def construct_numpy_representation_with_ordered_pairs_of_rankings_and_features_and_weights(
features: pd.DataFrame,
performances: pd.DataFrame,
max_pairs_per_instance=100,
seed=1,
order="asc",
skip_value=None):
"""Get numpy representation of features, performances and rankings
Arguments:
features {pd.DataFrame} -- Feature values
performances {pd.DataFrame} -- Performances of algorithms
Returns:
[type] -- Triple of numpy ndarrays, first stores the feature
values, the second stores the algirhtm performances and the
third stores the algorithm rankings
"""
rankings, weights = sample_pairs(performances,
pairs_per_instance=max_pairs_per_instance,
seed=seed,
skip_value=skip_value)
joined = rankings.join(features).join(performances,
lsuffix="_rank",
rsuffix="_performance")
np_features = joined[features.columns.values].values
np_performances = joined[[x for x in performances.columns]].values
np_rankings = joined[[x for x in rankings.columns]].values + 1
np_performances = np_performances[
np.arange(np_performances.shape[0])[:, np.newaxis], np_rankings - 1]
max_len = len(performances.columns)
print("performances", performances.head())
print("labels", rankings.head())
print("weight", weights.head())
np_weights = weights.to_numpy()
np_weights = np.amax(np_weights, axis=1)
# print("np_weights", np_weights)
np_weights = np.exp2(np_weights)
# print("exp np_weights", np_weights)
# TODO check for maximization problems
if order == "desc":
np_rankings = np.flip(np_rankings, axis=1)
np_performances = np.flip(np_performances, axis=1)
return np_features, np_performances, np_rankings, np_weights
def test_exp2():
fun = lambda x : 3.0 * np.exp2(x)
d_fun = grad(fun)
check_grads(fun, abs(npr.randn()))
check_grads(d_fun, abs(npr.randn()))
def test_exp2():
fun = lambda x : 3.0 * np.exp2(x)
d_fun = grad(fun)
check_grads(fun, abs(npr.randn()))
check_grads(d_fun, abs(npr.randn()))
def test_exp2():
fun = lambda x: 3.0 * np.exp2(x)
check_grads(fun)(abs(npr.randn()))
def test_exp2():
fun = lambda x : 3.0 * np.exp2(x)
check_grads(fun)(abs(npr.randn()))