overall_accuracy = 1.0 * (a[0][0] + a[1][1]) / (np.sum(a))
return precision, recall, overall_accuracy
to_consider = ["N2_K3_T50_CO", "N2_K4_T50_CO", "N2_K5_T50_CO","N2_K6_T50_CO",
"N3_K3_T50_CO", "N3_K4_T50_CO", "N3_K5_T50_CO","N3_K6_T50_CO",
"N4_K3_T50_CO", "N4_K4_T50_CO", "N4_K5_T50_CO","N4_K6_T50_CO",
"N2_K3_T50_FHMM", "N2_K4_T50_FHMM", "N2_K5_T50_FHMM","N2_K6_T50_FHMM",
"N3_K3_T50_FHMM", "N3_K4_T50_FHMM", "N3_K5_T50_FHMM","N3_K6_T50_FHMM",
"N4_K3_T50_FHMM", "N4_K4_T50_FHMM", "N4_K5_T50_FHMM","N4_K6_T50_FHMM",
]
for folder in to_consider:
df = pd.read_csv("../../data/hvac/minutes_%s.csv" % folder)
df["hvac_class_copy"] = df["hvac_class"].copy()
df = df[df.dataid.isin(find_common_dataids())]
if NUM_CLASSES == 2:
df.hvac_class[(df.hvac_class == "Average") | (df.hvac_class == "Good")] = "Not bad"
COLUMN_NAMES = ["Bad", "Not bad"]
else:
COLUMN_NAMES = ["Average", "Bad", "Good"]
np.random.seed(0)
clf = joblib.load(os.path.expanduser("~/git/nilm-actionable/data/hvac/rf_hvac.pkl"))
true_labels = df['hvac_class'].values
pred_labels = clf.predict(df[list(f)])
accur = accuracy_multiclass(true_labels, pred_labels)
print folder
print accur
return algo_name, algo_N, algo_K
def find_p_r_a(a):
precision = 1.0 * a[0][0] / (a[0][0] + a[1][0])
recall = 1.0 * a[0][0] / (a[0][0] + a[0][1])
overall_accuracy = 1.0 * (a[0][0] + a[1][1]) / (np.sum(a))
return precision, recall, overall_accuracy
to_consider = ["GT", "N4_K3_T50_CO", "N2_K3_T50_FHMM", "N2_K3_T50_Hart"]
for plot_num, folder in enumerate(to_consider):
df = pd.read_csv("../../data/hvac/minutes_%s.csv" % folder)
df["hvac_class_copy"] = df["hvac_class"].copy()
df = df[df.dataid.isin(find_common_dataids())]
df.index = range(len(df))
if NUM_CLASSES == 2:
df.hvac_class[(df.hvac_class == "Average") |
(df.hvac_class == "Good")] = "Not bad"
COLUMN_NAMES = ["Bad", "Not bad"]
else:
COLUMN_NAMES = ["Average", "Bad", "Good"]
np.random.seed(0)
clf = joblib.load(
os.path.expanduser("~/git/nilm-actionable/data/hvac/rf_hvac.pkl"))
true_labels = df['hvac_class'].values
pred_labels = clf.predict(df[list(f)])