def test_prep_data():
# check prep_data() returns approriate lists for classification
df = data.load_mouseday_features()
strain = df['strain']
features = df.iloc[:, 3:]
train_y, train_x, test_y, test_x = classification.prep_data(
strain, features)
assert train_y.shape == (1440, )
assert train_x.shape == (1440, 99)
assert test_y.shape == (481, )
assert test_x.shape == (481, 99)
def test_prep_data():
# check prep_data() returns approriate lists for classification
df = data.load_mouseday_features()
strain = df['strain']
features = df.iloc[:, 3:]
train_y, train_x, test_y, test_x = classification.prep_data(
strain, features)
assert train_y.shape == (1440, )
assert train_x.shape == (1440, 99)
assert test_y.shape == (481, )
assert test_x.shape == (481, 99)
def test_fit_svm():
# check svm() returns approriate data frame with
# prediction labels and true labels
# labels are integers from 0 to 15
df = data.load_mouseday_features()
strain = df['strain']
features = df.iloc[:200, 3:]
train_y, train_x, test_y, test_x = classification.prep_data(
strain, features)
result = classification.fit_svm(train_y, train_x, test_x, [1], [0.1])
assert features.shape == (200, 99)
assert result.shape == (50, 1)
assert all(result.iloc[:, 0] >= 0) & all(result.iloc[:, 0] <= 15)
assert all([i.is_integer() for i in result.iloc[:, 0]])
def test_fit_svm():
# check svm() returns approriate data frame with
# prediction labels and true labels
# labels are integers from 0 to 15
df = data.load_mouseday_features()
strain = df['strain']
features = df.iloc[:200, 3:]
train_y, train_x, test_y, test_x = classification.prep_data(
strain, features)
result = classification.fit_svm(train_y, train_x, test_x, [1], [0.1])
assert features.shape == (200, 99)
assert result.shape == (50, 1)
assert all(result.iloc[:, 0] >= 0) & all(result.iloc[:, 0] <= 15)
assert all([i.is_integer() for i in result.iloc[:, 0]])
def test_get_summary():
# check get_summary returns approriate data frame of precision,
# recall, f-1 measure, in terms of shape and range(0,1)
df = data.load_mouseday_features()
strain = df['strain']
features = df.iloc[:, 3:]
train_y, train_x, test_y, test_x = classification.prep_data(
strain, features)
result = classification.fit_random_forest(
train_y, train_x, test_x, [1], [10])[0]
summary = classification.get_summary(result, test_y)
assert summary.shape == (16, 3)
assert all(summary.iloc[:, 0] <= 1) & all(summary.iloc[:, 0] >= 0)
assert all(summary.iloc[:, 1] <= 1) & all(summary.iloc[:, 1] >= 0)
assert all(summary.iloc[:, 2] <= 1) & all(summary.iloc[:, 2] >= 0)
def test_get_summary():
# check get_summary returns approriate data frame of precision,
# recall, f-1 measure, in terms of shape and range(0,1)
df = data.load_mouseday_features()
strain = df['strain']
features = df.iloc[:, 3:]
train_y, train_x, test_y, test_x = classification.prep_data(
strain, features)
result = classification.fit_random_forest(train_y, train_x, test_x, [1],
[10])[0]
summary = classification.get_summary(result, test_y)
assert summary.shape == (16, 3)
assert all(summary.iloc[:, 0] <= 1) & all(summary.iloc[:, 0] >= 0)
assert all(summary.iloc[:, 1] <= 1) & all(summary.iloc[:, 1] >= 0)
assert all(summary.iloc[:, 2] <= 1) & all(summary.iloc[:, 2] >= 0)