def eval_pat_line_kfold(pat_classifier,
k_folds,
db_cred,
top_level_dir,
inject_percent_null,
inject_percent_outlier,
num_processors,
max_lines=100,
assume_multi_tables=True):
labels = ['BLANK', 'OTHER', 'HEADER','DATA', 'CONTEXT', 'FOOTNOTE', 'SUBHEADER']
con=connect(dbname=db_cred.database, user=db_cred.user, host = 'localhost', password=db_cred.password, port = 5532)
cur=con.cursor()
cur.execute(f"""DROP TABLE IF EXISTS "{k_folds}fold_cross_validation_pat_model" """)
con.commit()
cur.execute(f"""CREATE TABLE "{k_folds}fold_cross_validation_pat_model"(
fold_id integer,
fuzzy_rules json
) """)
con.commit()
cur.execute(f"""DROP TABLE IF EXISTS "{k_folds}fold_cross_validation_pat_cells" """)
con.commit()
cur.execute(f"""CREATE TABLE "{k_folds}fold_cross_validation_pat_cells"(
fold_id integer,
measure text
) """)
con.commit()
for label in labels:
cur.execute(f"""ALTER TABLE "{k_folds}fold_cross_validation_pat_cells" add column {label} real""")
con.commit()
cur.execute("DROP TABLE IF EXISTS pat_cell_predictions")
con.commit()
cur.execute("""CREATE TABLE pat_cell_predictions(
fold_id integer,
crawl_datafile_key integer,
line_index integer, column_index integer,
ground_truth_class text, pat_predicted_class text
)""")
con.commit()
cur.execute("DROP TABLE IF EXISTS pat_line_predictions")
con.commit()
cur.execute("""CREATE TABLE pat_line_predictions(
fold_id integer,
crawl_datafile_key integer, line_index integer,
gt_table_counter integer,pat_table_counter integer,
ground_truth_class text, pat_predicted_class text,
gt_fdl text, pat_fdl text,
gt_ldl text, pat_ldl text
)""")
con.commit()
cur.execute(f"""DROP TABLE IF EXISTS "{k_folds}fold_cross_validation_pat_lines" """)
con.commit()
cur.execute(f"""DROP TABLE IF EXISTS pat_table_confidences""")
con.commit()
cur.execute(f"""CREATE TABLE "{k_folds}fold_cross_validation_pat_lines"(
fold_id integer,
measure text
) """)
con.commit()
for label in labels:
cur.execute(f"""ALTER TABLE "{k_folds}fold_cross_validation_pat_lines" add column {label} real""")
con.commit()
print('\nCollecting experiment setup...')
folds=dict()
cur.execute(f"""SELECT fold_id, training, validation FROM "{k_folds}fold_cross_validation" """)
for result in cur:
folds[result[0]]= dict()
folds[result[0]]['train']= sorted(result[1])
folds[result[0]]['test']= sorted(result[2])
cur.close()
con.close()
print('Experiments collected!\n')
results = dict()
for fold_id in folds.keys():
results[fold_id] = dict()
print('\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
print(f'\n~~~~~~~~~~~~~~~~~~~~~ Cross Validation Fold {fold_id}: ~~~~~~~~~~~~~~~~~~')
print('\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
training_keys = folds[fold_id]['train']
print('Unique files in train: ', len(set(training_keys)))
test_keys = folds[fold_id]['test']
print('Total files in test: ', len(test_keys))
print('\nPreparing Training Data')
start = timer()
con=connect(dbname=db_cred.database, user=db_cred.user, host = 'localhost', password=db_cred.password, port = db_cred.port)
undersampled_cell_data = pd.read_sql_query(
sql = f"SELECT * FROM pat_data_cell_rules WHERE undersample=True and crawl_datafile_key in {tuple(training_keys)}", con=con)
con.close()
# print(f'undersampled_cell_data={undersampled_cell_data.head()}')
con=connect(dbname=db_cred.database, user=db_cred.user, host = 'localhost', password=db_cred.password, port = db_cred.port)
undersampled_line_data = pd.read_sql_query(
sql = f"SELECT * FROM pat_data_line_rules WHERE undersample=True and crawl_datafile_key in {tuple(training_keys)}", con=con)
con.close()
# print(f'undersampled_line_data={undersampled_line_data.head()}')
con=connect(dbname=db_cred.database, user=db_cred.user, host = 'localhost', password=db_cred.password, port = db_cred.port)
undersampled_cell_not_data = pd.read_sql_query(
sql = f"SELECT * FROM pat_not_data_cell_rules WHERE undersample=True and crawl_datafile_key in {tuple(training_keys)}", con=con)
con.close()
# print(f'undersampled_cell_not_data={undersampled_cell_not_data.head()}')
con=connect(dbname=db_cred.database, user=db_cred.user, host = 'localhost', password=db_cred.password, port = db_cred.port)
undersampled_line_not_data = pd.read_sql_query(
sql = f"SELECT * FROM pat_not_data_line_rules WHERE undersample=True and crawl_datafile_key in {tuple(training_keys)}", con=con)
con.close()
# print(f'undersampled_line_not_data={undersampled_line_not_data.head()}')
# input()
pat_classifier.train_rules(undersampled_cell_data, undersampled_cell_not_data, undersampled_line_data, undersampled_line_not_data)
end = timer()
con=connect(dbname=db_cred.database, user=db_cred.user, host = 'localhost', password=db_cred.password, port = db_cred.port)
# pp.pprint(pat_classifier.fuzzy_rules)
cur = con.cursor()
cur.execute(f""" INSERT INTO "{k_folds}fold_cross_validation_pat_model" (fold_id, fuzzy_rules) VALUES (%s, %s)""",
(fold_id, Json(pat_classifier.fuzzy_rules)))
con.commit()
cur.close()
con.close()
print(f'\n-training data generated in {timedelta(seconds=end - start)}')
print(f'Available CPUS:{pat.available_cpu_count()}')
NINPUTS = len(test_keys)
NPROC = min(num_processors,pat.available_cpu_count())
print(f'NINPUTS={NINPUTS}')
print(f'NPROC={NPROC}')
# Process files
processed_files=[]
with Pool(processes=NPROC) as pool:
with tqdm(total=NINPUTS) as pbar:
for r in pool.imap_unordered(process_csv_worker,
generate_process_csv_tasks(db_cred,
top_level_dir,
pat_classifier,
fold_id,
test_keys,
max_lines,
assume_multi_tables,
inject_percent_null,
inject_percent_outlier
)):
processed_files.append(r)
pbar.update(1)
print(f'\n-processed_files in fold {fold_id} in {timedelta(seconds=end - start)}')
#### SAVE Ytest,Ypred ###
start = timer()
line_prediction_list = [worker_output[0] for worker_output in processed_files]
line_prediction = pd.concat(line_prediction_list)
cell_prediction_list = [worker_output[1] for worker_output in processed_files]
cell_prediction = pd.concat(cell_prediction_list)
pat_line_classification_DATA = list(line_prediction.itertuples(index=False, name=None))
pat_cell_classification_DATA = list(cell_prediction.itertuples(index=False, name=None))
con = connect(dbname=db_cred.database,
user=db_cred.user,
host = 'localhost',
password=db_cred.password,
port = db_cred.port)
cur = con.cursor()
execute_values(cur,"""INSERT INTO pat_line_predictions
(fold_id, crawl_datafile_key, line_index, gt_table_counter, pat_table_counter,
ground_truth_class, pat_predicted_class,
gt_fdl, pat_fdl,
gt_ldl, pat_ldl
) VALUES %s""",
pat_line_classification_DATA)
execute_values(cur,"""INSERT INTO pat_cell_predictions
(fold_id, crawl_datafile_key, line_index,
column_index, ground_truth_class, pat_predicted_class
) VALUES %s""",
pat_cell_classification_DATA)
con.commit()
cur.close()
con.close()
#### PERFORMANCE ###
line_performance = evaluation_utilities.predict_performance(labels, line_prediction["annotated_label"], line_prediction["predicted_label"])
print(f'\n\nline_performance=\n\n{line_performance}')
results[fold_id]["line"] = line_performance
evaluation_utilities.save_performance(db_cred, line_performance, f'"{k_folds}fold_cross_validation_pat_lines"', fold_id)
boundary_performance = evaluation_utilities.predict_performance(["DATA_START"],
line_prediction["annotated_fdl"],
line_prediction["predicted_fdl"])
data_end_performance = evaluation_utilities.predict_performance(["DATA_END"],
line_prediction["annotated_ldl"],
line_prediction["predicted_ldl"])
boundary_performance = boundary_performance.join(data_end_performance, how='outer')
evaluation_utilities.save_performance(db_cred, boundary_performance, f'"{k_folds}fold_cross_validation_pat_boundary"', fold_id)
print(f'\n\nboundary_performance=\n\n{boundary_performance}')
results[fold_id]["boundary_performance"] = boundary_performance
first_table_line_performance = evaluation_utilities.predict_performance(labels,
line_prediction.query('predicted_table_counter<=1')["annotated_label"],
line_prediction.query('predicted_table_counter<=1')["predicted_label"])
results[fold_id]["first_table_line"] = first_table_line_performance
print(f'\n\nfirst_table_line_performance=\n\n{first_table_line_performance}')
evaluation_utilities.save_performance(db_cred, first_table_line_performance, f'"{k_folds}fold_cross_validation_pat_line_first"', fold_id)
first_table_boundary_performance = evaluation_utilities.predict_performance(["DATA_START"],
line_prediction.query('predicted_table_counter<=1')["annotated_fdl"],
line_prediction.query('predicted_table_counter<=1')["predicted_fdl"])
data_end_performance = evaluation_utilities.predict_performance(["DATA_END"],
line_prediction.query('predicted_table_counter<=1')["annotated_ldl"],
line_prediction.query('predicted_table_counter<=1')["predicted_ldl"])
first_table_boundary_performance=first_table_boundary_performance.join(data_end_performance, how='outer')
results[fold_id]["first_table_boundary_performance"] = first_table_boundary_performance
evaluation_utilities.save_performance(db_cred, first_table_boundary_performance, f'"{k_folds}fold_cross_validation_pat_boundary_first"', fold_id)
print(f'\n\nfirst_table_boundary_performance=\n\n{first_table_boundary_performance}')
cell_performance = evaluation_utilities.predict_performance(labels,
cell_prediction["annotated_label"],
cell_prediction["predicted_label"])
print(f'\ncell_performance=\n\n{cell_performance}')
results[fold_id]["cell"] = cell_performance
evaluation_utilities.save_performance(db_cred, cell_performance, f'"{k_folds}fold_cross_validation_pat_cell"', fold_id)
table_confusion_matrices = [worker_output[3] for worker_output in processed_files]
table_confusion_matrix = [[t.real_positive, t.true_positive_table, t.true_positive_data, t.predicted_positive_table, t.predicted_positive_data] for t in table_confusion_matrices]
real_positive, true_positive_table, true_positive_data, predicted_positive_table, predicted_positive_data = map(sum, zip(*table_confusion_matrix))
section_performance = pd.DataFrame(columns = ["Data", "Data_Header"]).astype(np.float)
section_performance.loc["precision","Data"] = evaluation_utilities.recall(true_positive_data, predicted_positive_data)
section_performance.loc["recall","Data"] = evaluation_utilities.recall(true_positive_data, real_positive)
section_performance.loc["fmeasure","Data"] = evaluation_utilities.fmeasure(section_performance.loc["precision","Data"], section_performance.loc["recall","Data"])
section_performance.loc["precision","Data_Header"] = evaluation_utilities.recall(true_positive_table, predicted_positive_table)
section_performance.loc["recall","Data_Header"] = evaluation_utilities.recall(true_positive_table, real_positive)
section_performance.loc["fmeasure","Data_Header"] = evaluation_utilities.fmeasure(section_performance.loc["precision","Data_Header"], section_performance.loc["recall","Data_Header"])
evaluation_utilities.save_performance(db_cred, section_performance, f'"{k_folds}fold_cross_validation_pat_relation"', fold_id)
results[fold_id]["table"] = section_performance
print(f'\n\nsection_performance=\n\n{section_performance}')
file_prediction_list = [worker_output[2] for worker_output in processed_files]
file_accuracy = file_prediction_list.count(True)/len(file_prediction_list)
print(f'\n\nfile_accuracy={file_accuracy}\n')
results[fold_id]["file"] = file_accuracy
file_data_jaccard = evaluation_utilities.jaccard_similarity_coefficient(['DATA', 'SUBHEADER'],
cell_prediction["annotated_label"],
cell_prediction["predicted_label"])
print(f'file_data_cell_jaccard={file_data_jaccard}')
results[fold_id]["file_jaccard"] = file_data_jaccard
end = timer()
print(f'>>> predict_performance calculated in {timedelta(seconds=end - start)}')
table_confidences_list = [worker_output[4] for worker_output in processed_files]
table_confidences = pd.concat(table_confidences_list)
table_confidences['avg_confidence']=table_confidences[['top_confidence','bottom_confidence']].mean(axis=1)
table_confidences['fold_id'] = fold_id
engine = create_engine(f'postgresql+psycopg2://{db_cred.user}:{db_cred.password}@localhost:{db_cred.port}/{db_cred.database}')
table_confidences.head(0).to_sql('pat_table_confidences', engine, if_exists='append',index=False)
conn = engine.raw_connection()
cur = conn.cursor()
output = io.StringIO()
table_confidences.to_csv(output, sep='\t', header=False, index=False)
output.seek(0)
contents = output.getvalue()
cur.copy_from(output, 'pat_table_confidences', null="") # null values become ''
conn.commit()
file_to_write=f"evaluate_pat_{k_folds}cross_val"
if inject_percent_null!=None:
file_to_write=os.path.join('add_nulls',file_to_write)
file_to_write=file_to_write+f"-{inject_percent_null}"
elif inject_percent_outlier!=None:
file_to_write=os.path.join('add_noise',file_to_write)
file_to_write=file_to_write+f"-{inject_percent_outlier}"
file_to_write=file_to_write+".pkl"
with open(file_to_write, "wb")as f:
pickle.dump(results, f)
average_results = evaluation_utilities.average_performance(file_to_write)
return average_results
import sys
from dotmap import DotMap
import pytheas
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"-w", "--weights", default="pytheas/trained_rules.json"
) # , description="Filepath to pre-trained rule weights")
parser.add_argument("-i", "--input_portals", nargs="*", default=[])
parser.add_argument("-p",
"--NPROC",
type=int,
default=pytheas.available_cpu_count())
parser.add_argument("-m", "--max_lines", type=int, default=10000)
parser.add_argument("-c",
"--db_cred_file",
default="database_credentials.json")
args = parser.parse_args(sys.argv[1:])
with open(args.db_cred_file) as f:
credentials = json.load(f)
args = parser.parse_args(sys.argv[1:])
# Database connection credentials
db_cred = DotMap()
db_cred.user = credentials["user"]
db_cred.password = credentials["password"]
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="")
parser.add_argument("-d", "--database", default="ground_truth_2k_canada", help="database for experimentation with ground truth")
parser.add_argument("-u", "--user", default="christina", help="user for the database connection")
parser.add_argument("-p", "--port", default=5532, help="port that postgresql database listens to")
parser.add_argument("-n", "--num_processors", default = 64, type=int, help="number of processors to be used")
parser.add_argument("-t", "--top_level_dir", default="/home/christina/OPEN_DATA_CRAWL_2018", help="path to Open Data Crawl")
parser.add_argument("-e", "--evaluation_method", default="cross", help="one of ['bootstrap', 'cross']")
parser.add_argument("-k", "--k_folds", default=10, type=int, help="number of folds in cross-validation")
parser.add_argument("-i", "--inject_percent_null", default='None')
parser.add_argument("-o", "--inject_percent_outlier", default='None')
args = parser.parse_args(sys.argv[1:])
num_processors=min(args.num_processors,pat.available_cpu_count())
top_level_dir=args.top_level_dir
evaluation_method = args.evaluation_method
inject_percent_null=eval(args.inject_percent_null)
inject_percent_outlier = eval(args.inject_percent_outlier)
if not os.path.exists('add_nulls'):
os.makedirs('add_nulls')
if not os.path.exists('add_noise'):
os.makedirs('add_noise')
print(f'inject_percent_null={inject_percent_null}')
print(f'inject_percent_outlier={inject_percent_outlier}')
print(f'num_processors={num_processors}')
from dotmap import DotMap
import os, argparse, sys
from psycopg2 import connect
import pytheas
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="")
parser.add_argument("-d", "--database", default="ground_truth_2k_canada", help="database for experimentation with ground truth")
parser.add_argument("-u", "--user", default="christina", help="user for the database connection")
parser.add_argument("-p", "--port", default=5532, help="port that postgresql database listens to")
parser.add_argument("-n", "--num_processors", default = 64, type=int, help="number of processors to be used")
parser.add_argument("-t", "--top_level_dir", default="/home/christina/OPEN_DATA_CRAWL_2018", help="path to Open Data Crawl")
args = parser.parse_args(sys.argv[1:])
num_processors=min(args.num_processors,pytheas.available_cpu_count())
top_level_dir = args.top_level_dir
# Database connection credentials
db_cred = DotMap()
db_cred.user = args.user
db_cred.database = args.database
db_cred.port = args.port
db_cred.password = ''
pytheas_model = pytheas.PYTHEAS()
pytheas_model.collect_rule_activation(db_cred, num_processors, top_level_dir)
print('\nLoading CACHED Training Data...')
