def predict(self,
when={},
from_data=None,
model_name='mdsb_model',
breakpoint=PHASE_END,
**kargs):
"""
:param predict:
:param when:
:param model_name:
:return:
"""
transaction_type = TRANSACTION_PREDICT
from_ds = None if from_data is None else getDS(from_data)
transaction_metadata = TransactionMetadata()
transaction_metadata.model_name = model_name
# This will become irrelevant as if we have trained a model with a predict we just need to pass when or from_data
# predict_columns = [predict] if type(predict) != type([]) else predict
# transaction_metadata.model_predict_columns = predict_columns
transaction_metadata.model_when_conditions = when
transaction_metadata.type = transaction_type
transaction_metadata.storage_file = self.storage_file
transaction_metadata.from_data = from_ds
transaction = self.session.newTransaction(transaction_metadata,
breakpoint)
return transaction.output_data
def predict(self,
predict,
from_data=None,
when={},
model_name='mdsb_model',
breakpoint=PHASE_END):
"""
:param predict:
:param when:
:param model_name:
:return:
"""
if not predict:
raise ValueError('Please provide valid predict value.')
transaction_type = TRANSACTION_PREDICT
from_ds = None if from_data is None else getDS(from_data)
predict_columns = [predict] if type(predict) != type([]) else predict
transaction_metadata = TransactionMetadata()
transaction_metadata.model_name = model_name
transaction_metadata.model_predict_columns = predict_columns
transaction_metadata.model_when_conditions = when
transaction_metadata.type = transaction_type
transaction_metadata.storage_file = self.storage_file
transaction_metadata.from_data = from_ds
transaction = self.session.newTransaction(transaction_metadata,
breakpoint)
return transaction.output_data
def predict(self,
predict,
from_data=None,
when={},
model_name='mdsb_model',
breakpoint=PHASE_END):
"""
:param predict:
:param when:
:param model_name:
:return:
"""
transaction_type = TRANSACTION_PREDICT
predict_columns = [predict] if type(predict) != type([]) else predict
transaction_metadata = TransactionMetadata()
transaction_metadata.model_name = model_name
transaction_metadata.model_predict_columns = predict_columns
transaction_metadata.model_when_conditions = when
transaction_metadata.type = transaction_type
transaction_metadata.storage_file = self.storage_file
transaction_metadata.from_data = from_data
transaction = self.session.newTransaction(transaction_metadata,
breakpoint)
return transaction.output_data
def learn(self,
predict,
from_file=None,
from_data=None,
model_name='mdsb_model',
test_from_data=None,
group_by=None,
window_size=MODEL_GROUP_BY_DEAFAULT_LIMIT,
order_by=[],
breakpoint=PHASE_END,
ignore_columns=[],
rename_strange_columns=True):
"""
:param from_query:
:param predict:
:param model_name:
:param test_query:
:return:
"""
if self._from_data is None:
from_ds = getDS(from_data) if from_file is None else getDS(
from_file)
else:
from_ds = getDS(self._from_data)
test_from_ds = test_from_data if test_from_data is None else getDS(
test_from_data)
transaction_type = TRANSACTION_LEARN
predict_columns_map = {}
predict_columns = [predict] if type(predict) != type([]) else predict
if rename_strange_columns is False:
for predict_col in predict_columns:
predict_col_as_in_df = from_ds.getColNameAsInDF(predict_col)
predict_columns_map[predict_col_as_in_df] = predict_col
predict_columns = list(predict_columns_map.keys())
else:
logging.warning(
'After version 1.0 rename_strange_columns in MindsDB().learn, the default value will be flipped from True to False '
)
transaction_metadata = TransactionMetadata()
transaction_metadata.model_name = model_name
transaction_metadata.model_predict_columns = predict_columns
transaction_metadata.model_columns_map = {} if rename_strange_columns else from_ds._col_map
transaction_metadata.model_group_by = group_by
transaction_metadata.model_order_by = order_by if type(
order_by) == type([]) else [order_by]
transaction_metadata.window_size = window_size
transaction_metadata.type = transaction_type
transaction_metadata.from_data = from_ds
transaction_metadata.test_from_data = test_from_ds
transaction_metadata.ignore_columns = ignore_columns
self.startInfoServer()
self.session.newTransaction(transaction_metadata, breakpoint)
def learn(self,
predict,
from_query=None,
from_file=None,
model_name='mdsb_model',
test_query=None,
group_by=None,
group_by_limit=MODEL_GROUP_BY_DEAFAULT_LIMIT,
order_by=[],
breakpoint=PHASE_END):
"""
:param from_query:
:param predict:
:param model_name:
:param test_query:
:return:
"""
if from_file is not None:
from_file_dest = os.path.basename(from_file).split('.')[0]
self.addTable(CSVFileDS(from_file), from_file_dest)
if from_query is None:
from_query = 'select * from {from_file_dest}'.format(
from_file_dest=from_file_dest)
logging.info('setting up custom learn query for file. ' +
from_query)
transaction_type = TRANSACTION_LEARN
predict_columns = [predict] if type(predict) != type([]) else predict
transaction_metadata = TransactionMetadata()
transaction_metadata.model_name = model_name
transaction_metadata.model_query = from_query
transaction_metadata.model_predict_columns = predict_columns
transaction_metadata.model_test_query = test_query
transaction_metadata.model_group_by = group_by
transaction_metadata.model_order_by = order_by if type(
order_by) == type([]) else [order_by]
transaction_metadata.model_group_by_limit = group_by_limit
transaction_metadata.type = transaction_type
self.startInfoServer()
self.session.newTransaction(transaction_metadata, breakpoint)
def learn(self,
predict,
from_file=None,
from_data=None,
model_name='mdsb_model',
test_from_data=None,
group_by=None,
window_size=MODEL_GROUP_BY_DEAFAULT_LIMIT,
order_by=[],
breakpoint=PHASE_END,
ignore_columns=[]):
"""
:param from_query:
:param predict:
:param model_name:
:param test_query:
:return:
"""
from_ds = getDS(from_data) if from_file is None else getDS(from_file)
test_from_ds = test_from_data if test_from_data is None else getDS(
test_from_data)
transaction_type = TRANSACTION_LEARN
predict_columns = [predict] if type(predict) != type([]) else predict
transaction_metadata = TransactionMetadata()
transaction_metadata.model_name = model_name
transaction_metadata.model_predict_columns = predict_columns
transaction_metadata.model_group_by = group_by
transaction_metadata.model_order_by = order_by if type(
order_by) == type([]) else [order_by]
transaction_metadata.window_size = window_size
transaction_metadata.type = transaction_type
transaction_metadata.from_data = from_ds
transaction_metadata.test_from_data = test_from_ds
transaction_metadata.ignore_columns = ignore_columns
self.startInfoServer()
self.session.newTransaction(transaction_metadata, breakpoint)
