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PERF: Performance improvements to regression/correlation.

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- ~7x speedup on PearsonR.
- ~30x speedup on SpearmanR (this will still be tight for hitting a 5
  minute compute timeout on Quantopian).
- ~25% speedup on LinearRegression.
This commit is contained in:
Scott Sanderson
2016-05-17 12:45:18 -04:00
committed by dmichalowicz
parent 1ec0bced6d
commit 877d0be798
1 changed files with 20 additions and 11 deletions
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zipline/pipeline/factors/technical.py
+20 -11
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@@ -8,7 +8,6 @@ from numpy import (
arange,
average,
clip,
corrcoef,
diff,
exp,
fmax,
@@ -22,7 +21,7 @@ from numpy import (
sum as np_sum,
)
from numexpr import evaluate
from scipy.stats import linregress, spearmanr
from scipy.stats import linregress, pearsonr, spearmanr
from zipline.pipeline.data import USEquityPricing
from zipline.pipeline.filters import SingleAsset
@@ -262,8 +261,10 @@ class RollingPearsonOfReturns(_RollingCorrelationOfReturns):
:class:`zipline.pipeline.factors.technical.RollingLinearRegressionOfReturns`
"""
def compute(self, today, assets, out, data, target):
asset_col = searchsorted(assets.values, target.sid)
out[:] = corrcoef(data, rowvar=0)[asset_col]
target_col = data[:, searchsorted(assets.values, target.sid)]
for i in range(len(out)):
# pearsonr returns the R-value and the P-value.
out[i] = pearsonr(data[:, i], target_col)[0]
class RollingSpearmanOfReturns(_RollingCorrelationOfReturns):
@@ -288,8 +289,10 @@ class RollingSpearmanOfReturns(_RollingCorrelationOfReturns):
:class:`zipline.pipeline.factors.technical.RollingLinearRegressionOfReturns`
"""
def compute(self, today, assets, out, data, target):
asset_col = searchsorted(assets.values, target.sid)
out[:] = spearmanr(data)[0][asset_col]
target_col = data[:, searchsorted(assets.values, target.sid)]
for i in range(len(out)):
# spearmanr returns the R-value and the P-value.
out[i] = spearmanr(data[:, i], target_col)[0]
class RollingLinearRegressionOfReturns(CustomFactor, SingleInputMixin):
@@ -402,16 +405,22 @@ class RollingLinearRegressionOfReturns(CustomFactor, SingleInputMixin):
def compute(self, today, assets, out, returns, target):
asset_col = searchsorted(assets.values, target.sid)
my_asset = returns[:, asset_col]
alpha = out.alpha
beta = out.beta
r_value = out.r_value
p_value = out.p_value
stderr = out.stderr
for i in range(len(out)):
other_asset = returns[:, i]
regr_results = linregress(y=other_asset, x=my_asset)
# `linregress` returns its results in the following order:
# slope, intercept, r-value, p-value, stderr
out.alpha[i] = regr_results[1]
out.beta[i] = regr_results[0]
out.r_value[i] = regr_results[2]
out.p_value[i] = regr_results[3]
out.stderr[i] = regr_results[4]
alpha[i] = regr_results[1]
beta[i] = regr_results[0]
r_value[i] = regr_results[2]
p_value[i] = regr_results[3]
stderr[i] = regr_results[4]
class _ExponentialWeightedFactor(SingleInputMixin, CustomFactor):