add test to check if polt work

tests/evaluation/test_evaluation.py

@@ -10,8 +10,8 @@ def mock_data():
     d = {'variable': ['education', 'education', 'education', 'education'],
          'label': ['1st-4th', '5th-6th', '7th-8th', '9th'],
          'pop_size': [0.002, 0.004, 0.009, 0.019],
-         'avg_incidence': [0.23, 0.23, 0.23, 0.23],
-         'incidence': [0.047, 0.0434, 0.054, 0.069]}
+         'global_avg_target': [0.23, 0.23, 0.23, 0.23],
+         'avg_target': [0.047, 0.0434, 0.054, 0.069]}
     return pd.DataFrame(d)
 
 def mock_preds(n, seed = 505):
@@ -22,6 +22,11 @@ def mock_preds(n, seed = 505):
 
     return y_true, y_pred
 
+
+
+
+
+
 class TestEvaluation:
 
     def test_plot_incidence_with_unsupported_model_type(self):
@@ -127,3 +132,30 @@ def test_fit_regression(self):
         for metric in ["R2", "MAE", "MSE", "RMSE"]:
             assert evaluator.scalar_metrics[metric] is not None
         assert evaluator.qq is not None
+
+class TestClassificationEvaluator:
+    y_true, y_pred = mock_preds(50)
+    y_true = (y_true > 0.5).astype(int)  # convert to 0-1 labels
+
+    evaluator = ClassificationEvaluator(n_bins=5)
+    evaluator.fit(y_true, y_pred)
+
+    def test_plot_roc_curve(self):
+        self.evaluator.plot_roc_curve()
+    def test_plot_confusion_matrix(self):
+        self.evaluator.plot_confusion_matrix()
+    def test_plot_cumulative_response_curve(self):
+        self.evaluator.plot_cumulative_response_curve()
+    def test_plot_lift_curve(self):
+        self.evaluator.plot_lift_curve()
+    def test_plot_cumulative_gains(self):
+        self.evaluator.plot_cumulative_gains()
+
+class TestRegressionEvaluator:
+    y_true, y_pred = mock_preds(50)
+    evaluator = RegressionEvaluator()
+    evaluator.fit(y_true, y_pred)
+    def test_plot_predictions(self):
+        self.evaluator.plot_predictions()
+    def test_plot_qq(self):
+        self.evaluator.plot_qq()

tests/evaluation/test_pig_tables.py

@@ -0,0 +1,19 @@
+import pytest
+import pandas as pd
+
+from cobra.evaluation import plot_incidence
+
+def mock_data():
+    d = {'variable': ['education', 'education', 'education', 'education'],
+         'label': ['1st-4th', '5th-6th', '7th-8th', '9th'],
+         'pop_size': [0.002, 0.004, 0.009, 0.019],
+         'global_avg_target': [0.23, 0.23, 0.23, 0.23],
+         'avg_target': [0.047, 0.0434, 0.054, 0.069]}
+    return pd.DataFrame(d)
+
+
+def test_plot_incidence():
+    plot_incidence(pig_tables=mock_data(),
+                   variable="education",
+                   model_type="regression",)
+

tests/evaluation/test_plotting_utils.py

@@ -0,0 +1,57 @@
+from pandas import DataFrame
+from cobra.evaluation import (plot_univariate_predictor_quality, 
+                              plot_correlation_matrix,
+                              plot_performance_curves,
+                              plot_variable_importance)
+
+def mock_df_rmse() -> DataFrame:
+    return DataFrame(
+        {'predictor': {0: 'weight', 1: 'displacement', 2: 'horsepower',
+                       3: 'cylinders',  4: 'origin',  5: 'model_year',
+                       6: 'name',  7: 'acceleration'},
+        'RMSE train': {0: 4.225088318760745, 1: 4.403878881676005,
+                       2: 4.3343326307873875, 3: 4.901531871261906,
+                       4: 6.6435969708016955, 5: 6.318271823003904,
+                       6: 1.4537996193882199, 7: 6.631180878197439},
+        'RMSE selection': {0: 4.006855931973032, 1: 4.146696570151399,
+                           2: 4.321365764687869, 3: 4.466259266291863,
+                           4: 5.833138420191894, 5: 5.979795941821068,
+                           6: 6.99641113758452, 7: 7.449190759856361},
+        'preselection': {0: True, 1: True, 2: True, 3: True, 4: True,
+                         5: True, 6: True, 7: True}}
+        )
+
+
+def mock_df_corr() -> DataFrame:
+    return DataFrame({
+        'cylinders': {'cylinders': 1.0, 'weight': 0.8767772796304492, 'horsepower': 0.8124872187173973},
+        'weight': {'cylinders': 0.8767772796304492, 'weight': 1.0, 'horsepower': 0.8786843186591881},
+        'horsepower': {'cylinders': 0.8124872187173973, 'weight': 0.8786843186591881, 'horsepower': 1.0}})
+
+def mock_performances() -> DataFrame:
+    return DataFrame({
+        'predictors': {0: ['weight_enc'], 1: ['weight_enc', 'horsepower_enc'], 2: ['horsepower_enc', 'weight_enc', 'cylinders_enc']},
+        'last_added_predictor': {0: 'weight_enc', 1: 'horsepower_enc', 2: 'cylinders_enc'},
+        'train_performance': {0: 4.225088318760745, 1: 3.92118718828259, 2: 3.8929681840552495},
+        'selection_performance': {0: 4.006855931973032, 1: 3.630079770314085, 2: 3.531305702221386},
+        'validation_performance': {0: 4.348180862267973, 1: 4.089638309577036, 2: 3.9989641017455995},
+        'model_type': {0: 'regression', 1: 'regression', 2: 'regression'}
+        })
+
+def mock_variable_importance() -> DataFrame:
+    return DataFrame({
+        'predictor': {0: 'weight', 1: 'horsepower', 2: 'model_year', 3: 'origin'},
+        'importance': {0: 0.8921354566046729, 1: 0.864633073581914, 2: 0.694399044392948, 3: 0.6442243718390968}
+        })
+
+def test_plot_univariate_predictor_quality():
+    plot_univariate_predictor_quality(mock_df_rmse())
+
+def test_plot_correlation_matrix():
+    plot_correlation_matrix(mock_df_corr())
+
+def test_plot_performance_curves():
+    plot_performance_curves(mock_performances())
+
+def test_plot_variable_importance():
+    plot_variable_importance(mock_variable_importance())