def evaluate_fix_classifier():
text_name = 'message'
classification_function = is_fix
classification_column = 'corrective_pred'
concept_column = 'Is_Corrective'
df = pd.read_csv(join(DATA_PATH, 'corrective_texts_tests.csv'))
#df = df[df.certain != 'FALSE']
df = df[~df.Is_Corrective.isna()]
#concept_column='is_corrective'
#df = pd.read_csv("/Users/idan/playground/commit-classification/data/change_samples.csv")
df = classifiy_commits_df(df,
classification_function=classification_function,
classification_column=classification_column,
text_name=text_name)
cm = evaluate_performance(df,
classification_column,
concept_column,
text_name=text_name)
print("corrective_labels CM")
print(cm)
df = df[(df['disable'] != 1)]
"""
fp = get_false_positives(df
, classifier_column=classification_column
, concept_column=concept_column)
print("False Positives")
pd.options.display.max_columns = 50
pd.options.display.max_rows = 2000
print(fp)
"""
fn = get_false_negatives(df,
classifier_column=classification_column,
concept_column=concept_column)
print("False Negatives")
pd.options.display.max_columns = 50
pd.options.display.max_rows = 2000
print(fn)
def evaluate_cc_fix_classifier():
text_name = 'message'
classification_function = is_cc_corrective
classification_column = 'corrective_pred'
concept_column = 'Is_Corrective'
df = pd.read_csv(join(DATA_PATH, 'conventional_commits.csv'))
df = classifiy_commits_df(df,
classification_function=classification_function,
classification_column=classification_column,
text_name=text_name)
cm = evaluate_performance(df,
classification_column,
concept_column,
text_name=text_name)
print("corrective_labels CM")
print(cm)
def evaluate_abstraction_classifier():
text_name = 'message'
classification_function = is_abstraction
classification_column = 'abstraction_pred'
concept_column = 'Is_abstraction'
df = pd.read_csv(join(DATA_PATH, 'abstraction_commits.csv'))
df = classifiy_commits_df(df,
classification_function=classification_function,
classification_column=classification_column,
text_name=text_name)
cm = evaluate_performance(df,
classification_column,
concept_column,
text_name=text_name)
print("Abstraction labels CM")
print(cm)
def evaluate_adaptive_classifier():
text_name = 'message'
classification_function = is_adaptive
classification_column = 'corrective_pred'
concept_column = 'Is_Adaptive'
df = pd.read_csv(join(DATA_PATH, 'commit_classification_batch2.csv'))
df = df[df.certain != 'FALSE']
df = df[~df.Is_Corrective.isna()]
"""
concept_column = 'is_adaptive'
df = pd.read_csv(join(DATA_PATH, "commit_classification_batch2.csv"))
df[concept_column] = df.expected.map(lambda x: not x)
"""
df = classifiy_commits_df(df,
classification_function=classification_function,
classification_column=classification_column,
text_name=text_name)
cm = evaluate_performance(df,
classification_column,
concept_column,
text_name=text_name)
print("corrective_labels CM")
print(cm)
"""
fp = get_false_positives(df
, classifier_column=classification_column
, concept_column=concept_column)
print("False Positives")
pd.options.display.max_columns = 50
pd.options.display.max_rows = 2000
print(fp)
"""
fn = get_false_negatives(df,
classifier_column=classification_column,
concept_column=concept_column)
print("False Negatives")
pd.options.display.max_columns = 50
pd.options.display.max_rows = 2000
print(fn)