def test_predict(self):
"""
function to test the predict
"""
HEADING()
Benchmark.Start()
LinearRegression.predict(X,X_shape_x,X_shape_y)
Benchmark.Stop()
assert True
def test_fit(self):
"""
function to test the fit
"""
HEADING()
Benchmark.Start()
LinearRegression.fit(X,y,X_shape_x,X_shape_y)
Benchmark.Stop()
assert True
def test_fit(self):
"""
function to test if the server is started and available to return
a successful http code
"""
HEADING()
Benchmark.Start()
LinearRegression.fit(X, y, sample_weight, X_shape_x, X_shape_y)
Benchmark.Stop()
assert True
def test_score(self):
"""
function to test the score
"""
HEADING()
Benchmark.Start()
score = LinearRegression.score(X,y,X_shape_x,X_shape_y)
Benchmark.Stop()
assert score > 0
from tests.generator import LinearRegression import numpy as np from sklearn.datasets import load_iris #X, y = load_iris(return_X_y=True) X = "X.csv" y = "y.csv" sample_weight = "sample_weight.csv" #print(np.array(y)) fit = LinearRegression.fit(X, y) print(LinearRegression.predict(X)) # print(LogisticRegression.decision_function(X,4,2)) #print(LinearRegression.predict_proba(X,4,2)) print(LinearRegression.score(X, y)) # from tests.generator import LinearRegression # from sklearn.datasets import load_iris # X, y = load_iris(return_X_y=True) # print(LinearRegression.fit(X,y,None)) # print(LinearRegression.predict(X[:2, :])) # print(LinearRegression.score(X,y,None)) # from tests.generator import RidgeClassifierCV as model # from sklearn.datasets import load_iris # # X, y = load_iris(return_X_y=True) # #print(X,y) # print(model.fit(X,y,None)) # print(model.predict(X[:2, :])) # print(model.score(X,y,None))