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tester.py
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tester.py
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#!/usr/bin/pickle
""" a basic script for importing student's POI identifier,
and checking the results that they get from it
requires that the algorithm, dataset, and features list
be written to my_classifier.pkl, my_dataset.pkl, and
my_feature_list.pkl, respectively
that process should happen at the end of poi_id.py
"""
import pickle
import sys
from sklearn.cross_validation import StratifiedShuffleSplit
sys.path.append("../tools/")
from feature_format import featureFormat, targetFeatureSplit
PERF_FORMAT_STRING = "\
\tAccuracy: {:>0.{display_precision}f}\tPrecision: {:>0.{display_precision}f}\t\
Recall: {:>0.{display_precision}f}\tF1: {:>0.{display_precision}f}\tF2: {:>0.{display_precision}f}"
RESULTS_FORMAT_STRING = "\tTotal predictions: {:4d}\tTrue positives: {:4d}\tFalse positives: {:4d}\
\tFalse negatives: {:4d}\tTrue negatives: {:4d}"
def test_classifier(clf, dataset, feature_list, folds = 1000):
data = featureFormat(dataset, feature_list, sort_keys = True)#,replace_median = True)
labels, features = targetFeatureSplit(data)
cv = StratifiedShuffleSplit(labels, folds, random_state = 42)
true_negatives = 0
false_negatives = 0
true_positives = 0
false_positives = 0
for train_idx, test_idx in cv:
features_train = []
features_test = []
labels_train = []
labels_test = []
for ii in train_idx:
features_train.append( features[ii] )
labels_train.append( labels[ii] )
for jj in test_idx:
features_test.append( features[jj] )
labels_test.append( labels[jj] )
## fit the classifier using training set, and test on test set
clf.fit(features_train, labels_train)
predictions = clf.predict(features_test)
for prediction, truth in zip(predictions, labels_test):
if prediction == 0 and truth == 0:
true_negatives += 1
elif prediction == 0 and truth == 1:
false_negatives += 1
elif prediction == 1 and truth == 0:
false_positives += 1
elif prediction == 1 and truth == 1:
true_positives += 1
else:
print "Warning: Found a predicted label not == 0 or 1."
print "All predictions should take value 0 or 1."
print "Evaluating performance for processed predictions:"
break
try:
total_predictions = true_negatives + false_negatives + false_positives + true_positives
accuracy = 1.0*(true_positives + true_negatives)/total_predictions
precision = 1.0*true_positives/(true_positives+false_positives)
recall = 1.0*true_positives/(true_positives+false_negatives)
f1 = 2.0 * true_positives/(2*true_positives + false_positives+false_negatives)
f2 = (1+2.0*2.0) * precision*recall/(4*precision + recall)
print clf
print PERF_FORMAT_STRING.format(accuracy, precision, recall, f1, f2, display_precision = 5)
print RESULTS_FORMAT_STRING.format(total_predictions, true_positives, false_positives, false_negatives, true_negatives)
print ""
except:
print "Got a divide by zero when trying out:", clf
print "Precision or recall may be undefined due to a lack of true positive predicitons."
CLF_PICKLE_FILENAME = "my_classifier.pkl"
DATASET_PICKLE_FILENAME = "my_dataset.pkl"
FEATURE_LIST_FILENAME = "my_feature_list.pkl"
def dump_classifier_and_data(clf, dataset, feature_list):
with open(CLF_PICKLE_FILENAME, "w") as clf_outfile:
pickle.dump(clf, clf_outfile)
with open(DATASET_PICKLE_FILENAME, "w") as dataset_outfile:
pickle.dump(dataset, dataset_outfile)
with open(FEATURE_LIST_FILENAME, "w") as featurelist_outfile:
pickle.dump(feature_list, featurelist_outfile)
def load_classifier_and_data():
with open(CLF_PICKLE_FILENAME, "r") as clf_infile:
clf = pickle.load(clf_infile)
with open(DATASET_PICKLE_FILENAME, "r") as dataset_infile:
dataset = pickle.load(dataset_infile)
with open(FEATURE_LIST_FILENAME, "r") as featurelist_infile:
feature_list = pickle.load(featurelist_infile)
return clf, dataset, feature_list
def main():
### load up student's classifier, dataset, and feature_list
clf, dataset, feature_list = load_classifier_and_data()
### Run testing script
test_classifier(clf, dataset, feature_list)
if __name__ == '__main__':
main()
#!/usr/bin/pickle
""" a basic script for importing student's POI identifier,
and checking the results that they get from it
requires that the algorithm, dataset, and features list
be written to my_classifier.pkl, my_dataset.pkl, and
my_feature_list.pkl, respectively
that process should happen at the end of poi_id.py
"""
import pickle
import sys
from sklearn.cross_validation import StratifiedShuffleSplit
sys.path.append("../tools/")
from feature_format import featureFormat, targetFeatureSplit
PERF_FORMAT_STRING = "\
\tAccuracy: {:>0.{display_precision}f}\tPrecision: {:>0.{display_precision}f}\t\
Recall: {:>0.{display_precision}f}\tF1: {:>0.{display_precision}f}\tF2: {:>0.{display_precision}f}"
RESULTS_FORMAT_STRING = "\tTotal predictions: {:4d}\tTrue positives: {:4d}\tFalse positives: {:4d}\
\tFalse negatives: {:4d}\tTrue negatives: {:4d}"
def test_classifier(clf, dataset, feature_list, folds = 1000):
data = featureFormat(dataset, feature_list, sort_keys = True)#,replace_median = True)
labels, features = targetFeatureSplit(data)
cv = StratifiedShuffleSplit(labels, folds, random_state = 42)
true_negatives = 0
false_negatives = 0
true_positives = 0
false_positives = 0
for train_idx, test_idx in cv:
features_train = []
features_test = []
labels_train = []
labels_test = []
for ii in train_idx:
features_train.append( features[ii] )
labels_train.append( labels[ii] )
for jj in test_idx:
features_test.append( features[jj] )
labels_test.append( labels[jj] )
## fit the classifier using training set, and test on test set
clf.fit(features_train, labels_train)
predictions = clf.predict(features_test)
for prediction, truth in zip(predictions, labels_test):
if prediction == 0 and truth == 0:
true_negatives += 1
elif prediction == 0 and truth == 1:
false_negatives += 1
elif prediction == 1 and truth == 0:
false_positives += 1
elif prediction == 1 and truth == 1:
true_positives += 1
else:
print "Warning: Found a predicted label not == 0 or 1."
print "All predictions should take value 0 or 1."
print "Evaluating performance for processed predictions:"
break
try:
total_predictions = true_negatives + false_negatives + false_positives + true_positives
accuracy = 1.0*(true_positives + true_negatives)/total_predictions
precision = 1.0*true_positives/(true_positives+false_positives)
recall = 1.0*true_positives/(true_positives+false_negatives)
f1 = 2.0 * true_positives/(2*true_positives + false_positives+false_negatives)
f2 = (1+2.0*2.0) * precision*recall/(4*precision + recall)
print clf
print PERF_FORMAT_STRING.format(accuracy, precision, recall, f1, f2, display_precision = 5)
print RESULTS_FORMAT_STRING.format(total_predictions, true_positives, false_positives, false_negatives, true_negatives)
print ""
except:
print "Got a divide by zero when trying out:", clf
print "Precision or recall may be undefined due to a lack of true positive predicitons."
CLF_PICKLE_FILENAME = "my_classifier.pkl"
DATASET_PICKLE_FILENAME = "my_dataset.pkl"
FEATURE_LIST_FILENAME = "my_feature_list.pkl"
def dump_classifier_and_data(clf, dataset, feature_list):
with open(CLF_PICKLE_FILENAME, "w") as clf_outfile:
pickle.dump(clf, clf_outfile)
with open(DATASET_PICKLE_FILENAME, "w") as dataset_outfile:
pickle.dump(dataset, dataset_outfile)
with open(FEATURE_LIST_FILENAME, "w") as featurelist_outfile:
pickle.dump(feature_list, featurelist_outfile)
def load_classifier_and_data():
with open(CLF_PICKLE_FILENAME, "r") as clf_infile:
clf = pickle.load(clf_infile)
with open(DATASET_PICKLE_FILENAME, "r") as dataset_infile:
dataset = pickle.load(dataset_infile)
with open(FEATURE_LIST_FILENAME, "r") as featurelist_infile:
feature_list = pickle.load(featurelist_infile)
return clf, dataset, feature_list
def main():
### load up student's classifier, dataset, and feature_list
clf, dataset, feature_list = load_classifier_and_data()
### Run testing script
test_classifier(clf, dataset, feature_list)
if __name__ == '__main__':
main()