Fixture for Augur tests (#406)

* Added fixture for test_augur.py with subsampled sc_sim_augur dataset obtained from the dataloader

* Deleted sc_sim.h5ad dataset from tests folder

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tests/tools/test_augur.py

@@ -16,90 +16,87 @@ class TestAugur:
     ag_lrc = pt.tl.Augur("logistic_regression_classifier", Params(random_state=42))
     ag_rfr = pt.tl.Augur("random_forest_regressor", Params(random_state=42))
 
-    def test_load(self):
+    @pytest.fixture
+    def adata(self):
+        adata = pt.dt.sc_sim_augur()
+        adata = sc.pp.subsample(adata, n_obs=200, copy=True, random_state=10)
+
+        return adata
+
+    def test_load(self, adata):
         """Test if load function creates anndata objects."""
-        sc_sim_adata = sc.read_h5ad(f"{CWD}/sc_sim.h5ad")
         ag = pt.tl.Augur(estimator="random_forest_classifier")
 
-        loaded_adata = ag.load(sc_sim_adata)
-        loaded_df = ag.load(sc_sim_adata.to_df(), meta=sc_sim_adata.obs, cell_type_col="cell_type", label_col="label")
+        loaded_adata = ag.load(adata)
+        loaded_df = ag.load(adata.to_df(), meta=adata.obs, cell_type_col="cell_type", label_col="label")
 
         assert loaded_adata.obs["y_"].equals(loaded_df.obs["y_"]) is True
-        assert sc_sim_adata.to_df().equals(loaded_adata.to_df()) is True and sc_sim_adata.to_df().equals(
-            loaded_df.to_df()
-        )
+        assert adata.to_df().equals(loaded_adata.to_df()) is True and adata.to_df().equals(loaded_df.to_df())
 
-    def test_random_forest_classifier(self):
+    def test_random_forest_classifier(self, adata):
         """Tests random forest for auc calculation."""
-        sc_sim_adata = sc.read_h5ad(f"{CWD}/sc_sim.h5ad")
-        sc_sim_adata = self.ag_rfc.load(sc_sim_adata)
-        sc.pp.highly_variable_genes(sc_sim_adata)
-        adata, results = self.ag_rfc.predict(
-            sc_sim_adata, n_threads=4, n_subsamples=3, random_state=42, select_variance_features=False
+        adata = self.ag_rfc.load(adata)
+        sc.pp.highly_variable_genes(adata)
+        h_adata, results = self.ag_rfc.predict(
+            adata, n_threads=4, n_subsamples=3, random_state=42, select_variance_features=False
         )
 
         assert results["CellTypeA"][2]["subsample_idx"] == 2
-        assert "augur_score" in adata.obs.columns
+        assert "augur_score" in h_adata.obs.columns
         assert np.allclose(results["summary_metrics"].loc["mean_augur_score"].tolist(), [0.634920, 0.933484, 0.902494])
         assert "feature_importances" in results.keys()
         assert len(set(results["summary_metrics"]["CellTypeA"])) == len(results["summary_metrics"]["CellTypeA"]) - 1
 
-    def test_logistic_regression_classifier(self):
+    def test_logistic_regression_classifier(self, adata):
         """Tests logistic classifier for auc calculation."""
-        sc_sim_adata = sc.read_h5ad(f"{CWD}/sc_sim.h5ad")
-        sc_sim_adata = self.ag_rfc.load(sc_sim_adata)
-        sc.pp.highly_variable_genes(sc_sim_adata)
-        adata, results = self.ag_lrc.predict(
-            sc_sim_adata, n_threads=4, n_subsamples=3, random_state=42, select_variance_features=False
+        adata = self.ag_rfc.load(adata)
+        sc.pp.highly_variable_genes(adata)
+        h_adata, results = self.ag_lrc.predict(
+            adata, n_threads=4, n_subsamples=3, random_state=42, select_variance_features=False
         )
 
-        assert "augur_score" in adata.obs.columns
+        assert "augur_score" in h_adata.obs.columns
         assert np.allclose(results["summary_metrics"].loc["mean_augur_score"].tolist(), [0.691232, 0.955404, 0.972789])
         assert "feature_importances" in results.keys()
 
-    def test_random_forest_regressor(self):
+    def test_random_forest_regressor(self, adata):
         """Tests random forest regressor for ccc calculation."""
-        sc_sim_adata = sc.read_h5ad(f"{CWD}/sc_sim.h5ad")
-        sc_sim_adata = self.ag_rfc.load(sc_sim_adata)
-        sc.pp.highly_variable_genes(sc_sim_adata)
+        adata = self.ag_rfc.load(adata)
+        sc.pp.highly_variable_genes(adata)
 
         with pytest.raises(ValueError):
-            self.ag_rfr.predict(sc_sim_adata, n_threads=4, n_subsamples=3, random_state=42)
+            self.ag_rfr.predict(adata, n_threads=4, n_subsamples=3, random_state=42)
 
-    def test_classifier(self):
+    def test_classifier(self, adata):
         """Test run cross validation with classifier."""
-        sc_sim_adata = sc.read_h5ad(f"{CWD}/sc_sim.h5ad")
-        sc_sim_adata = self.ag_rfc.load(sc_sim_adata)
-        sc.pp.highly_variable_genes(sc_sim_adata)
-        adata = sc.pp.subsample(sc_sim_adata, n_obs=100, random_state=42, copy=True)
+        adata = self.ag_rfc.load(adata)
+        sc.pp.highly_variable_genes(adata)
+        adata_subsampled = sc.pp.subsample(adata, n_obs=100, random_state=42, copy=True)
 
-        cv = self.ag_rfc.run_cross_validation(adata, subsample_idx=1, folds=3, random_state=42, zero_division=0)
+        cv = self.ag_rfc.run_cross_validation(
+            adata_subsampled, subsample_idx=1, folds=3, random_state=42, zero_division=0
+        )
         auc = 0.786412
         assert any([isclose(cv["mean_auc"], auc, abs_tol=10**-3)])
 
-        sc_sim_adata = sc.read_h5ad(f"{CWD}/sc_sim.h5ad")
-        sc_sim_adata = self.ag_lrc.load(sc_sim_adata)
-        sc.pp.highly_variable_genes(sc_sim_adata)
-        cv = self.ag_lrc.run_cross_validation(sc_sim_adata, subsample_idx=1, folds=3, random_state=42, zero_division=0)
+        cv = self.ag_lrc.run_cross_validation(adata, subsample_idx=1, folds=3, random_state=42, zero_division=0)
         auc = 0.978673
         assert any([isclose(cv["mean_auc"], auc, abs_tol=10**-3)])
 
-    def test_regressor(self):
+    def test_regressor(self, adata):
         """Test run cross validation with regressor."""
-        sc_sim_adata = sc.read_h5ad(f"{CWD}/sc_sim.h5ad")
-        sc_sim_adata = self.ag_rfc.load(sc_sim_adata)
-        cv = self.ag_rfr.run_cross_validation(sc_sim_adata, subsample_idx=1, folds=3, random_state=42, zero_division=0)
+        adata = self.ag_rfc.load(adata)
+        cv = self.ag_rfr.run_cross_validation(adata, subsample_idx=1, folds=3, random_state=42, zero_division=0)
         ccc = 0.168800
         r2 = 0.149887
         assert any([isclose(cv["mean_ccc"], ccc, abs_tol=10**-5), isclose(cv["mean_r2"], r2, abs_tol=10**-5)])
 
-    def test_subsample(self):
+    def test_subsample(self, adata):
         """Test default, permute and velocity subsampling process."""
-        sc_sim_adata = sc.read_h5ad(f"{CWD}/sc_sim.h5ad")
-        sc_sim_adata = self.ag_rfc.load(sc_sim_adata)
-        sc.pp.highly_variable_genes(sc_sim_adata)
+        adata = self.ag_rfc.load(adata)
+        sc.pp.highly_variable_genes(adata)
         categorical_subsample = self.ag_rfc.draw_subsample(
-            adata=sc_sim_adata,
+            adata=adata,
             augur_mode="default",
             subsample_size=20,
             feature_perc=0.3,
@@ -109,7 +106,7 @@ def test_subsample(self):
         assert len(categorical_subsample.obs_names) == 40
 
         non_categorical_subsample = self.ag_rfc.draw_subsample(
-            adata=sc_sim_adata,
+            adata=adata,
             augur_mode="default",
             subsample_size=20,
             feature_perc=0.3,
@@ -119,17 +116,17 @@ def test_subsample(self):
         assert len(non_categorical_subsample.obs_names) == 20
 
         permut_subsample = self.ag_rfc.draw_subsample(
-            adata=sc_sim_adata,
+            adata=adata,
             augur_mode="permute",
             subsample_size=20,
             feature_perc=0.3,
             categorical=True,
             random_state=42,
         )
-        assert (sc_sim_adata.obs.loc[permut_subsample.obs.index, "y_"] != permut_subsample.obs["y_"]).any()
+        assert (adata.obs.loc[permut_subsample.obs.index, "y_"] != permut_subsample.obs["y_"]).any()
 
         velocity_subsample = self.ag_rfc.draw_subsample(
-            adata=sc_sim_adata,
+            adata=adata,
             augur_mode="velocity",
             subsample_size=20,
             feature_perc=0.3,
@@ -142,13 +139,12 @@ def test_multiclass(self):
         """Test multiclass evaluation."""
         pass
 
-    def test_select_variance(self):
+    def test_select_variance(self, adata):
         """Test select variance implementation."""
-        sc_sim_adata = sc.read_h5ad(f"{CWD}/sc_sim.h5ad")
-        sc_sim_adata = self.ag_rfc.load(sc_sim_adata)
-        sc.pp.highly_variable_genes(sc_sim_adata)
-        adata = sc_sim_adata[sc_sim_adata.obs["cell_type"] == "CellTypeA"]
-        ad = self.ag_rfc.select_variance(adata, var_quantile=0.5, span=0.3, filter_negative_residuals=False)
+        adata = self.ag_rfc.load(adata)
+        sc.pp.highly_variable_genes(adata)
+        adata_cell_type = adata[adata.obs["cell_type"] == "CellTypeA"]
+        ad = self.ag_rfc.select_variance(adata_cell_type, var_quantile=0.5, span=0.3, filter_negative_residuals=False)
 
         assert 3672 == len(ad.var.index[ad.var["highly_variable"]])
 
@@ -170,17 +166,15 @@ def test_params(self):
             self.ag_rfr.create_estimator("random_forest_regressor", Params(unvalid=10))
 
     @pytest.mark.skip("Computationally expensive")
-    def test_differential_prioritization(self):
+    def test_differential_prioritization(self, adata):
         """Test differential prioritization run."""
-        sc_sim_adata = sc.read_h5ad(f"{CWD}/sc_sim.h5ad")
-
         ag = pt.tl.Augur("random_forest_classifier", Params(random_state=42))
-        ag.load(sc_sim_adata)
+        ag.load(adata)
 
-        adata, results1 = ag.predict(sc_sim_adata, n_threads=4, n_subsamples=3, random_state=2)
-        adata, results2 = ag.predict(sc_sim_adata, n_threads=4, n_subsamples=3, random_state=42)
+        adata, results1 = ag.predict(adata, n_threads=4, n_subsamples=3, random_state=2)
+        adata, results2 = ag.predict(adata, n_threads=4, n_subsamples=3, random_state=42)
 
-        a, permut1 = ag.predict(sc_sim_adata, augur_mode="permute", n_threads=4, n_subsamples=100, random_state=2)
-        a, permut2 = ag.predict(sc_sim_adata, augur_mode="permute", n_threads=4, n_subsamples=100, random_state=42)
+        a, permut1 = ag.predict(adata, augur_mode="permute", n_threads=4, n_subsamples=100, random_state=2)
+        a, permut2 = ag.predict(adata, augur_mode="permute", n_threads=4, n_subsamples=100, random_state=42)
         delta = ag.predict_differential_prioritization(results1, results2, permut1, permut2)
         assert not np.isnan(delta["z"]).any()