How to use mlxtend - 10 common examples

def spearman_similar_correlation(self, column,
                                      correlation_lower_bound,
                                      pvalue_threshold=0.05,
                                      num_rounds=3):
        correlation_info = stats.spearmanr(self.new_data[column],
                                           self.historical_data[column])
        p_value = permutation_test(
            self.new_data[column],
            self.historical_data[column],
            method="approximate",
            num_rounds=num_rounds,
            func=lambda x, y: stats.spearmanr(x, y).correlation,
            seed=0)
        if p_value > pvalue_threshold:
            return False
        if correlation_info.correlation < correlation_lower_bound:
            return False
        return True

def mann_whitney_u_similar_distribution(self, column,
                                            pvalue_threshold=0.05,
                                            num_rounds=3):
        p_value = permutation_test(
            self.new_data[column],
            self.historical_data[column],
            method="approximate",
            num_rounds=num_rounds,
            func=lambda x, y: stats.mannwhitneyu(x, y).statistic,
            seed=0)

        if p_value < pvalue_threshold:
            return False
        return True

def kruskal_similar_distribution(self, column,
                                      pvalue_threshold=0.05,
                                      num_rounds=3):
        p_value = permutation_test(
            self.new_data[column],
            self.historical_data[column],
            method="approximate",
            num_rounds=num_rounds,
            func=lambda x, y: stats.kruskal(x, y).statistic,
            seed=0)
        if p_value < pvalue_threshold:
            return False
        return True

def test_predict_2():
    X = np.array([[1], [2], [3]])
    est = _BaseEstimator(print_progress=0, random_seed=1)

    est.fit(X)
    est.predict(X)

def test_init():
    est = _BaseEstimator(print_progress=0, random_seed=1)
    assert hasattr(est, 'print_progress')
    assert hasattr(est, 'random_seed')

def test_multivariate_gradient_descent():
    gd_lr = LinearRegression(eta=0.001, epochs=500, solver='gd', random_seed=0)
    gd_lr.fit(X_rm_lstat_std, y_std)
    assert_almost_equal(gd_lr.w_, expect_rm_lstat_std, decimal=3)

def test_univariate_stochastic_gradient_descent():
    sgd_lr = LinearRegression(solver='sgd', eta=0.0001, epochs=100, random_seed=0)
    sgd_lr.fit(X_rm_std, y_std)
    assert_almost_equal(sgd_lr.w_, expect_rm_std, decimal=2)

def test_univariate_normal_equation_std():
    ne_lr = LinearRegression(solver='normal_equation')
    ne_lr.fit(X_rm_std, y_std)
    assert_almost_equal(ne_lr.w_, expect_rm_std, decimal=3)

def test_multivariate_normal_equation():
    ne_lr = LinearRegression(solver='normal_equation')
    ne_lr.fit(X_rm_lstat, y)
    assert_almost_equal(ne_lr.w_, expect_rm_lstat, decimal=3)

def test_multivariate_stochastic_gradient_descent():
    sgd_lr = LinearRegression(eta=0.0001, epochs=500, solver='sgd', random_seed=0)
    sgd_lr.fit(X_rm_lstat_std, y_std)
    assert_almost_equal(sgd_lr.w_, expect_rm_lstat_std, decimal=2)