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Author: omidbazgirTTU

Initial_MDS.py

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+import pickle
+import numpy as np
+import pandas as pd
+import cv2
+import matplotlib.pyplot as plt
+import scipy.misc
+import Toolbox
+from Toolbox import two_d_eq, Assign_features_to_pixels
+from sklearn.manifold import MDS
+from sklearn.metrics.pairwise import euclidean_distances
+import math
+
+#%% Loading the data
+Feat_DF = pd.read_csv("...\\normalized_padel_feats_NCI60_672_small.csv")
+
+X = Feat_DF.values; X = X[:,2:]
+original_input = pd.DataFrame(data = X)                              # The MDS input should be in a dataframe format
+feature_names_list = original_input.columns.tolist()                 # Extracting feature_names_list (gene_names or descriptor_names)
+print(">>>> Data  is loaded")
+
+#%% MDS
+nn = math.ceil(np.sqrt(len(feature_names_list))) 				     # Image dimension
+Nn = original_input.shape[1] 										 # Number of features
+    
+transposed_input = original_input.T 							     # The MDS input data must be transposed , because we want summarize each feature by two values (as compard to regular dimensionality reduction each sample will be described by two values)
+Euc_Dist = euclidean_distances(transposed_input) 					 # Euclidean distance
+Euc_Dist = np.maximum(Euc_Dist, Euc_Dist.transpose())   			 # Making the Euclidean distance matrix symmetric
+
+embedding = MDS(n_components=2)										 # Reduce the dimensionality by MDS into 2 components
+mds_xy = embedding.fit_transform(transposed_input)					 # Apply MDS			
+
+print(">>>> MDS dimensionality reduction is done")
+
+eq_xy = two_d_eq(mds_xy,Nn)
+Img = Assign_features_to_pixels(eq_xy,nn,verbose=1)					# Img is the none-overlapping coordinates generated by MDS
+
+#%% To be saved for hill climbing
+Desc = Feat_DF.columns.tolist();    Desc = Desc[2:]					# Drug descriptors name
+Dist = pd.DataFrame(data = Euc_Dist, columns = Desc, index = Desc)	# Generating a distance matrix which includes the Euclidean distance between each and every descriptor
+data = (Desc, Dist, Img	)  											# Preparing the hill climbing inputs
+
+with open("...\\Init_MDS_Euc.pickle", 'wb') as f:					# The hill climbing input is a pickle, therefore everything is saved as a pickle to be loaded by the hill climbing
+    pickle.dump(data, f)