/
assigner.py
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/
assigner.py
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"""
This module builds a predictor for the priority field for a bug report
"""
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from collections import defaultdict
from sklearn.cross_validation import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_selection import SelectFromModel
from sklearn.metrics import f1_score
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score
from sklearn.metrics import precision_recall_fscore_support
from sklearn.metrics import classification_report
from sklearn.metrics import cohen_kappa_score
import fselect
import preprocessing
def evaluate_performance(prefix=None, classifier=None, issues_train=None, priority_train=None,
issues_test=None, priority_test=None):
"""
Calculates performance metrics for a classifier.
:param prefix: A prefix, for identifying the classifier.
:param classifier: The classifier, previously fitted.
:param issues_train: Train features.
:param priority_train: Train class.
:param issues_test: Test features.
:param priority_test: Test class.
:return: Train accuracy , Test accuracy, Test weighted-f1 and F1 score per class.
"""
train_accuracy = None
if issues_train is not None and priority_train is not None:
train_accuracy = classifier.score(issues_train, priority_train)
print prefix, ': Training accuracy ', train_accuracy
train_predictions = classifier.predict(issues_train)
print prefix, " :TRAIN DATA SET"
print classification_report(y_true=priority_train, y_pred=train_predictions)
test_accuracy = classifier.score(issues_test, priority_test)
print prefix, ': Test accuracy ', test_accuracy
test_predictions = classifier.predict(issues_test)
test_kappa = cohen_kappa_score(priority_test, test_predictions)
print prefix, ": Test Kappa: ", test_kappa
print prefix, " :TEST DATA SET"
print classification_report(y_true=priority_test, y_pred=test_predictions)
labels = np.sort(np.unique(np.concatenate((priority_test.values, test_predictions))))
test_f1_score = f1_score(y_true=priority_test, y_pred=test_predictions, average='weighted')
precission_scores = precision_score(y_true=priority_test, y_pred=test_predictions, average=None)
all_scores = precision_recall_fscore_support(y_true=priority_test, y_pred=test_predictions, average=None)
precission_per_class = {label: score for label, score in zip(labels, precission_scores)}
recall_index = 1
recall_per_class = {label: support for label, support in zip(labels, all_scores[recall_index])}
return train_accuracy, test_accuracy, test_kappa, test_f1_score, \
defaultdict(lambda: 0, precission_per_class), \
defaultdict(lambda: 0, recall_per_class)
def select_features_l1(issues_train_std, priority_train, issues_test_std, priority_test):
"""
Applies a Logistic Regression using L1-regularization, in order to get a sparse solution.
:param issues_train_std: Issues for training.
:param priority_train: Priorities for training.
:param issues_test_std: Issues for testing.
:param priority_test: Priorities for testing
:return: The Logistic Regression classifier.
"""
logistic_regression = LogisticRegression(penalty='l1', C=0.1)
logistic_regression.fit(issues_train_std, priority_train)
print 'Intercept: ', logistic_regression.intercept_
print 'Coefficient: ', logistic_regression.coef_
evaluate_performance("LOGIT-L1", logistic_regression, issues_train_std, priority_train, issues_test_std,
priority_test)
return logistic_regression
def sequential_feature_selection(issues_train_std, priority_train, issues_test_std, priority_test):
"""
Applies a sequential feature selection algorithm and evaluates its performance using a k-neighbors classifier (5 neighbors).
:param issues_train_std: Train features.
:param priority_train: Train classes.
:param issues_test_std: Test features.
:param priority_test: Test classes.
:return: None.
"""
print "Applying sequential feature selection..."
# TODO: What is the optimal number of neighbors?
knn_classifier = KNeighborsClassifier(n_neighbors=5)
feature_selector = fselect.SBS(knn_classifier, k_features=1)
feature_selector.fit(issues_train_std.values, priority_train.values)
optimal_subset = None
for subset in feature_selector.subsets_:
if len(subset) == 5:
print "Number of features in subset: ", len(subset)
print issues_train_std.columns[list(subset)]
optimal_subset = list(subset)
num_features = [len(k) for k in feature_selector.subsets_]
figure, axes = plt.subplots(1, 1)
plt.plot(num_features, feature_selector.scores_, marker='o')
plt.ylim([0.4, 1.1])
plt.ylabel('Accuracy')
plt.xlabel('Number of features')
plt.grid()
plt.show()
knn_classifier.fit(issues_train_std, priority_train)
evaluate_performance("KNN-5NEIGH", knn_classifier, issues_train_std, priority_train, issues_test_std, priority_test)
new_train = issues_train_std.iloc[:, optimal_subset]
new_test = issues_test_std.iloc[:, optimal_subset]
knn_classifier.fit(new_train, priority_train)
evaluate_performance("KNN-OPT", knn_classifier, new_train, priority_train, new_test, priority_test)
return knn_classifier
def feature_importance_with_forest(rforest_classifier, issues_train, priority_train, issues_test, priority_test):
"""
Assess feature importance using a Random Forest.
:param rforest_classifier: An already fitted classifier.
:param issues_train: Train features.
:param priority_train: Train classes.
:param issues_test: Test features.
:param priority_test: Test classes.
:return: None
"""
importances = rforest_classifier.feature_importances_
indices = np.argsort(importances)[::-1]
for column_index in range(len(issues_train.columns)):
print column_index + 1, ") ", issues_train.columns[column_index], " ", importances[indices[column_index]]
figure, axes = plt.subplots(1, 1)
plt.title('Feature importance')
plt.bar(range(len(issues_train.columns)), importances[indices], color='lightblue', align='center')
plt.xticks(range(len(issues_train.columns)), issues_train.columns, rotation=90)
plt.xlim([-1, len(issues_train.columns)])
plt.tight_layout()
plt.show()
evaluate_performance("FOREST", rforest_classifier, issues_train, priority_train, issues_test, priority_test)
print "Selecting important features ..."
select = SelectFromModel(rforest_classifier, threshold=0.05, prefit=True)
train_selected = select.transform(issues_train)
test_selected = select.transform(issues_test)
rforest_classifier.fit(train_selected, priority_train)
evaluate_performance("FOREST-IMPORTANT", rforest_classifier, train_selected, priority_train, test_selected,
priority_test)
def train_and_predict(classifier, target_dataframe, training_dataframe, training_labels, class_label,
numerical_features,
nominal_features):
"""
Taking an issues dataframe, it performs predictions based on a classifier.It writes the resulting dataframe to a file.
:param classifier: Classifier to use.
:param target_dataframe: Dataframe to predict.
:param training_dataframe: Dataframe containing training instances.
:param training_labels: Labels for the training dataset.
:param class_label: Class label.
:param numerical_features: Numerical Features.
:param nominal_features: Nominal features.
:return:
"""
print "Missing data analysis ..."
print target_dataframe.isnull().sum()
# Temporarly, we are dropping NA values
target_dataframe = target_dataframe.dropna(subset=preprocessing.GIT_METRICS)
# The following repositories were not in the training dataset
repository_label = 'Git Repository'
target_dataframe = target_dataframe[~target_dataframe[repository_label].isin(['kylin', 'helix', 'mesos'])]
before_preprocessing = target_dataframe.copy()
print "Starting prediction process..."
target_features, target_labels = preprocessing.encode_and_split(target_dataframe, class_label,
numerical_features,
nominal_features)
training_std, target_features_std = preprocessing.escale_numerical_features(numerical_features, training_dataframe,
target_features)
features_for_training = training_std
features_for_prediction = target_features_std
if isinstance(classifier, RandomForestClassifier):
features_for_training = training_dataframe
features_for_prediction = target_features
print "Training classifier using a ", features_for_training.shape, " dataset ..."
classifier.fit(features_for_training, training_labels)
print "Training score: ", classifier.score(features_for_training, training_labels)
train_predictions = classifier.predict(features_for_training)
print classification_report(y_true=training_labels, y_pred=train_predictions)
print "Predicting ", len(features_for_prediction.index), " issues priority"
test_predictions = classifier.predict(features_for_prediction)
predicted_label = 'Predicted ' + class_label
print "Target dataframe after preprocessing: ", before_preprocessing.shape
print "Original class label ", target_labels.shape, target_labels.unique()
print "Predicted class label ", test_predictions.shape, np.unique(test_predictions)
before_preprocessing[class_label] = target_labels.values
before_preprocessing[predicted_label] = test_predictions
file_name = "Including_Prediction.csv"
before_preprocessing.to_csv(file_name, index=False)
print "File ready: ", file_name
results_dataframe = pd.DataFrame()
for repository in before_preprocessing[repository_label].unique():
issues_for_repo = before_preprocessing[before_preprocessing[repository_label] == repository]
issues_in_repo = len(issues_for_repo.index)
severe_issues = issues_for_repo[issues_for_repo[class_label]]
inflated_issues = severe_issues[~severe_issues[predicted_label]]
results_dataframe = results_dataframe.append(
[[repository, issues_in_repo, len(severe_issues.index), len(inflated_issues.index),
len(inflated_issues.index) / float(len(severe_issues.index))]],
ignore_index=True)
print "repository: ", repository, "\t Issues in Repo: ", issues_in_repo, "\t Reported Severe: ", len(
severe_issues.index), "\t Severe inflated: ", len(
inflated_issues.index), '\t ratio: ', len(inflated_issues.index) / float(len(severe_issues.index))
results_dataframe.columns = ["Repository", "Issues in Repository", "Reported Severe", "Severe Inflated",
"Inflation Ratio"]
results_dataframe.to_csv("prediction_results.csv", index=False)
def main():
original_dataframe = preprocessing.load_original_dataframe()
issues_dataframe = preprocessing.filter_issues_dataframe(original_dataframe)
# Plotting projects
figure, axes = plt.subplots(1, 1)
issues_dataframe['Git Repository'].value_counts(normalize=True).plot(kind='bar', ax=axes)
plt.show()
issues_dataframe, encoded_priorities = preprocessing.encode_and_split(issues_dataframe, preprocessing.CLASS_LABEL,
preprocessing.NUMERICAL_FEATURES,
preprocessing.NOMINAL_FEATURES)
# Plotting priorities
figure, axes = plt.subplots(1, 1)
encoded_priorities.value_counts(normalize=True, sort=True).plot(kind='bar', ax=axes)
plt.show()
issues_train, issues_test, priority_train, priority_test = train_test_split(issues_dataframe,
encoded_priorities,
test_size=0.2, random_state=0)
print len(issues_train.index), " issues on the train set."
issues_train_std, issues_test_std = preprocessing.escale_numerical_features(preprocessing.NUMERICAL_FEATURES,
issues_train,
issues_test)
logit_classifier = select_features_l1(issues_train_std, priority_train, issues_test_std, priority_test)
knn_classifier = sequential_feature_selection(issues_train_std, priority_train, issues_test_std, priority_test)
print "Building Random Forest Classifier ..."
rforest_classifier = RandomForestClassifier(n_estimators=10000, random_state=0, n_jobs=-1)
rforest_classifier.fit(issues_train, priority_train)
forest_classifier = feature_importance_with_forest(issues_train, priority_train, issues_test, priority_test)
rforest_classifier = RandomForestClassifier(n_estimators=10000, random_state=0, n_jobs=-1)
train_and_predict(rforest_classifier, original_dataframe, issues_dataframe, encoded_priorities,
preprocessing.CLASS_LABEL,
preprocessing.NUMERICAL_FEATURES,
preprocessing.NOMINAL_FEATURES)
if __name__ == "__main__":
main()