示例#1
0
def linreg_fit(input_dict):
    from discomll.regression import linear_regression

    fitmodel_url = linear_regression.fit(input_dict["dataset"],
                                         save_results=True)

    return {"fitmodel_url": fitmodel_url}
示例#2
0
def linreg_fit(input_dict):
    from discomll.regression import linear_regression

    fitmodel_url = linear_regression.fit(input_dict["dataset"],
                                         save_results=True)

    return {"fitmodel_url": fitmodel_url}
示例#3
0
    def test_lin_reg(self):
        # python -m unittest tests_regression.Tests_Regression.test_lin_reg
        from sklearn import linear_model
        from discomll.regression import linear_regression

        x_train, y_train, x_test, y_test = datasets.ex3()
        train_data, test_data = datasets.ex3_discomll()

        lin_reg = linear_model.LinearRegression()  # Create linear regression object
        lin_reg.fit(x_train, y_train)  # Train the model using the training sets
        thetas1 = [lin_reg.intercept_] + lin_reg.coef_[1:].tolist()
        prediction1 = lin_reg.predict(x_test)

        thetas_url = linear_regression.fit(train_data)
        thetas2 = [v for k, v in result_iterator(thetas_url["linreg_fitmodel"])]
        results = linear_regression.predict(test_data, thetas_url)
        prediction2 = [v[0] for k, v in result_iterator(results)]

        self.assertTrue(np.allclose(thetas1, thetas2))
        self.assertTrue(np.allclose(prediction1, prediction2))
    def test_lin_reg(self):
        # python -m unittest tests_regression.Tests_Regression.test_lin_reg
        from sklearn import linear_model
        from discomll.regression import linear_regression

        x_train, y_train, x_test, y_test = datasets.ex3()
        train_data, test_data = datasets.ex3_discomll()

        lin_reg = linear_model.LinearRegression(
        )  # Create linear regression object
        lin_reg.fit(x_train,
                    y_train)  # Train the model using the training sets
        thetas1 = [lin_reg.intercept_] + lin_reg.coef_[1:].tolist()
        prediction1 = lin_reg.predict(x_test)

        thetas_url = linear_regression.fit(train_data)
        thetas2 = [
            v for k, v in result_iterator(thetas_url["linreg_fitmodel"])
        ]
        results = linear_regression.predict(test_data, thetas_url)
        prediction2 = [v[0] for k, v in result_iterator(results)]

        self.assertTrue(np.allclose(thetas1, thetas2))
        self.assertTrue(np.allclose(prediction1, prediction2))
示例#5
0
from discomll import dataset
from discomll.regression import linear_regression

train = dataset.Data(
    data_tag=["http://ropot.ijs.si/data/fraction/train/xaaaaa.gz", "http://ropot.ijs.si/data/fraction/train/xaaabj.gz"],
    data_type="gzip",
    generate_urls=True,
    X_indices=range(1, 14),
    id_index=0,
    y_index=14,
    delimiter=",")

test = dataset.Data(
    data_tag=["http://ropot.ijs.si/data/fraction/test/xaaaaa.gz", "http://ropot.ijs.si/data/fraction/test/xaaabj.gz"],
    data_type="gzip",
    generate_urls=True,
    X_indices=range(1, 14),
    id_index=0,
    y_index=14,
    delimiter=",")

fit_model = linear_regression.fit(train)
predictions = linear_regression.predict(test, fit_model)
print predictions
示例#6
0
from discomll import dataset
from discomll.regression import linear_regression
from discomll.utils import model_view

# define training dataset
train = dataset.Data(data_tag=["test:ex3"],
                     data_type="chunk",
                     X_indices=[0, 1],
                     y_index=2)

# define test dataset
test = dataset.Data(data_tag=["test:ex3_test"],
                    data_type="chunk",
                    X_indices=[0, 1],
                    y_index=2)

# fit model on training dataset
fit_model = linear_regression.fit(train)

# output model
model = model_view.output_model(fit_model)
print model

# predict test dataset
predictions = linear_regression.predict(test, fit_model)

# output results
for k, v in result_iterator(predictions):
    print k, v[0]