def execute_custom_sql(): payload = request.json sql_text = payload['query'] records = execute_query(sql_text, None) result = format_data(records) return result, 201
def query_execution(query_id): parameter_names = list(request.args) paramerter_values = list() [paramerter_values.append(request.args[parm]) for parm in parameter_names] app.logger.debug('Query parameters for {}'.format(query_id)) app.logger.debug(parameter_names) app.logger.debug(paramerter_values) # Holds Query results records = list() parameters = [] if len(paramerter_values) == 0: parameters = None else: parameters = paramerter_values if query_id in qry.keys(): query = qry[query_id]['query'] records = execute_query(query, parameters) else: records.append('Exception: ' + query_id + ' not found in list of configured queries') # Format Query results in JSON form. result = format_data(records) return result
def main(): # import data if DEBUG: data_train = pd.read_csv( '../data/training-train.csv' ) #pd.read_csv('../data/training-small-train.csv')# data_validation = pd.read_csv( '../data/training-validate.csv' ) #pd.read_csv('../data/training-small-validate.csv')# else: data_train = pd.read_csv('../data/training.csv') data_validation = pd.read_csv('../data/testData.csv') data_train['train_flag'] = True data_validation['train_flag'] = False data = pd.concat((data_train, data_validation)) # keep missing flags for both training and validation ytr_missing = np.array( data_train.loc[:, 'COVAR_y1_MISSING':'COVAR_y3_MISSING']) yvl_missing = np.array( data_validation.loc[:, 'COVAR_y1_MISSING':'COVAR_y3_MISSING']) # remove temporary data del data_train del data_validation # basic formatting Xtr, ytr, Xvl, yvl = utils.format_data(data, preprocessing=USE_PREPROCESSING) del data # preprocess data if USE_PREPROCESSING: use_pca = False # apply PCA (True) or standard normalization (False) Xtr, Xvl = utils.preprocess(Xtr, Xvl, use_pca) # create RNN instance n_features = len(Xtr[0]) n_outputs = len(ytr[0]) nn_solver = RNN(n_features=n_features, n_outputs=n_outputs, n_neurons=hidden_size, param_update_scheme=param_update_scheme, learning_rate=learning_rate, activation_rule=activation_rule, use_batch_step=USE_BATCH_TRAINING, batch_step_size=batch_step_size, relu_neg_slope=relu_neg_slope, use_dropout_regularization=use_dropout_regularization, dropout_threshold=dropout_threshold, reg_strenght=reg_strenght, use_regularization=use_regularization, sgd_shuffle=sgd_shuffle) if not PREDICT_ONLY: trainAndTest(nn_solver, Xtr, ytr, ytr_missing, Xvl, yvl, yvl_missing) else: predictByModel(nn_solver, Xvl, '../models/DeepNN/model_2016-08-03T15_39_15.mat')
def datas_dict(self): self.datas['YEAR'] = [ date.strftime("%Y") for date in self.resultat_datas.keys().tolist() ] self.datas['EBITDA'] = utl.format_data(self.ebitda.values.tolist()) self.datas['Bénéfice Net'] = utl.format_data( self.benefice_net.values.tolist()) self.datas['Revenus Total'] = utl.format_data( self.revenue_total.values.tolist()) self.datas["Actifs Total"] = utl.format_data( self.actifs_total.values.tolist()) self.datas["Chiffre d'affaires"] = utl.format_data( self.chiffre_affaire.values.tolist()) self.datas["Trésorie"] = utl.format_data( self.cash_flow.values.tolist()) self.datas["Capitaux Propre"] = utl.format_data( self.total_capitaux_propre.values.tolist()) self.datas['Score'] = [self.total_score()] self.bna_years() self.per_years() self.debt_ratio() self.bvps_ratio() self.capitalisation_ratio() self.dividendes_ratio() self.roe_roa_ratio(roa=False) self.roe_roa_ratio(roa=True)
def report_sql_monitor(sql_id): query = 'SELECT DBMS_SQLTUNE.REPORT_SQL_MONITOR(' + "'" + sql_id + "')" + ' FROM DUAL' records = execute_query(query, '') # Data of this query is a LOB Object. Convert it to a String first. result = convert_lob_object_to_string(records[1][0]) records = [['STATUS'], [result]] result = format_data(records) return result
def send_api(self, data: dict): '''发送接口文件的指定接口数据''' raw = yaml.safe_dump(data) for k, v in self.params.items(): raw = raw.replace(f"${{{k}}}", repr(v)) data = yaml.safe_load(raw) url = data.get("url") method = data.get("method") params = data.get("params") jsons = data.get("json") raw = json.dumps(data, indent=2, ensure_ascii=False) logger.info("本次加载的数据为:\n{raw}".format(raw=raw)) r = requests.request(url=url, method=method, params=params, json=jsons) return format_data(r)
def get_list_of_data_dictionary_tables(): query = qry['data-dictionary-views'] records = execute_query(query, []) result = format_data(records) return result
def get_table(table): records = execute_query('SELECT * FROM ' + table + ' WHERE ROWNUM < 1000', []) result = format_data(records) return result
def capitalisation_ratio(self): # Capitalistion annee prece : prix * nbr actions capitalisation = [i * self.actions for i in self.price_dates] self.datas['Capitalisation'] = utl.format_data(capitalisation) self.data_analyse['Capitalisation'] = capitalisation