# List the names of the features generated
F['feature_name']
#Extract Feature Vectors.
H = em.extract_feature_vecs(I,
feature_table=F,
attrs_after='Match',
show_progress=False)
# compare stats to select best Matcher
#Logistic Regression in our case
result = em.select_matcher(
[dt, rf, svm, ln, lg, nb],
table=H,
exclude_attrs=['_id', 'ltable_ID', 'rtable_ID', 'Match'],
k=5,
target_attr='Match',
metric_to_select_matcher='f1',
random_state=0)
result['cv_stats']
#Read in the complete candidate set obtained after blocking (973 tuples)
C = em.read_csv_metadata("/mnt/c/Users/sreya/Downloads/candidates_large1.csv",
key='_id',
ltable=A,
rtable=B,
fk_ltable='ltable_ID',
fk_rtable='rtable_ID')
C
K = em.extract_feature_vecs(C, feature_table=F, show_progress=False)
# creating a set of learning-based matchers
dt = em.DTMatcher(name='DecisionTree', random_state=0)
svm = em.SVMMatcher(name='SVM', random_state=0)
rf = em.RFMatcher(name='RF', random_state=0)
lg = em.LogRegMatcher(name='LogReg', random_state=0)
ln = em.LinRegMatcher(name='LinReg')
nb = em.NBMatcher(name='NaiveBayes')
# Selecting the best matcher using cross-validation
# precision of matchers for 5-fold cross validations
result_p = em.select_matcher(
[dt, svm, rf, lg, ln, nb],
table=H,
exclude_attrs=['_id', 'ltable_id', 'rtable_id', 'label'],
k=5,
target_attr='label',
metric='precision',
random_state=0)
result_p['cv_stats']
# recall of matchers for 5-fold cross validations
result_r = em.select_matcher(
[dt, svm, rf, lg, ln, nb],
table=H,
exclude_attrs=['_id', 'ltable_id', 'rtable_id', 'label'],
k=5,
target_attr='label',
metric='recall',
random_state=0)
result_r['cv_stats']
def main():
# Read in data files
A = em.read_csv_metadata(FOLDER + 'A.csv', key='id') # imdb data
B = em.read_csv_metadata(FOLDER + 'B.csv', key='id') # tmdb data
G = em.read_csv_metadata(FOLDER + 'G.csv',
key='_id',
ltable=A,
rtable=B,
fk_ltable='l_id',
fk_rtable='r_id') # labeled data
# Split G into I and J for CV
IJ = em.split_train_test(G, train_proportion=0.5, random_state=0)
I = IJ['train']
J = IJ['test']
# Save I and J to files
I.to_csv(FOLDER + 'I.csv', index=False)
J.to_csv(FOLDER + 'J.csv', index=False)
# Generate features set F
F = em.get_features_for_matching(A, B, validate_inferred_attr_types=False)
#print(F.feature_name)
#print(type(F))
# Convert I to a set of feature vectors using F
H = em.extract_feature_vecs(I,
feature_table=F,
attrs_after='label',
show_progress=False)
#print(H.head)
# Check of missing values
#print(any(pd.notnull(H)))
excluded_attributes = ['_id', 'l_id', 'r_id', 'label']
# Fill in missing values with column's average
H = em.impute_table(H, exclude_attrs=excluded_attributes, strategy='mean')
# Create a set of matchers
dt = em.DTMatcher(name='DecisionTree', random_state=0)
svm = em.SVMMatcher(name='SVM', random_state=0)
rf = em.RFMatcher(name='RF', random_state=0)
lg = em.LogRegMatcher(name='LogReg', random_state=0)
ln = em.LinRegMatcher(name='LinReg')
nb = em.NBMatcher(name='NaiveBayes')
# Selecting best matcher with CV using F1-score as criteria
CV_result = em.select_matcher([dt, rf, svm, ln, lg, nb],
table=H,
exclude_attrs=excluded_attributes,
k=10,
target_attr='label',
metric_to_select_matcher='f1',
random_state=0)
print(CV_result['cv_stats']) # RF is the best matcher
# Train matchers on H
dt.fit(table=H, exclude_attrs=excluded_attributes, target_attr='label')
rf.fit(table=H, exclude_attrs=excluded_attributes, target_attr='label')
svm.fit(table=H, exclude_attrs=excluded_attributes, target_attr='label')
lg.fit(table=H, exclude_attrs=excluded_attributes, target_attr='label')
ln.fit(table=H, exclude_attrs=excluded_attributes, target_attr='label')
nb.fit(table=H, exclude_attrs=excluded_attributes, target_attr='label')
# Convert J into a set of features using F
L = em.extract_feature_vecs(J,
feature_table=F,
attrs_after='label',
show_progress=False)
# Fill in missing values with column's average
L = em.impute_table(L, exclude_attrs=excluded_attributes, strategy='mean')
# Predict on L with trained matchers
predictions_dt = dt.predict(table=L,
exclude_attrs=excluded_attributes,
append=True,
target_attr='predicted',
inplace=False,
return_probs=False,
probs_attr='proba')
predictions_rf = rf.predict(table=L,
exclude_attrs=excluded_attributes,
append=True,
target_attr='predicted',
inplace=False,
return_probs=False,
probs_attr='proba')
predictions_svm = svm.predict(table=L,
exclude_attrs=excluded_attributes,
append=True,
target_attr='predicted',
inplace=False,
return_probs=False,
probs_attr='proba')
predictions_lg = lg.predict(table=L,
exclude_attrs=excluded_attributes,
append=True,
target_attr='predicted',
inplace=False,
return_probs=False,
probs_attr='proba')
predictions_ln = ln.predict(table=L,
exclude_attrs=excluded_attributes,
append=True,
target_attr='predicted',
inplace=False,
return_probs=False,
probs_attr='proba')
predictions_nb = nb.predict(table=L,
exclude_attrs=excluded_attributes,
append=True,
target_attr='predicted',
inplace=False,
return_probs=False,
probs_attr='proba')
# Evaluate predictions
dt_eval = em.eval_matches(predictions_dt, 'label', 'predicted')
em.print_eval_summary(dt_eval)
rf_eval = em.eval_matches(predictions_rf, 'label', 'predicted')
em.print_eval_summary(rf_eval)
svm_eval = em.eval_matches(predictions_svm, 'label', 'predicted')
em.print_eval_summary(svm_eval)
lg_eval = em.eval_matches(predictions_lg, 'label', 'predicted')
em.print_eval_summary(lg_eval)
ln_eval = em.eval_matches(predictions_ln, 'label', 'predicted')
em.print_eval_summary(ln_eval)
nb_eval = em.eval_matches(predictions_nb, 'label', 'predicted')
em.print_eval_summary(nb_eval)
rtable_pk = "rtable_" + pk_B
dt = em.DTMatcher(name='DecisionTree', random_state=0)
svm = em.SVMMatcher(name='SVM', random_state=0)
rf = em.RFMatcher(name='RF', random_state=0)
lg = em.LogRegMatcher(name='LogReg', random_state=0)
ln = em.LinRegMatcher(name='LinReg')
nb = em.NBMatcher(name='NaiveBayes')
feature_table = em.get_features_for_matching(A, B, validate_inferred_attr_types=False)
H = em.extract_feature_vecs(I, feature_table=feature_table, attrs_after='label', show_progress=False)
H = em.impute_table(H, exclude_attrs=['_id', ltable_pk, rtable_pk, 'label'], strategy='mean', val_all_nans=0.0)
result = em.select_matcher(matchers=[dt, rf, svm, ln, lg, nb], table=H, exclude_attrs=['_id', ltable_pk, rtable_pk, 'label'], k=5, target_attr='label', metric_to_select_matcher='f1', random_state=0)
print("\n- Matcher selection Results:")
print(result['cv_stats'])
UV = em.split_train_test(H, train_proportion=0.5)
U = UV['train']
V = UV['test']
em.vis_debug_rf(rf, U, V,
exclude_attrs=['_id', ltable_pk, rtable_pk, 'label'],
target_attr='label')
em.vis_debug_dt(dt, U, V,
exclude_attrs=['_id', ltable_pk, rtable_pk, 'label'],
target_attr='label')
dt = em.DTMatcher(name='DecisionTree', random_state=0)
svm = em.SVMMatcher(name='SVM', random_state=0)
rf = em.RFMatcher(name='RF', random_state=0)
lg = em.LogRegMatcher(name='LogReg', random_state=0)
ln = em.LinRegMatcher(name='LinReg')
nb = em.NBMatcher(name='NaiveBayes')
feature_table = em.get_features_for_matching(songs, tracks)
H = em.extract_feature_vecs(I,
feature_table=feature_table,
attrs_after='gold',
show_progress=False)
result = em.select_matcher([dt, rf, svm, ln, lg, nb], table=H,
exclude_attrs=['_id', 'ltable_id', 'rtable_id', 'gold'],
k=5,
target_attr='gold', metric='precision', random_state=0)
L = em.extract_feature_vecs(J, feature_table=feature_table,
attrs_after='gold', show_progress=False)
lg.fit(table=H,
exclude_attrs=['_id', 'ltable_id', 'rtable_id', 'gold'],
target_attr='gold')
predictions = lg.predict(table=L, exclude_attrs=['_id', 'ltable_id', 'rtable_id', 'gold'],
append=True, target_attr='predicted', inplace=False)
eval_result = em.eval_matches(predictions, 'gold', 'predicted')
em.print_eval_summary(eval_result)
eval_result = em.eval_matches(predictions, 'gold', 'predicted')
em.print_eval_summary(eval_result)
############################## Magellan with RF ###############################
############################## Magellan with RF ###############################
############################## Magellan with RF ###############################
############################## Magellan with RF ###############################
IJ = em.split_train_test(G, train_proportion=0.001, random_state=0)
I = IJ['train']
J = IJ['test']
result = em.select_matcher([rf], table=H,
exclude_attrs=['_id', 'ltable_id', 'rtable_id', 'gold'],
k=5,
target_attr='gold', metric_to_select_matcher='precision', random_state=0)
result['cv_stats']
result = em.select_matcher([rf], table=H,
exclude_attrs=['_id', 'ltable_id', 'rtable_id', 'gold'],
k=5,
target_attr='gold', metric_to_select_matcher='recall', random_state=0)
result['cv_stats']
L = em.extract_feature_vecs(J, feature_table=feature_table,
attrs_after='gold', show_progress=False)
rf.fit(table=H,
exclude_attrs=['_id', 'ltable_id', 'rtable_id', 'gold'],
target_attr='gold')
F = em.get_features_for_matching(A, B, validate_inferred_attr_types=False);
H = em.extract_feature_vecs(I,
feature_table=F,
attrs_after='gold_labels',
show_progress=False)
H = em.impute_table(H,
exclude_attrs=['_id', 'ltable_ID', 'rtable_ID', 'gold_labels'],
strategy='mean');
# print(any(pd.notnull(H)));
result = em.select_matcher([dt, rf, svm, nb, lg, ln], table=H,
exclude_attrs=['_id', 'ltable_ID', 'rtable_ID', 'gold_labels'],
k=5,
target_attr='gold_labels', metric_to_select_matcher='f1', random_state=0);
print(result['cv_stats']);
Y = dt;
L = em.extract_feature_vecs(J,
feature_table=F,
attrs_after='gold_labels',
show_progress=False);
L = em.impute_table(L,
exclude_attrs=['_id', 'ltable_ID', 'rtable_ID', 'gold_labels'],
strategy='mean');
# Given the set of desired features **F**, we can now calculate the feature values for our training data and also impute the missing values in our data. In this case, we choose to replace the missing values with the mean of the column.
# In[28]:
train_features = em.extract_feature_vecs(train_data, feature_table=F, attrs_after='label', show_progress=False)
train_features = em.impute_table(train_features, exclude_attrs=['_id', 'l_id', 'r_id', 'label'], strategy='mean')
# Using the calculated features, we can evaluate the performance of different machine learning algorithms and select the best one for our matching task.
# In[29]:
result = em.select_matcher([dt, rf, svm, ln, lg, nb], table=train_features,
exclude_attrs=['_id', 'l_id', 'r_id', 'label'], k=5,
target_attr='label', metric='f1', random_state=0)
result['cv_stats']
# We can observe based on the reported accuracy of different techniques that the "random forest (RF)" algorithm achieves the best performance. Thus, it is best to use this technique for the matching.
# #### Substep F: Evaluating the quality of our matching
#
# It is important to evaluate the quality of our matching. We can now, use the traning set for this purpose and measure how well the random forest predicts the matches. We can see that we are obtaining a high accuracy and recall on the test set as well.
# In[30]:
best_model = result['selected_matcher']
best_model.fit(table=train_features, exclude_attrs=['_id', 'l_id', 'r_id', 'label'], target_attr='label')
import py_entitymatching as em
import logging
logging.basicConfig(level=logging.INFO)
print("Mem. usage before reading:{0} (GB)".format( psutil.virtual_memory().used/1e9))
A = em.read_csv_metadata('../datasets/sample_citeseer_100k.csv', key='id')
B = em.read_csv_metadata('../datasets/sample_dblp_100k.csv', key='id')
C = em.read_csv_metadata('../datasets/featvecs_citeseer.csv', key='_id', ltable=A, rtable=B, fk_ltable='l_id', fk_rtable='r_id')
C.fillna(value=0, inplace=True)
print("Mem. usage after reading:{0} (GB)".format(psutil.virtual_memory().used/1e9))
len(C)
dt = em.DTMatcher(name='DecisionTree', random_state=0)
svm = em.SVMMatcher(name='SVM', random_state=0)
rf = em.RFMatcher(name='RF', random_state=0)
lg = em.LogRegMatcher(name='LogReg', random_state=0)
nb = em.NBMatcher(name='NaiveBayes')
ln = em.LinRegMatcher(name='LinearRegression')
memUsageBefore = psutil.virtual_memory().used/1e9
timeBefore = time.time()
result = em.select_matcher([dt, lg, svm, rf, nb, ln], table=C,
exclude_attrs=['_id', 'l_id', 'r_id', 'label'],
k=len(C)-1,
target_attr='label', metric='f1', random_state=0 )
timeAfter = time.time()
memUsageAfter = psutil.virtual_memory().used/1e9
print('Mem.usage (after reading): {0} (GB), Mem.usage (after extract featvecs): {1} (GB), diff: {2} (GB)'.format(memUsageBefore, memUsageAfter, memUsageAfter-memUsageBefore))
print('Time. diff: {0} (secs)'.format(timeAfter-timeBefore))
H.head(3)
# Selecting the best matcher using cross-validation
# ------------------
# Now, we select the best matcher using k-fold cross-validation.
# For the purposes of this guide, we use ten fold cross validation and use 'precision' and 'recall' metric to select the best matcher
# In[120]:
## Select the best ML matcher using CV
result_precision = em.select_matcher(
matchers=[dt, rf, nb, logreg, linreg, svm],
table=H,
exclude_attrs=[],
target_attr='gold_labels',
metric='precision',
k=10)
result_precision['cv_stats']
# In[121]:
# Measuring recall
result_recall = em.select_matcher(matchers=[dt, rf, nb, logreg, linreg, svm],
table=H,
exclude_attrs=[],
target_attr='gold_labels',
metric='recall',
k=10)
result_recall['cv_stats']
def main():
# WELCOME TO MY MAGELLAN RUN SCRIPT
print("\n-------------WELCOME TO MY MAGELLAN RUN SCRIPT-------------\n")
# Get the datasets directory
datasets_dir = 'B:\McMaster\CAS 764 - Advance Topics in Data Management\Project\Data\\'
print("- Dataset directory: " + datasets_dir)
print("- List of folders/files: ")
print(os.listdir(datasets_dir))
print("- Please enter new dataset folder name:")
datasets_dir += input()
print("- Dataset directory set to: " + datasets_dir)
dateset_dir_files = os.listdir(datasets_dir)
print("- List of files in dataset folder: ")
print(dateset_dir_files)
# Get the path of the input table A
print("- Enter an index for Table A file (0-x):")
file_index_A = input()
filename_A = dateset_dir_files[int(file_index_A)]
print("Table A file set to: " + filename_A)
# Get the path of the input table
path_A = datasets_dir + os.sep + filename_A
# Get the path of the input table B
print("- Enter an index for Table B file (0-x):")
file_index_B = input()
filename_B = dateset_dir_files[int(file_index_B)]
print("Table B file set to: " + filename_B)
# Get the path of the input table
path_B = datasets_dir + os.sep + filename_B
# Print Table A column names
A = em.read_csv_metadata(path_A)
print("- List of columns of Table A: ")
print(list(A.columns))
# Get the Table A id/primary key column name
print('- Enter Table A primary key column index (ex. 0):')
pk_A_index = input()
pk_A = A.columns[int(pk_A_index)]
# Print Table B column names
B = em.read_csv_metadata(path_B)
print("- List of columns of Table B: ")
print(list(B.columns))
# Get the Table B id/primary key column name
print('- Enter Table B primary key column index (ex. 0):')
pk_B_index = input()
pk_B = A.columns[int(pk_A_index)]
# READING TABLES AND SETTING METADATA
print("\n-------------READING TABLES AND SETTING METADATA-------------\n")
# Both read csv and set metadata id as ID column
#A = em.read_csv_metadata(path_A, key=pk_A)
#B = em.read_csv_metadata(path_B, key=pk_B)
em.set_key(A, pk_A)
em.set_key(B, pk_B)
# Number of tables
print('- Number of tuples in A: ' + str(len(A)))
print('- Number of tuples in B: ' + str(len(B)))
print('- Number of tuples in A X B (i.e the cartesian product): ' +
str(len(A) * len(B)))
# Print first 5 tuples of tables
print(A.head())
print(B.head())
# Display the keys of the input tables
print("- Table A primary key: " + em.get_key(A))
print("- Table B primary key: " + em.get_key(B))
# DOWNSAMPLING
print("\n-------------DOWNSAMPING-------------\n")
print("- Do you want to use downsampling? (y or n):")
print("- Table A: " + str(len(A)) + ", Table B: " + str(len(B)))
print("- NOTE: Recommended if both tables have 100K+ tuples.")
is_downsample = input()
if (is_downsample == 'y'):
print("- Size of the downsampled tables (ex. 200):")
downsample_size = input()
# If the tables are large we can downsample the tables like this
A1, B1 = em.down_sample(A, B, downsample_size, 1, show_progress=False)
print("- Length of Table A1" + len(A1))
print("- Length of Table B1" + len(B1))
# BLOCKING
print("\n-------------BLOCKING-------------\n")
print("- Do you want to use blocking? (y or n):")
is_blocking = input()
if (is_blocking == 'y'):
# Check if the 2 tables column names are the same
if (list(A.columns) == list(B.columns)):
C_attr_eq = [] # Attr Equ blocker result list
C_overlap = [] # Overlap blocker result list
C_blackbox = [] # BlackBox blocker result list
# Left and right table attribute prefixes
l_prefix = "ltable_"
r_prefix = "rtable_"
print("\n- List of columns: ")
print(list(A.columns))
# Labeling output table column selection
print(
"\n- Enter the indexes of columns that you want to see in labeling table (0-"
+ str(len(A.columns) - 1) + "):")
out_attr = []
for i in range(1, len(A.columns)):
print("- Finish with empty character(enter+enter) " + str(i))
add_to_attr = input()
if (add_to_attr == ''):
break
# Get indexes from user and add columns into out_attr list
out_attr.append(A.columns[int(add_to_attr)])
# Print output attributes
print(out_attr)
# Loop for adding/combining new blockers
while (True):
# Blocker selection
print(
"\n- Do yo want to use Attribute Equivalence[ab] (same), Overlap[ob] (similar) or Blackbox[bb] blocker:"
)
blocker_selection = input()
# ----- Attribute Equivalence Blocker -----
if (blocker_selection == 'ab'):
# Create attribute equivalence blocker
ab = em.AttrEquivalenceBlocker()
# Counter for indexes
attr_eq_counter = 0
# Check if Overlap Blocker used before
if (C_overlap and not C_overlap[-1].empty):
print(
"\n- Do you want to work on Overlap Blocker candidate set or not (y or n):"
)
use_cand_set = input()
if (use_cand_set == 'y'):
C_attr_eq.append(
C_overlap[-1]) # Add last output of ob
attr_eq_counter += 1 # For skipping block_table function in first time
# Check if BlackBox Blocker used before
if (C_blackbox and not C_blackbox[-1].empty):
print(
"\n- Do you want to work on BlackBox Blocker candidate set or not (y or n):"
)
use_cand_set = input()
if (use_cand_set == 'y'):
C_attr_eq.append(
C_blackbox[-1]) # Add last output of ob
attr_eq_counter += 1 # For skipping block_table function in first time
# Loop for adding more columns/attributes into Attr Equ blocker
while (True):
# List column names
print("\n- List of columns: ")
print(list(A.columns))
# Get blocking attribute/column
print(
"\n- Which column (w/ index) to use for equivalence blocking? (ex. 1):"
)
blocking_col_index = input()
blocking_col = A.columns[int(blocking_col_index)]
print(
"\n- Do you want to add missing values into blocking? (y or n):"
)
add_missing_val = input()
if (add_missing_val == 'y'):
add_missing_val = True
else:
add_missing_val = False
# First time using Attr Equ blocker, use A and B
if (attr_eq_counter == 0):
# Block using selected (blocking_col) attribute on A and B
C_attr_eq.append(
ab.block_tables(A,
B,
blocking_col,
blocking_col,
l_output_attrs=out_attr,
r_output_attrs=out_attr,
l_output_prefix=l_prefix,
r_output_prefix=r_prefix,
allow_missing=add_missing_val,
n_jobs=-1))
# Not first time, add new constraint into previous candidate set
else:
# Block using selected (blocking_col) attribute on previous (last=-1) candidate set
C_attr_eq.append(
ab.block_candset(C_attr_eq[-1],
l_block_attr=blocking_col,
r_block_attr=blocking_col,
allow_missing=add_missing_val,
n_jobs=-1,
show_progress=False))
# DEBUG BLOCKING
print(
"\n- Attribute Equivalence Blocker Debugging...\n")
# Debug last blocker output
dbg = em.debug_blocker(C_attr_eq[-1],
A,
B,
output_size=200,
n_jobs=-1)
# Display first few tuple pairs from the debug_blocker's output
print("\n- Blocking debug results:")
print(dbg.head())
attr_eq_counter += 1 # Increase the counter
# Continue to use Attribute Equivalence Blocker or not
print("\n- Length of candidate set: " +
str(len(C_attr_eq[-1])))
print(
"- Add another column into Attribute Equivalence Blocker[a] OR Reset last blocker's output[r]:"
)
ab_next_operation = input()
if (not ab_next_operation.islower()):
ab_next_operation = ab_next_operation.lower(
) # Lower case
# Continue using Attribute Equivalence Blocker
if (ab_next_operation == 'a'):
continue
# Reset/remove last blocker's output from candidate set list
elif (ab_next_operation == 'r'):
del C_attr_eq[-1]
print("\n- Last blocker output removed!")
print(
"- Continue to use Attribute Equivalence Blocker (y or n):"
)
ab_next_operation = input()
if (ab_next_operation == 'n'):
break
# Finish Attribute Equivalence Blocker
else:
break
# ----- Overlap Blocker -----
elif (blocker_selection == 'ob'):
# Create attribute equivalence blocker
ob = em.OverlapBlocker()
# Counter for indexes
overlap_counter = 0
# Check if Attribute Equivalence Blocker used before
if (C_attr_eq and not C_attr_eq[-1].empty):
print(
"\n- Do you want to work on Attribute Equivalence Blocker candidate set or not (y or n):"
)
use_cand_set = input()
if (use_cand_set == 'y'):
C_overlap.append(
C_attr_eq[-1]) # Add last output of ab
overlap_counter += 1 # For skipping block_table function in first time
# Check if BlackBox Blocker used before
if (C_blackbox and not C_blackbox[-1].empty):
print(
"\n- Do you want to work on BlackBox Blocker candidate set or not (y or n):"
)
use_cand_set = input()
if (use_cand_set == 'y'):
C_overlap.append(
C_blackbox[-1]) # Add last output of ob
overlap_counter += 1 # For skipping block_table function in first time
# Loop for adding more columns/attributes into Overlap blocker
while (True):
# List column names
print("- List of columns: ")
print(list(A.columns))
# Get blocking attribute/column
print(
"- Which column (w/ index) to use for overlap blocking? (ex. 1):"
)
blocking_col_index = input()
blocking_col = A.columns[int(blocking_col_index)]
print(
"\n- Do you want to add missing values into blocking? (y or n):"
)
add_missing_val = input()
if (add_missing_val == 'y'):
add_missing_val = True
else:
add_missing_val = False
print("\n- Use words as a token? (y or n):")
use_world_level = input()
if (use_world_level == 'y'):
use_world_level = True
q_gram_value = None
else:
use_world_level = False
print(
"\n- Q-gram q value (ex. 2 --> JO HN SM IT H):"
)
q_gram_value = input()
q_gram_value = int(q_gram_value)
print(
"\n- Enter the overlap size (# of tokens that overlap):"
)
overlap_size = input()
overlap_size = int(overlap_size)
print(
"\n- Do you want to remove (a, an, the) from token set? (y or n):"
)
use_stop_words = input()
if (use_stop_words == 'y'):
use_stop_words = True
else:
use_stop_words = False
# First time using Overlap blocker, use A and B
if (overlap_counter == 0):
# Block using selected (blocking_col) attribute on A and B
C_overlap.append(
ob.block_tables(A,
B,
blocking_col,
blocking_col,
l_output_attrs=out_attr,
r_output_attrs=out_attr,
l_output_prefix=l_prefix,
r_output_prefix=r_prefix,
rem_stop_words=use_stop_words,
q_val=q_gram_value,
word_level=use_world_level,
overlap_size=overlap_size,
allow_missing=add_missing_val,
n_jobs=-1))
# Not first time, add new constraint into previous candidate set
else:
# Block using selected (blocking_col) attribute on previous (last=-1) candidate set
C_overlap.append(
ob.block_candset(C_overlap[-1],
l_overlap_attr=blocking_col,
r_overlap_attr=blocking_col,
rem_stop_words=use_stop_words,
q_val=q_gram_value,
word_level=use_world_level,
overlap_size=overlap_size,
allow_missing=add_missing_val,
n_jobs=-1,
show_progress=False))
# DEBUG BLOCKING
print("\n- Overlap Blocker Debugging...\n")
# Debug last blocker output
dbg = em.debug_blocker(C_overlap[-1],
A,
B,
output_size=200,
n_jobs=-1)
# Display first few tuple pairs from the debug_blocker's output
print("\n- Blocking debug results:")
print(dbg.head())
overlap_counter += 1 # Increase the counter
# Continue to use Attribute Equivalence Blocker or not
print("\n- Length of candidate set: " +
str(len(C_overlap[-1])))
print(
"- Add another column into Overlap Blocker[a] OR Reset last blocker's output[r]:"
)
ob_next_operation = input()
if (not ob_next_operation.islower()):
ob_next_operation = ob_next_operation.lower(
) # Lower case
# Continue using Overlap Blocker
if (ob_next_operation == 'a'):
continue
# Reset/remove last blocker's output from candidate set list
elif (ob_next_operation == 'r'):
del C_overlap[-1]
print("\n- Last blocker output removed!")
print(
"- Continue to use Overlap Blocker (y or n):")
ob_next_operation = input()
if (ob_next_operation == 'n'):
break
# Finish Overlap Blocker
else:
break
# ----- BlackBox Blocker -----
elif (blocker_selection == 'bb'):
# Create attribute equivalence blocker
bb = em.BlackBoxBlocker()
# Counter for indexes
blackbox_counter = 0
# Check if Overlap Blocker used before
if (C_attr_eq and not C_attr_eq[-1].empty):
print(
"\n- Do you want to work on Attribute Equivalence Blocker candidate set or not (y or n):"
)
use_cand_set = input()
if (use_cand_set == 'y'):
C_blackbox.append(
C_attr_eq[-1]) # Add last output of ob
blackbox_counter += 1 # For skipping block_table function in first time
# Check if Overlap Blocker used before
if (C_overlap and not C_overlap[-1].empty):
print(
"\n- Do you want to work on Overlap Blocker candidate set or not (y or n):"
)
use_cand_set = input()
if (use_cand_set == 'y'):
C_blackbox.append(
C_overlap[-1]) # Add last output of ob
blackbox_counter += 1 # For skipping block_table function in first time
# Loop for adding more columns/attributes into BlackBox blocker
while (True):
# Set function
bb.set_black_box_function(
number_10_percent_comparision)
# First time using Overlap blocker, use A and B
if (overlap_counter == 0):
# Block on A and B
C_blackbox.append(
bb.block_tables(A,
B,
l_output_attrs=out_attr,
r_output_attrs=out_attr,
l_output_prefix=l_prefix,
r_output_prefix=r_prefix,
n_jobs=-1,
show_progress=False))
# Not first time, add new constraint into previous candidate set
else:
# Block on previous (last=-1) candidate set
C_blackbox.append(
bb.block_candset(C_blackbox[-1],
n_jobs=-1,
show_progress=False))
# DEBUG BLOCKING
print("\n- BlackBox Blocker Debugging...\n")
# Debug last blocker output
dbg = em.debug_blocker(C_blackbox[-1],
A,
B,
output_size=200,
n_jobs=-1)
# Display first few tuple pairs from the debug_blocker's output
print("\n- Blocking debug results:")
print(dbg.head())
blackbox_counter += 1 # Increase the counter
# Continue to use Attribute Equivalence Blocker or not
print("\n- Length of candidate set: " +
str(len(C_blackbox[-1])))
print(
"- Add another column into BlackBox Blocker[a] OR Reset last blocker's output[r]:"
)
bb_next_operation = input()
if (not bb_next_operation.islower()):
bb_next_operation = bb_next_operation.lower(
) # Lower case
# Continue using Overlap Blocker
if (bb_next_operation == 'a'):
continue
# Reset/remove last blocker's output from candidate set list
elif (bb_next_operation == 'r'):
del C_blackbox[-1]
print("\n- Last blocker output removed!")
print(
"- Continue to use BlackBox Blocker (y or n):")
bb_next_operation = input()
if (bb_next_operation == 'n'):
break
# Finish BlackBox Blocker
else:
break
print("\n- Do you want to add/use another blocker? (y or n):")
blocker_decision = input()
if (blocker_decision == 'n'):
break
print(
"\n- Which blocker output you want to use? (Attr Equ-ab, Overlap-ob, BlackBox-bb, Union-un)"
)
blocker_output_selection = input()
# Attribute Equ
if (blocker_output_selection == "ab"):
C = C_attr_eq[-1]
# Overlap
elif (blocker_output_selection == "ob"):
C = C_overlap[-1]
# Overlap
elif (blocker_output_selection == "bb"):
C = C_blackbox[-1]
# Union of blockers
elif (blocker_output_selection == "un"):
# Combine/union blockers candidate sets
print("\n- TODO: Unions Attr Equ and Overlap only!")
if (C_attr_eq and C_overlap and not C_attr_eq[-1].empty and
not C_overlap[-1].empty): # Both blocker types used
C = em.combine_blocker_outputs_via_union(
[C_attr_eq[-1], C_overlap[-1]])
print(
"\n- Blockers candidate set outputs combined via union."
)
else: # Error
C = []
print(
"\n- ERROR: Candidate set C is empty! Check blockers' results."
)
# Error
else:
C = []
print(
"\n- ERROR: Candidate set C is empty! Check blockers' results."
)
print("\n- Length of C: " + str(len(C)))
else:
print(
"\n- 2 Tables column names are different, they must be the same"
)
print(list(A.columns))
print(list(B.columns))
# SAMPLING&LABELING
print("\n-------------SAMPLING&LABELING-------------\n")
print("- Choose sampling size (eg. 450):")
sampling_size = input()
while (int(sampling_size) > len(C)):
print("- Sampling size cannot be bigger than " + str(len(C)))
sampling_size = input()
# Sample candidate set
S = em.sample_table(C, int(sampling_size))
print("- New window will pop-up for " + sampling_size + " sized table.")
print("- If there is a match, change tuple's label value to 1")
# Label S
G = em.label_table(S, 'label')
#DEVELOPMENT AND EVALUATION
print("\n-------------DEVELOPMENT AND EVALUATION-------------\n")
# Split S into development set (I) and evaluation set (J)
IJ = em.split_train_test(G, train_proportion=0.7, random_state=0)
I = IJ['train']
J = IJ['test']
#SELECTING THE BEST MATCHER
print("\n-------------SELECTING THE BEST MATCHER-------------\n")
# Create a set of ML-matchers
dt = em.DTMatcher(name='DecisionTree', random_state=0)
svm = em.SVMMatcher(name='SVM', random_state=0)
rf = em.RFMatcher(name='RF', random_state=0)
lg = em.LogRegMatcher(name='LogReg', random_state=0)
ln = em.LinRegMatcher(name='LinReg')
nb = em.NBMatcher(name='NaiveBayes')
print(
"\n- 6 different ML-matchers created: DL, SVM, RF, LogReg, LinReg, NB")
print("\n- Creating features...")
# Generate features
feature_table = em.get_features_for_matching(
A, B, validate_inferred_attr_types=False)
print("\n- Features list:")
# List the names of the features generated
print(feature_table['feature_name'])
print("\n- Converting the development set to feature vectors...")
# Convert the I into a set of feature vectors using feature_table
H = em.extract_feature_vecs(I,
feature_table=feature_table,
attrs_after='label',
show_progress=False)
print("\n- Feature table first rows:")
# Display first few rows
print(H.head())
# Primary key of tables = prefix + pk = l_id, r_id
ltable_pk = l_prefix + pk_A
rtable_pk = r_prefix + pk_B
# Check if the feature vectors contain missing values
# A return value of True means that there are missing values
is_missing_values = any(pd.notnull(H))
print("\n- Does feature vector have missing values: " +
str(is_missing_values))
if (is_missing_values):
# Impute feature vectors with the mean of the column values.
H = em.impute_table(
H,
exclude_attrs=['_id', ltable_pk, rtable_pk, 'label'],
strategy='mean',
val_all_nans=0.0)
#print("\n- Feature table first rows:")
# Display first few rows
#print(H.head())
print("- Impute table function used for missing values.")
print("\n- Selecting the best matcher using cross-validation...")
# Select the best ML matcher using CV
result = em.select_matcher(
matchers=[dt, rf, svm, ln, lg, nb],
table=H,
exclude_attrs=['_id', ltable_pk, rtable_pk, 'label'],
k=5,
target_attr='label',
metric_to_select_matcher='f1',
random_state=0)
print("\n- Results:")
print(result['cv_stats'])
#DEBUGGING THE MATCHER
print("\n-------------DEBUGGING THE MATCHER-------------\n")
# Split feature vectors into train and test
UV = em.split_train_test(H, train_proportion=0.5)
U = UV['train']
V = UV['test']
# Debug decision tree using GUI
em.vis_debug_rf(rf,
U,
V,
exclude_attrs=['_id', ltable_pk, rtable_pk, 'label'],
target_attr='label')
print("\n- Do you want to add another feature?")
H = em.extract_feature_vecs(I,
feature_table=feature_table,
attrs_after='label',
show_progress=False)
# Check if the feature vectors contain missing values
# A return value of True means that there are missing values
is_missing_values = any(pd.notnull(H))
print("\n- Does feature vector have missing values: " +
str(is_missing_values))
if (is_missing_values):
# Impute feature vectors with the mean of the column values.
H = em.impute_table(
H,
exclude_attrs=['_id', ltable_pk, rtable_pk, 'label'],
strategy='mean')
print("\n- Feature table first rows:")
# Display first few rows
print(H.head())
# Select the best ML matcher using CV
result = em.select_matcher(
[dt, rf, svm, ln, lg, nb],
table=H,
exclude_attrs=['_id', ltable_pk, rtable_pk, 'label'],
k=5,
target_attr='label',
metric_to_select_matcher='f1',
random_state=0)
print("\n- Results:")
print(result['cv_stats'])
#EVALUATING THE MATCHING OUTPUT
print("\n-------------EVALUATING THE MATCHING OUTPUT-------------\n")
print("\n- Converting the evaluation set to feature vectors...")
# Convert J into a set of feature vectors using feature table
L = em.extract_feature_vecs(J,
feature_table=feature_table,
attrs_after='label',
show_progress=False)
# Check if the feature vectors contain missing values
# A return value of True means that there are missing values
is_missing_values = any(pd.notnull(L))
print("\n- Does feature vector have missing values: " +
str(is_missing_values))
if (is_missing_values):
# Impute feature vectors with the mean of the column values.
L = em.impute_table(
L,
exclude_attrs=['_id', ltable_pk, rtable_pk, 'label'],
strategy='mean')
print("\n- Feature table first rows:")
# Display first few rows
print(L.head())
print("\n- Training the selected matcher...")
# Train using feature vectors from I
rf.fit(table=H,
exclude_attrs=['_id', ltable_pk, rtable_pk, 'label'],
target_attr='label')
print("\n- Predicting the matches...")
# Predict on L
predictions = rf.predict(
table=L,
exclude_attrs=['_id', ltable_pk, rtable_pk, 'label'],
append=True,
target_attr='predicted',
inplace=False)
print("\n- Evaluating the prediction...")
# Evaluate the predictions
eval_result = em.eval_matches(predictions, 'label', 'predicted')
print(em.print_eval_summary(eval_result))
print("\n- Time elapsed:")
print(datetime.now() - startTime)
print("\n-------------END-------------\n")
