# ----------------
features = model.transform(population_table=population_table,
peripheral_tables=[peripheral_table])
# ----------------
features = model.transform(population_table=population_table,
peripheral_tables=[peripheral_table],
df_name="features")
# ----------------
yhat = model.predict(population_table=population_table,
peripheral_tables=[peripheral_table])
# ----------------
print(model.to_sql())
# ----------------
scores = model.score(population_table=population_table,
peripheral_tables=[peripheral_table])
print(scores)
# ----------------
engine.delete_project("examples")
def test_relboost_same_units():
"""Check if the same results will be obtained regardless of whether
the units are assigned to the DataFrame, to the Columns, or to the
RelboostModel.
"""
# ----------------------------------------------------------------
engine.set_project("examples")
seed = 33231
units = {"column_01": "column_01"}
# ----------------------------------------------------------------
# Generate artificial dataset
# The problem we create looks like this:
#
# SELECT COUNT( * )
# FROM POPULATION t1
# LEFT JOIN PERIPHERAL t2
# ON t1.join_key = t2.join_key
# WHERE (
# ( t1.time_stamp - t2.time_stamp <= 0.5 )
# ) AND t2.time_stamp <= t1.time_stamp
# GROUP BY t1.join_key,
# t1.time_stamp;
# ----------------------------------------------------------------
# Assign the units to the columns
population_table_columns, peripheral_table_columns = datasets.make_same_units_numerical(
random_state=seed)
population_table_columns.set_unit("column_01", "column_01")
peripheral_table_columns.set_unit("column_01", "column_01")
population_placeholder_columns = population_table_columns.to_placeholder()
peripheral_placeholder_columns = peripheral_table_columns.to_placeholder()
population_placeholder_columns.join(peripheral_placeholder_columns,
"join_key", "time_stamp")
# ----------------------------------------------------------------
model_columns, features_columns, yhat_columns, scores_columns = _fit_model(
population_table_columns, peripheral_table_columns,
population_placeholder_columns, peripheral_placeholder_columns, seed,
dict())
# ----------------------------------------------------------------
# Assign units to Model
population_table_model, peripheral_table_model = datasets.make_same_units_numerical(
random_state=seed)
population_placeholder_model = population_table_model.to_placeholder()
peripheral_placeholder_model = peripheral_table_model.to_placeholder()
population_placeholder_model.join(peripheral_placeholder_model, "join_key",
"time_stamp")
# ----------------------------------------------------------------
model_model, features_model, yhat_model, scores_model = _fit_model(
population_table_model, peripheral_table_model,
population_placeholder_model, peripheral_placeholder_model, seed,
units)
# ----------------------------------------------------------------
# Check whether the results are the same.
assert scores_model == scores_columns
assert (yhat_model == yhat_columns).all()
# ----------------------------------------------------------------
engine.delete_project("examples")
