def evaluate(category, clf, datamanager, data=(None, None)):
"""Run evaluation of a classifier, for one category.
If data isn't set explicitly, the test set is
used by default.
"""
log_file = os.path.join(datamanager.PATHS["LOGS"], "evaluation", class_name(clf), category)
log_file = os.path.join(log_file, str(datetime.now()) + ".log")
vcd = VisualConceptDetection(None, datamanager, log_file=log_file)
clf = vcd.load_object("Classifier", category, clf)
vcd.classifier = clf
if (data[0] is None) or (data[1] is None):
return vcd.evaluate_test_set(category)
else:
return vcd.evaluate(X_test=data[0], y_test=data[1])
class TestVisualConceptDetection(unittest.TestCase):
def setUp(self):
self.datamanager = MyTestDataManager()
clf = AdaBoostClassifier(n_estimators=14)
clf.base_estimator.max_depth = 10
self.vcd = VisualConceptDetection(clf, self.datamanager)
def test_base_dir(self):
path = os.path.join(BASE_PATH, "testdata", "bow")
self.assertEqual(self.datamanager.PATHS["BOW"], path)
# def test_dump_object(self):
def test_dump_and_load_object(self):
self.vcd.dump_object(self.vcd.classifier, "test_classifier")
# TODO: meh
self.assertIsInstance(self.vcd.load_object("test_classifier"),
self.vcd.classifier.__class__)
def test_count_methods(self):
classes = np.array([0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0])
self.assertEqual(self.vcd.count_positives(classes), 5)
self.assertEqual(self.vcd.count_negatives(classes), 7)
def test_sample_weights(self):
vec = np.array([1, 0, 0, 0, 1, 1, 0, 0, 0, 0])
p = 1.0
n = 3.0 / 7.0
weights = np.array([p, n, n, n, p, p, n, n, n, n])
calculated = self.vcd.calculate_weights(vec)
self.assertTrue((calculated == weights).all(),
"Should be:\n%s\nbut is:\n%s" % (weights, calculated))
class TestVisualConceptDetection(unittest.TestCase):
def setUp(self):
self.datamanager = MyTestDataManager()
clf = AdaBoostClassifier(n_estimators=14)
clf.base_estimator.max_depth = 10
self.vcd = VisualConceptDetection(clf, self.datamanager)
def test_base_dir(self):
path = os.path.join(BASE_PATH, "testdata", "bow")
self.assertEqual(self.datamanager.PATHS["BOW"], path)
# def test_dump_object(self):
def test_dump_and_load_object(self):
self.vcd.dump_object(self.vcd.classifier, "test_classifier")
# TODO: meh
self.assertIsInstance(self.vcd.load_object("test_classifier"), self.vcd.classifier.__class__)
def test_count_methods(self):
classes = np.array([0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0])
self.assertEqual(self.vcd.count_positives(classes), 5)
self.assertEqual(self.vcd.count_negatives(classes), 7)
def test_sample_weights(self):
vec = np.array([1, 0, 0, 0, 1, 1, 0, 0, 0, 0])
p = 1.0
n = 3.0 / 7.0
weights = np.array([p, n, n, n, p, p, n, n, n, n])
calculated = self.vcd.calculate_weights(vec)
self.assertTrue((calculated==weights).all(), "Should be:\n%s\nbut is:\n%s" % (weights, calculated))
def evaluate(category, clf, datamanager, data=(None, None)):
"""Run evaluation of a classifier, for one category.
If data isn't set explicitly, the test set is
used by default.
"""
log_file = os.path.join(datamanager.PATHS["LOGS"], "evaluation",
class_name(clf), category)
log_file = os.path.join(log_file, str(datetime.now()) + ".log")
vcd = VisualConceptDetection(None, datamanager, log_file=log_file)
clf = vcd.load_object("Classifier", category, clf)
vcd.classifier = clf
if (data[0] is None) or (data[1] is None):
return vcd.evaluate_test_set(category)
else:
return vcd.evaluate(X_test=data[0], y_test=data[1])
def setUp(self):
self.datamanager = MyTestDataManager()
clf = AdaBoostClassifier(n_estimators=14)
clf.base_estimator.max_depth = 10
self.vcd = VisualConceptDetection(clf, self.datamanager)
return result
if __name__ == "__main__":
# ada = AdaBoostClassifier()
# ada.n_estimators = 50
# ada.base_estimator.max_depth = 1
random_forest = RandomForestClassifier(n_estimators=100)
category = "trilobite"
dataset = "all"
datamanager = CaltechManager()
datamanager.PATHS["RESULTS"] = os.path.join(datamanager.PATHS["BASE"], "results_trilobite_rf_testing")
# vcd = VisualConceptDetection(ada, datamanager)
vcd = VisualConceptDetection(random_forest, datamanager)
clf = vcd.load_object("Classifier", category)
feature_importances = clf.feature_importances_
sample_matrix = vcd.datamanager.build_sample_matrix(dataset, category)
class_vector = vcd.datamanager.build_class_vector(dataset, category)
pred = clf.predict_proba(sample_matrix)
vis = EnsembleVisualization(datamanager)
del clf
image_titles = [vis.get_image_title(prediction, real) for prediction, real in
izip(pred, class_vector)]
del class_vector
del sample_matrix
import sys
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
from sklearn.svm import LinearSVC, SVC
from vcd import VisualConceptDetection
import os
import time
from util import svm
from datamanagers.CaltechManager import CaltechManager
import numpy as np
import pylab as pl
from runGridSearch import GridSearch
if __name__ == "__main__":
category = "airplanes"
total = time.time()
clf = RandomForestClassifier(n_estimators = 2000)
# clf = AdaBoostClassifier(n_estimators = 2000)
# clf.base_estimator.max_depth = 4
# clf = LinearSVC(C=100)
# clf = SVC(C=10)
dm = CaltechManager()
vcd = VisualConceptDetection(classifier=clf, datamanager=dm)
vcd.run(category)
print "Total execution time: %f minutes" % ((time.time() - total) / 60.0)
import sys
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
from sklearn.svm import LinearSVC, SVC
from vcd import VisualConceptDetection
import os
import time
from util import svm
from datamanagers.CaltechManager import CaltechManager
import numpy as np
import pylab as pl
from runGridSearch import GridSearch
if __name__ == "__main__":
category = "airplanes"
total = time.time()
clf = RandomForestClassifier(n_estimators=2000)
# clf = AdaBoostClassifier(n_estimators = 2000)
# clf.base_estimator.max_depth = 4
# clf = LinearSVC(C=100)
# clf = SVC(C=10)
dm = CaltechManager()
vcd = VisualConceptDetection(classifier=clf, datamanager=dm)
vcd.run(category)
print "Total execution time: %f minutes" % ((time.time() - total) / 60.0)
if __name__ == "__main__":
# ada = AdaBoostClassifier()
# ada.n_estimators = 50
# ada.base_estimator.max_depth = 1
random_forest = RandomForestClassifier(n_estimators=100)
category = "trilobite"
dataset = "all"
datamanager = CaltechManager()
datamanager.PATHS["RESULTS"] = os.path.join(
datamanager.PATHS["BASE"], "results_trilobite_rf_testing")
# vcd = VisualConceptDetection(ada, datamanager)
vcd = VisualConceptDetection(random_forest, datamanager)
clf = vcd.load_object("Classifier", category)
feature_importances = clf.feature_importances_
sample_matrix = vcd.datamanager.build_sample_matrix(dataset, category)
class_vector = vcd.datamanager.build_class_vector(dataset, category)
pred = clf.predict_proba(sample_matrix)
vis = EnsembleVisualization(datamanager)
del clf
image_titles = [
vis.get_image_title(prediction, real)
for prediction, real in izip(pred, class_vector)
]
del class_vector
def setUp(self):
self.datamanager = MyTestDataManager()
clf = AdaBoostClassifier(n_estimators=14)
clf.base_estimator.max_depth = 10
self.vcd = VisualConceptDetection(clf, self.datamanager)
def get_svm_importances(coef):
"""Normalize the SVM weights."""
factor = 1.0 / np.linalg.norm(coef)
return (coef * factor).ravel()
if __name__ == "__main__":
svm = LinearSVC(C=0.1)
category = "Faces"
dataset = "all"
datamanager = CaltechManager()
datamanager.PATHS["RESULTS"] = os.path.join(
datamanager.PATHS["BASE"], "results_Faces_LinearSVC_normalized")
vcd = VisualConceptDetection(svm, datamanager)
clf = vcd.load_object("Classifier", category)
importances = get_svm_importances(clf.coef_)
sample_matrix = vcd.datamanager.build_sample_matrix(dataset, category)
class_vector = vcd.datamanager.build_class_vector(dataset, category)
pred = clf.decision_function(sample_matrix)
del clf
image_titles = [
get_image_title(prediction, real)
for prediction, real in izip(pred, class_vector)
]
del class_vector
del sample_matrix
