Source code for scitex_ml.sk._clf

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Time-stamp: "2024-03-23 17:36:05 (ywatanabe)"

import numpy as np
from sklearn.decomposition import PCA
from sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier
from sklearn.feature_selection import SelectKBest, f_classif
from sklearn.linear_model import LogisticRegression, RidgeClassifierCV
from sklearn.pipeline import make_pipeline
from sklearn.svm import SVC, LinearSVC
from sktime.classification.deep_learning.cnn import CNNClassifier
from sktime.classification.deep_learning.inceptiontime import InceptionTimeClassifier
from sktime.classification.deep_learning.lstmfcn import LSTMFCNClassifier
from sktime.classification.dummy import DummyClassifier
from sktime.classification.feature_based import TSFreshClassifier
from sktime.classification.hybrid import HIVECOTEV2
from sktime.classification.interval_based import TimeSeriesForestClassifier
from sktime.classification.kernel_based import RocketClassifier, TimeSeriesSVC
from sktime.transformations.panel.reduce import Tabularizer
from sktime.transformations.panel.rocket import Rocket

# _rocket_pipeline = make_pipeline(
#     Rocket(n_jobs=-1),
#     RidgeClassifierCV(alphas=np.logspace(-3, 3, 10)),
# )


# def rocket_pipeline(*args, **kwargs):
#     return _rocket_pipeline



[docs]
def rocket_pipeline(*args, **kwargs):
    return make_pipeline(
        Rocket(*args, **kwargs),
        LogisticRegression(
            max_iter=1000
        ),  # Increase max_iter if needed for convergence
        # RidgeClassifierCV(alphas=np.logspace(-3, 3, 10)),
        # SVC(probability=True, kernel="linear"),
    )



# def rocket_pipeline(*args, **kwargs):
#     return make_pipeline(
#         Rocket(*args, **kwargs),
#         SelectKBest(f_classif, k=500),
#         PCA(n_components=100),
#         LinearSVC(dual=False, tol=1e-3, C=0.1, probability=True),
#     )


GB_pipeline = make_pipeline(
    Tabularizer(),
    GradientBoostingClassifier(),
)