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test_time_series_exogenous.py
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test_time_series_exogenous.py
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"""Module to test time_series forecasting - univariate with exogenous variables
"""
import numpy as np # type: ignore
import pandas as pd # type: ignore
import pytest
from pycaret.time_series import TSForecastingExperiment
from pycaret.utils.time_series import TSApproachTypes, TSExogenousPresent
pytestmark = pytest.mark.filterwarnings("ignore::UserWarning")
##############################
# Functions Start Here ####
##############################
############################
# Functions End Here ####
############################
##########################
# Tests Start Here ####
##########################
def test_create_tune_predict_finalize_model(load_uni_exo_data_target):
"""test create_model, tune_model, predict_model and finalize_model
functionality using exogenous variables
"""
data, target = load_uni_exo_data_target
fh = 12
data_for_modeling = data.iloc[:-12]
future_data = data.iloc[-12:]
future_exog = future_data.drop(columns=target)
exp = TSForecastingExperiment()
exp.setup(data=data_for_modeling, target=target, fh=fh, session_id=42)
#######################
# Test Create Model ##
#######################
model = exp.create_model("arima")
#########################
# Expected Values ####
#########################
expected_period_index = data_for_modeling.iloc[-fh:].index
final_expected_period_index = future_exog.index
########################
# Test Predict Model ##
########################
# Default prediction
y_pred = exp.predict_model(model)
assert isinstance(y_pred, pd.DataFrame)
assert np.all(y_pred.index == expected_period_index)
#####################
# Test Tune Model ##
#####################
tuned_model = exp.tune_model(model)
########################
# Test Predict Model ##
########################
# Default prediction
y_pred = exp.predict_model(tuned_model)
assert isinstance(y_pred, pd.DataFrame)
assert np.all(y_pred.index == expected_period_index)
#########################
# Test Finalize Model ##
#########################
final_model = exp.finalize_model(tuned_model)
y_pred = exp.predict_model(final_model, X=future_exog)
assert np.all(y_pred.index == final_expected_period_index)
def test_blend_models(load_uni_exo_data_target, load_models_uni_mix_exo_noexo):
"""test blending functionality.
NOTE: compare models does not enforce exog here for now.
TODO: Later when Reduced Regression Models also support exogenous variables,
we can add a test with only models that support exogenous variables (i.e.
with enforce_exogenous=True).
"""
data, target = load_uni_exo_data_target
fh = 12
data_for_modeling = data.iloc[:-12]
future_data = data.iloc[-12:]
future_exog = future_data.drop(columns=target)
#########################
# Expected Values ####
#########################
expected_period_index = data_for_modeling.iloc[-fh:].index
final_expected_period_index = future_exog.index
exp = TSForecastingExperiment()
exp.setup(
data=data_for_modeling,
target=target,
fh=fh,
enforce_exogenous=False,
session_id=42,
)
models_to_include = load_models_uni_mix_exo_noexo
best_models = exp.compare_models(include=models_to_include, n_select=3)
blender = exp.blend_models(best_models)
y_pred = exp.predict_model(blender)
assert isinstance(y_pred, pd.DataFrame)
assert np.all(y_pred.index == expected_period_index)
#########################
# Test Finalize Model ##
#########################
final_model = exp.finalize_model(blender)
y_pred = exp.predict_model(final_model, X=future_exog)
assert np.all(y_pred.index == final_expected_period_index)
def test_setup():
"""Test the setup with exogenous variables"""
length = 100
data = pd.DataFrame(np.random.rand(length, 7))
data.columns = "A B C D E F G".split()
data["B"] = pd.date_range("20130101", periods=length)
target = "A"
index = "B" # NOTE: When index is provided we do not need to pass seasonal_period
exp = TSForecastingExperiment()
######################################
# Univariate without exogenous ####
######################################
approach_type = TSApproachTypes.UNI
exogenous_present = TSExogenousPresent.NO
# Case 1: pd.Series ----
exp.setup(data=data[target])
assert exp.approach_type == approach_type
assert exp.exogenous_present == exogenous_present
assert exp.target_param == target
assert exp.exogenous_variables == []
# Case 2: pd.DataFrame with 1 column ----
exp.setup(data=pd.DataFrame(data[target]))
assert exp.approach_type == approach_type
assert exp.exogenous_present == exogenous_present
assert exp.target_param == target
assert exp.exogenous_variables == []
# Case 3: # Target specified & correct ----
exp.setup(data=data[target], target=target)
assert exp.approach_type == approach_type
assert exp.exogenous_present == exogenous_present
assert exp.target_param == target
assert exp.exogenous_variables == []
###################################
# Univariate with exogenous ####
###################################
approach_type = TSApproachTypes.UNI
exogenous_present = TSExogenousPresent.YES
# Case 1: `target` provided, `index` not provided, `ignore_features` not provided ----
exp.setup(data=data, target=target)
assert exp.approach_type == approach_type
assert exp.exogenous_present == exogenous_present
assert exp.target_param == target
assert exp.exogenous_variables == ["B", "C", "D", "E", "F", "G"]
# Case 2: `target` provided, `index` provided, `ignore_features` not provided ----
exp.setup(data=data, target=target, index=index)
assert exp.approach_type == approach_type
assert exp.exogenous_present == exogenous_present
assert exp.target_param == target
assert exp.exogenous_variables == ["C", "D", "E", "F", "G"]
# TODO: Add check for index values
# Case 3: `target` provided, `index` provided, `ignore_features` provided ----
exp.setup(data=data, target=target, index=index, ignore_features=["C", "E"])
assert exp.approach_type == approach_type
assert exp.exogenous_present == exogenous_present
assert exp.target_param == target
assert exp.exogenous_variables == ["D", "F", "G"]
# TODO: Add check for index values
# Case 4: `target` provided, `index` not provided, `ignore_features` provided ----
exp.setup(data=data, target=target, ignore_features=["C", "E"])
assert exp.approach_type == approach_type
assert exp.exogenous_present == exogenous_present
assert exp.target_param == target
assert exp.exogenous_variables == ["B", "D", "F", "G"]
def test_setup_raises():
"""Test the setup with exogenous variables when it raises errors"""
length = 100
data = pd.DataFrame(np.random.rand(length, 7))
data.columns = "A B C D E F G".split()
exp = TSForecastingExperiment()
##############################
# Target Not Specified ####
##############################
with pytest.raises(ValueError) as errmsg:
exp.setup(data=data)
exceptionmsg = errmsg.value.args[0]
assert (
exceptionmsg
== f"Data has {len(data.columns)} columns, but the target has not been specified."
)
################################
# Wrong Target Specified ####
################################
target = "WRONG"
# Case 1: Without exogenous ----
column = "A"
with pytest.raises(ValueError) as errmsg:
exp.setup(data=data[column], target=target)
exceptionmsg = errmsg.value.args[0]
assert (
exceptionmsg == f"Target = '{target}', but data only has '{column}'. "
"If you are passing a series (or a dataframe with 1 column) "
"to setup, you can leave `target=None`"
)
# Case 2: With exogenous ----
with pytest.raises(ValueError) as errmsg:
exp.setup(data=data, target=target)
exceptionmsg = errmsg.value.args[0]
assert exceptionmsg == f"Target Column '{target}' is not present in the data."