update

conf.yaml

@@ -71,14 +71,14 @@ test_conf: &test_conf
       
 main:
   confs: &main_confs
+    - DATASET_NAME: imdb
     - DATASET_NAME: rcv1
       DATASET_TARGET: CCAT
-  other_confs:
-    - DATASET_NAME: imdb
     - DATASET_NAME: rcv1
       DATASET_TARGET: GCAT
     - DATASET_NAME: rcv1
       DATASET_TARGET: MCAT
+  other_confs:
 
 sld_lr_conf: &sld_lr_conf
 
@@ -348,9 +348,7 @@ baselines_conf: &baselines_conf
     COMP_ESTIMATORS:
       - doc
       - atc_mc
-      - mandoline
-      - rca
-      - rca_star
+      - naive
     N_JOBS: -2
 
   confs: *main_confs
@@ -406,4 +404,4 @@ timing_conf: &timing_conf
 
   confs: *main_confs
 
-exec: *kde_lr_gs_conf
+exec: *baselines_conf

copy_source.sh

@@ -1,7 +1,9 @@
 #!/bin/bash
 
-CMD="cp"
-DEST="~/tesi_docker/"
+CMD="scp"
+DEST="andreaesuli@edge-nd1.isti.cnr.it:~/raid/lorenzo/"
+# CMD="cp"
+# DEST="~/tesi_docker/"
 
 bash -c "${CMD} -r quacc ${DEST}"
 bash -c "${CMD} -r baselines ${DEST}"

log

@@ -3,6 +3,8 @@
 if [[ "${1}" == "r" ]]; then
 	scp volpi@ilona.isti.cnr.it:~/tesi/quacc.log ~/tesi/remote.log &>/dev/null
 	ssh volpi@ilona.isti.cnr.it tail -n 500 -f /home/volpi/tesi/quacc.log | bat -P --language=log
+elif [[ "${1}" == "d" ]]; then
+	ssh andreaesuli@edge-nd1.isti.cnr.it tail -n 500 -f /home/andreaesuli/raid/lorenzo/quacc.log | bat -P --language=log
 else
 	tail -n 500 -f /home/lorev/tesi/quacc.log | bat --paging=never --language log
 fi

quacc/dataset.py

@@ -126,7 +126,9 @@ class DatasetProvider:
 
     # provare min_df=5
     def __imdb(self, **kwargs):
-        return qp.datasets.fetch_reviews("imdb", tfidf=True, min_df=3).train_test
+        return qp.datasets.fetch_reviews(
+            "imdb", data_home="./quapy_data", tfidf=True, min_df=3
+        ).train_test
 
     def __rcv1(self, target, **kwargs):
         n_train = 23149
@@ -135,7 +137,7 @@ class DatasetProvider:
         if target is None or target not in available_targets:
             raise ValueError(f"Invalid target {target}")
 
-        dataset = fetch_rcv1()
+        dataset = fetch_rcv1(data_home="./scikit_learn_data")
         target_index = np.where(dataset.target_names == target)[0]
         all_train_d = dataset.data[:n_train, :]
         test_d = dataset.data[n_train:, :]

quacc/evaluation/baseline.py

@@ -68,6 +68,38 @@ def kfcv(
     return report
 
 
+@baseline
+def naive(
+    c_model: BaseEstimator,
+    validation: LabelledCollection,
+    protocol: AbstractStochasticSeededProtocol,
+    predict_method="predict",
+):
+    c_model_predict = getattr(c_model, predict_method)
+    f1_average = "binary" if validation.n_classes == 2 else "macro"
+
+    val_preds = c_model_predict(validation.X)
+    val_acc = metrics.accuracy_score(validation.y, val_preds)
+    val_f1 = metrics.f1_score(validation.y, val_preds, average=f1_average)
+
+    report = EvaluationReport(name="naive")
+    for test in protocol():
+        test_preds = c_model_predict(test.X)
+        acc_score = metrics.accuracy_score(test.y, test_preds)
+        f1_score = metrics.f1_score(test.y, test_preds, average=f1_average)
+        meta_acc = abs(val_acc - acc_score)
+        meta_f1 = abs(val_f1 - f1_score)
+        report.append_row(
+            test.prevalence(),
+            acc_score=acc_score,
+            f1_score=f1_score,
+            acc=meta_acc,
+            f1=meta_f1,
+        )
+
+    return report
+
+
 @baseline
 def ref(
     c_model: BaseEstimator,
@@ -556,4 +588,3 @@ def kdex2(
         report.append_row(test.prevalence(), acc=meta_score, acc_score=estim_acc)
 
     return report
-

quacc/evaluation/method.py

@@ -380,9 +380,9 @@ __kde_lr_set = [
     M("mul_kde_lr_a",  __kde_lr(), "mul", conf=["max_conf", "entropy", "isoft"],         ),
     M("m3w_kde_lr_a",  __kde_lr(), "mul", conf=["max_conf", "entropy", "isoft"],  cf=True),
     # gs kde
-    G("bin_kde_lr_gs", __kde_lr(), "bin", pg="kde_lr", search="spider"         ),
-    G("mul_kde_lr_gs", __kde_lr(), "mul", pg="kde_lr", search="spider"         ),
-    G("m3w_kde_lr_gs", __kde_lr(), "mul", pg="kde_lr", search="spider", cf=True),
+    G("bin_kde_lr_gs", __kde_lr(), "bin", pg="kde_lr", search="grid"         ),
+    G("mul_kde_lr_gs", __kde_lr(), "mul", pg="kde_lr", search="grid"         ),
+    G("m3w_kde_lr_gs", __kde_lr(), "mul", pg="kde_lr", search="grid", cf=True),
     E("kde_lr_gs"),
 ]
 
@@ -458,6 +458,7 @@ __methods_set = (
     + __kde_lr_set
     + __dense_kde_lr_set
     + __dense_kde_rbf_set
+    + [E("QuAcc")]
 )
 
 _methods = {m.name: m for m in __methods_set}

quacc/evaluation/report.py

@@ -140,6 +140,14 @@ class CompReport:
                 "mul_kde_lr_gs",
                 "m3w_kde_lr_gs",
             ],
+            "QuAcc": [
+                "bin_sld_lr_gs",
+                "mul_sld_lr_gs",
+                "m3w_sld_lr_gs",
+                "bin_kde_lr_gs",
+                "mul_kde_lr_gs",
+                "m3w_kde_lr_gs",
+            ],
         }
 
         for name, methods in _mapping.items():

rates.md

@@ -0,0 +1,15 @@
+# Additional covariates percentage
+
+Rate of usage of additional covariates, recalibration and "balanced" class_weight
+during grid search:
+
+| method          | av %   | recalib % | rebalance % |
+| --------------: | :----: | :-------: | :---------: |
+| imdb_sld_lr     | 81.49% | 77.78%    | 59.26%      |
+| imdb_kde_lr     | 71.43% | NA        | 88.18%      |
+| rcv1_CCAT_sld_lr| 62.97% | 70.38%    | 77.78%      |
+| rcv1_CCAT_kde_lr| 78.06% | NA        | 84.82%      |
+| rcv1_GCAT_sld_lr| 76.93% | 61.54%    | 65.39%      |
+| rcv1_GCAT_kde_lr| 71.36% | NA        | 78.65%      |
+| rcv1_MCAT_sld_lr| 62.97% | 48.15%    | 74.08%      |
+| rcv1_MCAT_kde_lr| 71.03% | NA        | 68.70%      |

run.py

@@ -15,3 +15,7 @@ def run():
         run_local()
     elif args.remote:
         run_remote(detatch=args.detatch)
+
+
+if __name__ == "__main__":
+    run()