def performEvaluations(params, train_window = 3000, overload_dur = 5, overload_percentile = 70, predic_window=30): filename, METHOD, TYPE, OUTPUT, INPUT = params[:5] filename = filename.split('/')[-1] print OUTPUT+TYPE+"_"+METHOD+"/" + filename, "started..." cur_results = [] forecasts = np.nan_to_num(np.genfromtxt(OUTPUT+TYPE+"_"+METHOD+"/" + filename, delimiter=',',usecols=range(0,predic_window))).ravel() # ,usecols=range(0,30) if TYPE == 'pageviews' or TYPE == 'network': filename = filename.replace(".csv","") truevals = np.genfromtxt(INPUT+TYPE+"/"+filename)[:train_window+len(forecasts)] truevals = truevals/np.max(truevals) else: truevals = np.genfromtxt(INPUT+TYPE+"/"+filename, delimiter=',',skip_header=1)[:train_window+len(forecasts),1] # Normalize # truevals = np.divide(truevals, np.max(truevals)) threshold = np.percentile(truevals, overload_percentile) cur_results.append(eval.calc_RMSE(truevals[train_window:], forecasts)) for val in eval.calc_upper_lower_acc(truevals[train_window:], forecasts): cur_results.append(val) for val in eval.calc_persample_accuracy(truevals[train_window:], forecasts, threshold): cur_results.append(val) for val in eval.calc_overload_states_acc(truevals[train_window:], forecasts, threshold): cur_results.append(val) return cur_results
def performEvaluations(filename, train_window=3000, overload_dur=5, overload_percentile=70, steps=30): cur_results = [] forecasts = np.genfromtxt("d:/data/cpu_norm_forecasts/" + filename, delimiter=',', usecols=range(0, steps)).ravel() truevals = np.genfromtxt("d:/data/cpuRate/" + filename, delimiter=',', skip_header=1)[train_window:train_window + len(forecasts), 1] threshold = np.percentile(truevals, overload_percentile) cur_results.append(eval.calc_RMSE(truevals, forecasts)) for val in eval.calc_upper_lower_acc(truevals, forecasts): cur_results.append(val) for val in eval.calc_persample_accuracy(truevals, forecasts, threshold): cur_results.append(val) for val in eval.calc_overload_states_acc(truevals, forecasts, threshold): cur_results.append(val) return cur_results
def performEvaluations(filename, train_window=3000, overload_dur=5, overload_percentile=70, steps=30): cur_results = [] forecasts = np.nan_to_num( np.genfromtxt("d:/data/" + TYPE + "_" + METHOD + "/" + filename, delimiter=',', usecols=range(0, 30))).ravel() # ,usecols=range(0,30) truevals = np.genfromtxt("d:/data/" + TYPE + "/" + filename, delimiter=',', skip_header=1)[:train_window + len(forecasts), 1] # Normalize # truevals = np.divide(truevals, np.max(truevals)) threshold = np.percentile(truevals, overload_percentile) cur_results.append(eval.calc_RMSE(truevals[train_window:], forecasts)) for val in eval.calc_upper_lower_acc(truevals[train_window:], forecasts): cur_results.append(val) for val in eval.calc_persample_accuracy(truevals[train_window:], forecasts, threshold): cur_results.append(val) for val in eval.calc_overload_states_acc(truevals[train_window:], forecasts, threshold): cur_results.append(val) return cur_results
def performEvaluations(filename, train_window = 3000, overload_dur = 5, overload_percentile = 70, steps=30): cur_results = [] filename forecasts = np.nan_to_num(np.genfromtxt("d:/Wikipage data/"+TYPE+"_"+METHOD+"/" + filename, delimiter=',',usecols=range(0,30))).ravel() # # truevals = np.genfromtxt("d:/Wikipage data/"+TYPE+"/"+filename, delimiter=',',skip_header=1)[:train_window+len(forecasts),1] truevals = np.genfromtxt("d:/Wikipage data/"+TYPE+"/"+filename)[:train_window+len(forecasts)] truevals = truevals/np.max(truevals) cur_results.append(eval.calc_RMSE(truevals[train_window:], forecasts)) for val in eval.calc_upper_lower_acc(truevals[train_window:], forecasts): cur_results.append(val) return cur_results
def performEvaluations(filename, train_window = 3000, overload_dur = 5, overload_percentile = 70, steps=30): cur_results = [] forecasts = np.genfromtxt("d:/data/diskio_ar_forecasts/"+ filename,delimiter=',',usecols=range(0,steps)).ravel() truevals = np.genfromtxt("d:/data/diskio/"+filename, delimiter=',',skip_header=1)[train_window:train_window+len(forecasts),1] threshold = np.percentile(truevals, overload_percentile) cur_results.append(eval.calc_RMSE(truevals, forecasts)) for val in eval.calc_upper_lower_acc(truevals, forecasts): cur_results.append(val) for val in eval.calc_persample_accuracy(truevals, forecasts, threshold): cur_results.append(val) for val in eval.calc_overload_states_acc(truevals, forecasts, threshold): cur_results.append(val) return cur_results
def performEvaluations(filename, train_window = 3000, overload_dur = 5, overload_percentile = 70, steps=30): cur_results = [] forecasts = np.nan_to_num(np.genfromtxt("d:/data/"+TYPE+"_"+METHOD+"/" + filename, delimiter=',',usecols=range(0,30))).ravel() # ,usecols=range(0,30) truevals = np.genfromtxt("d:/data/"+TYPE+"/"+filename, delimiter=',',skip_header=1)[:train_window+len(forecasts),1] # Normalize # truevals = np.divide(truevals, np.max(truevals)) threshold = np.percentile(truevals, overload_percentile) cur_results.append(eval.calc_RMSE(truevals[train_window:], forecasts)) for val in eval.calc_upper_lower_acc(truevals[train_window:], forecasts): cur_results.append(val) for val in eval.calc_persample_accuracy(truevals[train_window:], forecasts, threshold): cur_results.append(val) for val in eval.calc_overload_states_acc(truevals[train_window:], forecasts, threshold): cur_results.append(val) return cur_results
def performEvaluations(filename, train_window=3000, overload_dur=5, overload_percentile=70, steps=30): cur_results = [] filename forecasts = np.nan_to_num( np.genfromtxt("d:/Wikipage data/" + TYPE + "_" + METHOD + "/" + filename, delimiter=',', usecols=range(0, 30))).ravel() # # truevals = np.genfromtxt("d:/Wikipage data/"+TYPE+"/"+filename, delimiter=',',skip_header=1)[:train_window+len(forecasts),1] truevals = np.genfromtxt("d:/Wikipage data/" + TYPE + "/" + filename)[:train_window + len(forecasts)] truevals = truevals / np.max(truevals) cur_results.append(eval.calc_RMSE(truevals[train_window:], forecasts)) for val in eval.calc_upper_lower_acc(truevals[train_window:], forecasts): cur_results.append(val) return cur_results