def main(): # Parse arguments parser = ArgumentParser() parser.add_argument( '-prev_state_pkl', required=True, help='Path to the pickle file holding the previous state.') parser.add_argument('-global_step_db', required=True, help='Path to db holding global step results.') args, unknown = parser.parse_known_args() fname_prev_state = path.abspath(args.prev_state_pkl) global_db = path.abspath(args.global_step_db) # Load local state local_state = StateData.load(fname_prev_state).get_data() # Load global node output globalStatistics = Global2Local_TD.load(global_db).get_data()['global_in'] #raise ValueError(globalStatistics,local_state['args_X']) # Run algorithm local step Hist = run_local_step( local_state['args_X'], local_state['args_Y'], local_state['args_bins'], local_state['dataSchema'], local_state['CategoricalVariablesWithDistinctValues'], local_state['dataFrame'], globalStatistics) # Pack results local_out = multipleHist2_Loc2Glob_TD( local_state['args_X'], local_state['args_Y'], local_state['CategoricalVariablesWithDistinctValues'], Hist) # Return the output data local_out.transfer()
def main(): # Parse arguments parser = ArgumentParser() parser.add_argument( '-cur_state_pkl', required=True, help='Path to the pickle file holding the current state.') parser.add_argument( '-prev_state_pkl', required=True, help='Path to the pickle file holding the previous state.') parser.add_argument('-global_step_db', required=True, help='Path to db holding global step results.') args, unknown = parser.parse_known_args() # raise ValueError(args) fname_cur_state = path.abspath(args.cur_state_pkl) fname_prev_state = path.abspath(args.prev_state_pkl) global_db = path.abspath(args.global_step_db) # Load local state local_state = StateData.load(fname_prev_state).data # Load global node output global_out = LogRegrIter_Glob2Loc_TD.load(global_db) # Run algorithm local iteration step local_state, local_out = logregr_local_iter(local_state=local_state, local_in=global_out) # Save local state local_state.save(fname=fname_cur_state) # Return local_out.transfer()
def main(): # Parse arguments parser = ArgumentParser() parser.add_argument( '-cur_state_pkl', required=True, help='Path to the pickle file holding the current state.') parser.add_argument( '-prev_state_pkl', required=True, help='Path to the pickle file holding the previous state.') parser.add_argument('-local_step_dbs', required=True, help='Path to db holding local step results.') args, unknown = parser.parse_known_args() fname_cur_state = path.abspath(args.cur_state_pkl) fname_prev_state = path.abspath(args.prev_state_pkl) local_dbs = path.abspath(args.local_step_dbs) # Load global state global_state = StateData.load(fname_prev_state).data # Load local nodes output local_out = LogRegrIter_Loc2Glob_TD.load(local_dbs) # Run algorithm global step global_state, global_out = logregr_global_iter(global_state=global_state, global_in=local_out) # Save global state global_state.save(fname=fname_cur_state) # Return the algorithm's output global_out.transfer()
def main(args): sys.argv =args # Parse arguments parser = ArgumentParser() parser.add_argument('-no_split_points', required=True, type=int, help='Number of split points') parser.add_argument('-cur_state_pkl', required=True, help='Path to the pickle file holding the current state.') parser.add_argument('-prev_state_pkl', required=True, help='Path to the pickle file holding the previous state.') parser.add_argument('-local_step_dbs', required=True, help='Path to db holding local step results.') args, unknown = parser.parse_known_args() fname_cur_state = path.abspath(args.cur_state_pkl) fname_prev_state = path.abspath(args.prev_state_pkl) local_dbs = path.abspath(args.local_step_dbs) # Load global state global_state = StateData.load(fname_prev_state).data # Load local nodes output activePaths = CartIter1_Loc2Glob_TD.load(local_dbs).get_data() # Run algorithm global iteration step activePaths = cart_step_1_global(global_state['args_X'], global_state['args_Y'], global_state['CategoricalVariables'], activePaths, args.no_split_points) global_out = Cart_Glob2Loc_TD( global_state['globalTree'], activePaths ) # Save global state # Save global state global_state = StateData( stepsNo = global_state['stepsNo'] + 1 , args_X = global_state['args_X'], args_Y = global_state['args_Y'], CategoricalVariables = global_state['CategoricalVariables'], globalTree = global_state['globalTree'], activePaths = activePaths, t1 = global_state['t1'] ) global_state.save(fname=fname_cur_state) # Return the algorithm's output global_out.transfer()
def main(args): sys.argv = args init_logger() # Parse arguments parser = ArgumentParser() parser.add_argument( '-cur_state_pkl', required=True, help='Path to the pickle file holding the current state.') parser.add_argument( '-prev_state_pkl', required=True, help='Path to the pickle file holding the previous state.') parser.add_argument('-local_step_dbs', required=True, help='Path to db holding local step results.') args, unknown = parser.parse_known_args() fname_cur_state = path.abspath(args.cur_state_pkl) fname_prev_state = path.abspath(args.prev_state_pkl) local_dbs = path.abspath(args.local_step_dbs) # Load global state global_state = StateData.load(fname_prev_state).data globalTreeJ = global_state['globalTree'].tree_to_json() myresult = {"result": [{"type": "application/json", "data": globalTreeJ}]} t1 = global_state['t1'] t2 = time.localtime(time.time()) t0 = ['yy', 'mm', 'dd', 'hh', 'min', 'sec', 'wday', 'yday', 'isdst'] logging.info(" Time: ") for i in range(len(t1)): logging.info([t0[i], t2[i], t1[i], t2[i] - t1[i]]) set_algorithms_output_data(myresult)
def main(args): sys.argv = args # Parse arguments parser = ArgumentParser() parser.add_argument( '-cur_state_pkl', required=True, help='Path to the pickle file holding the current state.') parser.add_argument( '-prev_state_pkl', required=True, help='Path to the pickle file holding the previous state.') parser.add_argument('-global_step_db', required=True, help='Path to db holding global step results.') args, unknown = parser.parse_known_args() fname_cur_state = path.abspath(args.cur_state_pkl) fname_prev_state = path.abspath(args.prev_state_pkl) global_db = path.abspath(args.global_step_db) # Load local state local_state = StateData.load(fname_prev_state).data # Load global node output globalTree, activePaths = Cart_Glob2Loc_TD.load(global_db).get_data() # Run algorithm local iteration step activePaths = cart_step_3_local(local_state['dataFrame'], local_state['args_X'], local_state['args_Y'], local_state['CategoricalVariables'], activePaths) ## Finished local_state = StateData( args_X=local_state['args_X'], args_Y=local_state['args_Y'], CategoricalVariables=local_state['CategoricalVariables'], dataFrame=local_state['dataFrame'], globalTree=globalTree, activePaths=activePaths) local_out = CartIter3_Loc2Glob_TD(activePaths) # Save local state local_state.save(fname=fname_cur_state) # Return local_out.transfer()
def main(): # Parse arguments parser = ArgumentParser() parser.add_argument( '-prev_state_pkl', required=True, help='Path to the pickle file holding the previous state.') parser.add_argument('-max_iter', type=int, required=True, help='Maximum number of iterations.') args, unknown = parser.parse_known_args() fname_prev_state = path.abspath(args.prev_state_pkl) max_iter = args.max_iter global_state = StateData.load(fname_prev_state).data termination_condition(global_state, max_iter)
def main(args): sys.argv =args # Parse arguments parser = ArgumentParser() parser.add_argument('-prev_state_pkl', required=True, help='Path to the pickle file holding the previous state.') parser.add_argument('-max_depth', type=int, required=True, help='Maximum depth of tree') args, unknown = parser.parse_known_args() fname_prev_state = path.abspath(args.prev_state_pkl) #max_iter = args.max_iter global_state = StateData.load(fname_prev_state).data if bool(global_state['activePaths']) == False or global_state['stepsNo'] > args.max_depth : set_algorithms_output_data('STOP') else: set_algorithms_output_data('CONTINUE')
def main(args): sys.argv = args # Parse arguments parser = ArgumentParser() parser.add_argument( '-cur_state_pkl', required=True, help='Path to the pickle file holding the current state.') parser.add_argument( '-prev_state_pkl', required=True, help='Path to the pickle file holding the previous state.') parser.add_argument('-global_step_db', required=True, help='Path to db holding global step results.') args, unknown = parser.parse_known_args() fname_cur_state = path.abspath(args.cur_state_pkl) fname_prev_state = path.abspath(args.prev_state_pkl) global_db = path.abspath(args.global_step_db) # Load local state local_state = StateData.load(fname_prev_state).data # Load global node output globalTree, activePaths = Cart_Glob2Loc_TD.load(global_db).get_data() # Run algorithm local iteration step activePaths = cart_step_2_local(local_state['dataFrame'], local_state['CategoricalVariables'], local_state['args_X'], local_state['args_Y'], activePaths) # # # Run algorithm local iteration step # for key in activePaths: # df = local_state['dataFrame'] # # For each unfinished path, find the subset of dataFrame (df) # for i in xrange(len(activePaths[key]['filter'])): # df = DataFrameFilter(df, activePaths[key]['filter'][i]["variable"], # activePaths[key]['filter'][i]["operator"], # activePaths[key]['filter'][i]["value"]) # if local_state['args_Y'][0] in local_state['CategoricalVariables']: #Classification Algorithm # resultJ = node_computations(df, local_state['args_X'], activePaths[key], local_state['args_Y'][0], local_state['CategoricalVariables'],"classNumbers") # activePaths[key]["classNumbersJ"] = dict(activePaths[key]["classNumbersJ"].items() + resultJ.items()) # elif local_state['args_Y'][0] not in local_state['CategoricalVariables']: # Regression Algorithm # resultJ = node_computations(df, local_state['args_X'], activePaths[key], local_state['args_Y'][0], local_state['CategoricalVariables'],"statistics") # activePaths[key]["statisticsJ"] = dict(activePaths[key]["statisticsJ"].items() + resultJ.items()) # #print activePaths ## Finished local_state = StateData( args_X=local_state['args_X'], args_Y=local_state['args_Y'], CategoricalVariables=local_state['CategoricalVariables'], dataFrame=local_state['dataFrame'], globalTree=globalTree, activePaths=activePaths) local_out = CartIter2_Loc2Glob_TD(activePaths) # Save local state local_state.save(fname=fname_cur_state) # Return local_out.transfer()