def test_rolling_with_pivot_df_and_single_metric():
pivot_df = pivot(
df=single_metric_df,
index=["dttm"],
columns=["country"],
aggregates={"sum_metric": {
"operator": "sum"
}},
flatten_columns=False,
reset_index=False,
)
rolling_df = rolling(
df=pivot_df,
rolling_type="sum",
window=2,
min_periods=0,
is_pivot_df=True,
)
# dttm UK US
# 0 2019-01-01 5 6
# 1 2019-01-02 12 14
assert rolling_df["UK"].to_list() == [5.0, 12.0]
assert rolling_df["US"].to_list() == [6.0, 14.0]
assert (rolling_df["dttm"].to_list() == to_datetime(
["2019-01-01", "2019-01-02"]).to_list())
rolling_df = rolling(
df=pivot_df,
rolling_type="sum",
window=2,
min_periods=2,
is_pivot_df=True,
)
assert rolling_df.empty is True
def test_rolling_should_not_side_effect():
_timeseries_df = timeseries_df.copy()
pp.rolling(
df=timeseries_df,
columns={"y": "y"},
rolling_type="sum",
window=2,
min_periods=0,
)
assert _timeseries_df.equals(timeseries_df)
def test_rolling_with_pivot_df_and_multiple_metrics(self):
pivot_df = proc.pivot(
df=multiple_metrics_df,
index=["dttm"],
columns=["country"],
aggregates={
"sum_metric": {
"operator": "sum"
},
"count_metric": {
"operator": "sum"
},
},
flatten_columns=False,
reset_index=False,
)
rolling_df = proc.rolling(
df=pivot_df,
rolling_type="sum",
window=2,
min_periods=0,
is_pivot_df=True,
)
# dttm count_metric, UK count_metric, US sum_metric, UK sum_metric, US
# 0 2019-01-01 1.0 2.0 5.0 6.0
# 1 2019-01-02 4.0 6.0 12.0 14.0
assert rolling_df["count_metric, UK"].to_list() == [1.0, 4.0]
assert rolling_df["count_metric, US"].to_list() == [2.0, 6.0]
assert rolling_df["sum_metric, UK"].to_list() == [5.0, 12.0]
assert rolling_df["sum_metric, US"].to_list() == [6.0, 14.0]
assert (rolling_df["dttm"].to_list() == to_datetime([
"2019-01-01",
"2019-01-02",
]).to_list())
def test_rolling_after_pivot_with_multiple_metrics():
pivot_df = pp.pivot(
df=multiple_metrics_df,
index=["dttm"],
columns=["country"],
aggregates={
"sum_metric": {
"operator": "sum"
},
"count_metric": {
"operator": "sum"
},
},
flatten_columns=False,
reset_index=False,
)
"""
count_metric sum_metric
country UK US UK US
dttm
2019-01-01 1 2 5 6
2019-01-02 3 4 7 8
"""
rolling_df = pp.rolling(
df=pivot_df,
columns={
"count_metric": "count_metric",
"sum_metric": "sum_metric",
},
rolling_type="sum",
window=2,
min_periods=0,
)
"""
count_metric sum_metric
country UK US UK US
dttm
2019-01-01 1.0 2.0 5.0 6.0
2019-01-02 4.0 6.0 12.0 14.0
"""
flat_df = pp.flatten(rolling_df)
"""
dttm count_metric, UK count_metric, US sum_metric, UK sum_metric, US
0 2019-01-01 1.0 2.0 5.0 6.0
1 2019-01-02 4.0 6.0 12.0 14.0
"""
assert flat_df.equals(
pd.DataFrame(
data={
"dttm": pd.to_datetime(["2019-01-01", "2019-01-02"]),
FLAT_COLUMN_SEPARATOR.join(["count_metric", "UK"]): [1.0, 4.0],
FLAT_COLUMN_SEPARATOR.join(["count_metric", "US"]): [2.0, 6.0],
FLAT_COLUMN_SEPARATOR.join(["sum_metric", "UK"]): [5.0, 12.0],
FLAT_COLUMN_SEPARATOR.join(["sum_metric", "US"]): [6.0, 14.0],
}))
def test_rolling_should_empty_df():
pivot_df = pp.pivot(
df=single_metric_df,
index=["dttm"],
columns=["country"],
aggregates={"sum_metric": {
"operator": "sum"
}},
)
rolling_df = pp.rolling(
df=pivot_df,
rolling_type="sum",
window=2,
min_periods=2,
columns={"sum_metric": "sum_metric"},
)
assert rolling_df.empty is True
def test_rolling_after_pivot_with_single_metric():
pivot_df = pp.pivot(
df=single_metric_df,
index=["dttm"],
columns=["country"],
aggregates={"sum_metric": {
"operator": "sum"
}},
)
"""
sum_metric
country UK US
dttm
2019-01-01 5 6
2019-01-02 7 8
"""
rolling_df = pp.rolling(
df=pivot_df,
columns={"sum_metric": "sum_metric"},
rolling_type="sum",
window=2,
min_periods=0,
)
"""
sum_metric
country UK US
dttm
2019-01-01 5.0 6.0
2019-01-02 12.0 14.0
"""
flat_df = pp.flatten(rolling_df)
"""
dttm sum_metric, UK sum_metric, US
0 2019-01-01 5.0 6.0
1 2019-01-02 12.0 14.0
"""
assert flat_df.equals(
pd.DataFrame(
data={
"dttm": pd.to_datetime(["2019-01-01", "2019-01-02"]),
FLAT_COLUMN_SEPARATOR.join(["sum_metric", "UK"]): [5.0, 12.0],
FLAT_COLUMN_SEPARATOR.join(["sum_metric", "US"]): [6.0, 14.0],
}))
def test_rolling():
# sum rolling type
post_df = pp.rolling(
df=timeseries_df,
columns={"y": "y"},
rolling_type="sum",
window=2,
min_periods=0,
)
assert post_df.columns.tolist() == ["label", "y"]
assert series_to_list(post_df["y"]) == [1.0, 3.0, 5.0, 7.0]
# mean rolling type with alias
post_df = pp.rolling(
df=timeseries_df,
rolling_type="mean",
columns={"y": "y_mean"},
window=10,
min_periods=0,
)
assert post_df.columns.tolist() == ["label", "y", "y_mean"]
assert series_to_list(post_df["y_mean"]) == [1.0, 1.5, 2.0, 2.5]
# count rolling type
post_df = pp.rolling(
df=timeseries_df,
rolling_type="count",
columns={"y": "y"},
window=10,
min_periods=0,
)
assert post_df.columns.tolist() == ["label", "y"]
assert series_to_list(post_df["y"]) == [1.0, 2.0, 3.0, 4.0]
# quantile rolling type
post_df = pp.rolling(
df=timeseries_df,
columns={"y": "q1"},
rolling_type="quantile",
rolling_type_options={"quantile": 0.25},
window=10,
min_periods=0,
)
assert post_df.columns.tolist() == ["label", "y", "q1"]
assert series_to_list(post_df["q1"]) == [1.0, 1.25, 1.5, 1.75]
# incorrect rolling type
with pytest.raises(InvalidPostProcessingError):
pp.rolling(
df=timeseries_df,
columns={"y": "y"},
rolling_type="abc",
window=2,
)
# incorrect rolling type options
with pytest.raises(InvalidPostProcessingError):
pp.rolling(
df=timeseries_df,
columns={"y": "y"},
rolling_type="quantile",
rolling_type_options={"abc": 123},
window=2,
)
def test_rolling(self):
# sum rolling type
post_df = proc.rolling(
df=timeseries_df,
columns={"y": "y"},
rolling_type="sum",
window=2,
min_periods=0,
)
self.assertListEqual(post_df.columns.tolist(), ["label", "y"])
self.assertListEqual(series_to_list(post_df["y"]),
[1.0, 3.0, 5.0, 7.0])
# mean rolling type with alias
post_df = proc.rolling(
df=timeseries_df,
rolling_type="mean",
columns={"y": "y_mean"},
window=10,
min_periods=0,
)
self.assertListEqual(post_df.columns.tolist(),
["label", "y", "y_mean"])
self.assertListEqual(series_to_list(post_df["y_mean"]),
[1.0, 1.5, 2.0, 2.5])
# count rolling type
post_df = proc.rolling(
df=timeseries_df,
rolling_type="count",
columns={"y": "y"},
window=10,
min_periods=0,
)
self.assertListEqual(post_df.columns.tolist(), ["label", "y"])
self.assertListEqual(series_to_list(post_df["y"]),
[1.0, 2.0, 3.0, 4.0])
# quantile rolling type
post_df = proc.rolling(
df=timeseries_df,
columns={"y": "q1"},
rolling_type="quantile",
rolling_type_options={"quantile": 0.25},
window=10,
min_periods=0,
)
self.assertListEqual(post_df.columns.tolist(), ["label", "y", "q1"])
self.assertListEqual(series_to_list(post_df["q1"]),
[1.0, 1.25, 1.5, 1.75])
# incorrect rolling type
self.assertRaises(
QueryObjectValidationError,
proc.rolling,
df=timeseries_df,
columns={"y": "y"},
rolling_type="abc",
window=2,
)
# incorrect rolling type options
self.assertRaises(
QueryObjectValidationError,
proc.rolling,
df=timeseries_df,
columns={"y": "y"},
rolling_type="quantile",
rolling_type_options={"abc": 123},
window=2,
)
