Merge branch 'datagen' of https://github.com/GANdy-team/GANdy into da…

…tagen

gandy/quality_est/datagen.py

@@ -1,10 +1,11 @@
-
+import deepchem as dc
 import numpy as np
-import pandas as pd
 import random
+import pandas as pd
+from pandas import Series, DataFrame
+from deepchem.molnet import load_qm9
 
-
-def create_data(to_csv = True):
+def generate_analytical_data(to_csv = True):
 
 	x1 = np.random.uniform(0, 1000, 1000)
 	x2 = np.random.uniform(0, 1000, 1000)
@@ -21,9 +22,53 @@ def g(x1, x2):
 
 	y = f(x1, x2) + g(x1, x2)
 
+	gen_data = pd.DataFrame({'X1':x1, 'X2':x2, 'Y':y})
+
 	if to_csv:
-		dataframe = pd.DataFrame({'Y':y, 'X1':x1, 'X2':x2})
-		dataframe.to_csv("test_data.csv", index=False, sep = ',')
-		gen_data = pd.read_csv('test_data.csv')
+		gen_data.to_csv("analytical_data.csv", index=False, sep = ',')
+
+	return gen_data
+
+
+def generate_qm9_noise_data(x1,x2,y):
+
+    #load data
+	qm9_tasks, datasets, transformers = load_qm9()
+	train_dataset, valid_dataset, test_dataset = datasets
+
+	c1=qm9_tasks[x1-1]
+	c2=qm9_tasks[x2-1]
+	c3=qm9_tasks[y-1]
+
+    #extrct the 'y'values
+	Y = test_dataset.y
+	YT = Y.T
+
+	X1 = YT[x1-1]
+	X2 = YT[x2-1]
+	Y_a = YT[y-1]
+
+	x1 = X1.tolist()
+	x2 = X2.tolist()
+	y_l = Y_a.tolist()
+	l = Y_a.shape
+
+	n = np.random.uniform(0, l, 1).astype(np.int) #set the number of noise added
+	ni = np.random.uniform(0, l, n) #n random values
+	an = len(ni)
+
+
+    #add noise to n numbers of y
+	for i in range(an):
+		mu = 0
+		sigma = (x1[i]+x2[i])/2
+		noise = np.random.normal(mu, np.abs(sigma), n)
+		g = noise.tolist()
+		y_l[i] += g[i]
+
+    #save to_csv:
+	dataframe = pd.DataFrame({'x1_'+c1:X1,'x2_'+c2:X2,'y_'+c3:y_l})
+	dataframe.to_csv("qm9_noise_data.csv", index=False, sep = ',')
+	gen_data = pd.read_csv('qm9_noise_data.csv')
 
 	return gen_data