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test_time_series_utils.py
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test_time_series_utils.py
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import numpy as np
import pandas as pd
import pytest
from pycaret.utils.time_series import (
SeasonalPeriod,
clean_time_index,
remove_harmonics_from_sp,
)
def test_harmonic_removal():
"""Tests the removal of harmonics"""
# 1.0 No harmonics removed ----
results = remove_harmonics_from_sp([2, 51, 5])
assert results == [2, 51, 5]
# 2.0 One base frequency removed ----
results = remove_harmonics_from_sp([2, 52, 3])
assert results == [52, 3]
# 3.0 Remove more than one base period ----
results = remove_harmonics_from_sp([50, 3, 11, 100, 39])
assert results == [11, 100, 39]
# 4.0 Order of replacement ----
# 4.1 Only one removed
# 4.1A Ordered by raw strength
results = remove_harmonics_from_sp([2, 3, 4, 50])
assert results == [3, 4, 50]
# 4.1B Ordered by harmonic max
results = remove_harmonics_from_sp(
[2, 3, 4, 50], harmonic_order_method="harmonic_max"
)
assert results == [50, 3, 4]
# 4.1C Ordered by harmonic strength
results = remove_harmonics_from_sp(
[2, 3, 4, 50], harmonic_order_method="harmonic_strength"
)
assert results == [4, 3, 50]
# 4.2 More than one removed
# 4.2A Ordered by raw strength
results = remove_harmonics_from_sp([3, 2, 6, 50])
assert results == [6, 50]
# 4.2B Ordered by harmonic max
results = remove_harmonics_from_sp(
[3, 2, 6, 50], harmonic_order_method="harmonic_max"
)
assert results == [6, 50]
results = remove_harmonics_from_sp(
[2, 3, 6, 50], harmonic_order_method="harmonic_max"
)
assert results == [50, 6]
# 4.2C Ordered by harmonic strength
results = remove_harmonics_from_sp(
[3, 2, 6, 50], harmonic_order_method="harmonic_strength"
)
assert results == [6, 50]
results = remove_harmonics_from_sp(
[2, 3, 6, 50], harmonic_order_method="harmonic_strength"
)
assert results == [6, 50]
# 4.2D Other variants
results = remove_harmonics_from_sp(
[10, 20, 30, 40, 50, 60], harmonic_order_method="harmonic_strength"
)
assert results == [20, 40, 60, 50]
results = remove_harmonics_from_sp(
[10, 20, 30, 40, 50, 60], harmonic_order_method="harmonic_max"
)
assert results == [60, 40, 50]
# 5.0 These were giving precision issues earlier. Now fixed by rounding internally. ----
# 5.1
results = remove_harmonics_from_sp([50, 100, 150, 49, 200, 51, 23, 27, 10, 250])
assert results == [150, 49, 200, 51, 23, 27, 250]
# 5.2
results = remove_harmonics_from_sp([49, 98, 18])
assert results == [98, 18]
# 5.3
results = remove_harmonics_from_sp([50, 16, 15, 17, 34, 2, 33, 49, 18, 100, 32])
assert results == [15, 34, 33, 49, 18, 100, 32]
def _get_seasonal_keys():
return [freq for freq, _ in SeasonalPeriod.__members__.items()]
@pytest.mark.parametrize("freq", _get_seasonal_keys())
@pytest.mark.parametrize("index", [True, False])
def test_clean_time_index_datetime(freq, index):
"""Test clean_time_index utility when index/column is of type DateTime"""
dates = pd.date_range("2019-01-01", "2022-01-30", freq=freq)
# At least 3 data points to allow test to insert a missing index in the middle
# but not so many data points that the code slows down too much.
if len(dates) > 100:
dates = dates[:100]
assert len(dates) >= 3
data = pd.DataFrame(
{
"date": dates,
"value": np.random.rand(len(dates)),
}
)
if index:
data.set_index("date", inplace=True)
index_col = None
else:
index_col = "date"
# -------------------------------------------------------------------------#
# Test 1: Cleaning without any missing time index
# -------------------------------------------------------------------------#
try:
cleaned = clean_time_index(data=data, index_col=index_col, freq=freq)
except AttributeError:
# Unsupported freq conversion from DatetimeIndex to PeriodIndex
return
assert len(cleaned) == len(data)
# -------------------------------------------------------------------------#
# Test 2: Cleaning with any missing time index
# -------------------------------------------------------------------------#
# Drop 2nd row
data_missing = data.copy()
data_missing = data_missing.drop(data_missing.index[1])
cleaned = clean_time_index(data=data_missing, index_col=index_col, freq=freq)
# Datetime Index missing values are filled in by clean_time_index
assert len(cleaned) == len(data)
@pytest.mark.parametrize("freq", _get_seasonal_keys())
@pytest.mark.parametrize("index", [False])
def test_clean_time_index_str_datetime(freq, index):
"""Test clean_time_index utility when index/column is of type str in format
acceptable to DatetimeIndex
NOTE: Index can not be string (only column). Code unchanges, just parameter
restricted to False
"""
dates = pd.date_range("2019-01-01 00:00:00", "2022-01-30 00:00:00", freq=freq)
# At least 3 data points to allow test to insert a missing index in the middle
# but not so many data points that the code slows down too much.
if len(dates) > 100:
dates = dates[:100]
assert len(dates) >= 3
dates = dates.strftime("%Y-%m-%d %H:%M:%S")
data = pd.DataFrame(
{
"date": dates,
"value": np.random.rand(len(dates)),
}
)
if index:
data.set_index("date", inplace=True)
index_col = None
else:
index_col = "date"
# -------------------------------------------------------------------------#
# Test 1: Cleaning without any missing time index
# -------------------------------------------------------------------------#
try:
cleaned = clean_time_index(data=data, index_col=index_col, freq=freq)
except AttributeError:
# Unsupported freq conversion from DatetimeIndex to PeriodIndex
return
assert len(cleaned) == len(data)
# -------------------------------------------------------------------------#
# Test 2: Cleaning with any missing time index
# -------------------------------------------------------------------------#
# Drop 2nd row
data_missing = data.copy()
data_missing = data_missing.drop(data_missing.index[1])
cleaned = clean_time_index(data=data_missing, index_col=index_col, freq=freq)
# Datetime Index missing values are filled in by clean_time_index
assert len(cleaned) == len(data)
@pytest.mark.parametrize("freq", _get_seasonal_keys())
@pytest.mark.parametrize("index", [True, False])
def test_clean_time_index_period(freq, index):
"""Test clean_time_index utility when index/column is of type Period"""
try:
dates = pd.period_range("2019-01-01", "2022-01-30", freq=freq)
except ValueError:
# Unsupported freq for PeriodIndex
return
# At least 3 data points to allow test to insert a missing index in the middle
# but not so many data points that the code slows down too much.
if len(dates) > 100:
dates = dates[:100]
assert len(dates) >= 3
data = pd.DataFrame(
{
"date": dates,
"value": np.random.rand(len(dates)),
}
)
if index:
data.set_index("date", inplace=True)
index_col = None
else:
index_col = "date"
# -------------------------------------------------------------------------#
# Test 1: Cleaning without any missing time index
# -------------------------------------------------------------------------#
cleaned = clean_time_index(data=data, index_col=index_col, freq=freq)
assert len(cleaned) == len(data)
# -------------------------------------------------------------------------#
# Test 2: Cleaning with any missing time index
# -------------------------------------------------------------------------#
# Drop 2nd row
data_missing = data.copy()
data_missing = data_missing.drop(data_missing.index[1])
cleaned = clean_time_index(data=data_missing, index_col=index_col, freq=freq)
# Period Index missing values are filled in by clean_time_index
assert len(cleaned) == len(data)
@pytest.mark.parametrize("freq", _get_seasonal_keys())
@pytest.mark.parametrize("index", [False])
def test_clean_time_index_str_period(freq, index):
"""Test clean_time_index utility when index/column is of type str in format
acceptable to PeriodIndex
NOTE: Index can not be string (only column). Code unchanges, just parameter
restricted to False
"""
try:
dates = pd.period_range("2019-01-01", "2022-01-30", freq=freq)
except ValueError:
# Unsupported freq for PeriodIndex
return
# At least 3 data points to allow test to insert a missing index in the middle
# but not so many data points that the code slows down too much.
if len(dates) > 100:
dates = dates[:100]
assert len(dates) >= 3
dates = dates.astype(str)
data = pd.DataFrame(
{
"date": dates,
"value": np.random.rand(len(dates)),
}
)
if index:
data.set_index("date", inplace=True)
index_col = None
else:
index_col = "date"
# -------------------------------------------------------------------------#
# Test 1: Cleaning without any missing time index
# -------------------------------------------------------------------------#
cleaned = clean_time_index(data=data, index_col=index_col, freq=freq)
assert len(cleaned) == len(data)
# -------------------------------------------------------------------------#
# Test 2: Cleaning with any missing time index
# -------------------------------------------------------------------------#
# Drop 2nd row
data_missing = data.copy()
data_missing = data_missing.drop(data_missing.index[1])
cleaned = clean_time_index(data=data_missing, index_col=index_col, freq=freq)
# Period Index missing values are filled in by clean_time_index
assert len(cleaned) == len(data)
@pytest.mark.parametrize("freq", _get_seasonal_keys())
@pytest.mark.parametrize("index", [True, False])
def test_clean_time_index_int(freq, index):
"""Test clean_time_index utility when index/column is of type Int"""
dates = np.arange(100)
# At least 3 data points to allow test to insert a missing index in the middle
# but not so many data points that the code slows down too much.
if len(dates) > 100:
dates = dates[:100]
assert len(dates) >= 3
data = pd.DataFrame(
{
"date": dates,
"value": np.random.rand(len(dates)),
}
)
if index:
data.set_index("date", inplace=True)
index_col = None
else:
index_col = "date"
# -------------------------------------------------------------------------#
# Test 1: Cleaning without any missing time index
# -------------------------------------------------------------------------#
cleaned = clean_time_index(data=data, index_col=index_col, freq=freq)
assert len(cleaned) == len(data)
# -------------------------------------------------------------------------#
# Test 2: Cleaning with any missing time index
# -------------------------------------------------------------------------#
# Drop 2nd row
data_missing = data.copy()
data_missing = data_missing.drop(data_missing.index[1])
cleaned = clean_time_index(data=data_missing, index_col=index_col, freq=freq)
# Int Index is untouched by clean_time_index
assert len(cleaned) == len(data) - 1