WISSE example

+# -*- coding: UTF-8 -*-
+import os
+from optparse import OptionParser
+import sys
+from time import time
+import matplotlib.pyplot as plt
+import re
+import numpy as np
+import matplotlib
+from sklearn.metrics.pairwise import cosine_similarity
+
+matplotlib.use('Qt4Agg')
+
+
+__author__ = 'CMendezC'
+
+# Objective: Plot vectors into 2D and 3D
+# with a color for vectors using different transformations
+
+# Parameters:
+#   1) --vectorPath Path to read vectors.
+#   2) --vectorFile File to read vectors.
+#   3) --outputPath Path to place plot files.
+#   4) --outputFormat Plot file format: pdf, png
+#   5) --absoluteValue Employ absolute values in vectors
+
+# Ouput:
+#   1) Plots
+
+# Execution:
+# C:\Anaconda3\python plot_Vectors_LSA.py
+# --outputPath C:\Users\cmendezc\Documents\GENOMICAS\GENSOR_UNITS\wordEmbeddings\lsa\plots
+# --vectorPath C:\Users\cmendezc\Documents\GENOMICAS\GENSOR_UNITS\wordEmbeddings\lsa
+# --vectorFile GU_lsa_local_vectors_2T.txt
+# --absoluteValue
+# --outputFormat pdf
+
+# C:\Anaconda3\python plot_Vectors_LSA.py --outputPath C:\Users\cmendezc\Documents\GENOMICAS\GENSOR_UNITS\wordEmbeddings\lsa\plots --vectorPath C:\Users\cmendezc\Documents\GENOMICAS\GENSOR_UNITS\wordEmbeddings\lsa --vectorFile GU_lsa_local_vectors_2T.txt --absoluteValue --outputFormat pdf
+# C:\Anaconda3\python plot_Vectors_LSA.py --outputPath C:\Users\cmendezc\Documents\GENOMICAS\GENSOR_UNITS\wordEmbeddings\lsa\plots --vectorPath C:\Users\cmendezc\Documents\GENOMICAS\GENSOR_UNITS\wordEmbeddings\lsa --vectorFile GU_lsa_local_vectors_10T.txt --absoluteValue --outputFormat pdf
+# C:\Anaconda3\python plot_Vectors_LSA.py --outputPath C:\Users\cmendezc\Documents\GENOMICAS\GENSOR_UNITS\wordEmbeddings\lsa\plots --vectorPath C:\Users\cmendezc\Documents\GENOMICAS\GENSOR_UNITS\wordEmbeddings\lsa --vectorFile GU_lsa_local_vectors_36T.txt --absoluteValue --outputFormat pdf
+# C:\Anaconda3\python plot_Vectors_LSA.py --outputPath C:\Users\cmendezc\Documents\GENOMICAS\GENSOR_UNITS\wordEmbeddings\lsa\plots --vectorPath C:\Users\cmendezc\Documents\GENOMICAS\GENSOR_UNITS\wordEmbeddings\lsa --vectorFile GU_lsa_local_vectors_88T.txt --absoluteValue --outputFormat pdf
+# C:\Anaconda3\python plot_Vectors_LSA.py --outputPath C:\Users\cmendezc\Documents\GENOMICAS\GENSOR_UNITS\wordEmbeddings\lsa\plots --vectorPath C:\Users\cmendezc\Documents\GENOMICAS\GENSOR_UNITS\wordEmbeddings\lsa --vectorFile GU_lsa_local_vectors_120T.txt --absoluteValue --outputFormat pdf
+
+###########################################################
+#                       MAIN PROGRAM                      #
+###########################################################
+
+if __name__ == "__main__":
+    # Parameter definition
+    parser = OptionParser()
+    parser.add_option("--vectorPath", dest="vectorPath",
+                      help="Path to read vector file", metavar="PATH")
+    parser.add_option("--vectorFile", dest="vectorFile",
+                      help="File to read vectors", metavar="FILE")
+    parser.add_option("--outputPath", dest="outputPath",
+                      help="Path to place clustering classified files", metavar="PATH")
+    parser.add_option("--outputFormat", dest="outputFormat", choices=('pdf', 'png'),
+                      help="Plot output format", metavar="PATH")
+    parser.add_option("--absoluteValue", default=False,
+                      action="store_true", dest="absoluteValue",
+                      help="Use vector absolute values?")
+
+    (options, args) = parser.parse_args()
+    if len(args) > 0:
+        parser.error("None parameters indicated.")
+        sys.exit(1)
+
+    # Printing parameter values
+    print('-------------------------------- PARAMETERS --------------------------------')
+    print("Path to read vector file: " + str(options.vectorPath))
+    print("File to read vectors: " + str(options.vectorFile))
+    print("Path to write plots: " + str(options.outputPath))
+    print("Plot output format: " + str(options.outputFormat))
+    print("Use vector absolute values? " + str(options.absoluteValue))
+
+    #regexLen = re.compile(r'_(?P<vectorLen>[0-9]+)T')
+    listVectors = []
+    listLabels = []
+    print("Reading vectors...")
+    #result = regexLen.search(options.vectorFile)
+    #vectorLen = 0
+    #if result:
+    #    vectorLen = int(result.group('vectorLen'))
+    #    print("Vector vectorLen: {}".format(vectorLen))
+    #else:
+    #    print("None vectorLen mentioned within name file!")
+    #    quit()
+    with open(os.path.join(options.vectorPath, options.vectorFile), mode="r", encoding='utf8') as iFile:
+        for line in iFile.readlines():
+            line = line.strip('\r\n')
+            listLine = line.split()
+            # print("Len listLine: {}".format(len(listLine)))
+            label = listLine[0][:12]
+            # print("   Label: {}".format(label))
+            vector = []
+            listValues = listLine[1:]
+            # print("   Len listValues: {}".format(len(listValues)))
+            #if len(listValues) != vectorLen:
+            #    print("Vector vectorLen does not match: {}".format(label))
+            #    continue
+            for elem in listValues:
+                if options.absoluteValue:
+                    vector.append(abs(float(elem)))
+                else:
+                    vector.append(float(elem))
+            listLabels.append(label)
+            listVectors.append(vector)
+    print("   Reading vectors done!")
+    print("    Len vectors: " + str(len(listVectors)))
+    print("    Len labels: " + str(len(listLabels)))
+
+    similarityMatrix = cosine_similarity(np.array(listVectors))
+    print("similarityMatrix shape: {}".format(similarityMatrix.shape))
+
+    t0 = time()
+    print("Plotting heatmap...")
+    # fig, ax = plt.subplots()
+    fig = plt.figure()
+    ax = fig.add_subplot(111)
+    # heatmap = ax.pcolor(similarityMatrix, cmap=plt.cm.Reds, alpha=0.8)
+    heatmap = ax.pcolor(similarityMatrix, cmap=plt.cm.Reds)
+    fig = plt.gcf()
+    fig.set_size_inches(16, 16)
+    ax.set_frame_on(False)
+    ax.set_yticks(np.arange(similarityMatrix.shape[0]) + 0.5, minor=False)
+    ax.set_xticks(np.arange(similarityMatrix.shape[1]) + 0.5, minor=False)
+    ax.invert_yaxis()
+    ax.xaxis.tick_top()
+    ax.set_xticklabels(listLabels, minor=False, size='xx-small')
+    ax.set_yticklabels(listLabels, minor=False, size='xx-small')
+    plt.xticks(rotation=90)
+    ax.grid(False)
+
+    # Turn off all the ticks
+    ax = plt.gca()
+
+    for t in ax.xaxis.get_major_ticks():
+        t.tick1On = False
+        t.tick2On = False
+    for t in ax.yaxis.get_major_ticks():
+        t.tick1On = False
+        t.tick2On = False
+
+    fig.tight_layout()
+    if options.absoluteValue:
+        fileName = options.vectorFile.replace('.txt', '.abs.' + options.outputFormat)
+    else:
+        fileName = options.vectorFile.replace('.txt', '.' + options.outputFormat)
+    fig.savefig(os.path.join(options.outputPath, fileName))
+
+    # plt.axis('tight')
+    # plt.show()
+    # plt.savefig('test.png', bbox_inches='tight')
+
+    print("   Plotting heatmap done in %fs" % (time() - t0))