def test_online_batch_consistent(): # Batch batch = ( preprocessing.StandardScaler() | multiclass.OneVsRestClassifier( linear_model.LogisticRegression() ) ) dataset = datasets.ImageSegments() batch_metric = metrics.MacroF1() for i, x in enumerate(pd.read_csv(dataset.path, chunksize=1)): y = x.pop('category') y_pred = batch.predict_many(x) batch.fit_many(x, y) for yt, yp in zip(y, y_pred): if yp is not None: batch_metric.update(yt, yp) if i == 30: break # Online online = ( preprocessing.StandardScaler() | multiclass.OneVsRestClassifier( linear_model.LogisticRegression() ) ) online_metric = metrics.MacroF1() X = pd.read_csv(dataset.path) Y = X.pop('category') for i, (x, y) in enumerate(stream.iter_pandas(X, Y)): y_pred = online.predict_one(x) online.fit_one(x, y) if y_pred is not None: online_metric.update(y, y_pred) if i == 30: break assert online_metric.get() == batch_metric.get()
def build_oracle(self) -> compose.Pipeline: model = compose.Pipeline( ('scale', preprocessing.StandardScaler()), ('learn', multiclass.OneVsRestClassifier( binary_classifier=linear_model.LogisticRegression()))) return model
else: inst = obj() yield inst @pytest.mark.parametrize('estimator, check', [ pytest.param( copy.deepcopy(estimator), check, id=f'{estimator}:{check.__name__}') for estimator in list(get_all_estimators()) + [ feature_extraction.TFIDF(), linear_model.LogisticRegression(), preprocessing.StandardScaler() | linear_model.LinearRegression(), preprocessing.StandardScaler() | linear_model.PAClassifier(), preprocessing.StandardScaler() | multiclass.OneVsRestClassifier(linear_model.LogisticRegression()), preprocessing.StandardScaler() | multiclass.OneVsRestClassifier(linear_model.PAClassifier()), naive_bayes.GaussianNB(), preprocessing.StandardScaler(), cluster.KMeans(n_clusters=5, seed=42), preprocessing.MinMaxScaler(), preprocessing.MinMaxScaler() + preprocessing.StandardScaler(), preprocessing.PolynomialExtender(), feature_selection.VarianceThreshold(), feature_selection.SelectKBest(similarity=stats.PearsonCorrelation()) ] for check in utils.estimator_checks.yield_checks(estimator) ]) def test_check_estimator(estimator, check): check(estimator)
elif issubclass(obj, multiclass.OneVsRestClassifier): inst = obj(binary_classifier=linear_model.LogisticRegression()) else: inst = obj() yield inst @pytest.mark.parametrize('estimator', [ pytest.param(copy.deepcopy(estimator), id=str(estimator)) for estimator in list(get_all_estimators()) + [ feature_extraction.TFIDF(), linear_model.LogisticRegression(), preprocessing.StandardScaler() | linear_model.LinearRegression(), preprocessing.StandardScaler() | linear_model.PAClassifier(), preprocessing.StandardScaler() | multiclass.OneVsRestClassifier(linear_model.LogisticRegression()), preprocessing.StandardScaler() | multiclass.OneVsRestClassifier(linear_model.PAClassifier()), naive_bayes.GaussianNB(), preprocessing.StandardScaler(), cluster.KMeans(n_clusters=5, seed=42), preprocessing.MinMaxScaler(), preprocessing.MinMaxScaler() + preprocessing.StandardScaler(), preprocessing.PolynomialExtender(), feature_selection.VarianceThreshold(), feature_selection.SelectKBest(similarity=stats.PearsonCorrelation()) ] ]) def test_check_estimator(estimator): utils.estimator_checks.check_estimator(estimator)
optimizer = optim.Adam(lr, beta_1, beta_2, eps) elif (opt == "FTRLProximal"): optimizer = optim.FTRLProximal(alpha, beta, l1, l2) elif (opt == "Momentum"): optimizer = optim.Momentum(lr, rho) elif (opt == "RMSProp"): optimizer = optim.RMSProp(lr, rho, eps) elif (opt == "VanillaSGD"): optimizer = optim.VanillaSGD(lr) elif (opt == "NesterovMomentum"): optimizer = optim.NesterovMomentum(lr, rho) else: optimizer = None log_reg = linear_model.LogisticRegression(optimizer, l2=l2) OVRClassifier = multiclass.OneVsRestClassifier(log_reg) output = {} while True: #wait request data = input() Xi = json.loads(data) y = float(Xi.pop(target)) output["Predict"] = OVRClassifier.predict_one(Xi) output["Truth"] = y model = OVRClassifier.fit_one(Xi, y)