raise a warning. if fk_ltable.startswith(l_prefix) is False: logger.warning( 'Foreign key for ltable is not starting with the given prefix (' '%s)', l_prefix) # Check if the fk_rtable is starting with the given prefix, if not its # not an error. Just raise a warning. if fk_rtable.startswith(r_prefix) is False: logger.warning( 'Foreign key for rtable is not starting with the given prefix (' '%s)', r_prefix) # Initialize lists # # keep track of projected tuple pair ids tuple_pair_ids = [] # # keep track of output attributes from the left table l_output_attrs = [] # # keep track of output attributes from the right table r_output_attrs = [] # for each DataFrame in the given list, project out tuple pair ids, get the # attributes from the ltable and rtable for data_frame in blocker_output_list: # Project out the tuple pair ids. A tuple pair id is a fk_ltable, # fk_rtable pair projected_tuple_pair_ids = data_frame[[fk_ltable, fk_rtable]] # Update the list that tracks tuple pair ids tuple_pair_ids.append(projected_tuple_pair_ids) # Get the columns, which should be segregated into the attributes # from the ltable and table col_set = ( gh.list_diff(list(data_frame.columns), [fk_ltable, fk_rtable, cm.get_key(data_frame)])) # Segregate the columns as attributes from the ltable and rtable l_attrs, r_attrs = _lr_cols(col_set, l_prefix, r_prefix) # Update the l_output_attrs, r_output_attrs l_output_attrs.extend(l_attrs) # the reason we use extend because l_attrs a list r_output_attrs.extend(r_attrs) ch.log_info(logger, 'Concatenating the tuple pair ids across given ' 'blockers ...', verbose) # concatenate the tuple pair ids from the list of input DataFrames concatenated_tuple_pair_ids = pd.concat(tuple_pair_ids) ch.log_info(logger, 'Concatenating the tuple pair ids ... DONE', verbose) ch.log_info(logger, 'Deduplicating the tuple pair ids ...', verbose) # Deduplicate the DataFrame. Now the returned DataFrame will contain # unique tuple pair ids. # noinspection PyUnresolvedReferences deduplicated_tuple_pair_ids = concatenated_tuple_pair_ids.drop_duplicates() ch.log_info(logger, 'Deduplicating the tuple pair ids ... DONE', verbose) # Construct output table # # Get unique list of attributes across different tables l_output_attrs = gh.list_drop_duplicates(l_output_attrs) r_output_attrs = gh.list_drop_duplicates(r_output_attrs) # Reset the index that might have lingered from concatenation. deduplicated_tuple_pair_ids.reset_index(inplace=True, drop=True) # Add the output attribtues from the ltable and rtable. # NOTE: This approach may be inefficient as it probes the ltable, rtable # to get the attribute values. A better way would be to fill the # attribute values from the input list of DataFrames. This attribute values # could be harvested (at the expense of some space) while we iterate the # input blocker output list for the first time. # noinspection PyProtectedMember consolidated_data_frame = gh._add_output_attributes( deduplicated_tuple_pair_ids, fk_ltable, fk_rtable, ltable, rtable, l_key, r_key, l_output_attrs, r_output_attrs, l_prefix, r_prefix, validate=False) # Sort the DataFrame ordered by fk_ltable and fk_rtable. # The function "sort" will be depreciated in the newer versions of # pandas DataFrame, and it will replaced by 'sort_values' function. So we # will first try to use sort_values if this fails we will use sort. try: consolidated_data_frame.sort_values([fk_ltable, fk_rtable], inplace=True) except AttributeError: consolidated_data_frame.sort([fk_ltable, fk_rtable], inplace=True) # update the catalog for the consolidated DataFrame # First get a column name for the key key = ch.get_name_for_key(consolidated_data_frame.columns) # Second, add the column name as the key consolidated_data_frame = ch.add_key_column(consolidated_data_frame, key) # Third, reset the index to remove any out of order index values from # the sort. consolidated_data_frame.reset_index(inplace=True, drop=True) # Finally, set the properties for the consolidated DataFrame in the catalog cm.set_candset_properties(consolidated_data_frame, key, fk_ltable, fk_rtable, ltable, rtable) # Return the consolidated DataFrame return consolidated_data_frame