Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

fix+perf: Benford correlation and entropy features #58

Merged
merged 4 commits into from
Sep 30, 2023
Merged

fix+perf: Benford correlation and entropy features #58

Changes from 1 commit
Commits
Show all changes
4 commits
Select commit Hold shift + click to select a range
1c21723
perf improv and bug fix
abstractqqq Sep 30, 2023
d1d1f45
use eager mode distinct way to compute it. fixed another bug I missed…
abstractqqq Sep 30, 2023
13bcedd
faster binned entropy, removed some indirection
abstractqqq Sep 30, 2023
1e0d739
no need to take abs in str, added - sign back as that seems faster
abstractqqq Sep 30, 2023
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
Prev Previous commit
Next Next commit
faster binned entropy, removed some indirection
  • Loading branch information
@abstractqqq
abstractqqq committed Sep 30, 2023
commit 13bceddf12a98fb6658d915c9f44f23773f55f4a
42 changes: 17 additions & 25 deletions functime/feature_extraction/tsfresh.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -103,7 +103,7 @@ def approximate_entropies(
"""
sigma = x.std()
rs = [sigma * r for r in filtering_levels]
N = x.len()
N = x.count()
phis_m = _phis(x, m=run_length, N=N, rs=rs)
phis_m_plus_1 = _phis(x, m=run_length + 1, N=N, rs=rs)
return [phis_m[i] - phis_m_plus_1[i] for i in range(len(phis_m))]
Expand Down Expand Up @@ -249,7 +249,7 @@ def benford_correlation(x: TIME_SERIES_T) -> FLOAT_EXPR:
[4] Newcomb, S. (1881). Note on the frequency of use of the different digits in natural numbers. American Journal of
mathematics.
"""
y = x.cast(pl.Utf8).str.strip_chars_start("-0.")
y = x.abs().cast(pl.Utf8).str.strip_chars_start("0.")
if isinstance(x, pl.Series):
counts = (
y.filter(y != "").str.slice(0, 1).cast(
Expand Down Expand Up @@ -339,18 +339,12 @@ def binned_entropy(x: TIME_SERIES_T, bin_count: int = 10) -> FLOAT_EXPR:
probs[probs == 0] = 1.0
return -np.sum(probs * np.log(probs))
else:
step_size = 1 / bin_count
breaks = np.linspace(step_size, stop=1 - step_size, num=bin_count - 1)
scaled_x = (x - x.min()) / (
x.max() - x.min()
) # This step slows down the calculation
# Left closed because we want to micmic NumPy's histogram's behavior
return (
scaled_x.cut(breaks, left_closed=True)
.value_counts()
(x - x.min()).floordiv(pl.lit(1e-12) + (x.max() - x.min())/pl.lit(bin_count))
.drop_nulls().value_counts()
.sort()
.struct.field("counts")
.entropy()
.suffix("_binned_entropy")
.entropy().suffix("_binned_entropy")
)


Expand Down Expand Up @@ -621,7 +615,7 @@ def first_location_of_maximum(x: TIME_SERIES_T) -> FLOAT_EXPR:
-------
float | Expr
"""
return x.arg_max() / x.len()
return x.arg_max() / x.count()


def first_location_of_minimum(x: TIME_SERIES_T) -> FLOAT_EXPR:
Expand All @@ -638,7 +632,7 @@ def first_location_of_minimum(x: TIME_SERIES_T) -> FLOAT_EXPR:
-------
float | Expr
"""
return x.arg_min() / x.len()
return x.arg_min() / x.count()


def fourier_entropy(x: TIME_SERIES_T, n_bins: int = 10) -> float:
Expand Down Expand Up @@ -788,7 +782,7 @@ def index_mass_quantile(x: TIME_SERIES_T, q: float) -> FLOAT_EXPR:
"""
x_abs = x.abs()
x_sum = x.sum()
n = x.len()
n = x.count()
mass_center = x_abs.cumsum() / x_sum
return ((mass_center >= q) + 1).arg_max() / n

Expand Down Expand Up @@ -834,7 +828,7 @@ def last_location_of_maximum(x: TIME_SERIES_T) -> FLOAT_EXPR:
-------
float | Expr
"""
return (x.len() - x.reverse().arg_max()) / x.len()
return (x.count() - x.reverse().arg_max()) / x.count()


def last_location_of_minimum(x: TIME_SERIES_T) -> FLOAT_EXPR:
Expand All @@ -851,7 +845,7 @@ def last_location_of_minimum(x: TIME_SERIES_T) -> FLOAT_EXPR:
-------
float | Expr
"""
return (x.len() - x.reverse().arg_min()) / x.len()
return (x.count() - x.reverse().arg_min()) / x.count()


def lempel_ziv_complexity(x: pl.Series, n_bins: int) -> List[float]:
Expand Down Expand Up @@ -901,7 +895,7 @@ def linear_trend(x: TIME_SERIES_T) -> Mapping[str, float]:
-------
Mapping | Expr
"""
x_range = pl.int_range(1, x.len() + 1)
x_range = pl.int_range(1, x.count() + 1)
beta = pl.cov(x, x_range) / x.var()
alpha = x.mean() - beta * x_range.mean()
resid = x - beta * x_range + alpha
Expand Down Expand Up @@ -1118,7 +1112,7 @@ def percent_reocurring_points(x: TIME_SERIES_T) -> float:
float
"""
count = x.unique_counts()
return count.filter(count > 1).sum() / x.len()
return count.filter(count > 1).sum() / x.count()


def percent_reoccuring_values(x: TIME_SERIES_T) -> FLOAT_EXPR:
Expand All @@ -1142,7 +1136,7 @@ def percent_reoccuring_values(x: TIME_SERIES_T) -> FLOAT_EXPR:
float | Expr
"""
count = x.unique_counts()
return (count > 1).sum() / count.len()
return (count > 1).sum() / count.count()


def number_peaks(x: TIME_SERIES_T, support: int) -> INT_EXPR:
Expand Down Expand Up @@ -1298,7 +1292,7 @@ def ratio_n_unique_to_length(x: TIME_SERIES_T) -> FLOAT_EXPR:
-------
float | Expr
"""
return x.n_unique() / x.len()
return x.n_unique() / x.count()


def root_mean_square(x: TIME_SERIES_T) -> FLOAT_EXPR:
Expand All @@ -1314,14 +1308,12 @@ def root_mean_square(x: TIME_SERIES_T) -> FLOAT_EXPR:
-------
float | Expr
"""
# This will be a little bit faster because x.count() is commonly used
# and we have a high chance of hitting CSE
return (x.dot(x) / x.count()).sqrt()
return x.pow(2).mean().sqrt()


def _into_sequential_chunks(x: pl.Series, m: int) -> np.ndarray:
name = x.name
n_rows = x.len() - m + 1
n_rows = len(x) - m + 1
df = (
x.to_frame()
.select(
Expand Down