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339 lines
11 KiB
339 lines
11 KiB
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# The new class FASTsearch. Every DB can be represented in Lists. The Brain actually is constituted from lists. Access to all Documents almost the same moment.
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# TODO GPU Multithreading has to be implemented.
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# USAGE: Learn scikit-learn count vectorizer on a database of lines or docs.
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from sklearn.externals import joblib
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from sklearn.feature_extraction.text import CountVectorizer
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import numpy as np
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import scipy as sc
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import tensorflow as tf
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import _pickle as cPickle
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import hickle as hkl
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import os
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# Define function to convert scipy csr matrix to tf tensor for working on gpu
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def convert_sparse_matrix_to_sparse_tensor(X):
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coo = sc.sparse.coo_matrix(X)
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indices = np.mat([coo.row, coo.col]).transpose()
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return tf.SparseTensorValue(indices, coo.data, coo.shape)
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# The whole class is initialized with input of the database in [['word','word2'],[],[],[]] List format, 2 dimensional, the index of the list in the matrix defines its id
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## in every list element of the input, each document is represented by one string
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# This list must be saved as a hkl dump and then loaded into the database.
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def my_tokenizer(s):
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return s.split('\+')
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class FASTsearch(object):
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def __init__(self, DatabaseDir):
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self.DatabaseDir = DatabaseDir[:-4]
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database = []
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hkl_load = hkl.load(DatabaseDir)
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for element in hkl_load:
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#print('element',element)
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#print('joined element', ' '.join(element))
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database.append(' '.join(element))
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# input has to be hkl format
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self.database = database
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def Gen_BoW_Model(self, max_features, analyzer, punctuation = False):
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print("Creating the bag of words...\n")
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from sklearn.feature_extraction.text import CountVectorizer
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# Initialize the "CountVectorizer" object, which is scikit-learn's
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# bag of words tool.
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if punctuation == False:
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vectorizer = CountVectorizer(analyzer = analyzer, \
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tokenizer = None, \
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preprocessor = None, \
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stop_words = None, \
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max_features = max_features)
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if punctuation == True:
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vectorizer = CountVectorizer(analyzer = analyzer, \
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tokenizer = my_tokenizer, \
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preprocessor = None, \
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stop_words = None, \
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max_features = max_features)
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# token_pattern = r'(?u)\w')
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# fit_transform() does two functions: First, it fits the model
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# and learns the vocabulary; second, it transforms our training data
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# into feature vectors. The input to fit_transform should be a list of
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# strings.
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train_data_features = vectorizer.fit_transform(self.database)
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joblib.dump(vectorizer, 'bagofwords' + self.DatabaseDir + '.pkl')
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print('dumping the data to hkl format..')
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hkl.dump(train_data_features, 'DataBaseOneZeros' + self.DatabaseDir + '.hkl', mode='w', compression='gzip')
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print('done')
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return vectorizer
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def Load_BoW_Model(self, BoWModelDir, DatabaseOneZerosDir):
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# input has to be pkl format
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self.vectorizer = joblib.load(BoWModelDir)
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self.dbOZ = hkl.load(DatabaseOneZerosDir).astype('float32')
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return self.vectorizer
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# input: string to search for in the documents, the numberofmatches to get the best n documents
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# output the numberofmatches documents with their indexes on the database which is searched, the highest accordance number plus index [index, number]
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def search(self, string , numberofmatches):
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numberofmatches = numberofmatches
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# Convert user input to Zeros and Ones
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user_array = []
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user_array.append(string)
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user_input_OnesZeros = self.vectorizer.transform(user_array)
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uOZ = user_input_OnesZeros.toarray()[0].astype(np.float32, copy=False)
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uiOZ = uOZ[np.newaxis, :]
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uiOZ = uiOZ.transpose()
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sess = tf.Session()
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with sess.as_default():
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uiOZ_tensor = tf.constant(uiOZ)
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dbOZ_tensor_sparse = convert_sparse_matrix_to_sparse_tensor(self.dbOZ)
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#uiOZ_tensor_sparse =tf.contrib.layers.dense_to_sparse(uiOZ_tensor, eos_token=0, outputs_collections=None, scope=None )
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#dbOZ_tensor_sparse =tf.contrib.layers.dense_to_sparse(dbOZ_tensor, eos_token=0, outputs_collections=None, scope=None )
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#wordCountDoku = tf.matmul(uiOZ_tensor, dbOZ_tensor)
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wordCountDoku = tf.sparse_tensor_dense_matmul(dbOZ_tensor_sparse, uiOZ_tensor)
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wCD = np.array(wordCountDoku.eval())
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indexedwCD = []
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for n in range(len(wCD)):
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indexedwCD.append([n,wCD[n][0]])
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indexedwCD = sorted(indexedwCD[::-1], key=lambda tup: tup[1], reverse=True)
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best_n_documents = []
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eq_number = 0
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for number in uiOZ:
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#print(number)
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eq_number += number ** 2
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#print(eq_number)
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n = 0
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done = False
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while n < len(indexedwCD) and done == False:
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n += 1
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if indexedwCD[n][1] == eq_number:
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best_n_documents = indexedwCD[n][0]
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done = True
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if indexedwCD[n][1] < eq_number:
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best_n_documents = indexedwCD[n - 1][0]
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done = True
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#for n in range(numberofmatches):
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#best_n_documents.append([indexedwCD[n][0], indexedwCD[n][1]])
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return best_n_documents, indexedwCD[0]
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def search_with_highest_multiplikation_Output(self, string , numberofmatches):
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numberofmatches = numberofmatches
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# Convert user input to Zeros and Ones
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user_array = []
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user_array.append(string)
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user_input_OnesZeros = self.vectorizer.transform(user_array)
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uOZ = user_input_OnesZeros.toarray()[0].astype(np.float32, copy=False)
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uiOZ = uOZ[np.newaxis, :]
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uiOZ = uiOZ.transpose()
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sess = tf.Session()
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with sess.as_default():
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uiOZ_tensor = tf.constant(uiOZ)
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dbOZ_tensor_sparse = convert_sparse_matrix_to_sparse_tensor(self.dbOZ)
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#uiOZ_tensor_sparse =tf.contrib.layers.dense_to_sparse(uiOZ_tensor, eos_token=0, outputs_collections=None, scope=None )
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#dbOZ_tensor_sparse =tf.contrib.layers.dense_to_sparse(dbOZ_tensor, eos_token=0, outputs_collections=None, scope=None )
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#wordCountDoku = tf.matmul(uiOZ_tensor, dbOZ_tensor)
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wordCountDoku = tf.sparse_tensor_dense_matmul(dbOZ_tensor_sparse, uiOZ_tensor)
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wCD = np.array(wordCountDoku.eval())
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indexedwCD = []
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for n in range(len(wCD)):
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indexedwCD.append([n,wCD[n][0]])
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indexedwCD = sorted(indexedwCD[::-1], key=lambda tup: tup[1], reverse=True)
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best_n_documents = []
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for n in range(numberofmatches):
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best_n_documents.append(indexedwCD[n][0])
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return best_n_documents, indexedwCD[0]
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def searchPatternMatch(self, string , numberofmatches):
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numberofmatches = numberofmatches
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# Convert user input to Zeros and Ones
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user_array = []
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user_array.append(string)
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user_input_OnesZeros = self.vectorizer.transform(user_array)
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uOZ = user_input_OnesZeros.toarray()[0].astype(np.float32, copy=False)
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uiOZ = uOZ[np.newaxis, :]
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uiOZ = uiOZ.transpose()
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sess = tf.Session()
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with sess.as_default():
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uiOZ_tensor = tf.constant(uiOZ)
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dbOZ_tensor_sparse = convert_sparse_matrix_to_sparse_tensor(self.dbOZ)
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#uiOZ_tensor_sparse =tf.contrib.layers.dense_to_sparse(uiOZ_tensor, eos_token=0, outputs_collections=None, scope=None )
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#dbOZ_tensor_sparse =tf.contrib.layers.dense_to_sparse(dbOZ_tensor, eos_token=0, outputs_collections=None, scope=None )
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#wordCountDoku = tf.matmul(uiOZ_tensor, dbOZ_tensor)
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wordCountDoku = tf.sparse_tensor_dense_matmul(dbOZ_tensor_sparse, uiOZ_tensor)
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wCD = np.array(wordCountDoku.eval())
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indexedwCD = []
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for n in range(len(wCD)):
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indexedwCD.append([n,wCD[n][0]])
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# Sort the biggest matches
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indexedwCD = sorted(indexedwCD[::-1], key=lambda tup: tup[1], reverse=True)
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best_n_documents = []
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best_docs_surrounding = []
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# Get the number which is result when same words would be in the document as in one grammar scheme
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eq_number = 0
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for number in uiOZ:
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#print(number)
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eq_number += number ** 2
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print(eq_number)
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# Create new array of closest grammar schemes, I have chosen around 3 (in the matchnumber, not regarding words or so)
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n = 0
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done = False
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while n < len(indexedwCD) and done == False:
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n += 1
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#print('a',indexedwCD)
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#print('oo', indexedwCD[n])
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if indexedwCD[n][1] == eq_number:
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best_docs_surrounding.append(indexedwCD[n][0])
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#if indexedwCD[n][1] < eq_number:
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#best_docs_surrounding.append(indexedwCD[n][0])
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if indexedwCD[n][1] < eq_number :
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done = True
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# Count for these docs in surrounding the matches of wordnumbers per word
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# would be much faster when using the sparse class
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best_docs_surrounding_new = []
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for doc in best_docs_surrounding:
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dok_BoW = self.dbOZ[doc].toarray()[0].astype(np.float32, copy=False)
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Number_equal_words = 0
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for n in range(len(uiOZ)):
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#print(uiOZ[n])
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#print(dok_BoW[n])
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#print('dok_BoW',dok_BoW)
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if uiOZ[n] == dok_BoW[n]:
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Number_equal_words += 1
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best_docs_surrounding_new.append([doc , Number_equal_words])
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# Sort the result again with the original indexes
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best_n_documents = sorted(best_docs_surrounding_new[::-1], key=lambda tup: tup[1], reverse=True)
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#for n in range(numberofmatches):
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#best_n_documents.append([indexedwCD[n][0], indexedwCD[n][1]])
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return best_n_documents
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