def test_rocket_on_power_demand(): # load power demand data X_train, y_train = load_italy_power_demand(split="train", return_X_y=True) X_test, y_test = load_italy_power_demand(split="test", return_X_y=True) indices = np.random.RandomState(0).permutation(100) # train ROCKET rocket = ROCKETClassifier(num_kernels=1000, random_state=0) rocket.fit(X_train, y_train) score = rocket.score(X_test.iloc[indices], y_test[indices]) assert score >= 0.92
def test_rocket_on_gunpoint(): # load gunpoint data X_train, y_train = load_gunpoint(split="train", return_X_y=True) X_test, y_test = load_gunpoint(split="test", return_X_y=True) indices = np.random.RandomState(0).permutation(10) # train ROCKET rocket = ROCKETClassifier(num_kernels=1000, random_state=0) rocket.fit(X_train.iloc[indices], y_train[indices]) # assert probabilities are the same probas = rocket.predict_proba(X_test.iloc[indices]) testing.assert_array_equal(probas, rocket_gunpoint_probas)
def test_rocket_ensemble_on_gunpoint(n_jobs, ensemble_config): ensemble_size, ensemble, n_estimators = ensemble_config # load gunpoint data X_train, y_train = load_gunpoint(split="train", return_X_y=True) X_test, y_test = load_gunpoint(split="test", return_X_y=True) indices = np.random.RandomState(0).permutation(10) # train ROCKET ensemble rocket_e = ROCKETClassifier( num_kernels=1000, ensemble_size=ensemble_size, ensemble=ensemble, n_estimators=n_estimators, random_state=0, n_jobs=n_jobs, ) rocket_e.fit(X_train.iloc[indices], y_train[indices]) # assert probabilities are the same probas = rocket_e.predict_proba(X_test.iloc[indices]) testing.assert_array_equal(probas, rocket_e_gunpoint_probas)
def set_classifier(cls, resampleId=None): """ Basic way of creating the classifier to build using the default settings. This set up is to help with batch jobs for multiple problems to facilitate easy reproducability. You can set up bespoke classifier in many other ways. :param cls: String indicating which classifier you want :param resampleId: classifier random seed :return: A classifier. """ name = cls.lower() # Distance based if name == "pf" or name == "proximityforest": return ProximityForest(random_state=resampleId) elif name == "pt" or name == "proximitytree": return ProximityTree(random_state=resampleId) elif name == "ps" or name == "proximityStump": return ProximityStump(random_state=resampleId) elif name == "dtwcv" or name == "kneighborstimeseriesclassifier": return KNeighborsTimeSeriesClassifier(metric="dtw") elif name == "ee" or name == "elasticensemble": return ElasticEnsemble() elif name == "shapedtw": return ShapeDTW() # Dictionary based elif name == "boss" or name == "bossensemble": return BOSSEnsemble(random_state=resampleId) elif name == "cboss" or name == "contractableboss": return ContractableBOSS(random_state=resampleId) elif name == "tde" or name == "temporaldictionaryensemble": return TemporalDictionaryEnsemble(random_state=resampleId) elif name == "weasel": return WEASEL(random_state=resampleId) elif name == "muse": return MUSE(random_state=resampleId) # Interval based elif name == "rise" or name == "randomintervalspectralforest": return RandomIntervalSpectralForest(random_state=resampleId) elif name == "tsf" or name == "timeseriesforest": return TimeSeriesForest(random_state=resampleId) elif name == "cif" or name == "canonicalintervalforest": return CanonicalIntervalForest(random_state=resampleId) elif name == "drcif": return DrCIF(random_state=resampleId) # Shapelet based elif name == "stc" or name == "shapelettransformclassifier": return ShapeletTransformClassifier(random_state=resampleId, time_contract_in_mins=1) elif name == "mrseql" or name == "mrseqlclassifier": return MrSEQLClassifier(seql_mode="fs", symrep=["sax", "sfa"]) elif name == "rocket": return ROCKETClassifier(random_state=resampleId) # Hybrid elif name == "catch22": return Catch22ForestClassifier(random_state=resampleId) elif name == "hivecotev1": return HIVECOTEV1(random_state=resampleId) else: raise Exception("UNKNOWN CLASSIFIER")
norm_data = data_input.copy() norm_data = norm_data.apply(lambda x: (x-x.min())/(x.max()-x.min()), axis=1) X_norm = norm_data.values #label binário lb = LabelBinarizer() y = lb.fit_transform(label) y = y.reshape(-1)[:] #será necessário converter os dados de tabular para nested para aplicar algoritmos da sktime X_nested = from_2d_array_to_nested(X_norm)[:] #definição dos modelos e parametros model_params = { 'ROCKET' : { 'model': ROCKETClassifier(), 'params': { 'num_kernels': [10000,8000,5000] } } } #definição das métricas e parametros scoring = {'acc': 'accuracy', 'prec': make_scorer(precision_score,pos_label=pos_label), 'avg_prec': make_scorer(average_precision_score,pos_label=pos_label), 'recall': make_scorer(recall_score,pos_label=pos_label), 'f1': make_scorer(f1_score,pos_label=pos_label), 'bal_acc': 'balanced_accuracy' }