Comments (8)
WenjieZ
commented on July 23, 2024
Could you please provide a minimal, self-complete example to reproduce this error?
from tscv.
MislavSag
commented on July 23, 2024
sample X:
from sklearn.linear_model import SGDClassifier
import pandas as pd
from pandas import Timestamp
from tscv import GapWalkForward
from sklearn.model_selection import cross_val_predict
X = {'open': {Timestamp('1998-01-05 14:41:00'): 0.0,
Timestamp('1998-01-05 14:42:00'): 0.00160616768390609,
Timestamp('1998-01-05 14:43:00'): -0.0006418485237483784,
Timestamp('1998-01-05 14:44:00'): -0.00256657042027586,
Timestamp('1998-01-05 14:45:00'): -0.0025673940949936247,
Timestamp('1998-01-05 14:46:00'): -0.0009627727856226231,
Timestamp('1998-01-05 14:48:00'): -0.0009621552277100376,
Timestamp('1998-01-05 14:49:00'): 0.0,
Timestamp('1998-01-05 14:50:00'): 0.0022515278224508606,
Timestamp('1998-01-05 14:51:00'): 0.002574002574002643},
'high': {Timestamp('1998-01-05 14:41:00'): 0.0,
Timestamp('1998-01-05 14:42:00'): 0.00160616768390609,
Timestamp('1998-01-05 14:43:00'): 0.0,
Timestamp('1998-01-05 14:44:00'): -0.0028864656831302238,
Timestamp('1998-01-05 14:45:00'): -0.0025673940949936247,
Timestamp('1998-01-05 14:46:00'): -0.0012832852101379855,
Timestamp('1998-01-05 14:48:00'): -0.0009621552277100376,
Timestamp('1998-01-05 14:49:00'): -0.0009624639076034613,
Timestamp('1998-01-05 14:50:00'): 0.0022515278224508606,
Timestamp('1998-01-05 14:51:00'): 0.0038610038610038533},
'low': {Timestamp('1998-01-05 14:41:00'): 0.0,
Timestamp('1998-01-05 14:42:00'): 0.0012853470437017567,
Timestamp('1998-01-05 14:43:00'): -0.0009627727856226231,
Timestamp('1998-01-05 14:44:00'): -0.002887391722810384,
Timestamp('1998-01-05 14:45:00'): -0.0025673940949936247,
Timestamp('1998-01-05 14:46:00'): -0.0009627727856226231,
Timestamp('1998-01-05 14:48:00'): -0.0012836970474967568,
Timestamp('1998-01-05 14:49:00'): 0.00032123353678126243,
Timestamp('1998-01-05 14:50:00'): 0.002574002574002643,
Timestamp('1998-01-05 14:51:00'): 0.002574002574002643},
'close': {Timestamp('1998-01-05 14:41:00'): 0.0003209242618742447,
Timestamp('1998-01-05 14:42:00'): 0.0016066838046271403,
Timestamp('1998-01-05 14:43:00'): -0.0012832852101379855,
Timestamp('1998-01-05 14:44:00'): -0.003207184092366866,
Timestamp('1998-01-05 14:45:00'): -0.0025673940949936247,
Timestamp('1998-01-05 14:46:00'): -0.0012832852101379855,
Timestamp('1998-01-05 14:48:00'): -0.0016041065126723986,
Timestamp('1998-01-05 14:49:00'): 0.00032123353678126243,
Timestamp('1998-01-05 14:50:00'): 0.002574002574002643,
Timestamp('1998-01-05 14:51:00'): 0.0038610038610038533},
'volume': {Timestamp('1998-01-05 14:41:00'): -0.20512820512820518,
Timestamp('1998-01-05 14:42:00'): 9.0,
Timestamp('1998-01-05 14:43:00'): 28.0,
Timestamp('1998-01-05 14:44:00'): 1.0,
Timestamp('1998-01-05 14:45:00'): 7.225806451612904,
Timestamp('1998-01-05 14:46:00'): -0.967741935483871,
Timestamp('1998-01-05 14:48:00'): -0.7166666666666667,
Timestamp('1998-01-05 14:49:00'): -0.9885057471264368,
Timestamp('1998-01-05 14:50:00'): -0.7,
Timestamp('1998-01-05 14:51:00'): -0.7647058823529411}}
sample y:
y = {Timestamp('1998-01-05 14:41:00'): False,
Timestamp('1998-01-05 14:42:00'): False,
Timestamp('1998-01-05 14:43:00'): True,
Timestamp('1998-01-05 14:44:00'): True,
Timestamp('1998-01-05 14:45:00'): True,
Timestamp('1998-01-05 14:46:00'): True,
Timestamp('1998-01-05 14:48:00'): True,
Timestamp('1998-01-05 14:49:00'): True,
Timestamp('1998-01-05 14:50:00'): False,
Timestamp('1998-01-05 14:51:00'): True}
and then:
X = pd.DataFrame.from_dict(X)
y = pd.DataFrame.from_dict(y, orient='index').set_axis(['y'], axis=1)
cv = GapWalkForward(n_splits=3, gap_size=1, test_size=2)
predictions = cross_val_predict(estimator=SGDClassifier(), X=X,
y=y, cv=cv, n_jobs=6)
from tscv.
WenjieZ
commented on July 23, 2024
Where did you import SGDClassifier?
from tscv.
WenjieZ
commented on July 23, 2024
Where did you import Timestamp?
from tscv.
MislavSag
commented on July 23, 2024
I changed code, it should work now.
from tscv.
WenjieZ
commented on July 23, 2024
My newly uploaded crosspredict branch solves this issue. It enables the combination of GapWalkForward and cross_val_predict.
If you don't know how to install a package from a GitHub repo branch, you can download this file, put it in your package search path, and then try to do something similar to the following example.
Example:
from sklearn.model_selection import cross_val_predict
from split import GapWalkForward
class Foo:
def get_params(self, deep):
return dict()
def fit(self, X, y):
pass
def predict(self, X):
return [0 for _ in X]
cv = GapWalkForward(n_splits=3, gap_size=1, test_size=2)
cross_val_predict(estimator=Foo(), X=range(6), y=range(6), cv=cv)Output:
array([0, 0, 0, 0, 0, 0])
from tscv.
MislavSag
commented on July 23, 2024
I got the same error as before:
ValueError: cross_val_predict only works for partitions
I have to say I get the same error if I use cross_val_predict with sklearn TimeSeriesSplit. I would have to figure out how to implement cross validation predictions ith time series splits...
from tscv.
WenjieZ
commented on July 23, 2024
Superseded by #9
from tscv.
Related Issues (20)
- [Docs] Use this package for Nested Cross-Validation
- Intution on setting number of gaps HOT 1
- split.py depends on deprecated / newly private method `_safe_indexing` in scikit-learn 0.24.0 HOT 3
- Stratify? HOT 1
- Double count in `n_splits` in `GapLeavePOut` HOT 1
- Improve the user experience of `gap_train_test_split`
- Retrained version of GapWalkForward: GapRollForward HOT 1
- Continuous Integration
- Documentation
- Warning once is not enough HOT 1
- Documentation HOT 3
- GapWalkForward Issue with Scikit-learn 0.24.1 HOT 2
- Deprecation message for `GapWalkForward` HOT 1
- Publish on conda
- Implement Rep-Holdout HOT 11
- Error when Importing TSCV Gapwalkforward HOT 2
- Import error with latest sklearn version HOT 3
- Does this work with sklearn 1.2? HOT 4
- GapKFold CV not working with sklearn cross_val_predict HOT 1
- GapLeavePOut CV not working with sklearn cross_val_predict HOT 1
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from tscv.