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)