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 = []
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
