new classifier

bengrn/base.py

@@ -286,39 +286,50 @@ def train_classifier(
         adj, da, random_state=0, train_size=train_size, shuffle=shuffle
     )
     print("doing classification....")
-    clf = LogisticRegression(
-        penalty="l1",
-        C=C,
-        solver="saga",
+    from sklearn.linear_model import RidgeClassifier
+
+    clf = RidgeClassifier(
+        alpha=C,
+        fit_intercept=False,
         class_weight=class_weight,
+        # solver="saga",
         max_iter=max_iter,
-        n_jobs=8,
-        fit_intercept=False,
-        **kwargs,
+        positive=True,
     )
+    # clf = LogisticRegression(
+    #    penalty="l1",
+    #    C=C,
+    #    solver="saga",
+    #    class_weight=class_weight,
+    #    max_iter=max_iter,
+    #    n_jobs=8,
+    #    fit_intercept=False,
+    #    verbose=10,
+    #    **kwargs,
+    # )
     clf.fit(X_train, y_train)
     pred = clf.predict(X_test)
-    epr = compute_epr(clf, X_test, y_test)
+    # epr = compute_epr(clf, X_test, y_test)
     metrics = {
         "used_heads": (clf.coef_ != 0).sum(),
         "precision": (pred[y_test == 1] == 1).sum() / (pred == 1).sum(),
         "random_precision": y_test.sum() / len(y_test),
         "recall": (pred[y_test == 1] == 1).sum() / y_test.sum(),
         "predicted_true": pred.sum(),
         "number_of_true": y_test.sum(),
-        "epr": epr,
+        # "epr": epr,
     }
     if doplot:
         print("metrics", metrics)
         PrecisionRecallDisplay.from_estimator(
             clf, X_test, y_test, plot_chance_level=True
         )
         plt.show()
-    adj = grn.varp["GRN"]
     if return_full:
-        grn.varp["classified"] = clf.predict_proba(
-            adj.reshape(-1, adj.shape[-1])
-        ).reshape(len(grn.var), len(grn.var), 2)[:, :, 1]
+        adj = grn.varp["GRN"]
+        grn.varp["classified"] = clf.predict(adj.reshape(-1, adj.shape[-1])).reshape(
+            len(grn.var), len(grn.var), 2
+        )[:, :, 1]
     return grn, metrics, clf