class Session:
"""
Session class controls the entire pipeline of HC
"""
def __init__(self, env, name="session"):
"""
Constructor for Holoclean session
:param env: Holoclean environment
:param name: Name for the Holoclean session
"""
# use DEBUG logging level if verbose enabled
if env['verbose']:
root_logger.setLevel(logging.DEBUG)
gensim_logger.setLevel(logging.DEBUG)
logging.debug('initiating session with parameters: %s', env)
# Initialize random seeds.
random.seed(env['seed'])
torch.manual_seed(env['seed'])
np.random.seed(seed=env['seed'])
# Initialize members
self.name = name
self.env = env
self.ds = Dataset(name, env)
self.dc_parser = Parser(env, self.ds)
self.domain_engine = DomainEngine(env, self.ds)
self.detect_engine = DetectEngine(env, self.ds)
self.repair_engine = RepairEngine(env, self.ds)
self.eval_engine = EvalEngine(env, self.ds)
def load_data(self,
name,
fpath,
na_values=None,
entity_col=None,
src_col=None):
"""
load_data takes the filepath to a CSV file to load as the initial dataset.
:param name: (str) name to initialize dataset with.
:param fpath: (str) filepath to CSV file.
:param na_values: (str) value that identifies a NULL value
:param entity_col: (st) column containing the unique
identifier/ID of an entity. For fusion tasks, rows with
the same ID will be fused together in the output.
If None, assumes every row is a unique entity.
:param src_col: (str) if not None, for fusion tasks
specifies the column containing the source for each "mention" of an
entity.
"""
status, load_time = self.ds.load_data(name,
fpath,
na_values=na_values,
entity_col=entity_col,
src_col=src_col)
logging.info(status)
logging.debug('Time to load dataset: %.2f secs', load_time)
def load_dcs(self, fpath):
"""
load_dcs ingests the Denial Constraints for initialized dataset.
:param fpath: filepath to TXT file where each line contains one denial constraint.
"""
status, load_time = self.dc_parser.load_denial_constraints(fpath)
logging.info(status)
logging.debug('Time to load dirty data: %.2f secs', load_time)
def get_dcs(self):
return self.dc_parser.get_dcs()
def detect_errors(self, detect_list):
status, detect_time = self.detect_engine.detect_errors(detect_list)
logging.info(status)
logging.debug('Time to detect errors: %.2f secs', detect_time)
def setup_domain(self):
status, domain_time = self.domain_engine.setup()
logging.info(status)
logging.debug('Time to setup the domain: %.2f secs', domain_time)
def repair_errors(self, featurizers):
status, feat_time = self.repair_engine.setup_featurized_ds(featurizers)
logging.info(status)
logging.debug('Time to featurize data: %.2f secs', feat_time)
status, setup_time = self.repair_engine.setup_repair_model()
logging.info(status)
logging.debug('Time to setup repair model: %.2f secs', feat_time)
status, fit_time = self.repair_engine.fit_repair_model()
logging.info(status)
logging.debug('Time to fit repair model: %.2f secs', fit_time)
status, infer_time = self.repair_engine.infer_repairs()
logging.info(status)
logging.debug('Time to infer correct cell values: %.2f secs',
infer_time)
status, time = self.ds.get_inferred_values()
logging.info(status)
logging.debug('Time to collect inferred values: %.2f secs', time)
status, time = self.ds.get_repaired_dataset()
logging.info(status)
logging.debug('Time to store repaired dataset: %.2f secs', time)
if self.env['print_fw']:
status, time = self.repair_engine.get_featurizer_weights()
logging.info(status)
logging.debug('Time to store featurizer weights: %.2f secs', time)
return status
def evaluate(self, fpath, tid_col, attr_col, val_col, na_values=None):
"""
evaluate generates an evaluation report with metrics (e.g. precision,
recall) given a test set.
:param fpath: (str) filepath to test set (ground truth) CSV file.
:param tid_col: (str) column in CSV that corresponds to the TID.
:param attr_col: (str) column in CSV that corresponds to the attribute.
:param val_col: (str) column in CSV that corresponds to correct value
for the current TID and attribute (i.e. cell).
:param na_values: (Any) how na_values are represented in the data.
Returns an EvalReport named tuple containing the experiment results.
"""
name = self.ds.raw_data.name + '_clean'
print("name")
status, load_time = self.eval_engine.load_data(name,
fpath,
tid_col,
attr_col,
val_col,
na_values=na_values)
logging.info(status)
logging.debug('Time to evaluate repairs: %.2f secs', load_time)
status, report_time, eval_report = self.eval_engine.eval_report()
logging.info(status)
logging.debug('Time to generate report: %.2f secs', report_time)
return eval_report
def explain_repairs(self, detectors):
"""
TODO Document
:param detectors: (list) of ErrorDetector objects
"""
graph_time = self.explain_engine.build_constraints_graphs()
logging.debug('Time to build constraints graph: %.2f secs', graph_time)
explanation, explain_time = self.explain_engine.explain_repairs(
detectors)
logging.info(explanation)
logging.debug('Time to generate explanation: %.2f secs', explain_time)
for e in explanation:
self.explain_engine.plot_explanation(e)
return explanation
class Session:
"""
Session class controls the entire pipeline of HC
"""
def __init__(self, env, name="session"):
"""
Constructor for Holoclean session
:param env: Holoclean environment
:param name: Name for the Holoclean session
"""
# use DEBUG logging level if verbose enabled
if env['verbose']:
root_logger.setLevel(logging.DEBUG)
gensim_logger.setLevel(logging.DEBUG)
logging.debug('initiating session with parameters: %s', env)
# Initialize random seeds.
random.seed(env['seed'])
torch.manual_seed(env['seed'])
np.random.seed(seed=env['seed'])
# Initialize members
self.name = name
self.env = env
self.ds = Dataset(name, env)
self.dc_parser = Parser(env, self.ds)
self.domain_engine = DomainEngine(env, self.ds)
self.detect_engine = DetectEngine(env, self.ds)
self.repair_engine = RepairEngine(env, self.ds)
self.eval_engine = EvalEngine(env, self.ds)
def load_data(self,
name,
fpath,
na_values=None,
entity_col=None,
src_col=None,
exclude_attr_cols=None,
numerical_attrs=None):
"""
load_data takes the filepath to a CSV file to load as the initial dataset.
:param name: (str) name to initialize dataset with.
:param fpath: (str) filepath to CSV file.
:param na_values: (str) value that identifies a NULL value
:param entity_col: (st) column containing the unique
identifier/ID of an entity. For fusion tasks, rows with
the same ID will be fused together in the output.
If None, assumes every row is a unique entity.
:param src_col: (str) if not None, for fusion tasks
specifies the column containing the source for each "mention" of an
entity.
:param exclude_attr_cols: (str list)
:param numerical_attrs: (str list)
"""
status, load_time = self.ds.load_data(
name,
fpath,
na_values=na_values,
entity_col=entity_col,
src_col=src_col,
exclude_attr_cols=exclude_attr_cols,
numerical_attrs=numerical_attrs)
logging.info(status)
logging.debug('Time to load dataset: %.2f secs', load_time)
def load_dcs(self, fpath):
"""
load_dcs ingests the Denial Constraints for initialized dataset.
:param fpath: filepath to TXT file where each line contains one denial constraint.
"""
status, load_time = self.dc_parser.load_denial_constraints(fpath)
logging.info(status)
logging.debug('Time to load dirty data: %.2f secs', load_time)
def get_dcs(self):
return self.dc_parser.get_dcs()
def detect_errors(self, detect_list):
status, detect_time = self.detect_engine.detect_errors(detect_list)
logging.info(status)
logging.debug('Time to detect errors: %.2f secs', detect_time)
def disable_quantize(self):
self.do_quantization = False
self.ds.do_quantization = False
self.domain_engine.do_quantization = False
def quantize_numericals(self, num_attr_groups_bins):
"""
:param num_attr_groups_bins: list[tuple] where each tuple consists of
(# of bins, list[str]) where the list[str] is a group of attribues to be
treated as numerical.
"""
self.do_quantization = True
self.ds.do_quantization = True
self.domain_engine.do_quantization = True
status, quantize_time, quantized_data = \
quantize_km(self.env, self.ds.get_raw_data(), num_attr_groups_bins)
logging.info(status)
logging.debug('Time to quantize the dataset: %.2f secs' %
quantize_time)
self.load_quantized_data(quantized_data)
return quantized_data
def load_quantized_data(self, df):
tic = time.time()
name = self.ds.raw_data.name + '_quantized'
self.ds.quantized_data = Table(name, Source.DF, df=df)
# Re-store to DB, ensuring numerical values are stored as floats.
df_correct_type = df.copy()
for attr in self.ds.numerical_attrs:
df_correct_type.loc[df_correct_type[attr] == NULL_REPR,
attr] = np.nan
df_correct_type[attr] = df_correct_type[attr].astype(float)
df_correct_type.to_sql(name,
self.ds.engine.engine,
if_exists='replace',
index=False,
index_label=None)
for attr in self.ds.quantized_data.get_attributes():
self.ds.quantized_data.create_db_index(self.ds.engine, [attr])
logging.debug('Time to load quantized dataset: %.2f secs' %
(time.time() - tic))
def generate_domain(self):
status, domain_time = self.domain_engine.setup()
logging.info(status)
logging.debug('Time to generate the domain: %.2f secs', domain_time)
def run_estimator(self):
"""
Uses estimator to weak label and prune domain.
"""
self.domain_engine.run_estimator()
def repair_errors(self, featurizers):
return self._repair_errors(featurizers)
def repair_validate_errors(self,
featurizers,
fpath,
tid_col,
attr_col,
val_col,
validate_period,
na_values=None):
return self._repair_errors(featurizers, fpath, tid_col, attr_col,
val_col, na_values, validate_period)
def _repair_errors(self,
featurizers,
fpath=None,
tid_col=None,
attr_col=None,
val_col=None,
na_values=None,
validate_period=None):
"""
Repair errors and optionally runs validation set per epoch.
Must specify the following parameters if validation required:
:param fpath: (str) filepath to test set (ground truth) CSV file.
:param tid_col: (str) column in CSV that corresponds to the TID.
:param attr_col: (str) column in CSV that corresponds to the attribute.
:param val_col: (str) column in CSV that corresponds to correct value
for the current TID and attribute (i.e. cell).
:param na_values: (Any) how na_values are represented in the data.
:param validate_period: (int) perform validation every nth epoch.
"""
status, feat_time = self.repair_engine.setup_featurized_ds(featurizers)
logging.info(status)
logging.debug('Time to featurize data: %.2f secs', feat_time)
status, setup_time = self.repair_engine.setup_repair_model()
logging.info(status)
logging.debug('Time to setup repair model: %.2f secs', feat_time)
# If validation fpath provided, fit and validate
if fpath is None:
status, fit_time = self.repair_engine.fit_repair_model()
else:
# Set up validation set
name = self.ds.raw_data.name + '_clean'
status, load_time = self.eval_engine.load_data(name,
fpath,
tid_col,
attr_col,
val_col,
na_values=na_values)
logging.info(status)
logging.debug('Time to evaluate repairs: %.2f secs', load_time)
status, fit_time = self.repair_engine.fit_validate_repair_model(
self.eval_engine, validate_period)
logging.info(status)
logging.debug('Time to fit repair model: %.2f secs', fit_time)
status, infer_time = self.repair_engine.infer_repairs()
logging.info(status)
logging.debug('Time to infer correct cell values: %.2f secs',
infer_time)
status, time = self.ds.get_inferred_values()
logging.info(status)
logging.debug('Time to collect inferred values: %.2f secs', time)
status, time = self.ds.get_repaired_dataset()
logging.info(status)
logging.debug('Time to store repaired dataset: %.2f secs', time)
if self.env['print_fw']:
status, time = self.repair_engine.get_featurizer_weights()
logging.info(status)
logging.debug('Time to store featurizer weights: %.2f secs', time)
return status
def evaluate(self, fpath, tid_col, attr_col, val_col, na_values=None):
"""
evaluate generates an evaluation report with metrics (e.g. precision,
recall) given a test set.
:param fpath: (str) filepath to test set (ground truth) CSV file.
:param tid_col: (str) column in CSV that corresponds to the TID.
:param attr_col: (str) column in CSV that corresponds to the attribute.
:param val_col: (str) column in CSV that corresponds to correct value
for the current TID and attribute (i.e. cell).
:param na_values: (Any) how na_values are represented in the data.
Returns an EvalReport named tuple containing the experiment results.
"""
name = self.ds.raw_data.name + '_clean'
status, load_time = self.eval_engine.load_data(name,
fpath,
tid_col,
attr_col,
val_col,
na_values=na_values)
logging.info(status)
logging.debug('Time to evaluate repairs: %.2f secs', load_time)
status, report_time, eval_report = self.eval_engine.eval_report()
logging.info(status)
logging.debug('Time to generate report: %.2f secs', report_time)
return eval_report
def get_predictions(self):
"""
Returns a dataframe with 3 columns:
- tid, attribute, inferred_val, proba
"""
query = """
SELECT
_tid_, attribute, inferred_val, prob
FROM {dom}
INNER JOIN {inf_vals} USING(_vid_)
""".format(inf_vals=AuxTables.inf_values_idx.name,
dom=AuxTables.cell_domain.name)
res = self.ds.engine.execute_query(query)
df_preds = pd.DataFrame(
res,
columns=['tid', 'attribute', 'inferred_val', 'proba'],
dtype=str)
return df_preds