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test_transformerhd.py
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test_transformerhd.py
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"""Test HD transformers."""
import numpy as np
import pandas as pd
import pytest
from sklearn.linear_model import LinearRegression
import adtk.transformer as transformer
from adtk._base import _TrainableModel
nan = float("nan")
testCases = [
{
"model": transformer.CustomizedTransformerHD,
"params": {"transform_func": lambda x: x.sum(axis=1) > 0},
"df": [
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
],
"t": [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
},
{
"model": transformer.CustomizedTransformerHD,
"params": {
"transform_func": lambda x, a: x.sum(axis=1) > a,
"transform_func_params": {"a": 0},
},
"df": [
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
],
"t": [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
},
{
"model": transformer.CustomizedTransformerHD,
"params": {
"transform_func": lambda x, a: x.sum(axis=1) > a,
"fit_func": lambda x: {"a": x.sum(axis=1).median()},
},
"df": [
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
],
"t": [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
},
{
"model": transformer.CustomizedTransformerHD,
"params": {
"transform_func": lambda x, a: x.sum(axis=1) > a,
"fit_func": lambda x, q: {"a": x.sum(axis=1).quantile(q)},
"fit_func_params": {"q": 0.5},
},
"df": [
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
],
"t": [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
},
{
"model": transformer.CustomizedTransformerHD,
"params": {
"transform_func": lambda x, a, b: (x.sum(axis=1) > a)
| (x.sum(axis=1) < b),
"transform_func_params": {"b": -0.5},
"fit_func": lambda x: {"a": x.sum(axis=1).median()},
},
"df": [
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0],
],
"t": [0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0],
},
{
"model": transformer.CustomizedTransformerHD,
"params": {
"transform_func": lambda x, a, b: (x.sum(axis=1) > a)
| (x.sum(axis=1) < b),
"transform_func_params": {"b": -0.5},
"fit_func": lambda x, q: {"a": x.sum(axis=1).quantile(q)},
"fit_func_params": {"q": 0.5},
},
"df": [
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0],
],
"t": [0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0],
},
{
"model": transformer.CustomizedTransformerHD,
"params": {
"transform_func": lambda x: pd.DataFrame(
{"min": x.min(axis=1) > 0, "max": x.max(axis=1) > 0}
)
},
"df": [
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
],
"t": {
"min": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
"max": [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
},
},
{
"model": transformer.CustomizedTransformerHD,
"params": {
"transform_func": lambda x, a: pd.DataFrame(
{"min": x.min(axis=1) > a, "max": x.max(axis=1) > a}
),
"transform_func_params": {"a": 0},
},
"df": [
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
],
"t": {
"min": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
"max": [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
},
},
{
"model": transformer.CustomizedTransformerHD,
"params": {
"transform_func": lambda x, a: pd.DataFrame(
{"min": x.min(axis=1) > a, "max": x.max(axis=1) > a}
),
"fit_func": lambda x: {"a": x.sum(axis=1).median()},
},
"df": [
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
],
"t": {
"min": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
"max": [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
},
},
{
"model": transformer.CustomizedTransformerHD,
"params": {
"transform_func": lambda x, a: pd.DataFrame(
{"min": x.min(axis=1) > a, "max": x.max(axis=1) > a}
),
"fit_func": lambda x, q: {"a": x.sum(axis=1).quantile(q)},
"fit_func_params": {"q": 0.5},
},
"df": [
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
],
"t": {
"min": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
"max": [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
},
},
{
"model": transformer.CustomizedTransformerHD,
"params": {
"transform_func": lambda x, a, b: pd.DataFrame(
{
"min": (x.min(axis=1) > a) | (x.min(axis=1) < b),
"max": (x.max(axis=1) > a) | (x.max(axis=1) < b),
}
),
"transform_func_params": {"b": -0.5},
"fit_func": lambda x: {"a": x.sum(axis=1).median()},
},
"df": [
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0],
],
"t": {
"min": [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],
"max": [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
},
},
{
"model": transformer.CustomizedTransformerHD,
"params": {
"transform_func": lambda x, a, b: pd.DataFrame(
{
"min": (x.min(axis=1) > a) | (x.min(axis=1) < b),
"max": (x.max(axis=1) > a) | (x.max(axis=1) < b),
}
),
"transform_func_params": {"b": -0.5},
"fit_func": lambda x, q: {"a": x.sum(axis=1).quantile(q)},
"fit_func_params": {"q": 0.5},
},
"df": [
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0],
],
"t": {
"min": [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],
"max": [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
},
},
{
"model": transformer.RegressionResidual,
"params": {"regressor": LinearRegression(), "target": 1},
"df": [
[0, 1, 2, 3, 4, 5, 6, 7, 7, 8, 9],
[9, 8, 7, 6, 5, 4, 3, 2, nan, 1, 0],
[9] * 11,
],
"t": [0] * 8 + [nan] + [0] * 2,
},
{
"model": transformer.PcaProjection,
"params": {"k": 1},
"df": [[0, 1, 2, 3, 4, 4, nan, 5, 6], [0, 1, 2, 3, nan, 4, 5, 5, 6]],
"t": {
"pc0": [
3 * 2 ** 0.5,
2 * 2 ** 0.5,
1 * 2 ** 0.5,
0 * 2 ** 0.5,
nan,
-1 * 2 ** 0.5,
nan,
-2 * 2 ** 0.5,
-3 * 2 ** 0.5,
]
},
},
{
"model": transformer.PcaReconstruction,
"params": {"k": 1},
"df": [
[0, 1, 2, 3, 3.9, 4.1, 5, 6, 7, 7, 8, 9],
[0, 1, 2, 3, 4.1, 3.9, 5, 6, 7, nan, 8, 9],
],
"t": {
0: [0, 1, 2, 3, 4, 4, 5, 6, 7, nan, 8, 9],
1: [0, 1, 2, 3, 4, 4, 5, 6, 7, nan, 8, 9],
},
},
{
"model": transformer.PcaReconstructionError,
"params": {"k": 1},
"df": [
[0, 1, 2, 3, 3.9, 4.1, 5, 6, 7, 7, 8, 9],
[0, 1, 2, 3, 4.1, 3.9, 5, 6, 7, nan, 8, 9],
],
"t": [0, 0, 0, 0, 0.02, 0.02, 0, 0, 0, nan, 0, 0],
},
]
@pytest.mark.parametrize("testCase", testCases)
def test_fit_transform(testCase):
"""Test fit_transform the transformer."""
df = pd.DataFrame(
np.array(testCase["df"]).T,
pd.date_range(
start="2017-1-1", periods=len(testCase["df"][0]), freq="D"
),
)
model = testCase["model"](**testCase["params"])
if isinstance(model, _TrainableModel):
t = model.fit_transform(df)
else:
t = model.transform(df)
if not isinstance(testCase["t"], dict):
t_true = pd.Series(testCase["t"], index=df.index)
pd.testing.assert_series_equal(t, t_true, check_dtype=False)
else:
t_true = pd.DataFrame(testCase["t"], index=df.index)
pd.testing.assert_frame_equal(t, t_true, check_dtype=False)
@pytest.mark.parametrize("testCase", testCases)
def test_fit_and_transform(testCase):
"""Test fit the transformer and then transform."""
df = pd.DataFrame(
np.array(testCase["df"]).T,
pd.date_range(
start="2017-1-1", periods=len(testCase["df"][0]), freq="D"
),
)
model = testCase["model"](**testCase["params"])
if isinstance(model, _TrainableModel):
model.fit(df)
t = model.transform(df)
if not isinstance(testCase["t"], dict):
t_true = pd.Series(testCase["t"], index=df.index)
pd.testing.assert_series_equal(t, t_true, check_dtype=False)
else:
t_true = pd.DataFrame(testCase["t"], index=df.index)
pd.testing.assert_frame_equal(t, t_true, check_dtype=False)