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test_series_name.py
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test_series_name.py
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"Check if the series name or column name is correctly kept."
import sys
import pandas as pd
import numpy as np
import pytest
import adtk.detector as detector
import adtk.transformer as transformer
from sklearn.neighbors import LocalOutlierFactor
from sklearn.cluster import KMeans
from sklearn.linear_model import LinearRegression
one2one_models = [
detector.ThresholdAD(),
detector.QuantileAD(),
detector.InterQuartileRangeAD(),
detector.GeneralizedESDTestAD(),
detector.PersistAD(),
detector.LevelShiftAD(),
detector.VolatilityShiftAD(),
detector.AutoregressionAD(),
detector.SeasonalAD(freq=2),
transformer.RollingAggregate(agg="median"),
transformer.RollingAggregate(agg="quantile", agg_params={"q": 0.5}),
transformer.DoubleRollingAggregate(agg="median"),
transformer.DoubleRollingAggregate(
agg="quantile", agg_params={"q": [0.1, 0.5, 0.9]}
),
transformer.DoubleRollingAggregate(
agg="hist", agg_params={"bins": [30, 50, 70]}
),
transformer.StandardScale(),
transformer.ClassicSeasonalDecomposition(freq=2),
]
one2many_models = [
transformer.RollingAggregate(
agg="quantile", agg_params={"q": [0.1, 0.5, 0.9]}
),
transformer.RollingAggregate(
agg="hist", agg_params={"bins": [20, 50, 80]}
),
transformer.Retrospect(n_steps=3),
]
many2one_models = [
detector.MinClusterDetector(KMeans(n_clusters=2)),
detector.OutlierDetector(
LocalOutlierFactor(n_neighbors=20, contamination=0.1)
),
detector.RegressionAD(regressor=LinearRegression()),
detector.PcaAD(),
transformer.SumAll(),
transformer.RegressionResidual(LinearRegression()),
transformer.PcaReconstructionError(),
]
@pytest.mark.parametrize("model", one2one_models)
def test_one2one_s2s_wo_name(model):
s_name = pd.Series(
np.arange(100),
index=pd.date_range(start="2017-1-1", periods=100, freq="D"),
name="A",
)
result = model.fit_predict(s_name)
assert result.name == "A"
@pytest.mark.parametrize("model", one2one_models)
def test_one2one_s2s_w_name(model):
s_no_name = pd.Series(
np.arange(100),
index=pd.date_range(start="2017-1-1", periods=100, freq="D"),
)
result = model.fit_predict(s_no_name)
assert result.name is None
@pytest.mark.parametrize("model", one2one_models)
def test_one2one_df2df(model):
df = pd.DataFrame(
np.arange(300).reshape(100, 3),
index=pd.date_range(start="2017-1-1", periods=100, freq="D"),
columns=["A", "B", "C"],
)
result = model.fit_predict(df)
assert list(result.columns) == ["A", "B", "C"]
@pytest.mark.parametrize("model", one2one_models)
def test_one2one_df2list(model):
if hasattr(model, "fit_detect"):
df = pd.DataFrame(
np.arange(300).reshape(100, 3),
index=pd.date_range(start="2017-1-1", periods=100, freq="D"),
columns=["A", "B", "C"],
)
result = model.fit_detect(df, return_list=True)
if sys.version_info[1] >= 6:
assert list(result.keys()) == ["A", "B", "C"]
else:
assert set(result.keys()) == {"A", "B", "C"}
@pytest.mark.parametrize("model", one2many_models)
def test_one2many_s2df_w_name(model):
s_name = pd.Series(
np.arange(100),
index=pd.date_range(start="2017-1-1", periods=100, freq="D"),
name="A",
)
result = model.fit_predict(s_name)
assert all([col[:2] == "A_" for col in result.columns])
assert all([col[2:4] != "A_" for col in result.columns])
@pytest.mark.parametrize("model", one2many_models)
def test_one2many_s2df_wo_name(model):
s_no_name = pd.Series(
np.arange(100),
index=pd.date_range(start="2017-1-1", periods=100, freq="D"),
)
result = model.fit_predict(s_no_name)
assert all([col[:2] != "A_" for col in result.columns])
@pytest.mark.parametrize("model", one2many_models)
def test_one2many_df2df(model):
df = pd.DataFrame(
np.arange(300).reshape(100, 3),
index=pd.date_range(start="2017-1-1", periods=100, freq="D"),
columns=["A", "B", "C"],
)
result = model.fit_predict(df)
n_cols = round(len(result.columns) / 3)
assert all([col[:2] == "A_" for col in result.columns[:n_cols]])
assert all([col[2:4] != "A_" for col in result.columns[:n_cols]])
assert all(
[col[:2] == "B_" for col in result.columns[n_cols : 2 * n_cols]]
)
assert all(
[col[2:4] != "B_" for col in result.columns[n_cols : 2 * n_cols]]
)
assert all([col[:2] == "C_" for col in result.columns[2 * n_cols :]])
assert all([col[2:4] != "C_" for col in result.columns[2 * n_cols :]])
@pytest.mark.parametrize("model", many2one_models)
def test_many2one(model):
df = pd.DataFrame(
np.arange(300).reshape(100, 3),
index=pd.date_range(start="2017-1-1", periods=100, freq="D"),
columns=["A", "B", "C"],
)
result = model.fit_predict(df)
assert result.name is None
def test_pca_reconstruction():
df = pd.DataFrame(
np.arange(300).reshape(100, 3),
index=pd.date_range(start="2017-1-1", periods=100, freq="D"),
columns=["A", "B", "C"],
)
result = transformer.PcaReconstruction(k=2).fit_predict(df)
assert list(result.columns) == ["A", "B", "C"]