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test: Streak lengths #100

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merged 5 commits into from
Oct 27, 2023
Merged

test: Streak lengths #100

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5492982
fix: sort mode before taking first value
TomBurdge Oct 23, 2023
871d5ef
handle no streaks
TomBurdge Oct 25, 2023
3649a38
add tests for streak_lenghth_stats
TomBurdge Oct 25, 2023
f5dc54d
Merge branch 'main' into test-streak-length-stats
TomBurdge Oct 25, 2023
e40fba4
Merge branch 'main' into test-streak-length-stats
abstractqqq Oct 27, 2023
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114 changes: 72 additions & 42 deletions functime/feature_extraction/tsfresh.py
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Original file line number Diff line number Diff line change
Expand Up @@ -41,8 +41,8 @@ def absolute_energy(x: TIME_SERIES_T) -> FLOAT_INT_EXPR:
float | Expr
"""
if isinstance(x, pl.Series):
if x.len() > 300_000: # Would be different for different machines
return np.dot(x,x)
if x.len() > 300_000: # Would be different for different machines
return np.dot(x, x)
return x.dot(x)


Expand Down Expand Up @@ -244,20 +244,20 @@ def autoregressive_coefficients(x: TIME_SERIES_T, n_lags: int) -> List[float]:
"""
if isinstance(x, pl.Series):
length = len(x) - n_lags
y = x.fill_null(0.)
y = x.fill_null(0.0)
data_x = (
y.to_frame()
.select(
*(
pl.col(y.name).slice(n_lags - i, length=length).alias(str(i))
for i in range(1, n_lags + 1)
),
pl.lit(1)
pl.lit(1),
)
.to_numpy()
)
y_ = y.tail(length).to_numpy(zero_copy_only=True).reshape((-1,1))
out:np.ndarray = rs_faer_lstsq(data_x, y_)
y_ = y.tail(length).to_numpy(zero_copy_only=True).reshape((-1, 1))
out: np.ndarray = rs_faer_lstsq(data_x, y_)
return out.ravel()
else:
logger.info("Expression version of autoregressive_coefficients is not yet implemented due to "
Expand Down Expand Up @@ -445,6 +445,7 @@ def change_quantiles(

return expr.implode()


# Revisit later.
def cid_ce(x: TIME_SERIES_T, normalize: bool = False) -> FLOAT_EXPR:
"""
Expand Down Expand Up @@ -495,6 +496,7 @@ def count_above(x: TIME_SERIES_T, threshold: float = 0.0) -> FLOAT_EXPR:
"""
return 100 * (x >= threshold).sum() / x.len()


# Should this be percentage?
def count_above_mean(x: TIME_SERIES_T) -> INT_EXPR:
"""
Expand Down Expand Up @@ -787,15 +789,15 @@ def index_mass_quantile(x: TIME_SERIES_T, q: float) -> FLOAT_EXPR:
x_cumsum = x.abs().cumsum().set_sorted()
if isinstance(x, pl.Series):
if x.is_integer():
idx = x_cumsum.search_sorted(int(q*x_cumsum[-1]) + 1, "left")
else: # Search sorted is sensitive to dtype.
idx = x_cumsum.search_sorted(q*x_cumsum[-1], "left")
idx = x_cumsum.search_sorted(int(q * x_cumsum[-1]) + 1, "left")
else: # Search sorted is sensitive to dtype.
idx = x_cumsum.search_sorted(q * x_cumsum[-1], "left")
return (idx + 1) / x.len()
else:
# In lazy case we have to cast, which kind of wasts some time, but this is on
# par with the old implementation. Use max here because... believe it or not
# max (with fast track because I did set_sorted()) is faster than .last() ???
idx = x_cumsum.cast(pl.Float64).search_sorted(q*x_cumsum.max(), "left")
idx = x_cumsum.cast(pl.Float64).search_sorted(q * x_cumsum.max(), "left")
return (idx + 1) / x.len()


Expand Down Expand Up @@ -864,7 +866,7 @@ def last_location_of_minimum(x: TIME_SERIES_T) -> FLOAT_EXPR:


def lempel_ziv_complexity(
x: TIME_SERIES_T, threshold: Union[float, pl.Expr], as_ratio:bool=True
x: TIME_SERIES_T, threshold: Union[float, pl.Expr], as_ratio: bool = True
) -> FLOAT_EXPR:
"""
Calculate a complexity estimate based on the Lempel-Ziv compression algorithm. The
Expand All @@ -880,7 +882,7 @@ def lempel_ziv_complexity(
Either a number, or an expression representing a comparable quantity. If x > value,
then it will be binarized as 1 and 0 otherwise. If x is eager, then value must also
be eager as well.
as_ratio: bool
as_ratio: bool
If true, return the complexity / length of sequence

Returns
Expand Down Expand Up @@ -920,13 +922,15 @@ def linear_trend(x: TIME_SERIES_T) -> MAP_EXPR:
if isinstance(x, pl.Series):
n = x.len()
if n == 1:
return {"slope": np.nan, "intercept":np.nan, "rss": np.nan}
return {"slope": np.nan, "intercept": np.nan, "rss": np.nan}

m = n - 1
x_range_mean = m / 2
x_range = np.arange(start=0, stop=n)
x_mean = x.mean()
beta = np.dot(x - x_mean, x_range - x_range_mean) / (m * np.var(x_range, ddof=1))
beta = np.dot(x - x_mean, x_range - x_range_mean) / (
m * np.var(x_range, ddof=1)
)
alpha = x_mean - beta * x_range_mean
resid = x - (beta * x_range + alpha)
return {"slope": beta, "intercept": alpha, "rss": np.dot(resid, resid)}
Expand Down Expand Up @@ -964,7 +968,12 @@ def longest_streak_above_mean(x: TIME_SERIES_T) -> INT_EXPR:
result = y.filter(y.struct.field("values")).struct.field("lengths").max()
return 0 if result is None else result
else:
return y.filter(y.struct.field("values")).struct.field("lengths").max().fill_null(0)
return (
y.filter(y.struct.field("values"))
.struct.field("lengths")
.max()
.fill_null(0)
)


def longest_streak_below_mean(x: TIME_SERIES_T) -> INT_EXPR:
Expand All @@ -988,7 +997,13 @@ def longest_streak_below_mean(x: TIME_SERIES_T) -> INT_EXPR:
result = y.filter(y.struct.field("values")).struct.field("lengths").max()
return 0 if result is None else result
else:
return y.filter(y.struct.field("values")).struct.field("lengths").max().fill_null(0)
return (
y.filter(y.struct.field("values"))
.struct.field("lengths")
.max()
.fill_null(0)
)


def mean_abs_change(x: TIME_SERIES_T) -> FLOAT_EXPR:
"""
Expand Down Expand Up @@ -1042,7 +1057,11 @@ def mean_change(x: TIME_SERIES_T) -> FLOAT_EXPR:
return 0
return (x[-1] - x[0]) / (x.len() - 1)
else:
return pl.when(x.len() - 1 > 0).then((x.last() - x.first()) / (x.len() - 1)).otherwise(0)
return (
pl.when(x.len() - 1 > 0)
.then((x.last() - x.first()) / (x.len() - 1))
.otherwise(0)
)


def mean_n_absolute_max(x: TIME_SERIES_T, n_maxima: int) -> FLOAT_EXPR:
Expand Down Expand Up @@ -1083,10 +1102,10 @@ def mean_second_derivative_central(x: TIME_SERIES_T) -> FLOAT_EXPR:
return np.nan
return (x[-1] - x[-2] - x[1] + x[0]) / (2 * (x.len() - 2))
else:
return pl.when(x.len() < 3).then(
np.nan
).otherwise(
x.tail(2).sub(x.head(2)).diff().last() / (2 * (x.len() - 2))
return (
pl.when(x.len() < 3)
.then(np.nan)
.otherwise(x.tail(2).sub(x.head(2)).diff().last() / (2 * (x.len() - 2)))
)


Expand All @@ -1112,6 +1131,7 @@ def number_crossings(x: TIME_SERIES_T, crossing_value: float = 0.0) -> FLOAT_EXP
return y.eq(y.shift(1)).not_().sum()



def number_cwt_peaks(x: pl.Series, max_width: int = 5) -> float:
"""
Number of different peaks in x.
Expand Down Expand Up @@ -1266,28 +1286,28 @@ def permutation_entropy(
expr = permutation_entropy(pl.col(x.name), tau=tau, n_dims=n_dims, base=base)
return frame.select(expr).item(0, 0)
else:
if tau == 1: # Fast track the most common use case
if tau == 1: # Fast track the most common use case
return (
pl.concat_list(
x,
*(x.shift(-i) for i in range(1, n_dims))
).head(x.len() - n_dims + 1)
.list.eval(
pl.element().arg_sort()
).value_counts() # groupby and count, but returns a struct
.struct.field("counts") # extract the field named "counts"
pl.concat_list(x, *(x.shift(-i) for i in range(1, n_dims)))
.head(x.len() - n_dims + 1)
.list.eval(pl.element().arg_sort())
.value_counts() # groupby and count, but returns a struct
.struct.field("counts") # extract the field named "counts"
.entropy(base=base, normalize=True)
)
else:
max_shift = -n_dims + 1
return (
pl.concat_list(
x.take_every(tau),
*(x.shift(-i).take_every(tau) for i in range(1, n_dims))
).filter( # This is the correct way to filter (with respect to tau) in this case.
pl.repeat(True, x.len()).shift(max_shift, fill_value=False)
*(x.shift(-i).take_every(tau) for i in range(1, n_dims)),
)
.filter( # This is the correct way to filter (with respect to tau) in this case.
pl.repeat(True, x.len())
.shift(max_shift, fill_value=False)
.take_every(tau)
).list.eval(
)
.list.eval(
pl.element().arg_sort()
) # for each inner list, do an argsort
.value_counts() # groupby and count, but returns a struct
Expand Down Expand Up @@ -1398,7 +1418,7 @@ def _into_sequential_chunks(x: pl.Series, m: int) -> np.ndarray:


# Only works on series
def sample_entropy(x: TIME_SERIES_T, ratio: float = 0.2, m:int = 2) -> FLOAT_EXPR:
def sample_entropy(x: TIME_SERIES_T, ratio: float = 0.2, m: int = 2) -> FLOAT_EXPR:
"""
Calculate the sample entropy of a time series.
This only works for Series input right now.
Expand Down Expand Up @@ -1633,7 +1653,7 @@ def harmonic_mean(x: TIME_SERIES_T) -> FLOAT_EXPR:
-------
float | Expr
"""
return x.len() / (1.0/x).sum()
return x.len() / (1.0 / x).sum()


def range_over_mean(x: TIME_SERIES_T) -> FLOAT_EXPR:
Expand Down Expand Up @@ -1705,25 +1725,25 @@ def streak_length_stats(x: TIME_SERIES_T, above: bool, threshold: float) -> MAP_
y = y.filter(y.struct.field("values")).struct.field("lengths")
if isinstance(x, pl.Series):
return {
"min": y.min(),
"min": y.min() or 0,
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I didn't know Python supports this lol 👍

"max": y.max(),
"mean": y.mean(),
"std": y.std(),
"10-percentile": y.quantile(0.1),
"median": y.median(),
"90-percentile": y.quantile(0.9),
"mode": y.mode()[0],
"mode": y.mode().sort()[0] if not y.is_empty() else None,
}
else:
return pl.struct(
y.min().alias("min"),
y.min().clip_min(0).alias("min"),
y.max().alias("max"),
y.mean().alias("mean"),
y.std().alias("std"),
y.quantile(0.1).alias("10-percentile"),
y.median().alias("median"),
y.quantile(0.9).alias("90-percentile"),
y.mode().first().alias("mode"),
y.mode().sort(nulls_last=True).first().alias("mode"),
)


Expand All @@ -1750,7 +1770,12 @@ def longest_streak_above(x: TIME_SERIES_T, threshold: float) -> TIME_SERIES_T:
streak_max = y.filter(y.struct.field("values")).struct.field("lengths").max()
return 0 if streak_max is None else streak_max
else:
return y.filter(y.struct.field("values")).struct.field("lengths").max().fill_null(0)
return (
y.filter(y.struct.field("values"))
.struct.field("lengths")
.max()
.fill_null(0)
)


def longest_streak_below(x: TIME_SERIES_T, threshold: float) -> TIME_SERIES_T:
Expand All @@ -1775,7 +1800,12 @@ def longest_streak_below(x: TIME_SERIES_T, threshold: float) -> TIME_SERIES_T:
streak_max = y.filter(y.struct.field("values")).struct.field("lengths").max()
return 0 if streak_max is None else streak_max
else:
return y.filter(y.struct.field("values")).struct.field("lengths").max().fill_null(0)
return (
y.filter(y.struct.field("values"))
.struct.field("lengths")
.max()
.fill_null(0)
)


def longest_winning_streak(x: TIME_SERIES_T) -> TIME_SERIES_T:
Expand Down
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