class_docstr: Tests the 'plot_predictions' function for different combinations of main, group,
  and panel variables.
class_docstr_tok: ["Tests","the","'plot_predictions","'","function","for","different","combinations","of","main",",","group",",","and","panel","variables","."]
docstr: Test plot cap default and specified values for target argument
docstr_tok: ["Test","plot","cap","default","and","specified","values","for","target","argument"]
code: def test_multiple_outputs_with_alias(self, pps):
      """Test plot cap default and specified values for target argument"""
      rng = np.random.default_rng(121195)
      N = 200
      a, b = 0.5, 1.1
      x = rng.uniform(-1.5, 1.5, N)
      shape = np.exp(0.3 + x * 0.5 + rng.normal(scale=0.1, size=N))
      y = rng.gamma(shape, np.exp(a + b * x) / shape, N)
      data_gamma = pd.DataFrame({"x": x, "y": y})
  
      formula = bmb.Formula("y ~ x", "alpha ~ x")
      model = bmb.Model(formula, data_gamma, family="gamma")
  
      # Without alias
      idata = model.fit(tune=100, draws=100, random_seed=1234)
      # Test default target
      plot_predictions(model, idata, "x", pps=pps)
      # Test user supplied target argument
      plot_predictions(model, idata, "x", target="alpha", pps=False)
  
      # With alias
      alias = {
          "alpha": {
              "Intercept": "sd_intercept",
              "x": "sd_x",
              "alpha": "sd_alpha",
          }
      }
      model.set_alias(alias)
      idata = model.fit(tune=100, draws=100, random_seed=1234)
  
      # Test user supplied target argument
      plot_predictions(model, idata, "x", target="alpha", pps=False)
  
imports: from os.path import dirname, join
  import numpy
  import pandas
  import matplotlib.pyplot
  import pytest
  import bambi
  from bambi.interpret import plot_comparisons, plot_predictions, plot_slopes
label_desc: Use image node_id 9 for calling the TestPredictions obj's underlying member method code with example usage: obj.test_multiple_outputs_with_alias(pps) without return types