class RandomTable(DataFrameModule):
def __init__(self, columns, rows=-1, random=np.random.rand, throttle=False, force_valid_ids=False, **kwds):
super(RandomTable, self).__init__(**kwds)
self.default_step_size = 1000
if isinstance(columns, int):
self.columns = list(range(1, columns + 1))
elif isinstance(columns, (list, np.ndarray)):
self.columns = columns
else:
raise ProgressiveError("Invalid type for columns")
cols = len(self.columns)
self.columns.append(self.UPDATE_COLUMN)
self.rows = rows
self.random = random
if throttle and isinstance(throttle, (int, float)):
self.throttle = throttle
else:
self.throttle = False
self._df = create_dataframe(self.columns, types=cols * [np.dtype(float)] + [np.dtype(int)])
if force_valid_ids:
force_valid_id_columns(self._df)
self.columns = self._df.columns # reuse the pandas index structure
self._buffer = BufferedDataFrame()
def run_step(self, run_number, step_size, howlong):
if step_size == 0: # bug
logger.error("Received a step_size of 0")
return self._return_run_step(self.state_ready, steps_run=0, creates=0)
logger.info("generating %d lines", step_size)
if self.throttle:
step_size = np.min([self.throttle, step_size])
if self.rows >= 0 and (len(self._df) + step_size) > self.rows:
step_size = self.rows - len(self._df)
if step_size <= 0:
raise StopIteration
logger.info("truncating to %d lines", step_size)
values = {}
for c in self.columns[:-1]:
s = pd.Series(self.random(step_size))
values[c] = s
values[self.UPDATE_COLUMN] = pd.Series(step_size * [run_number], dtype=np.dtype(int))
df = pd.DataFrame(values, columns=self.columns)
with self.lock:
self._buffer.append(df)
self._df = self._buffer.df()
next_state = self.state_blocked if self.throttle else self.state_ready
return self._return_run_step(next_state, steps_run=step_size)
def validate_outputs(self):
valid = super(MBKMeans, self).validate_inputs()
if valid:
opt_slot = self.get_output_slot('labels')
if opt_slot:
logger.debug('Maintaining labels')
self._buffer = BufferedDataFrame()
else:
logger.debug('Not maintaining labels')
return valid
def __init__(self, filename, dtype=np.float64, **kwds):
self._add_slots(kwds,'output_descriptors',
[SlotDescriptor('array', type=csr_matrix, required=False)])
super(VECLoader, self).__init__(**kwds)
self._dtype = dtype
self.default_step_size = kwds.get('chunksize', 100) # initial guess
openf=open
if filename.endswith('.bz2'):
openf=BZ2File
elif filename.endswith('.gz') or filename.endswith('.Z'):
openf=GzipFile
self.f = openf(filename)
# When created with a specified chunksize, it returns the parser
self._rows_read = 0
self._csr_matrix = None
self._buffer = BufferedDataFrame()
def __init__(self, filepath_or_buffer=None, filter=None, force_valid_ids=False, **kwds):
"""HDFLoader(filepath_or_buffer=None, force_valid_ids=False, id=None,scheduler=None,tracer=None,predictor=None,storage=None)
"""
super(HDFLoader, self).__init__(**kwds)
assert False, "Not working yet"
self.default_step_size = kwds.get('chunksize', 1000) # initial guess
kwds.setdefault('chunksize', self.default_step_size)
# Filter out the module keywords from the csv loader keywords
hdf_kwds = self._filter_kwds(kwds, pd.read_hdf)
# When called with a specified chunksize, it returns a parser
self.filepath_or_buffer = filepath_or_buffer
self.force_valid_ids = force_valid_ids
self.hdf_kwds = hdf_kwds
self._rows_read = 0
if filter is not None and not callable(filter):
raise ProgressiveError('filter parameter should be callable or None')
self._filter = filter
self._buffer = BufferedDataFrame()
def __init__(self, filepath_or_buffer=None, filter=None, force_valid_ids=False, **kwds):
"""CSVLoader(filepath_or_buffer=None, sep=', ', dialect=None, compression='infer', doublequote=True, escapechar=None, quotechar='"', quoting=0, skipinitialspace=False, lineterminator=None, header='infer', index_col=None, names=None, prefix=None, skiprows=None, skipfooter=None, skip_footer=0, na_values=None, na_fvalues=None, true_values=None, false_values=None, delimiter=None, converters=None, dtype=None, usecols=None, engine=None, delim_whitespace=False, as_recarray=False, na_filter=True, compact_ints=False, use_unsigned=False, low_memory=True, buffer_lines=None, warn_bad_lines=True, error_bad_lines=True, keep_default_na=True, thousands=None, comment=None, decimal='.', parse_dates=False, keep_date_col=False, dayfirst=False, date_parser=None, memory_map=False, float_precision=None, nrows=None, chunksize=None, verbose=False, encoding=None, squeeze=False, mangle_dupe_cols=True, tupleize_cols=False, infer_datetime_format=False, skip_blank_lines=True, force_valid_ids=False, id=None,scheduler=None,tracer=None,predictor=None,storage=None,input_descriptors=[],output_descriptors=[])
"""
self._add_slots(kwds,'input_descriptors',
[SlotDescriptor('filenames', type=pd.DataFrame,required=False)])
super(CSVLoader, self).__init__(**kwds)
self.default_step_size = kwds.get('chunksize', 1000) # initial guess
kwds.setdefault('chunksize', self.default_step_size)
# Filter out the module keywords from the csv loader keywords
csv_kwds = self._filter_kwds(kwds, pd.read_csv)
# When called with a specified chunksize, it returns a parser
self.filepath_or_buffer = filepath_or_buffer
self.force_valid_ids = force_valid_ids
self.parser = None
self.csv_kwds = csv_kwds
self._rows_read = 0
if filter is not None and not callable(filter):
raise ProgressiveError('filter parameter should be callable or None')
self._filter = filter
self._buffer = BufferedDataFrame()
def __init__(self, columns, rows=-1, random=np.random.rand, throttle=False, force_valid_ids=False, **kwds):
super(RandomTable, self).__init__(**kwds)
self.default_step_size = 1000
if isinstance(columns, int):
self.columns = list(range(1, columns + 1))
elif isinstance(columns, (list, np.ndarray)):
self.columns = columns
else:
raise ProgressiveError("Invalid type for columns")
cols = len(self.columns)
self.columns.append(self.UPDATE_COLUMN)
self.rows = rows
self.random = random
if throttle and isinstance(throttle, (int, float)):
self.throttle = throttle
else:
self.throttle = False
self._df = create_dataframe(self.columns, types=cols * [np.dtype(float)] + [np.dtype(int)])
if force_valid_ids:
force_valid_id_columns(self._df)
self.columns = self._df.columns # reuse the pandas index structure
self._buffer = BufferedDataFrame()
class CSVLoader(DataFrameModule):
def __init__(self, filepath_or_buffer=None, filter=None, force_valid_ids=False, **kwds):
"""CSVLoader(filepath_or_buffer=None, sep=', ', dialect=None, compression='infer', doublequote=True, escapechar=None, quotechar='"', quoting=0, skipinitialspace=False, lineterminator=None, header='infer', index_col=None, names=None, prefix=None, skiprows=None, skipfooter=None, skip_footer=0, na_values=None, na_fvalues=None, true_values=None, false_values=None, delimiter=None, converters=None, dtype=None, usecols=None, engine=None, delim_whitespace=False, as_recarray=False, na_filter=True, compact_ints=False, use_unsigned=False, low_memory=True, buffer_lines=None, warn_bad_lines=True, error_bad_lines=True, keep_default_na=True, thousands=None, comment=None, decimal='.', parse_dates=False, keep_date_col=False, dayfirst=False, date_parser=None, memory_map=False, float_precision=None, nrows=None, chunksize=None, verbose=False, encoding=None, squeeze=False, mangle_dupe_cols=True, tupleize_cols=False, infer_datetime_format=False, skip_blank_lines=True, force_valid_ids=False, id=None,scheduler=None,tracer=None,predictor=None,storage=None,input_descriptors=[],output_descriptors=[])
"""
self._add_slots(kwds,'input_descriptors',
[SlotDescriptor('filenames', type=pd.DataFrame,required=False)])
super(CSVLoader, self).__init__(**kwds)
self.default_step_size = kwds.get('chunksize', 1000) # initial guess
kwds.setdefault('chunksize', self.default_step_size)
# Filter out the module keywords from the csv loader keywords
csv_kwds = self._filter_kwds(kwds, pd.read_csv)
# When called with a specified chunksize, it returns a parser
self.filepath_or_buffer = filepath_or_buffer
self.force_valid_ids = force_valid_ids
self.parser = None
self.csv_kwds = csv_kwds
self._rows_read = 0
if filter is not None and not callable(filter):
raise ProgressiveError('filter parameter should be callable or None')
self._filter = filter
self._buffer = BufferedDataFrame()
def rows_read(self):
return self._rows_read
def is_ready(self):
fn = self.get_input_slot('filenames')
if fn and fn.has_created():
return True
return super(CSVLoader, self).is_ready()
def validate_parser(self, run_number):
if self.parser is None:
if self.filepath_or_buffer is not None:
try:
self.parser = pd.read_csv(self.filepath_or_buffer, **self.csv_kwds)
except IOError as e:
logger.error('Cannot open file %s: %s', self.filepath_or_buffer, e)
self.parser = None
return self.state_terminated
self.filepath_or_buffer = None
else:
fn_slot = self.get_input_slot('filenames')
if fn_slot is None or fn_slot.output_module is None:
return self.state_terminated
with fn_slot.lock:
fn_slot.update(run_number)
if fn_slot.has_deleted() or fn_slot.has_updated():
raise ProgressiveError('Cannot handle input file changes')
df = fn_slot.data()
while self.parser is None:
indices = fn_slot.next_created(1)
if indices.stop==indices.start:
return self.state_blocked
filename = df.at[indices.start, 'filename']
try:
self.parser = pd.read_csv(filename, **self.csv_kwds)
except IOError as e:
logger.error('Cannot open file %s: %s', filename, e)
self.parser = None
# fall through
return self.state_ready
def run_step(self,run_number,step_size, howlong):
if step_size==0: # bug
logger.error('Received a step_size of 0')
return self._return_run_step(self.state_ready, steps_run=0, creates=0)
status = self.validate_parser(run_number)
if status==self.state_terminated:
raise StopIteration('no more filenames')
elif status==self.state_blocked:
return self._return_run_step(status, steps_run=0, creates=0)
elif status != self.state_ready:
logger.error('Invalid state returned by validate_parser: %d', status)
raise StopIteration('Unexpected situation')
logger.info('loading %d lines', step_size)
try:
with self.lock:
df = self.parser.read(step_size) # raises StopIteration at EOF
except StopIteration:
fn_slot = self.get_input_slot('filenames')
if fn_slot is None or fn_slot.output_module is None:
raise
self.parser = None
return self._return_run_step(self.state_ready, steps_run=0, creates=0)
creates = len(df)
if creates == 0: # should not happen
logger.error('Received 0 elements')
raise StopIteration
if self._filter != None:
df = self._filter(df)
creates = len(df)
if creates == 0:
logger.info('frame has been filtered out')
else:
self._rows_read += creates
logger.info('Loaded %d lines', self._rows_read)
if self.force_valid_ids:
force_valid_id_columns(df)
df[self.UPDATE_COLUMN] = run_number
with self.lock:
self._buffer.append(df)
self._df = self._buffer.df()
return self._return_run_step(self.state_ready, steps_run=creates)
class HDFLoader(DataFrameModule):
def __init__(self, filepath_or_buffer=None, filter=None, force_valid_ids=False, **kwds):
"""HDFLoader(filepath_or_buffer=None, force_valid_ids=False, id=None,scheduler=None,tracer=None,predictor=None,storage=None)
"""
super(HDFLoader, self).__init__(**kwds)
assert False, "Not working yet"
self.default_step_size = kwds.get('chunksize', 1000) # initial guess
kwds.setdefault('chunksize', self.default_step_size)
# Filter out the module keywords from the csv loader keywords
hdf_kwds = self._filter_kwds(kwds, pd.read_hdf)
# When called with a specified chunksize, it returns a parser
self.filepath_or_buffer = filepath_or_buffer
self.force_valid_ids = force_valid_ids
self.hdf_kwds = hdf_kwds
self._rows_read = 0
if filter is not None and not callable(filter):
raise ProgressiveError('filter parameter should be callable or None')
self._filter = filter
self._buffer = BufferedDataFrame()
def rows_read(self):
return self._rows_read
def is_ready(self):
fn = self.get_input_slot('filenames')
if fn and fn.has_created():
return True
return super(HDFLoader, self).is_ready()
def validate_parser(self, run_number):
if self.parser is None:
if self.filepath_or_buffer is not None:
try:
self.parser = pd.read_hdf(self.filepath_or_buffer, **self.hdf_kwds)
except IOError as e:
logger.error('Cannot open file %s: %s', self.filepath_or_buffer, e)
self.parser = None
return self.state_terminated
self.filepath_or_buffer = None
return self.state_ready
def run_step(self,run_number,step_size, howlong):
if step_size==0: # bug
logger.error('Received a step_size of 0')
return self._return_run_step(self.state_ready, steps_run=0, creates=0)
status = self.validate_parser(run_number)
if status==self.state_terminated:
raise StopIteration()
elif status != self.state_ready:
logger.error('Invalid state returned by validate_parser: %d', status)
raise StopIteration('Unexpected situation')
logger.info('loading %d lines', step_size)
df = self.parser.read(step_size) # raises StopIteration at EOF
creates = len(df)
if creates == 0: # should not happen
logger.error('Received 0 elements')
raise StopIteration
if self._filter != None:
df = self._filter(df)
creates = len(df)
if creates == 0:
logger.info('frame has been filtered out')
else:
self._rows_read += creates
logger.info('Loaded %d lines', self._rows_read)
if self.force_valid_ids:
self.force_valid_id_columns(df)
df[self.UPDATE_COLUMN] = run_number
with self.lock:
self._buffer.append(df)
self._df = self._buffer.df()
return self._return_run_step(self.state_ready, steps_run=creates)
class VECLoader(DataFrameModule):
pattern = re.compile(r"\(([0-9]+),([-+.0-9]+)\)[ ]*")
def __init__(self, filename, dtype=np.float64, **kwds):
self._add_slots(kwds,'output_descriptors',
[SlotDescriptor('array', type=csr_matrix, required=False)])
super(VECLoader, self).__init__(**kwds)
self._dtype = dtype
self.default_step_size = kwds.get('chunksize', 100) # initial guess
openf=open
if filename.endswith('.bz2'):
openf=BZ2File
elif filename.endswith('.gz') or filename.endswith('.Z'):
openf=GzipFile
self.f = openf(filename)
# When created with a specified chunksize, it returns the parser
self._rows_read = 0
self._csr_matrix = None
self._buffer = BufferedDataFrame()
def rows_read(self):
return self._rows_read
def toarray(self):
if self._csr_matrix is None:
docs = self.df()['document']
dv=DictVectorizer()
#TODO: race condition when using threads, cleanup_run can reset between
#setting the value here and returning it at the next instruction
self._csr_matrix = dv.fit_transform(docs)
return self._csr_matrix
def cleanup_run(self, run_number):
self._csr_matrix = None
super(VECLoader, self).cleanup_run(run_number)
def get_data(self, name):
if name=='array':
return self.toarray()
return super(VECLoader, self).get_data(name)
def run_step(self,run_number,step_size, howlong):
if self.f is None:
raise StopIteration()
dataset = []
try:
while len(dataset) < step_size:
line = self.f.next()
line=line.rstrip('\n\r')
if len(line)==0:
continue
doc = {}
for match in re.finditer(self.pattern, line):
termidx = int(match.group(1))
termfrx = self._dtype(match.group(2))
doc[termidx] = termfrx
if len(doc)!=0:
dataset.append(doc)
except StopIteration:
self.f.close()
self.f = None
creates = len(dataset)
if creates==0:
raise StopIteration()
df = pd.DataFrame({'document': dataset,
self.UPDATE_COLUMN: run_number})
self._rows_read += creates
with self.lock:
self._buffer.append(df)
self._df = self._buffer.df()
return self._return_run_step(self.state_ready, steps_run=creates, creates=creates)
class MBKMeans(DataFrameModule):
"""
Mini-batch k-means using the sklearn implementation.
"""
def __init__(self, n_clusters, columns=None, batch_size=100, tol=0.0, is_input=True, random_state=None,**kwds):
self._add_slots(kwds, 'input_descriptors',
[SlotDescriptor('df', type=pd.DataFrame, required=True)])
self._add_slots(kwds,'output_descriptors',
[SlotDescriptor('labels', type=pd.DataFrame, required=False)])
super(MBKMeans, self).__init__(**kwds)
self.mbk = MiniBatchKMeans(n_clusters=n_clusters, batch_size=batch_size,
verbose=True,
tol=tol, random_state=random_state)
self.columns = columns
self.n_clusters = n_clusters
self.default_step_size = 100
self._buffer = None
self._labels = None
self._is_input = is_input
def reset(self, init='k-means++'):
print "Reset, init=", init
self.mbk = MiniBatchKMeans(n_clusters=self.mbk.n_clusters,
batch_size=self.mbk.batch_size,
init=init,
#tol=self._rel_tol,
random_state=self.mbk.random_state)
dfslot = self.get_input_slot('df')
dfslot.reset()
if self._buffer is not None:
self._buffer.reset()
self._df = None
self._labels = None
self.set_state(self.state_ready)
def validate_outputs(self):
valid = super(MBKMeans, self).validate_inputs()
if valid:
opt_slot = self.get_output_slot('labels')
if opt_slot:
logger.debug('Maintaining labels')
self._buffer = BufferedDataFrame()
else:
logger.debug('Not maintaining labels')
return valid
def labels(self):
return self._labels
def get_data(self, name):
if name=='labels':
return self.labels()
return super(MBKMeans, self).get_data(name)
def run_step(self, run_number, step_size, howlong):
dfslot = self.get_input_slot('df')
dfslot.update(run_number)
if dfslot.has_deleted() or dfslot.has_updated():
logger.debug('has deleted or updated, reseting')
self.reset()
dfslot.update(run_number)
print('dfslot has buffered %d elements'% dfslot.created_length())
if dfslot.created_length() < self.mbk.n_clusters:
# Should add more than k items per loop
return self._return_run_step(self.state_blocked, steps_run=0)
indices = dfslot.next_created(step_size) # returns a slice
steps = indices_len(indices)
if steps==0:
return self._return_run_step(self.state_blocked, steps_run=0)
input_df = dfslot.data()
X = self.filter_columns(input_df, fix_loc(indices)).values
batch_size = self.mbk.batch_size or 100
for batch in gen_batches(steps, batch_size):
self.mbk.partial_fit(X[batch])
if self._buffer is not None:
df = pd.DataFrame({'labels': self.mbk.labels_})
df[self.UPDATE_COLUMN] = run_number
self._buffer.append(df)
with self.lock:
self._df = pd.DataFrame(self.mbk.cluster_centers_, columns=self.columns)
self._df[self.UPDATE_COLUMN] = run_number
if self._buffer is not None:
logger.debug('Setting the labels')
self._labels = self._buffer.df()
return self._return_run_step(dfslot.next_state(), steps_run=steps)
def is_visualization(self):
return False
def to_json(self, short=False):
json = super(MBKMeans, self).to_json(short)
if short:
return json
return self._centers_to_json(json)
def _centers_to_json(self, json):
if self._df is not None:
json['cluster_centers'] = self._df.to_json()
return json
def set_centroid(self, c, values):
try:
c = int(c)
except:
pass
centroids = self._df
if c not in centroids.index:
raise ProgressiveError('Expected %s values, received %s', len(self.columns), values)
if len(values)!=len(self.columns):
raise ProgressiveError('Expected %s of values, received %s', len(self.columns), values)
run_number = self.scheduler().for_input(self)
centroids.loc[c, self.columns] = values
centroids.loc[c, self.UPDATE_COLUMN] = run_number
self.mbk.cluster_centers_[c] = centroids.loc[c, self.columns]
return values
def is_input(self):
return self._is_input
def from_input(self, msg):
logger.info('Received message %s', msg)
for c in msg:
self.set_centroid(c, msg[c])
self.reset(init=self.mbk.cluster_centers_)
