def test_dt_invariance(dummy_titanic_dt, dummy_passengers):
model = dummy_titanic_dt
p1, p2 = dummy_passengers
# Get original survival probability of passenger 1
test_df = pd.DataFrame.from_dict([p1], orient='columns')
X, y = get_feats_and_labels(prep_df(test_df))
p1_prob = model.predict(X)[0] # 0.09
# Change name from Owen to Mary (without changing gender or title)
p1_name = p1.copy()
p1_name['Name'] = ' Mr. Mary'
test_df = pd.DataFrame.from_dict([p1_name], orient='columns')
X, y = get_feats_and_labels(prep_df(test_df))
p1_name_prob = model.predict(X)[0] # 0.09
# Change ticket number from 'A/5 21171' to 'PC 17599'
p1_ticket = p1.copy()
p1_ticket['ticket'] = 'PC 17599'
test_df = pd.DataFrame.from_dict([p1_ticket], orient='columns')
X, y = get_feats_and_labels(prep_df(test_df))
p1_ticket_prob = model.predict(X)[0] # 0.09
# Change embarked port from 'S' to 'C'
p1_port = p1.copy()
p1_port['Embarked'] = 'C'
test_df = pd.DataFrame.from_dict([p1_port], orient='columns')
X, y = get_feats_and_labels(prep_df(test_df))
p1_port_prob = model.predict(X)[0] # 0.09
# Get original survival probability of passenger 2
test_df = pd.DataFrame.from_dict([p2], orient='columns')
X, y = get_feats_and_labels(prep_df(test_df))
p2_prob = model.predict(X)[0] # 1.0
# Change name from John to Berns (without changing gender or title)
p2_name = p2.copy()
p2_name['Name'] = ' Mrs. Berns'
test_df = pd.DataFrame.from_dict([p2_name], orient='columns')
X, y = get_feats_and_labels(prep_df(test_df))
p2_name_prob = model.predict(X)[0] # 1.0
# Change ticket number from 'PC 17599' to 'A/5 21171'
p2_ticket = p2.copy()
p2_ticket['ticket'] = 'A/5 21171'
test_df = pd.DataFrame.from_dict([p2_ticket], orient='columns')
X, y = get_feats_and_labels(prep_df(test_df))
p2_ticket_prob = model.predict(X)[0] # 1.0
# Change embarked port from 'C' to 'Q'
p2_port = p2.copy()
p2_port['Embarked'] = 'Q'
test_df = pd.DataFrame.from_dict([p2_port], orient='columns')
X, y = get_feats_and_labels(prep_df(test_df))
p2_port_prob = model.predict(X)[0] # 1.0
assert p1_prob == p1_name_prob == p1_ticket_prob == p1_port_prob
assert p2_prob == p2_name_prob == p2_ticket_prob == p2_port_prob
def dummy_titanic():
df = load_df()
df = prep_df(df)
train, test = split_df(df)
X_train, y_train = get_feats_and_labels(train)
X_test, y_test = get_feats_and_labels(test)
return X_train, y_train, X_test, y_test
def test_dt_directional_expectation(dummy_titanic_dt, dummy_passengers):
model = dummy_titanic_dt
p1, p2 = dummy_passengers
# Get original survival probability of passenger 1
test_df = pd.DataFrame.from_dict([p1], orient='columns')
X, y = get_feats_and_labels(prep_df(test_df))
p1_prob = model.predict(X)[0] # 0.09
# Change gender from male to female
p1_female = p1.copy()
p1_female['Name'] = ' Mrs. Owen'
p1_female['Sex'] = 'female'
test_df = pd.DataFrame.from_dict([p1_female], orient='columns')
X, y = get_feats_and_labels(prep_df(test_df))
p1_female_prob = model.predict(X)[0] # 0.65
# Change passenger class from 3 to 1
p1_class = p1.copy()
p1_class['Pclass'] = 1
test_df = pd.DataFrame.from_dict([p1_class], orient='columns')
X, y = get_feats_and_labels(prep_df(test_df))
p1_class_prob = model.predict(X)[0] # 0.36
assert p1_prob < p1_female_prob, 'Changing gender from male to female should increase survival probability.'
assert p1_prob < p1_class_prob, 'Changing class from 3 to 1 should increase survival probability.'
# Get original survival probability of passenger 2
test_df = pd.DataFrame.from_dict([p2], orient='columns')
X, y = get_feats_and_labels(prep_df(test_df))
p2_prob = model.predict(X)[0] # 1.0
# Change gender from female to male
p2_male = p2.copy()
p2_male['Name'] = ' Mr. John'
p2_male['Sex'] = 'male'
test_df = pd.DataFrame.from_dict([p2_male], orient='columns')
X, y = get_feats_and_labels(prep_df(test_df))
p2_male_prob = model.predict(X)[0] # 0.56
# Change class from 1 to 3
p2_class = p2.copy()
p2_class['Pclass'] = 3
test_df = pd.DataFrame.from_dict([p2_class], orient='columns')
X, y = get_feats_and_labels(prep_df(test_df))
p2_class_prob = model.predict(X)[0] # 0.0
# Lower fare from 71.2833 to 5
p2_fare = p2.copy()
p2_fare['Fare'] = 5
test_df = pd.DataFrame.from_dict([p2_fare], orient='columns')
X, y = get_feats_and_labels(prep_df(test_df))
p2_fare_prob = model.predict(X)[0] # 0.85
assert p2_prob > p2_male_prob, 'Changing gender from female to male should decrease survival probability.'
assert p2_prob > p2_class_prob, 'Changing class from 1 to 3 should decrease survival probability.'
assert p2_prob > p2_fare_prob, 'Changing fare from 72 to 5 should decrease survival probability.'
def test_prep_steps():
df = load_df()
df = prep_df(df)
train, test = split_df(df)
X_train, y_train = get_feats_and_labels(train)
X_test, y_test = get_feats_and_labels(test)
assert X_train.shape == (712, 8)
assert y_train.shape == (712,)
assert X_test.shape == (179, 8)
assert y_test.shape == (179,)