population_table = population_table.rename(
index=str, columns={"time_stamp_population": "time_stamp"})
peripheral_table = peripheral_table.rename(
index=str, columns={"time_stamp_peripheral": "time_stamp"})
# Replace NaN targets with 0.0 - target values may never be NaN!.
population_table.targets = np.where(np.isnan(population_table['targets']), 0,
population_table['targets'])
# ----------------
# Build model
population_placeholder = data.Placeholder(name="POPULATION",
numerical=["column_01"],
join_keys=["join_key"],
targets=["targets"])
peripheral_placeholder = data.Placeholder(name="PERIPHERAL",
numerical=["column_01"],
join_keys=["join_key"])
population_placeholder.join(peripheral_placeholder, "join_key")
predictor = predictors.LinearRegression()
#predictor = predictors.XGBoostRegressor()
model = models.RelboostModel(population=population_placeholder,
peripheral=[peripheral_placeholder],
loss_function=loss_functions.SquareLoss(),
# Here we load the first part of the pandas.DataFrame...
population_on_engine = data.DataFrame(name="POPULATION",
roles={
"join_key": ["join_key"],
"numerical": ["column_01"],
"time_stamp": ["time_stamp"],
"target": ["targets"]
}).read_pandas(population_table[:20])
# ...and now we load the second part
population_on_engine.read_pandas(population_table[20:], append=True)
# ----------------
# Build model
population_placeholder = data.Placeholder(name="numerical_population")
peripheral_placeholder = data.Placeholder(name="numerical_peripheral")
population_placeholder.join(peripheral_placeholder, "join_key", "time_stamp")
predictor = predictors.LinearRegression()
model = models.MultirelModel(
aggregation=[aggregations.Count, aggregations.Sum],
population=population_placeholder,
peripheral=[peripheral_placeholder],
loss_function=loss_functions.SquareLoss(),
predictor=predictor,
num_features=10,
share_aggregations=1.0,
"target": ["column_01"],
"time_stamp": ["time_stamp_lagged"]
}).read_pandas(time_series)
peripheral_on_engine = data.DataFrame(name="PERIPHERAL",
roles={
"join_key": ["join_key"],
"numerical": ["column_01"],
"time_stamp":
["time_stamp", "upper_time_stamp"]
}).read_pandas(time_series)
# ----------------
# Build model
population_placeholder = data.Placeholder(name="TIME_SERIES")
peripheral_placeholder = data.Placeholder(name="TIME_SERIES")
population_placeholder.join(peripheral_placeholder,
join_key="join_key",
time_stamp="time_stamp_lagged",
other_time_stamp="time_stamp",
upper_time_stamp="upper_time_stamp")
predictor = predictors.LinearRegression()
model = models.MultirelModel(
aggregation=[aggregations.Count, aggregations.Sum],
population=population_placeholder,
peripheral=[peripheral_placeholder],