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utils.py
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utils.py
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import numpy as np
import scipy.sparse as sp
import math
import tensorflow as tf
from sklearn.metrics import mean_squared_error, mean_absolute_error
def normalize_adj(adj, symmetric=True):
if symmetric:
d = sp.diags(np.power(np.array(adj.sum(1)), -0.5).flatten(), 0)
a_norm = adj.dot(d).transpose().dot(d).tocsr()
else:
d = sp.diags(np.power(np.array(adj.sum(1)), -1).flatten(), 0)
a_norm = d.dot(adj).tocsr()
return a_norm
def preprocess_adj(adj, symmetric=True):
adj = adj + sp.eye(adj.shape[0])
adj = normalize_adj(adj, symmetric)
return adj
def sample_mask(idx, l):
mask = np.zeros(l)
mask[idx] = 1
return np.array(mask, dtype=np.bool)
def get_splits(y):
idx_train = range(6400)
idx_val = range(6400,7999)
idx_test = range(8000, 9599)
y_train = np.zeros(y.shape)
y_val = np.zeros(y.shape)
y_test = np.zeros(y.shape)
y_train[idx_train] = y[idx_train]
y_val[idx_val] = y[idx_val]
y_test[idx_test] = y[idx_test]
train_mask = sample_mask(idx_train, y.shape[0])
return y_train, y_val, y_test, idx_train, idx_val, idx_test, train_mask
def root_mse(preds, labels):
return math.sqrt(mean_squared_error(labels, preds))
def pred_error_baseline(preds, labels):
preds = preds.reshape(1,-1)
labels = labels.reshape(1,-1)
diff = np.fabs(preds - labels)
pred_error = np.true_divide(diff, labels)
pred_error = tf.convert_to_tensor(pred_error)
#avg_pred_error = np.mean(pred_error)
return tf.keras.backend.mean(pred_error)
def evaluate_preds_baseline(preds, labels):
mse_loss = list()
predict_error = list()
mse_loss.append(root_mse(preds, labels))
predict_error.append(pred_error(preds, labels))
return mse_loss, predict_error
def pred_error(preds, labels):
preds = preds.reshape(1,-1)
labels = labels.reshape(1,-1)
diff = np.fabs(preds - labels)
pred_error = np.true_divide(diff, labels)
avg_pred_error = np.mean(pred_error)
return avg_pred_error
def evaluate_preds(preds, labels, indices):
rmse_loss = list()
predict_error = list()
MAE = list()
for y_split, idx_split in zip(labels, indices):
rmse_loss.append(root_mse(preds[idx_split], y_split[idx_split]))
predict_error.append(pred_error(preds[idx_split], y_split[idx_split]))
MAE.append(mean_absolute_error(y_split[idx_split], preds[idx_split]))
return rmse_loss, predict_error, MAE
def sparse_to_tuple(sparse_mx):
if not sp.isspmatrix_coo(sparse_mx):
sparse_mx = sparse_mx.tocoo()
coords = np.vstack((sparse_mx.row, sparse_mx.col)).transpose()
values = sparse_mx.data
shape = sparse_mx.shape
return coords, values, shape
def backend_reshape(x, out):
return tf.keras.backend.reshape(x,(-1,out))
def year_built(x):
if x <1850 :
return "<1850"
elif x >= 1850 and x < 1900:
return "1850-1900"
elif x >= 1900 and x < 1910:
return "1900-1910"
elif x >= 1910 and x < 1920:
return "1910-1920"
elif x >= 1920 and x < 1930:
return "1920-1930"
elif x >= 1930 and x < 1940:
return "1930-1940"
elif x >= 1940 and x < 1950:
return "1940-1950"
elif x >= 1950 and x < 1960:
return "1950-1960"
elif x >= 1960 and x < 1970:
return "1960-1970"
elif x >= 1970 and x < 1980:
return "1970-1980"
elif x >= 1980 and x < 1990:
return "1980-1990"
elif x >= 1990 and x < 2000:
return "1990-2000"
else :
return ">2000"
def condition_residential(x):
if x == 0 :
return "nonresidential"
elif x > 0 and x < 5:
return "small_residential"
elif x>= 5 and x <=10:
return "smmed_residential"
elif x>10 and x <=25 :
return "medium_residential"
else :
return "large_residential"
def condition_commercial(x):
if x == 0 :
return "noncommercial"
elif x > 0 and x < 5:
return "small_commerce"
elif x>= 5 and x <=10:
return "smmed_commerce"
elif x>10 and x <=25 :
return "medium_commerce"
else :
return "large_commerce"