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test_time_series_setup.py
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test_time_series_setup.py
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"""Module to test time_series "setup" functionality
"""
import math
import numpy as np
import pandas as pd
import pytest
from time_series_test_utils import (
_get_seasonal_values,
_get_seasonal_values_alphanumeric,
_return_setup_args_raises,
_return_splitter_args,
)
from pycaret.datasets import get_data
from pycaret.time_series import TSForecastingExperiment
##############################
# Functions Start Here ####
##############################
# NOTE: Fixtures can not be used to parameterize tests
# https://stackoverflow.com/questions/52764279/pytest-how-to-parametrize-a-test-with-a-list-that-is-returned-from-a-fixture
# Hence, we have to create functions and create the parameterized list first
# (must happen during collect phase) before passing it to mark.parameterize.
_splitter_args = _return_splitter_args()
_setup_args_raises = _return_setup_args_raises()
############################
# Functions End Here ####
############################
##########################
# Tests Start Here ####
##########################
@pytest.mark.parametrize("fold, fh, fold_strategy", _splitter_args)
def test_splitter_using_fold_and_fh(fold, fh, fold_strategy, load_pos_and_neg_data):
"""Tests the splitter creation using fold, fh and a string value for fold_strategy."""
from sktime.forecasting.model_selection._split import (
ExpandingWindowSplitter,
SlidingWindowSplitter,
)
from pycaret.time_series import setup
exp_name = setup(
data=load_pos_and_neg_data,
fold=fold,
fh=fh,
fold_strategy=fold_strategy,
)
allowed_fold_strategies = ["expanding", "rolling", "sliding"]
if fold_strategy in allowed_fold_strategies:
if (fold_strategy == "expanding") or (fold_strategy == "rolling"):
assert isinstance(exp_name.fold_generator, ExpandingWindowSplitter)
elif fold_strategy == "sliding":
assert isinstance(exp_name.fold_generator, SlidingWindowSplitter)
if isinstance(fh, int):
# Since fh is an int, we can check as follows ----
assert np.all(exp_name.fold_generator.fh == np.arange(1, fh + 1))
assert exp_name.fold_generator.step_length == fh
else:
assert np.all(exp_name.fold_generator.fh == fh)
# When fh has np gaps: e.g. fh = np.arange(1, 37), step length = 36
# When fh has gaps, e.g. fh = np.arange(25, 37), step length = 12
assert exp_name.fold_generator.step_length == len(fh)
def test_splitter_pass_cv_object(load_pos_and_neg_data):
"""Tests the passing of a `sktime` cv splitter to fold_strategy"""
from sktime.forecasting.model_selection._split import ExpandingWindowSplitter
from pycaret.time_series import setup
fold = 3
fh = np.arange(1, 13) # regular horizon of 12 months
fh_extended = np.arange(1, 25) # extended horizon of 24 months
fold_strategy = ExpandingWindowSplitter(
initial_window=72,
step_length=12,
# window_length=12,
fh=fh,
start_with_window=True,
)
exp_name = setup(
data=load_pos_and_neg_data,
fold=fold, # should be ignored since we are passing explicit fold_strategy
fh=fh_extended, # should be ignored since we are passing explicit fold_strategy
fold_strategy=fold_strategy,
)
assert exp_name.fold_generator.initial_window == fold_strategy.initial_window
assert np.all(exp_name.fold_generator.fh == fold_strategy.fh)
assert exp_name.fold_generator.step_length == fold_strategy.step_length
num_folds = exp_name.get_config("fold_param")
y_train = exp_name.get_config("y_train")
expected = fold_strategy.get_n_splits(y=y_train)
assert num_folds == expected
@pytest.mark.parametrize("fold, fh, fold_strategy", _setup_args_raises)
def test_setup_raises(fold, fh, fold_strategy, load_pos_and_neg_data):
"""Tests conditions that raise an error due to lack of data"""
from pycaret.time_series import setup
with pytest.raises(ValueError) as errmsg:
_ = setup(
data=load_pos_and_neg_data,
fold=fold,
fh=fh,
fold_strategy=fold_strategy,
)
exceptionmsg = errmsg.value.args[0]
assert exceptionmsg == "Not Enough Data Points, set a lower number of folds or fh"
def test_enforce_pi(load_pos_and_neg_data):
"""Tests the enforcement of prediction interval"""
data = load_pos_and_neg_data
# With enforcement ----
exp1 = TSForecastingExperiment()
exp1.setup(data=data, point_alpha=0.5)
num_models1 = len(exp1.models())
# Without enforcement ----
exp2 = TSForecastingExperiment()
exp2.setup(data=data, point_alpha=None)
num_models2 = len(exp2.models())
# We know that some models do not offer PI capability, so the following
# check is valid for now.
assert num_models1 < num_models2
def test_enforce_exogenous_no_exo_data(load_pos_and_neg_data):
"""Tests the enforcement of exogenous variable support in models when
univariate data without exogenous variables is passed."""
data = load_pos_and_neg_data
exp1 = TSForecastingExperiment()
exp1.setup(data=data, enforce_exogenous=True)
num_models1 = len(exp1.models())
exp2 = TSForecastingExperiment()
exp2.setup(data=data, enforce_exogenous=False)
num_models2 = len(exp2.models())
# Irrespective of the enforce_exogenous flag, all models are enabled when
# the data does not contain exogenous variables.
assert num_models1 == num_models2
def test_enforce_exogenous_exo_data(load_uni_exo_data_target):
"""Tests the enforcement of exogenous variable support in models when
univariate data with exogenous variables is passed."""
data, target = load_uni_exo_data_target
exp1 = TSForecastingExperiment()
exp1.setup(data=data, target=target, enforce_exogenous=True)
num_models1 = len(exp1.models())
exp2 = TSForecastingExperiment()
exp2.setup(data=data, target=target, enforce_exogenous=False)
num_models2 = len(exp2.models())
# We know that some models do not offer exogenous variables support, so the
# following check is valid for now.
assert num_models1 < num_models2
def test_sp_to_use_using_index():
"""Seasonal Period detection using Indices (used before 3.0.0rc5)."""
exp = TSForecastingExperiment()
data = get_data("airline", verbose=False)
# 1.1 Airline Data with seasonality of 12
exp.setup(
data=data,
sp_detection="index",
verbose=False,
session_id=42,
)
assert exp.candidate_sps == [12]
assert exp.significant_sps == [12]
assert exp.significant_sps_no_harmonics == [12]
assert exp.all_sps_to_use == [12]
assert exp.primary_sp_to_use == 12
# 1.2 Airline Data with seasonality of M (12), 6
exp.setup(
data=data,
sp_detection="index",
verbose=False,
session_id=42,
seasonal_period=["M", 6],
num_sps_to_use=-1,
)
assert exp.candidate_sps == [12, 6]
assert exp.significant_sps == [12, 6]
assert exp.significant_sps_no_harmonics == [12]
assert exp.all_sps_to_use == [12, 6]
assert exp.primary_sp_to_use == 12
# 1.3 White noise Data with seasonality of 12
data = get_data("1", folder="time_series/white_noise", verbose=False)
exp.setup(
data=data,
sp_detection="index",
seasonal_period=12,
verbose=False,
session_id=42,
)
# Should get 1 even though we passed 12
assert exp.candidate_sps == [12]
assert exp.significant_sps == [1]
assert exp.significant_sps_no_harmonics == [1]
assert exp.all_sps_to_use == [1]
assert exp.primary_sp_to_use == 1
def test_sp_to_use_using_auto():
"""Seasonal Period detection using Statistical tests (used on and after 3.0.0rc5)."""
exp = TSForecastingExperiment()
data = get_data("airline", verbose=False)
# 1.1 Auto Detection of Seasonal Period ----
exp.setup(
data=data,
sp_detection="auto",
verbose=False,
session_id=42,
)
assert exp.candidate_sps == [12, 24, 36, 11, 48]
assert exp.significant_sps == [12, 24, 36, 11, 48]
assert exp.significant_sps_no_harmonics == [48, 36, 11]
assert exp.all_sps_to_use == [12]
assert exp.primary_sp_to_use == 12
# 1.2 Auto Detection with multiple values allowed ----
# 1.2.1 Multiple Seasonalities < tested and detected ----
exp.setup(
data=data,
sp_detection="auto",
num_sps_to_use=2,
verbose=False,
session_id=42,
)
assert exp.candidate_sps == [12, 24, 36, 11, 48]
assert exp.significant_sps == [12, 24, 36, 11, 48]
assert exp.significant_sps_no_harmonics == [48, 36, 11]
assert exp.all_sps_to_use == [12, 24]
assert exp.primary_sp_to_use == 12
# 1.2.2 Multiple Seasonalities > tested and detected ----
exp.setup(
data=data,
sp_detection="auto",
num_sps_to_use=100,
verbose=False,
session_id=42,
)
assert exp.candidate_sps == [12, 24, 36, 11, 48]
assert exp.significant_sps == [12, 24, 36, 11, 48]
assert exp.significant_sps_no_harmonics == [48, 36, 11]
assert exp.all_sps_to_use == [12, 24, 36, 11, 48]
assert exp.primary_sp_to_use == 12
def test_sp_to_use_upto_max_sp():
"""Seasonal Period detection upto a max seasonal period provided by user."""
data = get_data("airline", verbose=False)
# 1.0 Max SP not specified ----
exp = TSForecastingExperiment()
exp.setup(
data=data, fh=12, session_id=42, remove_harmonics=False, max_sp_to_consider=None
)
assert exp.candidate_sps == [12, 24, 36, 11, 48]
assert exp.significant_sps == [12, 24, 36, 11, 48]
assert exp.significant_sps_no_harmonics == [48, 36, 11]
assert exp.all_sps_to_use == [12]
assert exp.primary_sp_to_use == 12
# 2.0 Max SP more than at least some detected values ----
# 2.1 Without removing harmonics
exp = TSForecastingExperiment()
exp.setup(
data=data, fh=12, session_id=42, remove_harmonics=False, max_sp_to_consider=24
)
assert exp.candidate_sps == [12, 24, 11]
assert exp.significant_sps == [12, 24, 11]
assert exp.significant_sps_no_harmonics == [24, 11]
assert exp.all_sps_to_use == [12]
assert exp.primary_sp_to_use == 12
# 2.2 Removing harmonics
exp = TSForecastingExperiment()
exp.setup(
data=data, fh=12, session_id=42, remove_harmonics=True, max_sp_to_consider=24
)
assert exp.candidate_sps == [12, 24, 11]
assert exp.significant_sps == [12, 24, 11]
assert exp.significant_sps_no_harmonics == [24, 11]
assert exp.all_sps_to_use == [24]
assert exp.primary_sp_to_use == 24
# 3.0 Max SP less than all detected values ----
# 3.1 Without removing harmonics
exp = TSForecastingExperiment()
exp.setup(
data=data, fh=12, session_id=42, remove_harmonics=False, max_sp_to_consider=2
)
assert exp.candidate_sps == []
assert exp.significant_sps == [1]
assert exp.significant_sps_no_harmonics == [1]
assert exp.all_sps_to_use == [1]
assert exp.primary_sp_to_use == 1
# 3.2 Removing harmonics
exp = TSForecastingExperiment()
exp.setup(
data=data, fh=12, session_id=42, remove_harmonics=True, max_sp_to_consider=2
)
assert exp.candidate_sps == []
assert exp.significant_sps == [1]
assert exp.significant_sps_no_harmonics == [1]
assert exp.all_sps_to_use == [1]
assert exp.primary_sp_to_use == 1
@pytest.mark.parametrize("seasonal_key, seasonal_value", _get_seasonal_values())
def test_setup_seasonal_period_int(load_pos_and_neg_data, seasonal_key, seasonal_value):
exp = TSForecastingExperiment()
fh = np.arange(1, 13)
fold = 2
data = load_pos_and_neg_data
exp.setup(
data=data,
fh=fh,
fold=fold,
fold_strategy="sliding",
verbose=False,
seasonal_period=seasonal_value,
)
assert exp.candidate_sps == [seasonal_value]
@pytest.mark.parametrize("seasonal_period, seasonal_value", _get_seasonal_values())
def test_setup_seasonal_period_str(
load_pos_and_neg_data, seasonal_period, seasonal_value
):
exp = TSForecastingExperiment()
fh = np.arange(1, 13)
fold = 2
data = load_pos_and_neg_data
exp.setup(
data=data,
fh=fh,
fold=fold,
fold_strategy="sliding",
verbose=False,
session_id=42,
seasonal_period=seasonal_period,
)
assert exp.candidate_sps == [seasonal_value]
@pytest.mark.parametrize(
"prefix, seasonal_period, seasonal_value", _get_seasonal_values_alphanumeric()
)
def test_setup_seasonal_period_alphanumeric(
load_pos_and_neg_data, prefix, seasonal_period, seasonal_value
):
"""Tests the get_sp_from_str function with different values of frequency"""
seasonal_period = prefix + seasonal_period
prefix = int(prefix)
lcm = abs(seasonal_value * prefix) // math.gcd(seasonal_value, prefix)
expected_candidate_sps = [int(lcm / prefix)]
exp = TSForecastingExperiment()
fh = np.arange(1, 13)
fold = 2
data = load_pos_and_neg_data
exp.setup(
data=data,
fh=fh,
fold=fold,
fold_strategy="sliding",
verbose=False,
seasonal_period=seasonal_period,
)
assert exp.candidate_sps == expected_candidate_sps
def test_train_test_split_uni_no_exo(load_pos_and_neg_data):
"""Tests the train-test splits for univariate time series without exogenous variables"""
data = load_pos_and_neg_data
####################################
# Continuous fh without Gaps ####
####################################
# Integer fh ----
exp = TSForecastingExperiment()
fh = 12
exp.setup(data=data, fh=fh, session_id=42)
assert np.all(exp.dataset.index == data.index)
assert np.all(exp.train.index == data.iloc[: (len(data) - fh)].index)
assert np.all(exp.test.index == data.iloc[-fh:].index)
assert exp.X is None
assert np.all(exp.y.index == data.index)
assert exp.X_train is None
assert exp.X_test is None
assert np.all(exp.y_train.index == data.iloc[: (len(data) - fh)].index)
assert np.all(exp.y_test.index == data.iloc[-fh:].index)
assert np.all(exp.dataset_transformed.index == data.index)
assert np.all(exp.train_transformed.index == data.iloc[: (len(data) - fh)].index)
assert np.all(exp.test_transformed.index == data.iloc[-fh:].index)
assert exp.X_transformed is None
assert np.all(exp.y_transformed.index == data.index)
assert exp.X_train_transformed is None
assert exp.X_test_transformed is None
assert np.all(exp.y_train_transformed.index == data.iloc[: (len(data) - fh)].index)
assert np.all(exp.y_test_transformed.index == data.iloc[-fh:].index)
# Numpy fh ----
exp = TSForecastingExperiment()
fh = np.arange(1, 10) # 9 values
exp.setup(data=data, fh=fh, session_id=42)
assert np.all(exp.dataset.index == data.index)
assert np.all(exp.train.index == data.iloc[: (len(data) - max(fh))].index)
assert np.all(exp.test.index == data.iloc[-len(fh) :].index)
assert exp.X is None
assert np.all(exp.y.index == data.index)
assert exp.X_train is None
assert exp.X_test is None
assert np.all(exp.y_train.index == data.iloc[: (len(data) - max(fh))].index)
assert np.all(exp.y_test.index == data.iloc[-len(fh) :].index)
assert np.all(exp.dataset_transformed.index == data.index)
assert np.all(
exp.train_transformed.index == data.iloc[: (len(data) - max(fh))].index
)
assert np.all(exp.test_transformed.index == data.iloc[-len(fh) :].index)
assert exp.X_transformed is None
assert np.all(exp.y_transformed.index == data.index)
assert exp.X_train_transformed is None
assert exp.X_test_transformed is None
assert np.all(
exp.y_train_transformed.index == data.iloc[: (len(data) - max(fh))].index
)
assert np.all(exp.y_test_transformed.index == data.iloc[-len(fh) :].index)
# List fh ----
exp = TSForecastingExperiment()
fh = [1, 2, 3, 4, 5, 6]
exp.setup(data=data, fh=fh, session_id=42)
assert np.all(exp.dataset.index == data.index)
assert np.all(exp.train.index == data.iloc[: (len(data) - max(fh))].index)
assert np.all(exp.test.index == data.iloc[-len(fh) :].index)
assert exp.X is None
assert np.all(exp.y.index == data.index)
assert exp.X_train is None
assert exp.X_test is None
assert np.all(exp.y_train.index == data.iloc[: (len(data) - max(fh))].index)
assert np.all(exp.y_test.index == data.iloc[-len(fh) :].index)
assert np.all(exp.dataset_transformed.index == data.index)
assert np.all(
exp.train_transformed.index == data.iloc[: (len(data) - max(fh))].index
)
assert np.all(exp.test_transformed.index == data.iloc[-len(fh) :].index)
assert exp.X_transformed is None
assert np.all(exp.y_transformed.index == data.index)
assert exp.X_train_transformed is None
assert exp.X_test_transformed is None
assert np.all(
exp.y_train_transformed.index == data.iloc[: (len(data) - max(fh))].index
)
assert np.all(exp.y_test_transformed.index == data.iloc[-len(fh) :].index)
#################################
# Continuous fh with Gaps ####
#################################
# Numpy fh ----
exp = TSForecastingExperiment()
fh = np.arange(7, 13) # 6 values
exp.setup(data=data, fh=fh, session_id=42)
assert np.all(exp.dataset.index == data.index)
assert np.all(exp.train.index == data.iloc[: (len(data) - max(fh))].index)
assert len(exp.test) == len(fh)
assert exp.X is None
assert np.all(exp.y.index == data.index)
assert exp.X_train is None
assert exp.X_test is None
assert np.all(exp.y_train.index == data.iloc[: (len(data) - max(fh))].index)
assert len(exp.y_test) == len(fh)
assert np.all(exp.dataset_transformed.index == data.index)
assert np.all(
exp.train_transformed.index == data.iloc[: (len(data) - max(fh))].index
)
assert len(exp.test_transformed) == len(fh)
assert exp.X_transformed is None
assert np.all(exp.y_transformed.index == data.index)
assert exp.X_train_transformed is None
assert exp.X_test_transformed is None
assert np.all(
exp.y_train_transformed.index == data.iloc[: (len(data) - max(fh))].index
)
assert len(exp.y_test_transformed) == len(fh)
# List fh ----
exp = TSForecastingExperiment()
fh = [4, 5, 6]
exp.setup(data=data, fh=fh, session_id=42)
assert np.all(exp.dataset.index == data.index)
assert np.all(exp.train.index == data.iloc[: (len(data) - max(fh))].index)
assert len(exp.test) == len(fh)
assert exp.X is None
assert np.all(exp.y.index == data.index)
assert exp.X_train is None
assert exp.X_test is None
assert np.all(exp.y_train.index == data.iloc[: (len(data) - max(fh))].index)
assert len(exp.y_test) == len(fh)
assert np.all(exp.dataset_transformed.index == data.index)
assert np.all(
exp.train_transformed.index == data.iloc[: (len(data) - max(fh))].index
)
assert len(exp.test_transformed) == len(fh)
assert exp.X_transformed is None
assert np.all(exp.y_transformed.index == data.index)
assert exp.X_train_transformed is None
assert exp.X_test_transformed is None
assert np.all(
exp.y_train_transformed.index == data.iloc[: (len(data) - max(fh))].index
)
assert len(exp.y_test_transformed) == len(fh)
####################################
# Discontinuous fh with Gaps ####
####################################
# Numpy fh ----
exp = TSForecastingExperiment()
fh = np.array([4, 5, 6, 10, 11, 12]) # 6 values
exp.setup(data=data, fh=fh, session_id=42)
assert np.all(exp.dataset.index == data.index)
assert np.all(exp.train.index == data.iloc[: (len(data) - max(fh))].index)
assert len(exp.test) == len(fh)
assert exp.X is None
assert np.all(exp.y.index == data.index)
assert exp.X_train is None
assert exp.X_test is None
assert np.all(exp.y_train.index == data.iloc[: (len(data) - max(fh))].index)
assert len(exp.y_test) == len(fh)
assert np.all(exp.dataset_transformed.index == data.index)
assert np.all(
exp.train_transformed.index == data.iloc[: (len(data) - max(fh))].index
)
assert len(exp.test_transformed) == len(fh)
assert exp.X_transformed is None
assert np.all(exp.y_transformed.index == data.index)
assert exp.X_train_transformed is None
assert exp.X_test_transformed is None
assert np.all(
exp.y_train_transformed.index == data.iloc[: (len(data) - max(fh))].index
)
assert len(exp.y_test_transformed) == len(fh)
# List fh ----
exp = TSForecastingExperiment()
fh = [4, 5, 6, 10, 11, 12]
exp.setup(data=data, fh=fh, session_id=42)
assert np.all(exp.dataset.index == data.index)
assert np.all(exp.train.index == data.iloc[: (len(data) - max(fh))].index)
assert len(exp.test) == len(fh)
assert exp.X is None
assert np.all(exp.y.index == data.index)
assert exp.X_train is None
assert exp.X_test is None
assert np.all(exp.y_train.index == data.iloc[: (len(data) - max(fh))].index)
assert len(exp.y_test) == len(fh)
assert np.all(exp.dataset_transformed.index == data.index)
assert np.all(
exp.train_transformed.index == data.iloc[: (len(data) - max(fh))].index
)
assert len(exp.test_transformed) == len(fh)
assert exp.X_transformed is None
assert np.all(exp.y_transformed.index == data.index)
assert exp.X_train_transformed is None
assert exp.X_test_transformed is None
assert np.all(
exp.y_train_transformed.index == data.iloc[: (len(data) - max(fh))].index
)
assert len(exp.y_test_transformed) == len(fh)
def test_train_test_split_uni_exo(load_uni_exo_data_target):
"""Tests the train-test splits for univariate time series with exogenous variables"""
data, target = load_uni_exo_data_target
####################################
# Continuous fh without Gaps ####
####################################
# Integer fh ----
exp = TSForecastingExperiment()
fh = 12
exp.setup(data=data, target=target, fh=fh, session_id=42)
assert np.all(exp.dataset.index == data.index)
assert np.all(exp.train.index == data.iloc[: (len(data) - fh)].index)
assert np.all(exp.test.index == data.iloc[-fh:].index)
assert np.all(exp.X.index == data.index)
assert np.all(exp.y.index == data.index)
assert np.all(exp.X_train.index == data.iloc[: (len(data) - fh)].index)
assert np.all(exp.X_test.index == data.iloc[-fh:].index)
assert np.all(exp.y_train.index == data.iloc[: (len(data) - fh)].index)
assert np.all(exp.y_test.index == data.iloc[-fh:].index)
assert np.all(exp.dataset_transformed.index == data.index)
assert np.all(exp.train_transformed.index == data.iloc[: (len(data) - fh)].index)
assert np.all(exp.test_transformed.index == data.iloc[-fh:].index)
assert np.all(exp.X_transformed.index == data.index)
assert np.all(exp.y_transformed.index == data.index)
assert np.all(exp.X_train_transformed.index == data.iloc[: (len(data) - fh)].index)
assert np.all(exp.X_test_transformed.index == data.iloc[-fh:].index)
assert np.all(exp.y_train_transformed.index == data.iloc[: (len(data) - fh)].index)
assert np.all(exp.y_test_transformed.index == data.iloc[-fh:].index)
# Numpy fh ----
exp = TSForecastingExperiment()
fh = np.arange(1, 10) # 9 values
exp.setup(data=data, target=target, fh=fh, session_id=42)
assert np.all(exp.dataset.index == data.index)
assert np.all(exp.train.index == data.iloc[: (len(data) - max(fh))].index)
assert np.all(exp.test.index == data.iloc[-len(fh) :].index)
assert np.all(exp.X.index == data.index)
assert np.all(exp.y.index == data.index)
assert np.all(exp.X_train.index == data.iloc[: (len(data) - max(fh))].index)
assert np.all(exp.X_test.index == data.iloc[-len(fh) :].index)
assert np.all(exp.y_train.index == data.iloc[: (len(data) - max(fh))].index)
assert np.all(exp.y_test.index == data.iloc[-len(fh) :].index)
assert np.all(exp.dataset_transformed.index == data.index)
assert np.all(
exp.train_transformed.index == data.iloc[: (len(data) - max(fh))].index
)
assert np.all(exp.test_transformed.index == data.iloc[-len(fh) :].index)
assert np.all(exp.X_transformed.index == data.index)
assert np.all(exp.y_transformed.index == data.index)
assert np.all(
exp.X_train_transformed.index == data.iloc[: (len(data) - max(fh))].index
)
assert np.all(exp.X_test_transformed.index == data.iloc[-len(fh) :].index)
assert np.all(
exp.y_train_transformed.index == data.iloc[: (len(data) - max(fh))].index
)
assert np.all(exp.y_test_transformed.index == data.iloc[-len(fh) :].index)
# List fh ----
exp = TSForecastingExperiment()
fh = [1, 2, 3, 4, 5, 6]
exp.setup(data=data, target=target, fh=fh, session_id=42)
assert np.all(exp.dataset.index == data.index)
assert np.all(exp.train.index == data.iloc[: (len(data) - max(fh))].index)
assert np.all(exp.test.index == data.iloc[-len(fh) :].index)
assert np.all(exp.X.index == data.index)
assert np.all(exp.y.index == data.index)
assert np.all(exp.X_train.index == data.iloc[: (len(data) - max(fh))].index)
assert np.all(exp.X_test.index == data.iloc[-len(fh) :].index)
assert np.all(exp.y_train.index == data.iloc[: (len(data) - max(fh))].index)
assert np.all(exp.y_test.index == data.iloc[-len(fh) :].index)
assert np.all(exp.dataset_transformed.index == data.index)
assert np.all(
exp.train_transformed.index == data.iloc[: (len(data) - max(fh))].index
)
assert np.all(exp.test_transformed.index == data.iloc[-len(fh) :].index)
assert np.all(exp.X_transformed.index == data.index)
assert np.all(exp.y_transformed.index == data.index)
assert np.all(
exp.X_train_transformed.index == data.iloc[: (len(data) - max(fh))].index
)
assert np.all(exp.X_test_transformed.index == data.iloc[-len(fh) :].index)
assert np.all(
exp.y_train_transformed.index == data.iloc[: (len(data) - max(fh))].index
)
assert np.all(exp.y_test_transformed.index == data.iloc[-len(fh) :].index)
#################################
# Continuous fh with Gaps ####
#################################
# Numpy fh ----
exp = TSForecastingExperiment()
fh = np.arange(7, 13) # 6 values
exp.setup(data=data, target=target, fh=fh, session_id=42)
assert np.all(exp.dataset.index == data.index)
assert np.all(exp.train.index == data.iloc[: (len(data) - max(fh))].index)
# `test`` call still refers to y_test indices and not X_test indices
assert len(exp.test) == len(fh)
assert np.all(exp.X.index == data.index)
assert np.all(exp.y.index == data.index)
assert np.all(exp.X_train.index == data.iloc[: (len(data) - max(fh))].index)
# Exogenous variables will not have any gaps (only target has gaps)
assert np.all(exp.X_test.index == data.iloc[-max(fh) :].index)
assert np.all(exp.y_train.index == data.iloc[: (len(data) - max(fh))].index)
assert len(exp.y_test) == len(fh)
assert np.all(exp.dataset_transformed.index == data.index)
assert np.all(
exp.train_transformed.index == data.iloc[: (len(data) - max(fh))].index
)
assert len(exp.test_transformed) == len(fh)
assert np.all(exp.X_transformed.index == data.index)
assert np.all(exp.y_transformed.index == data.index)
# List fh ----
exp = TSForecastingExperiment()
fh = [4, 5, 6]
exp.setup(data=data, target=target, fh=fh, session_id=42)
assert np.all(exp.dataset.index == data.index)
assert np.all(exp.train.index == data.iloc[: (len(data) - max(fh))].index)
# `test`` call still refers to y_test indices and not X_test indices
assert len(exp.test) == len(fh)
assert np.all(exp.X.index == data.index)
assert np.all(exp.y.index == data.index)
assert np.all(exp.X_train.index == data.iloc[: (len(data) - max(fh))].index)
# Exogenous variables will not have any gaps (only target has gaps)
assert np.all(exp.X_test.index == data.iloc[-max(fh) :].index)
assert np.all(exp.y_train.index == data.iloc[: (len(data) - max(fh))].index)
assert len(exp.y_test) == len(fh)
assert np.all(exp.dataset_transformed.index == data.index)
assert np.all(
exp.train_transformed.index == data.iloc[: (len(data) - max(fh))].index
)
assert len(exp.test_transformed) == len(fh)
assert np.all(exp.X_transformed.index == data.index)
assert np.all(exp.y_transformed.index == data.index)
####################################
# Discontinuous fh with Gaps ####
####################################
# Numpy fh ----
exp = TSForecastingExperiment()
fh = np.array([4, 5, 6, 10, 11, 12]) # 6 values
exp.setup(data=data, target=target, fh=fh, session_id=42)
assert np.all(exp.dataset.index == data.index)
assert np.all(exp.train.index == data.iloc[: (len(data) - max(fh))].index)
# `test`` call still refers to y_test indices and not X_test indices
assert len(exp.test) == len(fh)
assert np.all(exp.X.index == data.index)
assert np.all(exp.y.index == data.index)
assert np.all(exp.X_train.index == data.iloc[: (len(data) - max(fh))].index)
# Exogenous variables will not have any gaps (only target has gaps)
assert np.all(exp.X_test.index == data.iloc[-max(fh) :].index)
assert np.all(exp.y_train.index == data.iloc[: (len(data) - max(fh))].index)
assert len(exp.y_test) == len(fh)
assert np.all(exp.dataset_transformed.index == data.index)
assert np.all(
exp.train_transformed.index == data.iloc[: (len(data) - max(fh))].index
)
assert len(exp.test_transformed) == len(fh)
assert np.all(exp.X_transformed.index == data.index)
assert np.all(exp.y_transformed.index == data.index)
assert np.all(
exp.X_train_transformed.index == data.iloc[: (len(data) - max(fh))].index
)
# Exogenous variables will not have any gaps (only target has gaps)
assert np.all(exp.X_test_transformed.index == data.iloc[-max(fh) :].index)
assert np.all(
exp.y_train_transformed.index == data.iloc[: (len(data) - max(fh))].index
)
assert len(exp.y_test_transformed) == len(fh)
# List fh ----
exp = TSForecastingExperiment()
fh = [4, 5, 6, 10, 11, 12]
exp.setup(data=data, target=target, fh=fh, session_id=42)
assert np.all(exp.dataset.index == data.index)
assert np.all(exp.train.index == data.iloc[: (len(data) - max(fh))].index)
# `test`` call still refers to y_test indices and not X_test indices
assert len(exp.test) == len(fh)
assert np.all(exp.X.index == data.index)
assert np.all(exp.y.index == data.index)
assert np.all(exp.X_train.index == data.iloc[: (len(data) - max(fh))].index)
# Exogenous variables will not have any gaps (only target has gaps)
assert np.all(exp.X_test.index == data.iloc[-max(fh) :].index)
assert np.all(exp.y_train.index == data.iloc[: (len(data) - max(fh))].index)
assert len(exp.y_test) == len(fh)
assert np.all(exp.dataset_transformed.index == data.index)
assert np.all(
exp.train_transformed.index == data.iloc[: (len(data) - max(fh))].index
)
assert len(exp.test_transformed) == len(fh)
assert np.all(exp.X_transformed.index == data.index)
assert np.all(exp.y_transformed.index == data.index)
assert np.all(
exp.X_train_transformed.index == data.iloc[: (len(data) - max(fh))].index
)
# Exogenous variables will not have any gaps (only target has gaps)
assert np.all(exp.X_test_transformed.index == data.iloc[-max(fh) :].index)
assert np.all(
exp.y_train_transformed.index == data.iloc[: (len(data) - max(fh))].index
)
assert len(exp.y_test_transformed) == len(fh)
def test_missing_indices():
"""Tests setup when data has missing indices"""
data = pd.read_csv(
"https://raw.githubusercontent.com/facebook/prophet/main/examples/example_wp_log_peyton_manning.csv"
)
data["ds"] = pd.to_datetime(data["ds"])
data.set_index("ds", inplace=True)
data.index = data.index.to_period("D")
data.info()
exp = TSForecastingExperiment()
with pytest.raises(ValueError) as errmsg:
exp.setup(data=data, fh=365, session_id=42)
exceptionmsg = errmsg.value.args[0]
assert "Data has missing indices!" in exceptionmsg