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
# 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])
# ----------------
features = model.transform(population_table=population_table,
peripheral_tables=[peripheral_table])
n_estimators=100,
n_jobs=6,
max_depth=7,
reg_lambda=500
)
#predictor = predictors.LogisticRegression()
model = models.RelboostModel(
population=population_placeholder,
peripheral=[expd_placeholder, memd_placeholder],
loss_function=loss_functions.CrossEntropyLoss(),
shrinkage=0.1,
gamma=0.0,
min_num_samples=200,
num_features=20,
share_selected_features=1.0,
reg_lambda=0.01,
sampling_factor=1.0,
predictor=predictor,
feature_selector=feature_selector,
num_threads=4
).send()
# ----------------
# Build a hyperparameter space
param_space = dict()
param_space['max_depth'] = [3, 10]
param_space['min_num_samples'] = [100, 500]
