def load_engine(sdf_files, feature_matrix, dimension):
"""
Function that converts the given sdf_files into instances of the sdf_class, then loads them into nearpy Engine.
Parameters
sdf_files: a list of sdf_files with their pathname from the current directory. Intended to be fed in from `find_sdf(root_dir)`
feature_matrix: matrix of training data features to be loaded into engine
dimension: dimensionality of the feature vectors used for LSH (here: number of cluster centers)
Returns
engine: instance of a nearpy engine with all of sdf_files loaded
Sample Usage
>>> engine = load_engine(sdf_files)
"""
#dimension here can be altered as well
rbp = RandomBinaryProjections('rbp',10)
engine = Engine(dimension, lshashes=[rbp])
count = 0
for index,file_ in enumerate(sdf_files):
#print file_
if count % 100 == 0:
print 'Converted %d files' %(count)
converted = SDF(file_)
converted.set_feature_vector(feature_matrix[index])
converted.add_to_nearpy_engine(engine)
count += 1
return engine
def adddir_25(self,dir_to_add):
"""add files in a directory only with dimension 12"""
sdf_files = []
for root,dirs,files in walk(dir_to_add):
for file_ in files:
if file_.endswith(".sdf"):
tempsdf=SDF(path.join(root,file_))
if tempsdf.dimensions()[0]==25*25*25:
sdf_files.append(path.join(root,file_))
self.all_files_+=sdf_files
def transform(self, sdf_list):
features = np.array([])
for file_ in sdf_list:
#print file_
converted = SDF(file_)
my_windows = np.array(converted.make_windows(self.W, self.S, self.FILTER))
prediction = histogramize(self.Kmeans_model.predict(my_windows), self.NUM_CLUSTERS)
if len(features) == 0:
features = prediction
else:
features = np.concatenate((features,prediction), axis=0)
return features
def transform(self, sdf_list):
features = np.array([])
for file_ in sdf_list:
#print file_
converted = SDF(file_)
my_windows = np.array(
converted.make_windows(self.W, self.S, self.FILTER))
prediction = histogramize(self.Kmeans_model.predict(my_windows),
self.NUM_CLUSTERS)
if len(features) == 0:
features = prediction
else:
features = np.concatenate((features, prediction), axis=0)
return features
def fit(self, sdf_list, K):
windows = np.array([])
for file_ in sdf_list:
#print file_
converted = SDF(file_)
my_windows = np.array(converted.make_windows(self.W, self.S, self.FILTER))
#print my_windows.shape
if len(windows) == 0:
windows = my_windows
else:
windows = np.concatenate((windows,my_windows), axis=0)
self.Kmeans_model = KMeans(n_clusters=K)
self.Kmeans_model.fit(windows)
#print windows.shape, 'win shape'
self.cluster_centers = self.Kmeans_model.cluster_centers_
return self.transform(sdf_list) #used to map back for sdf_class purposes/LSH pipeline
def fit(self, sdf_list, K):
windows = np.array([])
for file_ in sdf_list:
#print file_
converted = SDF(file_)
my_windows = np.array(
converted.make_windows(self.W, self.S, self.FILTER))
#print my_windows.shape
if len(windows) == 0:
windows = my_windows
else:
windows = np.concatenate((windows, my_windows), axis=0)
self.Kmeans_model = KMeans(n_clusters=K)
self.Kmeans_model.fit(windows)
#print windows.shape, 'win shape'
self.cluster_centers = self.Kmeans_model.cluster_centers_
return self.transform(
sdf_list) #used to map back for sdf_class purposes/LSH pipeline
def load_engine(sdf_files, feature_matrix, dimension):
"""
Function that converts the given sdf_files into instances of the sdf_class, then loads them into nearpy Engine.
Parameters
sdf_files: a list of sdf_files with their pathname from the current directory. Intended to be fed in from `find_sdf(root_dir)`
feature_matrix: matrix of training data features to be loaded into engine
dimension: dimensionality of the feature vectors used for LSH (here: number of cluster centers)
Returns
engine: instance of a nearpy engine with all of sdf_files loaded
Sample Usage
>>> engine = load_engine(sdf_files)
"""
#dimension here can be altered as well
rbp = RandomBinaryProjections('rbp', 10)
engine = Engine(dimension, lshashes=[rbp])
count = 0
for index, file_ in enumerate(sdf_files):
#print file_
if count % 100 == 0:
print 'Converted %d files' % (count)
converted = SDF(file_)
converted.set_feature_vector(feature_matrix[index])
converted.add_to_nearpy_engine(engine)
count += 1
return engine
def train_and_test_lsh(num_train, num_test, root_dir, K = 1, clusters=10):
"""
Function that generates a list of sdf files given a root_dir, and loads a random num_train of them into a nearpy engine. It then queries the LSH engine for a
random num_test other sdf files. num_train+num_test must be less than the total number of sdf_files
Parameters
num_train: number of files to load into the engine
num_test: number of files to query after
sdf_files: list of sdf files to draw from
K: number of neighbors to check
Returns
accuracy: float representing the accuracy of querying the nearpy engine with the test results
engine: the trained and "tested" nearpy engine
test_results: dictionary of the results from the "testing" for each of the sdf_files
Sample Usage
>>> train_and_test_lsh(100,5,"datasets/Cat50_ModelDatabase")
"""
test_results = {}
confusion = {}
sdf_files = find_sdf(root_dir, 'clean.sdf')
print 'Found %d SDF files' %(len(sdf_files))
assert num_train+num_test <= len(sdf_files)
#Randomly permutes the indices of the sdf_files list.
np.random.seed(100)
permuted_indices = np.random.permutation(len(sdf_files))
get_training = itemgetter(*permuted_indices[:num_train])
get_testing = itemgetter(*permuted_indices[num_train:num_train+num_test])
training = get_training(sdf_files)
model = SDFBagOfWords(clusters)
predictions = model.fit(training, clusters)
print "DONE FITTING"
#print predictions
engine = load_engine(training,predictions, clusters)
print "LOADED TO LSH ENGINE"
if num_test > 1:
test_files = get_testing(sdf_files)
else:
test_files = [get_testing(sdf_files)]
featurized = model.transform(test_files)
print "TRANSFORMED TEST"
# setup confusion matrix
confusion[UNKNOWN_TAG] = {}
for file_ in sdf_files:
category = cat50_file_category(file_)
confusion[category] = {}
for query_cat in confusion.keys():
for pred_cat in confusion.keys():
confusion[query_cat][pred_cat] = 0
for index,file_ in enumerate(test_files):
#NOTE: This is assuming the file structure is: data/<dataset_name>/<category>/...
query_category = cat50_file_category(file_)
print "Querying: %s with category %s "%(file_, query_category)
converted = SDF(file_)
converted.set_feature_vector(featurized[index])
closest_names, closest_vals = converted.query_nearpy_engine(engine)
# check if top K items contains the query category
pred_category = UNKNOWN_TAG
if len(closest_names) > 0:
closest_category = closest_names[0]
pred_category = cat50_file_category(closest_category)
for i in range(1, min(K, len(closest_names))):
closest_category = closest_names[i]
potential_category = cat50_file_category(closest_category)
if potential_category == query_category:
pred_category = potential_category
print "Result Category: %s"%(pred_category)
confusion[query_category][pred_category] += 1
test_results[file_]= [(closest_names, closest_vals)]
# convert the dictionary to a numpy array
row_names = confusion.keys()
confusion_mat = np.zeros([len(row_names), len(row_names)])
i = 0
for query_cat in confusion.keys():
j = 0
for pred_cat in confusion.keys():
confusion_mat[i,j] = confusion[query_cat][pred_cat]
j += 1
i += 1
# get true positives, etc for each category
num_preds = len(test_files)
tp = np.diag(confusion_mat)
fp = np.sum(confusion_mat, axis=0) - np.diag(confusion_mat)
fn = np.sum(confusion_mat, axis=1) - np.diag(confusion_mat)
tn = num_preds * np.ones(tp.shape) - tp - fp - fn
# compute useful statistics
recall = tp / (tp + fn)
tnr = tn / (fp + tn)
precision = tp / (tp + fp)
npv = tn / (tn + fn)
fpr = fp / (fp + tn)
accuracy = np.sum(tp) / num_preds # correct predictions over entire dataset
# remove nans
recall[np.isnan(recall)] = 0
tnr[np.isnan(tnr)] = 0
precision[np.isnan(precision)] = 0
npv[np.isnan(npv)] = 0
fpr[np.isnan(fpr)] = 0
return accuracy, engine, test_results
def train_and_test_lsh(num_train, num_test, root_dir, K=1, clusters=10):
"""
Function that generates a list of sdf files given a root_dir, and loads a random num_train of them into a nearpy engine. It then queries the LSH engine for a
random num_test other sdf files. num_train+num_test must be less than the total number of sdf_files
Parameters
num_train: number of files to load into the engine
num_test: number of files to query after
sdf_files: list of sdf files to draw from
K: number of neighbors to check
Returns
accuracy: float representing the accuracy of querying the nearpy engine with the test results
engine: the trained and "tested" nearpy engine
test_results: dictionary of the results from the "testing" for each of the sdf_files
Sample Usage
>>> train_and_test_lsh(100,5,"datasets/Cat50_ModelDatabase")
"""
test_results = {}
confusion = {}
sdf_files = find_sdf(root_dir, 'clean.sdf')
print 'Found %d SDF files' % (len(sdf_files))
assert num_train + num_test <= len(sdf_files)
#Randomly permutes the indices of the sdf_files list.
np.random.seed(100)
permuted_indices = np.random.permutation(len(sdf_files))
get_training = itemgetter(*permuted_indices[:num_train])
get_testing = itemgetter(*permuted_indices[num_train:num_train + num_test])
training = get_training(sdf_files)
model = SDFBagOfWords(clusters)
predictions = model.fit(training, clusters)
print "DONE FITTING"
#print predictions
engine = load_engine(training, predictions, clusters)
print "LOADED TO LSH ENGINE"
if num_test > 1:
test_files = get_testing(sdf_files)
else:
test_files = [get_testing(sdf_files)]
featurized = model.transform(test_files)
print "TRANSFORMED TEST"
# setup confusion matrix
confusion[UNKNOWN_TAG] = {}
for file_ in sdf_files:
category = cat50_file_category(file_)
confusion[category] = {}
for query_cat in confusion.keys():
for pred_cat in confusion.keys():
confusion[query_cat][pred_cat] = 0
for index, file_ in enumerate(test_files):
#NOTE: This is assuming the file structure is: data/<dataset_name>/<category>/...
query_category = cat50_file_category(file_)
print "Querying: %s with category %s " % (file_, query_category)
converted = SDF(file_)
converted.set_feature_vector(featurized[index])
closest_names, closest_vals = converted.query_nearpy_engine(engine)
# check if top K items contains the query category
pred_category = UNKNOWN_TAG
if len(closest_names) > 0:
closest_category = closest_names[0]
pred_category = cat50_file_category(closest_category)
for i in range(1, min(K, len(closest_names))):
closest_category = closest_names[i]
potential_category = cat50_file_category(closest_category)
if potential_category == query_category:
pred_category = potential_category
print "Result Category: %s" % (pred_category)
confusion[query_category][pred_category] += 1
test_results[file_] = [(closest_names, closest_vals)]
# convert the dictionary to a numpy array
row_names = confusion.keys()
confusion_mat = np.zeros([len(row_names), len(row_names)])
i = 0
for query_cat in confusion.keys():
j = 0
for pred_cat in confusion.keys():
confusion_mat[i, j] = confusion[query_cat][pred_cat]
j += 1
i += 1
# get true positives, etc for each category
num_preds = len(test_files)
tp = np.diag(confusion_mat)
fp = np.sum(confusion_mat, axis=0) - np.diag(confusion_mat)
fn = np.sum(confusion_mat, axis=1) - np.diag(confusion_mat)
tn = num_preds * np.ones(tp.shape) - tp - fp - fn
# compute useful statistics
recall = tp / (tp + fn)
tnr = tn / (fp + tn)
precision = tp / (tp + fp)
npv = tn / (tn + fn)
fpr = fp / (fp + tn)
accuracy = np.sum(
tp) / num_preds # correct predictions over entire dataset
# remove nans
recall[np.isnan(recall)] = 0
tnr[np.isnan(tnr)] = 0
precision[np.isnan(precision)] = 0
npv[np.isnan(npv)] = 0
fpr[np.isnan(fpr)] = 0
return accuracy, engine, test_results
