def _fit_model(population_df, peripheral_df, population_ph, peripheral_ph, seed, units):
# ----------------------------------------------------------------
predictor = predictors.LinearRegression()
# ----------------------------------------------------------------
model = models.MultirelModel(
aggregation=[
aggregations.Count,
aggregations.Sum
],
population=population_ph,
peripheral=[peripheral_ph],
loss_function=loss_functions.SquareLoss(),
predictor=predictor,
num_features=10,
share_aggregations=1.0,
max_length=3,
num_threads=1,
seed=seed,
units=units
).send()
# ----------------------------------------------------------------
model = model.fit(
population_table=population_df,
peripheral_tables=[peripheral_df]
)
# ----------------------------------------------------------------
features = model.transform(
population_table=population_df,
peripheral_tables=[peripheral_df]
)
# ----------------------------------------------------------------
yhat = model.predict(
population_table=population_df,
peripheral_tables=[peripheral_df]
)
# ----------------------------------------------------------------
scores = model.score(
population_table=population_df,
peripheral_tables=[peripheral_df]
)
# ----------------------------------------------------------------
return model, features, yhat, scores
def _fit_model(population_df, peripheral_df, population_ph, peripheral_ph,
seed, units):
# ----------------------------------------------------------------
predictor = predictors.LinearRegression()
# ----------------------------------------------------------------
model = models.RelboostModel(population=population_ph,
peripheral=[peripheral_ph],
loss_function=loss_functions.SquareLoss(),
predictor=predictor,
num_features=10,
max_depth=1,
reg_lambda=0.0,
shrinkage=0.3,
num_threads=1,
seed=seed,
units=units).send()
# ----------------------------------------------------------------
model = model.fit(population_table=population_df,
peripheral_tables=[peripheral_df])
# ----------------------------------------------------------------
features = model.transform(population_table=population_df,
peripheral_tables=[peripheral_df])
# ----------------------------------------------------------------
yhat = model.predict(population_table=population_df,
peripheral_tables=[peripheral_df])
# ----------------------------------------------------------------
scores = model.score(population_table=population_df,
peripheral_tables=[peripheral_df])
# ----------------------------------------------------------------
return model, features, yhat, scores
# 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;
population_table, peripheral_table = datasets.make_numerical()
population_placeholder = population_table.to_placeholder()
peripheral_placeholder = peripheral_table.to_placeholder()
population_placeholder.join(peripheral_placeholder, "join_key", "time_stamp")
predictor = predictors.LinearRegression()
model = models.RelboostModel(population=population_placeholder,
peripheral=[peripheral_placeholder],
loss_function=loss_functions.SquareLoss(),
predictor=predictor,
num_features=10,
max_depth=1,
reg_lambda=0.0,
shrinkage=0.3,
num_threads=0).send()
# ----------------
model = model.fit(population_table=population_table,
peripheral_tables=[peripheral_table])