random_state=42,
n_jobs=-1)
# Fit the random search model
rf_random.fit(features, labels)
pprint(rf_random.best_params_)
def evaluate(model, test_features, test_labels):
predictions = model.predict(test_features)
accuracy = accuracy_score(test_labels, predictions)
print('Model Performance')
print('Accuracy = {:0.2f}%.'.format(accuracy * 100))
return accuracy
# Split the data into training and testing sets
train_features, test_features, train_labels, test_labels = train_test_split(
features, labels, test_size=0.25, random_state=43)
base_model = RandomForestClassifier(**get_rf_parameters())
base_model.fit(train_features, train_labels)
base_accuracy = evaluate(base_model, test_features, test_labels)
best_random = rf_random.best_estimator_
random_accuracy = evaluate(best_random, test_features, test_labels)
print('Improvement of {:0.2f}%.'.format(
100 * (random_accuracy - base_accuracy) / base_accuracy))
'ch.uzh.ciclassifier.features.configuration.UseCache',
'ch.uzh.ciclassifier.features.configuration.UseDeploy',
# 'ch.uzh.ciclassifier.features.github.OwnerType',
# 'ch.uzh.ciclassifier.features.github.PrimaryLanguage',
# 'ch.uzh.ciclassifier.features.repository.CommitsUntilConfigAdded',
# 'ch.uzh.ciclassifier.features.repository.ConfigChangeFrequency',
# 'ch.uzh.ciclassifier.features.repository.DaysUntilConfigAdded',
# 'ch.uzh.ciclassifier.features.repository.NumberOfConfigurationFileChanges',
# 'ch.uzh.ciclassifier.features.repository.NumberOfContributorsOnConfigurationFile',
# 'ch.uzh.ciclassifier.features.repository.ProjectName',
# 'ch.uzh.ciclassifier.features.travisci.BuildSuccessRatio',
# 'ch.uzh.ciclassifier.features.travisci.BuildTimeAverage',
# 'ch.uzh.ciclassifier.features.travisci.BuildTimeLatestAverage',
# 'ch.uzh.ciclassifier.features.travisci.ManualInteractionRatio',
# 'ch.uzh.ciclassifier.features.travisci.PullRequestRatio',
# 'ch.uzh.ciclassifier.features.travisci.TimeToFixAverage',
# 'ch.uzh.ciclassifier.features.travisci.TimeToFixLatestAverage',
]
# Labels are the values we want to predict
labels = np.array(raw_data['actual'])
features = raw_data[used_features]
# Instantiate model with 1000 decision trees
rf = RandomForestClassifier(**get_rf_parameters())
# Train the model on training data
rf.fit(features, labels)
pickle.dump(rf, open(MODEL_PATH, 'wb'))
FEATURES_FILE = 'data/truth.csv'
LANGAUGES = ['Ruby', 'JavaScript', 'Python', 'Java', 'C++', 'PHP']
results = []
NUMBER_OF_RUNS = 10
for language in LANGAUGES:
raw_data = pd.read_csv(FEATURES_FILE)
subset = raw_data.loc[raw_data['language'] == language]
features = subset[get_features()]
labels = np.array(subset['actual'])
features = np.array(features)
cv = RepeatedKFold(n_splits=10, n_repeats=10, random_state=42)
rf = RandomForestClassifier(**get_rf_parameters())
scores = cross_val_score(rf,
features,
labels,
scoring='accuracy',
cv=cv,
n_jobs=-1)
accuracies = []
precisions = []
recalls = []
for run in range(NUMBER_OF_RUNS):
raw_data = pd.read_csv(FEATURES_FILE)
subset_train = raw_data.loc[raw_data['language'] != language]
subset_test = raw_data.loc[raw_data['language'] == language]
