[GRASS-SVN] r71010 - in grass-addons/grass7/raster: . r.object.activelearning r.object.activelearning/examples
svn_grass at osgeo.org
svn_grass at osgeo.org
Wed May 3 01:09:28 PDT 2017
Author: mlennert
Date: 2017-05-03 01:09:28 -0700 (Wed, 03 May 2017)
New Revision: 71010
Added:
grass-addons/grass7/raster/r.object.activelearning/
grass-addons/grass7/raster/r.object.activelearning/Makefile
grass-addons/grass7/raster/r.object.activelearning/examples/
grass-addons/grass7/raster/r.object.activelearning/examples/test_set.csv
grass-addons/grass7/raster/r.object.activelearning/examples/training_set.csv
grass-addons/grass7/raster/r.object.activelearning/examples/unlabeled_set.csv
grass-addons/grass7/raster/r.object.activelearning/examples/update.csv
grass-addons/grass7/raster/r.object.activelearning/r.object.activelearning.html
grass-addons/grass7/raster/r.object.activelearning/r.object.activelearning.py
grass-addons/grass7/raster/r.object.activelearning/report.pdf
grass-addons/grass7/raster/r.object.activelearning/tests.py
Log:
r.object.activelearning: New addon for active learning in OBIA classification
Added: grass-addons/grass7/raster/r.object.activelearning/Makefile
===================================================================
--- grass-addons/grass7/raster/r.object.activelearning/Makefile (rev 0)
+++ grass-addons/grass7/raster/r.object.activelearning/Makefile 2017-05-03 08:09:28 UTC (rev 71010)
@@ -0,0 +1,7 @@
+MODULE_TOPDIR = ../..
+
+PGM = r.object.activelearning
+
+include $(MODULE_TOPDIR)/include/Make/Script.make
+
+default: script
Property changes on: grass-addons/grass7/raster/r.object.activelearning/Makefile
___________________________________________________________________
Added: svn:mime-type
+ text/x-makefile
Added: svn:eol-style
+ native
Added: grass-addons/grass7/raster/r.object.activelearning/examples/test_set.csv
===================================================================
--- grass-addons/grass7/raster/r.object.activelearning/examples/test_set.csv (rev 0)
+++ grass-addons/grass7/raster/r.object.activelearning/examples/test_set.csv 2017-05-03 08:09:28 UTC (rev 71010)
@@ -0,0 +1,586 @@
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Added: grass-addons/grass7/raster/r.object.activelearning/examples/training_set.csv
===================================================================
--- grass-addons/grass7/raster/r.object.activelearning/examples/training_set.csv (rev 0)
+++ grass-addons/grass7/raster/r.object.activelearning/examples/training_set.csv 2017-05-03 08:09:28 UTC (rev 71010)
@@ -0,0 +1,74 @@
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Added: grass-addons/grass7/raster/r.object.activelearning/examples/unlabeled_set.csv
===================================================================
--- grass-addons/grass7/raster/r.object.activelearning/examples/unlabeled_set.csv (rev 0)
+++ grass-addons/grass7/raster/r.object.activelearning/examples/unlabeled_set.csv 2017-05-03 08:09:28 UTC (rev 71010)
@@ -0,0 +1,772 @@
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Added: grass-addons/grass7/raster/r.object.activelearning/examples/update.csv
===================================================================
--- grass-addons/grass7/raster/r.object.activelearning/examples/update.csv (rev 0)
+++ grass-addons/grass7/raster/r.object.activelearning/examples/update.csv 2017-05-03 08:09:28 UTC (rev 71010)
@@ -0,0 +1,6 @@
+cat,Class_num
+8441476,12
+12377913,34
+9984762,14
+9951298,21
+15321846,34
\ No newline at end of file
Added: grass-addons/grass7/raster/r.object.activelearning/r.object.activelearning.html
===================================================================
--- grass-addons/grass7/raster/r.object.activelearning/r.object.activelearning.html (rev 0)
+++ grass-addons/grass7/raster/r.object.activelearning/r.object.activelearning.html 2017-05-03 08:09:28 UTC (rev 71010)
@@ -0,0 +1,189 @@
+<h2>DESCRIPTION</h2>
+This module uses SVM from the <em> scikit-learn </em> python package to perform
+classification on regions of raster maps. These regions can be the output of
+<em><a href="i.segment.html">i.segment</a></em>
+or
+<em><a href="r.clump.html">r.clump</a></em>.
+
+<p>
+The module enables learning with only a small initial labeled data set via
+<em>active learning</em>. This semi-supervised learning algorithm interactively
+query the user to label the regions that are most useful to improve the overall
+classification score. With this technique, the number of examples to learn the
+classification is often much lower than the number of examples needed in normal
+supervised algorithms. You should start the classification with a small training
+set and run the module multiple times to label new informative samples to
+improve the classification score. The score metric is the number of correctly
+predicted labels over the total number of samples in the test set.
+
+<p>
+The samples that are chosen to be labeled are the ones where the class
+prediction is the most uncertain [2]. Moreover, from the more uncertain
+samples, only the most different samples are kept [1]. This diversity heuristic
+takes into account for each uncertain sample the distance to its closest
+neighbour and the average distance to all other samples. This ensures that newly
+labeled samples are not redundant with each other.
+
+<p>
+The learning data should be composed of features extracted from the regions, for
+example with the
+<em><a href="i.segment.stats.html">i.segment.stats</a></em> module.
+The features of the training set, the test set and the unlabeled set should be
+in three different files in csv format. The first line of each file must be a
+header containing the features' name. Every regions should be uniquely
+identified by the first attribute. The classes for the training and test
+examples should be the second attribute.
+
+<p>
+Example of a training and test files :
+ <div class="code">
+ <pre>
+ cat,Class_num,attr1,attr2,attr3
+ 167485,4,3.546,456.76,6.76
+ 183234,6,5.76,1285.54,9.45
+ 173457,2,5.65,468.76,6.78
+ </pre>
+ </div>
+
+<p>
+Example of an unlabeled file :
+ <div class="code">
+ <pre>
+ cat,attr1,attr2,attr3
+ 167485,3.546,456.76,6.76
+ 183234,5.76,1285.54,9.45
+ 173457,5.65,468.76,6.78
+ </pre>
+ </div>
+
+<p>
+The training set can be easily updated once you have labeled new samples. Create
+a file to specify what label you give to which sample.
+This file in csv format should have a header and two attributes per line : the
+ID of the sample you have labeled and the label itself. The module will
+transfer the newly labeled samples from the unlabeled set to the training set,
+adding the class you have provided. This is done internally and does not modify
+your original files.
+
+<p>
+If the user wants to save the changes in new files according to the updates, new
+files can be created with the new labeled samples added to the training file and
+removed from the unlabeled file. Just specify the path of those output files in
+the parameters (training_updated, unlabeled_updated).
+
+<p>
+Example of an update file :
+ <div class="code">
+ <pre>
+ cat,Class_num
+ 194762,2
+ 153659,6
+ 178350,2
+ </pre>
+ </div>
+
+<p>
+Here are more details on a few parameters :
+<ul>
+ <li><b>learning_steps</b> : This is the number of samples that
+ the module will ask to label at each run.</li>
+ <li><b>nbr_uncertainty</b> : Number of uncertain samples to
+ choose before applying the diversity filter. This number should
+ be higher than <em>learning_steps</em></li>
+ <li><b>diversity_lambda</b> : Parameter used in the diversity
+ heuristic. If close to 0 only take into account the average
+ distance to all other samples. If close to 1 only take into
+ account the distance to the closest neighbour</li>
+ <li><b>c_SVM</b> : Penalty parameter C of the error term. If
+ it is too large there is a risk of overfitting the training
+ data. If it is too small you may have underfitting.</li>
+ <li><b>gamma_SVM</b> : Kernel coefficient. 1/#features is
+ often a good value to start with.</li>
+ If C or gamma is left empty (or both), a good value based on the
+ data distibution is found by cross-validation (at least 3
+ samples per class in the training set is needed, more is
+ better). This automatic parameter tuning requires more
+ computation time as the training set grows.
+ <li><b>search_iter</b> :Number of parameter settings that are
+ sampled in the automatic parameter search (C, gamma).
+ search_iter trades off runtime vs quality of the solution.</li>
+</ul>
+
+<h2>EXAMPLES</h2>
+
+The following examples are based on the data files found in this module
+repository.
+
+<h3>Simple run without an update file</h3>
+
+<div class="code">
+ <pre>
+ r.object.activelearning training_set=/path/to/training_set.csv \
+ test_set=/path/to/test_set.csv \
+ unlabeled_set=/path/to/unlabeled_set.csv
+
+ Parameters used : C=146.398423284, gamma=0.0645567086567, lambda=0.25
+ 12527959
+ 9892568
+ 13731120
+ 15445003
+ 13767630
+ Class predictions written to predictions.csv
+ Training set : 70
+ Test set : 585
+ Unlabeled set : 792
+ Score : 0.321367521368
+ </pre>
+</div>
+
+<h3>With an update file</h3>
+
+The five samples output at the previous example have been labeled and added to
+the update file.
+
+<div class="code">
+ <pre>
+ r.object.activelearning training_set=/path/to/training_set.csv \
+ test_set=/path/to/test_set.csv \
+ unlabeled_set=/path/to/unlabeled_set.csv \
+ update=/path/to/update.csv
+
+ Parameters used : C=101.580687073, gamma=0.00075388337475, lambda=0.25
+ Class predictions written to predictions.csv
+ Training set : 75
+ Test set : 585
+ Unlabeled set : 787
+ Score : 0.454700854701
+ 8691475
+ 9321017
+ 14254774
+ 14954255
+ 15838185
+ </pre>
+</div>
+
+
+<h2>NOTES</h2>
+
+This module requires the <em> scikit-learn </em> python package. This module
+needs to be installed in your GRASS GIS Python environment. Please refer to <a
+ href="r.learn.ml.html"><em>r.learn.ml</em></a>'s notes on how to install
+this package.
+
+<p>
+The memory usage for ~1450 samples of 52 features each is around ~650 kb. This
+number can vary due to the unpredictablity of the garbage collector's behaviour.
+Everything is computed in memory; therefore the size of the data is limited by
+the amount of RAM available.
+
+<h2>REFERENCES</h2>
+[1] Bruzzone, L. and Persello, C. (2009). Active learning for classification of
+remote sensing images. 2009 IEEE International Geoscience and Remote Sensing
+Symposium. doi:10.1109/igarss.2009.5417857<br>
+[2] Tuia, D. et al (2011). A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification. IEEE Journal of Selected Topics in Signal Processing, 5(3), 606-617. doi:10.1109/jstsp.2011.2139193
+
+
+<h2>AUTHOR</h2>
+Lucas Lefèvre (ULB, Brussels, Belgium)
+
+<p><i>Last changed: $Date$</i>
Property changes on: grass-addons/grass7/raster/r.object.activelearning/r.object.activelearning.html
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+ text/html
Added: svn:keywords
+ Author Date Id
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+ native
Added: grass-addons/grass7/raster/r.object.activelearning/r.object.activelearning.py
===================================================================
--- grass-addons/grass7/raster/r.object.activelearning/r.object.activelearning.py (rev 0)
+++ grass-addons/grass7/raster/r.object.activelearning/r.object.activelearning.py 2017-05-03 08:09:28 UTC (rev 71010)
@@ -0,0 +1,579 @@
+#!/usr/bin/env python
+# encoding: utf-8
+#******************************************************************
+#*
+#* MODULE: r.object.activelearning
+#*
+#* AUTHOR(S) Lucas Lefèvre
+#*
+#* PURPOSE: Active learning for classifying raster objects
+#*
+#* COPYRIGHT: (C) 2017 Lucas Lefèvre, and the GRASS Development Team
+# This program is free software under the GNU General Public
+# License (>=v2). Read the file COPYING that comes with GRASS
+# for details.
+#*
+#******************************************************************
+
+#%module
+#% description: Active learning for classifying raster objects
+#%end
+#%option G_OPT_F_INPUT
+#% key: training_set
+#% description: Training set (csv format)
+#% required: yes
+#%end
+#%option G_OPT_F_INPUT
+#% key: test_set
+#% description: Test set (csv format)
+#% required: yes
+#%end
+#%option G_OPT_F_INPUT
+#% key: unlabeled_set
+#% description: Unlabeled samples (csv format)
+#% required: yes
+#%end
+#%option
+#% key: learning_steps
+#% type: integer
+#% description: Number of samples to label at each iteration
+#% answer: 5
+#% required: no
+#%end
+#%option
+#% key: nbr_uncertainty
+#% type: integer
+#% description: Number of samples to select (based on uncertainty criterion) before applying the diversity criterion.
+#% answer: 15
+#% required: no
+#%end
+#%option
+#% key: diversity_lambda
+#% type: double
+#% description: Lambda parameter used in the diversity heuristic
+#% answer: 0.25
+#% required: no
+#%end
+#%option
+#% key: c_svm
+#% type: double
+#% description: Penalty parameter C of the error term
+#% required: no
+#%end
+#%option
+#% key: gamma_parameter
+#% type: double
+#% description: Kernel coefficient
+#% required: no
+#%end
+#%option
+#% key: search_iter
+#% type: integer
+#% description: Number of parameter settings that are sampled in the automatic parameter search (C, gamma).
+#% answer: 15
+#% required: no
+#%end
+#%option G_OPT_F_INPUT
+#% key: update
+#% description: Training set update file
+#% required: no
+#%end
+#%option G_OPT_F_OUTPUT
+#% key: predictions
+#% description: Output file for class predictions
+#% required: no
+#%end
+#%option G_OPT_F_OUTPUT
+#% key: training_updated
+#% description: Output file for the updated training file
+#% required: no
+#%end
+#%option G_OPT_F_OUTPUT
+#% key: unlabeled_updated
+#% description: Output file for the updated unlabeled file
+#% required: no
+#%end
+
+
+
+try :
+ from sklearn import svm
+ from sklearn import preprocessing
+ from sklearn.model_selection import train_test_split
+ from sklearn.model_selection import RandomizedSearchCV
+ from sklearn.model_selection import StratifiedKFold
+ from sklearn.metrics.pairwise import rbf_kernel
+except ImportError :
+ gcore.fatal("This module requires the scikit-learn python package. Please install it.")
+
+try : # You can run the tests outside of grass where those imports are not available
+ import grass as grass
+ import grass.script as gcore
+except ImportError :
+ pass
+
+import numpy as np
+import scipy
+import os.path
+import sys
+
+import matplotlib.pyplot as plt
+
+
+
+def load_data(file_path, labeled=False, skip_header=1, scale=True) :
+
+ """
+ Load the data from a csv file
+
+ :param file_path: Path to the csv data file
+ :param labeled: True if the data is labeled (default=False)
+ :param skip_header: Header size (in line) (default=1)
+ :param scale: True if the data should be normalize (default=True)
+
+ :type file_path: string
+ :type labeled: boolean
+ :type skip_header: int
+ :type scale: boolean
+
+ :return: Return 4 arrays, the features X, the IDs, the labels y and the header
+ :rtype: ndarray
+ """
+ data = np.genfromtxt(file_path, delimiter=',', skip_header=0, dtype=None)
+
+
+ header = np.array([])
+
+ if skip_header != 0 :
+ header = data[0:skip_header,:]
+ data = data[skip_header:, :] #Remove header
+ data = data.astype(np.float)
+
+ ID = data[:,0] #get only row 0s
+ if labeled :
+ y = data[:,1] #get only row 1
+ X = data[:,2:] #remove ID and label
+ else :
+ y = []
+ X = data[:,1:] #remove ID
+
+ if scale :
+ X = preprocessing.scale(X)
+
+ return X, ID, y, header
+
+def write_result_file(ID, X_unlabeled, predictions, header, filename) :
+ """
+ Write all samples with their ID and their class prediction in csv file. Also add the header to this csv file.
+
+ :param ID: Samples'IDs
+ :X_unlabeled: Samples'features
+ :predictions: Class predictin for each sample
+ :header: Header of the csv file
+ :filename: Name of the csv file
+ """
+ data = np.copy(X_unlabeled)
+ data = np.insert(data, 0, map(str, ID), axis=1)
+ data = np.insert(data, 1, map(str, predictions), axis=1)
+
+ if header.size != 0 :
+ header = np.insert(header, 1, ['Class'])
+ data = np.insert(data.astype(str), 0, header , axis=0)
+ np.savetxt(filename, data, delimiter=",",fmt="%s")
+ return True
+
+def update(update_file, X_train, ID_train, y_train, X_unlabeled, ID_unlabeled) :
+ """
+ Transfer features and labels from the unlabeled arrays to the training arrays based on the update file.
+
+ :param update_file: Path to the update file
+ :param X_train: Features for the training samples
+ :param ID_train: IDs of the training samples
+ :param y_train: Labels of the training samples
+ :param X_unlabeled: Features for the training samples
+ :param ID_unlabeled: IDs of the unlabeled samples
+ """
+ update = np.genfromtxt(update_file, delimiter=',', skip_header=1)
+ if update.size == 0 :
+ return X_train, ID_train, y_train
+ elif update.ndim == 1 :
+ update = [update]
+ for index_update, row in enumerate(update) :
+ index = np.where(ID_unlabeled == row[0]) # Find in 'unlabeled' the line corresping to the ID
+ if index[0].size != 0 : # Check if row exists
+ features = X_unlabeled[index[0][0]] # Features
+ ID = ID_unlabeled[index[0][0]]
+ label = row[1]
+ X_train = np.append(X_train, [features], axis=0)
+ ID_train = np.append(ID_train, [ID], axis=0)
+ y_train = np.append(y_train, [label], axis=0)
+ else :
+ gcore.warning("The following sample could not be found :{}".format(row[0]))
+
+ return X_train, ID_train, y_train
+
+def write_update(update_file, training_file, unlabeled_file, new_training_filename, new_unlabeled_filename) :
+ """
+ Transfer samples from the unlabeled set to the training set based on an update file
+ with IDs of samples to transfer and their classes.
+
+ :param update_file: Path to the update file
+ :param training_file: Path to the training file
+ :param unlabeled_file: Path to the unlabeled file
+ :param new_training_filename: Path to the new training file that will be created
+ :param new_unlabeled_filename: Path to the new unlabeled file that will be created
+
+ :type update_file: string
+ :type training_file: string
+ :type unlabeled_file: string
+ :type new_training_filename: string
+ :type new_unlabeled_filename: string
+ """
+ update = np.genfromtxt(update_file, delimiter=',', skip_header=1)
+ training = np.genfromtxt(training_file, delimiter=',', skip_header=0, dtype=None)
+ unlabeled = np.genfromtxt(unlabeled_file, delimiter=',', skip_header=0, dtype=None)
+ successful_updates = []
+
+ if update.size == 0 :
+ return
+ elif update.ndim == 1 :
+ update = [update]
+
+ for index_update, row in enumerate(update) :
+ index = np.where(unlabeled == str(row[0])) # Find in 'unlabeled' the line corresping to the ID
+ if index[0].size != 0 : # Check if row exists
+ data = unlabeled[index[0][0]][1:] # Features
+ data = np.insert(data, 0, row[0], axis=0) # ID
+ data = np.insert(data, 1, row[1], axis=0) # Class
+ training = np.append(training, [data], axis=0)
+ unlabeled = np.delete(unlabeled, index[0][0], axis=0)
+ successful_updates.append(index_update)
+ else :
+ gcore.warning("Unable to update completely: the following sample could not be found in the unlabeled set:{}".format(row[0]))
+
+ with open(update_file) as f:
+ header = f.readline()
+ header = header.split(',')
+
+ update = np.delete(update, successful_updates, axis=0)
+ update = np.insert(update.astype(str), 0, header, axis=0)
+
+ # Save files
+ if new_training_filename != '' :
+ write_updated_file(new_training_filename, training)
+ gcore.message("New training file written to {}".format(new_training_filename))
+ if new_unlabeled_filename != '':
+ write_updated_file(new_unlabeled_filename, unlabeled)
+ gcore.message("New unlabeled file written to {}".format(new_unlabeled_filename))
+
+def write_updated_file(file_path, data) :
+ """
+ Write to disk some csv data. Add '_updated' at the end of the filename
+ :param filename: location where the file will be saved
+ :param data: data to save
+
+ :type file_path: string
+ :type data: ndarray
+ """
+
+ np.savetxt(file_path, data, delimiter=",",fmt="%s")
+
+def linear_scale(data) :
+ """
+ Linearly scale values : 5th percentile to 0 and 95th percentile to 1
+
+ :param data: Features
+ :type data: ndarray(#samples x #features)
+
+ :return: Linearly scaled data
+ :rtype: ndarray(#samples x #features)
+ """
+ p5 = np.percentile(data, 5, axis=0, interpolation='nearest')[np.newaxis] # 5th percentiles as a 2D array (-> newaxis)
+ p95 = np.percentile(data, 95, axis=0, interpolation='nearest')[np.newaxis] # 95th percentiles as a 2D array (-> newaxis)
+
+ return (data-p5)/(p95-p5)
+
+def train(X, y, c_svm, gamma_parameter) :
+ """
+ Train a SVM classifier.
+
+ :param c: Penalty parameter C of the error term.
+ :param gamma: Kernel coefficient
+ :param X: Features of the training samples
+ :param y: Labels of the training samples
+
+ :return: Returns the trained classifier
+ :rtype: sklearn.svm.SVC
+ """
+ classifier = svm.SVC(kernel='rbf', C=c_svm, gamma=gamma_parameter, probability=False,decision_function_shape='ovr', random_state=1938475632)
+ classifier.fit(X, y)
+
+ return classifier
+
+def active_diversity_sample_selection(X_unlabled, nbr, classifier) :
+ """
+ Select a number of samples to label based on uncertainety and diversity
+
+ :param X_unlabeled: Pool of unlabeled samples
+ :param nbr: Number of samples to select from the pool
+ :param classifier: Used to predict the class of each sample
+
+ :type X_unlabeled: ndarray(#samples x #features)
+ :type nbr: int
+ :type classifier: sklearn.svm.SVC
+
+ :return: Indexes of selected samples
+ :rtype: ndarray
+ """
+
+ batch_size = nbr_uncertainty # Number of samples to select with the uncertainty criterion
+
+ uncertain_samples_index = uncertainty_filter(X_unlabled, batch_size, classifier) # Take twice as many samples as needed
+ uncertain_samples = X_unlabled[uncertain_samples_index]
+
+ return diversity_filter(uncertain_samples, uncertain_samples_index, nbr, diversity_lambda)
+
+def uncertainty_filter(samples, nbr, classifier) :
+ """
+ Keep only a few samples based on an uncertainty criterion
+ Return the indexes of samples to keep
+
+ :param samples: Pool of unlabeled samples to select from
+ :param nbr: number of samples to select from the pool
+ :param classifier: Used to predict the class of each sample
+
+ :type X_unlabeled: ndarray(#samples x #features)
+ :type nbr: int
+ :type classifier: sklearn.svm.SVC
+
+ :return: Indexes of selected samples
+ :rtype: ndarray
+ """
+ NBR_NEW_SAMPLE = nbr
+ decision_function = np.absolute(classifier.decision_function(samples))
+
+ # Check if the number of samples to return is not
+ # bigger than the total number of samples
+ if (nbr >= samples.shape[0]) :
+ NBR_NEW_SAMPLE = samples.shape[0] - 1
+
+
+ # Get the max distance to each class hyperplane for each example
+ max_index = np.argmax(decision_function[:,:], axis=1)
+ max_values = decision_function[np.arange(len(decision_function)), max_index]
+
+ # Make the max values very small.
+ # The max value is now the second best
+ decision_function[np.arange(len(decision_function)), max_index] = np.NINF
+
+ # Get the second max distance to each class to hyperplane for each example
+ second_max_index = np.argmax(decision_function[:,:], axis=1)
+ second_max_values = decision_function[np.arange(len(decision_function)), second_max_index]
+
+ # "Functionnal margin" for multiclass classifiers for each sample
+ f_MC = max_values - second_max_values
+
+
+ selected_sample_index = np.argpartition(f_MC, NBR_NEW_SAMPLE)[:NBR_NEW_SAMPLE]
+
+ return selected_sample_index
+
+def diversity_filter(samples, uncertain_samples_index, nbr, diversity_lambda=0.25) :
+ """
+ Keep only 'nbr' samples based on a diversity criterion (bruzzone2009 : Active Learning For Classification Of Remote Sensing Images)
+ Return the indexes of samples to keep
+
+ :param samples: Pool of unlabeled samples
+ :param uncertain_samples: Indexes of uncertain samples in the arry of samples
+ :param nbr: number of samples to select from the pool
+ :param diversity_lambda: Heuristic parameter, between 0 and 1. Weight between the average distance to other samples and the distance to the closest sample. (default=0.25)
+
+ :type X_unlabeled: ndarray(#samples x #features)
+ :type uncertain_samples_index: ndarray(#uncertain_samples)
+ :type nbr: int
+ :type diversity_lambda: float
+
+ :return: Indexes of selected samples
+ :rtype: ndarray
+ """
+ L = diversity_lambda
+ m = samples.shape[0] # Number of samples
+ samples_cpy = np.empty(samples.shape)
+ samples_cpy[:] = samples
+
+ selected_sample_index = uncertain_samples_index # At the begining, take all samples
+
+ while (selected_sample_index.shape[0] > nbr) :
+
+ dist_to_closest = distance_to_closest(samples_cpy)
+ average_dist = average_distance(samples_cpy)
+ discard = np.argmax(L*dist_to_closest + (1-L) * (1./m) * average_dist)
+ selected_sample_index = np.delete(selected_sample_index, discard) # Remove the sample to discard
+ samples_cpy = np.delete(samples_cpy, discard, axis=0)
+
+ return selected_sample_index
+
+def distance_to_closest(samples) :
+ """
+ For each sample, computes the distance to its closest neighbour
+
+ :param samples: Samples to consider
+ :type samples: ndarray(#samples x #features)
+
+ :return: For each sample, the distance to its closest neighbour
+ :rtype: ndarray(#samples)
+ """
+ dist_with_samples = rbf_kernel(samples, samples) # Distance between each samples (symetric matrix)
+ np.fill_diagonal(dist_with_samples, np.NINF) # Do not take into acount the distance between a sample and itself (values on the diagonal)
+ dist_with_closest = dist_with_samples.max(axis=0) # For each sample, the distance to the closest other sample
+
+ return dist_with_closest
+
+
+def average_distance(samples) :
+ """
+ For each sample, computes the average distance to all other samples
+
+ :param samples: Samples to consider
+ :type samples: ndarray(#samples x #features)
+
+ :return: For each sample, the average distance to all other samples
+ :rtype: ndarray(#samples)
+ """
+ samples = np.asarray(samples)
+ nbr_samples = samples.shape[0]
+ dist_with_samples = rbf_kernel(samples, samples)
+ average_dist = (dist_with_samples.sum(axis=1) - 1)/(nbr_samples-1) # Remove dist to itself (=1)
+
+ return average_dist
+
+def learning(X_train, y_train, X_test, y_test, X_unlabeled, ID_unlabeled, steps, sample_selection) :
+ """
+ Train a SVM classifier with the training data, compute the score of the classifier based on testing data and
+ make a class prediction for each sample in the unlabeled data.
+ Find the best samples to label that would increase the most the classification score
+
+ :param X_train: Features of training samples
+ :param y_train: Labels of training samples
+ :param X_test: Features of test samples
+ :param y_test: Labels of test samples
+ :param X_unlabeled: Features of unlabeled samples
+ :param ID_unlabeled: IDs of unlabeled samples
+ :param steps: Number of samples to label
+ :param sample_selection: Function used to select the samples to label (different heuristics)
+
+ :type X_train: ndarray(#samples x #features)
+ :type y_train: ndarray(#samples)
+ :type X_test: ndarray(#samples x #features)
+ :type y_test: ndarray(#samples)
+ :type X_unlabeled: ndarray(#samples x #features)
+ :type ID_unlabeled: ndarray(#samples)
+ :type steps: int
+ :type samples_selection: callable
+
+ :return: The IDs of samples to label, the score of the classifier and the prediction for all unlabeled samples
+ :rtype indexes: ndarray(#steps)
+ :rtype score: float
+ :rtype predictions: ndarray(#unlabeled_samples)
+ """
+
+ if(X_unlabeled.size == 0) :
+ raise Exception("Pool of unlabeled samples empty")
+
+ c_svm, gamma_parameter = SVM_parameters(options['c_svm'], options['gamma_parameter'], X_train, y_train, search_iter)
+ gcore.message('Parameters used : C={}, gamma={}, lambda={}'.format(c_svm, gamma_parameter, diversity_lambda))
+
+ classifier = train(X_train, y_train, c_svm, gamma_parameter)
+ score = classifier.score(X_test, y_test)
+
+ predictions = classifier.predict(X_unlabeled)
+
+ samples_to_label = sample_selection(X_unlabeled, steps, classifier)
+
+ return ID_unlabeled[samples_to_label], score, predictions
+
+def SVM_parameters(c, gamma, X_train, y_train, n_iter) :
+ """
+ Determine the parameters (C and gamma) for the SVM classifier.
+ If a parameter is specified in the parameters, keep this value.
+ If it is not specified, compute the 'best' value by grid search (cross validation set)
+
+ :param c: Penalty parameter C of the error term.
+ :param gamma: Kernel coefficient
+ :param X_train: Features of the training samples
+ :param y_train: Labels of the training samples
+ :param n_iter: Number of parameter settings that are sampled. n_iter trades off runtime vs quality of the solution.
+
+ :type c: string
+ :type gamma: string
+ :type X_train: ndarray
+ :type Y_train: ndarray
+ :type n_iter: int
+
+ :return: The c and gamma parameters
+ :rtype: floats
+ """
+
+ parameters = {}
+ if c == '' or gamma == '':
+ parameters = {'C': scipy.stats.expon(scale=100), 'gamma': scipy.stats.expon(scale=.1),
+ 'kernel': ['rbf'], 'class_weight':['balanced', None]}
+
+ if parameters != {} :
+ svr = svm.SVC()
+ clf = RandomizedSearchCV(svr, parameters, n_iter=n_iter, n_jobs=-1, verbose=0)
+ clf.fit(X_train, y_train)
+
+ if c == '' :
+ c = clf.best_params_['C']
+ if gamma == '' :
+ gamma = clf.best_params_['gamma']
+ return float(c), float(gamma)
+
+def main() :
+ global learning_steps
+ global diversity_lambda
+ global nbr_uncertainty
+ global search_iter
+
+ learning_steps = int(options['learning_steps']) if options['learning_steps'] != '0' else 5
+ search_iter = int(options['search_iter']) if options['search_iter'] != '0' else 10 # Number of samples to label at each iteration
+ diversity_lambda = float(options['diversity_lambda']) if options['diversity_lambda'] != '' else 0.25 # Lambda parameter used in the diversity heuristic
+ nbr_uncertainty = int(options['nbr_uncertainty']) if options['nbr_uncertainty'] != '0' else 15 # Number of samples to select (based on uncertainty criterion) before applying the diversity criterion. Must be at least greater or equal to [LEARNING][steps]
+
+ X_train, ID_train, y_train, header_train = load_data(options['training_set'], labeled = True)
+ X_test, ID_test, y_test, header_test = load_data(options['test_set'], labeled = True)
+ X_unlabeled, ID_unlabeled, y_unlabeled, header_unlabeled = load_data(options['unlabeled_set'])
+
+ nbr_train = ID_train.shape[0]
+
+ if (options['update'] !='') : # If an update file has been specified, transfer samples
+ X_train, ID_train, y_train = update(options['update'], X_train, ID_train, y_train, X_unlabeled, ID_unlabeled)
+ if (options['training_updated'] != '' or options['unlabeled_updated'] != '') :
+ write_update(options['update'], options['training_set'], options['unlabeled_set'], options['training_updated'], options['unlabeled_updated'])
+ elif (options['update'] =='' and (options['training_updated'] != '' or options['unlabeled_updated'] != '')) :
+ gcore.warning('No update file specified : could not write the updated files.')
+ nbr_new_train = ID_train.shape[0]
+
+ samples_to_label_IDs, score, predictions = learning(X_train, y_train, X_test, y_test, X_unlabeled, ID_unlabeled, learning_steps, active_diversity_sample_selection)
+
+ X_unlabeled, ID_unlabeled, y_unlabeled, header_unlabeled = load_data(options['unlabeled_set'], scale=False) # Load unscaled data
+
+ predictions_file = options['predictions']
+ if (predictions_file != '') : # Write the class prediction only if an output file has been specified by the user
+ write_result_file(ID_unlabeled, X_unlabeled, predictions, header_unlabeled, predictions_file)
+ gcore.message("Class predictions written to {}".format(predictions_file))
+
+
+ gcore.message('Training set : {}'.format(X_train.shape[0]))
+ gcore.message('Test set : {}'.format(X_test.shape[0]))
+ gcore.message('Unlabeled set : {}'.format(X_unlabeled.shape[0] - (nbr_new_train - nbr_train)))
+ gcore.message('Score : {}'.format(score))
+
+ for ID in samples_to_label_IDs :
+ print(int(ID))
+
+
+if __name__ == '__main__' :
+ options, flags = grass.script.parser()
+ main()
Property changes on: grass-addons/grass7/raster/r.object.activelearning/r.object.activelearning.py
___________________________________________________________________
Added: svn:executable
+ *
Added: svn:mime-type
+ text/x-python
Added: svn:eol-style
+ native
Added: grass-addons/grass7/raster/r.object.activelearning/report.pdf
===================================================================
(Binary files differ)
Property changes on: grass-addons/grass7/raster/r.object.activelearning/report.pdf
___________________________________________________________________
Added: svn:mime-type
+ application/pdf
Added: grass-addons/grass7/raster/r.object.activelearning/tests.py
===================================================================
--- grass-addons/grass7/raster/r.object.activelearning/tests.py (rev 0)
+++ grass-addons/grass7/raster/r.object.activelearning/tests.py 2017-05-03 08:09:28 UTC (rev 71010)
@@ -0,0 +1,188 @@
+#!/usr/bin/env python
+# encoding: utf-8
+import unittest
+import os
+import numpy as np
+from sklearn.metrics.pairwise import rbf_kernel
+
+#from r.objects.activelearning import * --> doesn't work because of the dots in the file name
+import imp
+open_file, file_name, description = imp.find_module('r.objects.activelearning')
+al = imp.load_source('al', file_name, open_file)
+
+def silent_remove(filename) :
+ """
+ Remove the file if it exists or do nothing if it does not exists
+ """
+ try :
+ os.remove(filename)
+ except OSerror as e :
+ if e.errno != errno.ENOENT :
+ raise
+
+class Test (unittest.TestCase) :
+
+
+ def test_linear_scale(self) :
+
+ X = np.random.randint(0,100, (10,5))
+ #print(X)
+ X = al.linear_scale(X)
+ #print(X)
+ p1 = np.percentile(X, 1, interpolation='nearest', axis=0)
+ p5 = np.percentile(X, 5, interpolation='nearest', axis=0)
+ p95 = np.percentile(X, 95, interpolation='nearest', axis=0)
+ p99 = np.percentile(X, 99, interpolation='nearest', axis=0)
+
+ self.assertTrue((p1 <= 0).all())
+ self.assertTrue((p5 == 0).all())
+ self.assertTrue((p95 == 1).all())
+ self.assertTrue((p99 >= 0).all())
+
+ X = np.random.rand(100,100)/2
+
+ X = al.linear_scale(X)
+ p1 = np.percentile(X, 1, interpolation='nearest', axis=0)
+ p5 = np.percentile(X, 5, interpolation='nearest', axis=0)
+ p95 = np.percentile(X, 95, interpolation='nearest', axis=0)
+ p99 = np.percentile(X, 99, interpolation='nearest', axis=0)
+
+ self.assertTrue((p1 <= 0).all())
+ self.assertTrue((p5 == 0).all())
+ self.assertTrue((p95 == 1).all())
+ self.assertTrue((p99 >= 0).all())
+
+ def test_dist_to_closest(self) :
+ # 5 samples
+ a = [1, 3]
+ b = [0, 4]
+ c = [2, 5]
+ d = [-1, 2]
+ e = [-1, 2]
+ samples = np.array([a, b, c, d, e])
+
+ dist = al.distance_to_closest(samples)
+ self.assertEqual(dist[0], rbf_kernel([a], [b])[0][0]) # closest to a
+ self.assertEqual(dist[1], rbf_kernel([b], [a])[0][0]) # closest to b
+ self.assertEqual(dist[2], rbf_kernel([c], [b])[0][0]) # closest to c
+ self.assertEqual(dist[3], rbf_kernel([d], [e])[0][0]) # closest to d
+ self.assertEqual(dist[4], rbf_kernel([d], [e])[0][0]) # closest to e
+
+
+ def test_average_dist(self) :
+ # 3 samples
+ a = [1, 3]
+ b = [0, 4]
+ c = [2, 5]
+ samples = np.array([a, b, c])
+ dist = al.average_distance(samples)
+
+ avg_a = (rbf_kernel([a], [b])[0][0] + rbf_kernel([a], [c])[0][0])/2
+ avg_b = (rbf_kernel([b], [a])[0][0] + rbf_kernel([b], [c])[0][0])/2
+ avg_c = (rbf_kernel([c], [a])[0][0] + rbf_kernel([c], [b])[0][0])/2
+
+ self.assertAlmostEqual(dist[0],avg_a)
+ self.assertAlmostEqual(dist[1],avg_b)
+ self.assertAlmostEqual(dist[2],avg_c)
+
+ def test_diversity_criterion(self) :
+ # 9 samples -> plot them for a better visualization (e.g. with GeoGebra)
+ a = [2, 4]
+ b = [4, 1]
+ b_bis = [4, 1]
+ c = [-2, 1]
+ d = [-1, 5]
+ e = [1.6, 3.6]
+ f = [3, 1]
+ g = [1, 2]
+ h = [9, 5]
+ i = [49, 4] # This sample is not sent to the diversity filter
+ samples = np.array([a, b, b_bis, c, d, e, f, g, h, i])
+
+ selected_samples = al.diversity_filter(samples, np.arange(9), 4)
+
+ self.assertTrue(0 not in selected_samples)
+ self.assertTrue((1 in selected_samples) ^ (2 in selected_samples)) # Either b or b_bis is kept
+ self.assertTrue(3 in selected_samples)
+ self.assertTrue(4 in selected_samples)
+ self.assertTrue(5 not in selected_samples)
+ self.assertTrue(6 not in selected_samples)
+ self.assertTrue(7 not in selected_samples)
+ self.assertTrue(8 in selected_samples)
+
+ self.assertTrue((samples == np.array([a, b, b_bis, c, d, e, f, g, h, i])).all()) # Check that the original array was not modified
+
+
+ def test_write_result_file(self) :
+ X = np.array([
+ [11., 3.5, 4.7],
+ [22., 4.5, 6.7]
+ ])
+ header = np.array(['ID', 'attr1','attr2', 'attr3'])
+ ID = np.array([1., 2.])
+ predictions = np.array([0,1])
+ filename = 'unittest_prediction.csv'
+
+ #ID, X_unlabeled, predictions, header, filename
+
+ al.write_result_file(ID, X, predictions, header, filename)
+
+ result = np.genfromtxt(filename, delimiter=',', dtype=str)
+
+ data = np.array([
+ ['ID', 'Class', 'attr1','attr2', 'attr3'],
+ [1., 0., 11., 3.5, 4.7],
+ [2., 1., 22., 4.5, 6.7]
+ ])
+
+ self.assertTrue((data == result).all())
+
+ silent_remove(filename)
+
+ def test_update(self) :
+ #Create a fake update file
+ update_file = 'update.csv'
+ update_data = np.array([
+ ['cat', 'Class'],
+ [123, 1],
+ [456, 2]
+ ])
+
+ np.savetxt(update_file, update_data, delimiter=",",fmt="%s")
+ X_train = np.array([
+ [1,2,3],
+ [4,5,6]
+ ])
+ ID_train = np.array([111,222])
+ y_train = np.array([1,2])
+
+ X_unlabeled = np.array([
+ [11,22,33],
+ [44,55,66],
+ [77,88,99]
+ ])
+ ID_unlabeled = np.array([123,456, 789])
+
+ ####
+ X_train, ID_train, y_train = al.update(update_file, X_train, ID_train, y_train, X_unlabeled, ID_unlabeled)
+ ####
+ X_train_should_be = np.array([
+ [1,2,3],
+ [4,5,6],
+ [11,22,33],
+ [44,55,66]
+ ])
+
+ ID_train_should_be = np.array([111,222,123,456])
+ y_train_should_be = np.array([1,2,1,2])
+
+ self.assertTrue((X_train == X_train_should_be).all())
+ self.assertTrue((ID_train_should_be == ID_train).all())
+ self.assertTrue((y_train_should_be == y_train).all())
+
+ silent_remove(update_file)
+
+
+
+if __name__ == '__main__' :
+ unittest.main()
\ No newline at end of file
Property changes on: grass-addons/grass7/raster/r.object.activelearning/tests.py
___________________________________________________________________
Added: svn:executable
+ *
Added: svn:mime-type
+ text/x-python
Added: svn:eol-style
+ native
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