def run(self, args, opts):
if len(args) < 1:
raise UsageError()
elif len(args) > 1:
raise UsageError(
"running 'scrapy benchmark' with more than one argument is not supported"
)
classifier_name = args[0]
status = Status()
CF = ClassifierFactory(status.classifiers[classifier_name])
if opts.reviewed and opts.unreviewed:
CF.create_data_set("both")
elif opts.reviewed:
CF.create_data_set("reviewed")
elif opts.unreviewed:
CF.create_data_set("unreviewed")
results = []
lc = CF.create_classifier(
LogisticRegression(C=1e5),
status.classifiers[classifier_name]['features']())
lc.benchmark(opts.topn, opts.print_cm, opts.print_report, verbose=True)
## for clf, name in (
## (RidgeClassifier(tol=1e-2, solver="lsqr"), "Ridge Classifier"),
## (Perceptron(n_iter=50), "Perceptron"),
## (PassiveAggressiveClassifier(n_iter=50), "Passive-Aggressive")
## ):
## print('=' * 80)
## print(name)
## c = CF.create_classifier(clf, status.classifiers[classifier_name]['features']())
## results.append(c.benchmark(opts.topn, opts.print_cm, opts.print_report, verbose=True))
#Multiple classifier comparison
# results.append(classifier)
## indices = np.arange(len(results))
## results = [[x[i] for x in results] for i in range(4)]
## clf_names, score, training_time, test_time = results
## training_time = np.array(training_time) / np.max(training_time)
## test_time = np.array(test_time) / np.max(test_time)
## plt.figure(figsize=(12, 8))
## plt.title("Score")
## plt.barh(indices, score, .2, label="score", color='r')
## plt.barh(indices + .3, training_time, .2, label="training time", color='g')
## plt.barh(indices + .6, test_time, .2, label="test time", color='b')
## plt.yticks(())
## plt.legend(loc='best')
## plt.subplots_adjust(left=.25)
## plt.subplots_adjust(top=.95)
## plt.subplots_adjust(bottom=.05)
##
## for i, c in zip(indices, clf_names):
## plt.text(-.3, i, c)
## plt.show()
def __init__(self):
self.status = Status()
self.classifiers = []
self.exporters = {}
for classifier in self.status.classifiers.keys():
CF = ClassifierFactory(self.status.classifiers[classifier])
CF.create_data_set("both")
lc = lc = CF.create_classifier(LogisticRegression(C=1e5), self.status.classifiers[classifier]['features']())
lc.fit()
self.classifiers.append((classifier, lc))
self.classifiers = sorted(self.classifiers, key = lambda a: a[1].estimate_accuracy(5, verbose=True))
print "Classifier {0} needs the most improvement; selected for export".format(self.classifiers[0][0])
for classification in self.status.classifiers[self.classifiers[0][0]]['classifications']:
f = file("{0}.json".format(classification), "wb")
self.exporters[classification] = JsonItemExporter(f)
def run(self, args, opts):
if len(args) < 1:
raise UsageError()
elif len(args) > 1:
raise UsageError("running 'scrapy benchmark' with more than one argument is not supported")
classifier_name = args[0]
status = Status()
CF = ClassifierFactory(status.classifiers[classifier_name])
if opts.reviewed and opts.unreviewed:
CF.create_data_set("both")
elif opts.reviewed:
CF.create_data_set("reviewed")
elif opts.unreviewed:
CF.create_data_set("unreviewed")
results = []
lc = CF.create_classifier(LogisticRegression(C=1e5), status.classifiers[classifier_name]['features']())
lc.benchmark(opts.topn, opts.print_cm, opts.print_report, verbose=True)
## for clf, name in (
## (RidgeClassifier(tol=1e-2, solver="lsqr"), "Ridge Classifier"),
## (Perceptron(n_iter=50), "Perceptron"),
## (PassiveAggressiveClassifier(n_iter=50), "Passive-Aggressive")
## ):
## print('=' * 80)
## print(name)
## c = CF.create_classifier(clf, status.classifiers[classifier_name]['features']())
## results.append(c.benchmark(opts.topn, opts.print_cm, opts.print_report, verbose=True))
#Multiple classifier comparison
# results.append(classifier)
## indices = np.arange(len(results))
## results = [[x[i] for x in results] for i in range(4)]
## clf_names, score, training_time, test_time = results
## training_time = np.array(training_time) / np.max(training_time)
## test_time = np.array(test_time) / np.max(test_time)
## plt.figure(figsize=(12, 8))
## plt.title("Score")
## plt.barh(indices, score, .2, label="score", color='r')
## plt.barh(indices + .3, training_time, .2, label="training time", color='g')
## plt.barh(indices + .6, test_time, .2, label="test time", color='b')
## plt.yticks(())
## plt.legend(loc='best')
## plt.subplots_adjust(left=.25)
## plt.subplots_adjust(top=.95)
## plt.subplots_adjust(bottom=.05)
##
## for i, c in zip(indices, clf_names):
## plt.text(-.3, i, c)
## plt.show()
def __init__(self):
self.status = Status()
self.classifiers = []
self.exporters = {}
for classifier in self.status.classifiers.keys():
CF = ClassifierFactory(self.status.classifiers[classifier])
CF.create_data_set("both")
lc = lc = CF.create_classifier(
LogisticRegression(C=1e5),
self.status.classifiers[classifier]['features']())
lc.fit()
self.classifiers.append((classifier, lc))
self.classifiers = sorted(
self.classifiers,
key=lambda a: a[1].estimate_accuracy(5, verbose=True))
print "Classifier {0} needs the most improvement; selected for export".format(
self.classifiers[0][0])
for classification in self.status.classifiers[self.classifiers[0]
[0]]['classifications']:
f = file("{0}.json".format(classification), "wb")
self.exporters[classification] = JsonItemExporter(f)
