content: import pytest
  import numpy as np
  from numpy.testing import assert_allclose
  
  from sklearn.datasets import load_iris
  from sklearn.linear_model import LogisticRegression
  
  from sklearn.metrics import det_curve
  from sklearn.metrics import plot_det_curve
  
  
  @pytest.fixture(scope="module")
  def data():
      return load_iris(return_X_y=True)
  
  
  @pytest.fixture(scope="module")
  def data_binary(data):
      X, y = data
      return X[y < 2], y[y < 2]
  
  
  @pytest.mark.parametrize(
      "response_method", ["predict_proba", "decision_function"]
  )
  @pytest.mark.parametrize("with_sample_weight", [True, False])
  @pytest.mark.parametrize("with_strings", [True, False])
  def test_plot_det_curve(
      pyplot,
      response_method,
      data_binary,
      with_sample_weight,
      with_strings
  ):
      X, y = data_binary
  
      pos_label = None
      if with_strings:
          y = np.array(["c", "b"])[y]
          pos_label = "c"
  
      if with_sample_weight:
          rng = np.random.RandomState(42)
          sample_weight = rng.randint(1, 4, size=(X.shape[0]))
      else:
          sample_weight = None
  
      lr = LogisticRegression()
      lr.fit(X, y)
  
      viz = plot_det_curve(
          lr, X, y, alpha=0.8, sample_weight=sample_weight,
      )
  
      y_pred = getattr(lr, response_method)(X)
      if y_pred.ndim == 2:
          y_pred = y_pred[:, 1]
  
      fpr, fnr, _ = det_curve(
          y, y_pred, sample_weight=sample_weight, pos_label=pos_label,
      )
  
      assert_allclose(viz.fpr, fpr)
      assert_allclose(viz.fnr, fnr)
  
      assert viz.estimator_name == "LogisticRegression"
  
      # cannot fail thanks to pyplot fixture
      import matplotlib as mpl  # noqal
      assert isinstance(viz.line_, mpl.lines.Line2D)
      assert viz.line_.get_alpha() == 0.8
      assert isinstance(viz.ax_, mpl.axes.Axes)
      assert isinstance(viz.figure_, mpl.figure.Figure)
      assert viz.line_.get_label() == "LogisticRegression"
  
      expected_pos_label = 1 if pos_label is None else pos_label
      expected_ylabel = (
          f"False Negative Rate (Positive label: {expected_pos_label})"
      )
      expected_xlabel = (
          f"False Positive Rate (Positive label: {expected_pos_label})"
      )
      assert viz.ax_.get_ylabel() == expected_ylabel
      assert viz.ax_.get_xlabel() == expected_xlabel
  