Refactoring completed

quacc/data.py

@@ -7,7 +7,6 @@ from typing import List, Optional
 class ExtendedCollection(LabelledCollection):
     def __init__(
         self,
-        b_coll: LabelledCollection,
         instances: np.ndarray | sp.csr_matrix,
         labels: np.ndarray,
         classes: Optional[List] = None,

quacc/error.py

@@ -0,0 +1,15 @@
+import quapy as qp
+
+def from_name(err_name):
+    if err_name == 'f1e':
+        return f1e
+    else:
+        return qp.error.from_name(err_name)
+    
+def f1e(prev):
+    return 1 - f1_score(prev)
+
+def f1_score(prev):
+    recall = prev[0] / (prev[0] + prev[1])
+    precision = prev[0] / (prev[0] + prev[2])
+    return 2 * (precision * recall) / (precision + recall)

quacc/estimator.py

@@ -46,7 +46,7 @@ def _extend_collection(base: LabelledCollection, pred_proba) -> ExtendedCollecti
         ]
     )
 
-    return ExtendedCollection(n_x, n_y, [*range(0, n_classes * n_classes)])
+    return ExtendedCollection(n_x, n_y, classes=[*range(0, n_classes * n_classes)])
 
 
 class AccuracyEstimator:
@@ -65,7 +65,7 @@ class AccuracyEstimator:
         # self.model.fit(*train.Xy)
         if isinstance(train, LabelledCollection):
             pred_prob_train = cross_val_predict(
-                self.model, train.Xy, method="predict_proba"
+                self.model, *train.Xy, method="predict_proba"
             )
 
             self.e_train = _extend_collection(train, pred_prob_train)
@@ -84,5 +84,5 @@ class AccuracyEstimator:
         estim_prev = self.q_model.quantify(e_inst)
 
         return _check_prevalence_classes(
-            e_inst.classes_, self.q_model.classes_, estim_prev
+            self.e_train.classes_, self.q_model.classes_, estim_prev
         )

quacc/evaluation.py

@@ -2,8 +2,12 @@ from quapy.protocol import (
     OnLabelledCollectionProtocol,
     AbstractStochasticSeededProtocol,
 )
+import quapy as qp
+from typing import Iterable, Callable, Union
 
 from .estimator import AccuracyEstimator
+import pandas as pd
+import quacc.error as error
 
 
 def estimate(estimator: AccuracyEstimator, protocol: AbstractStochasticSeededProtocol):
@@ -21,5 +25,43 @@ def estimate(estimator: AccuracyEstimator, protocol: AbstractStochasticSeededPro
     return base_prevs, true_prevs, estim_prevs
 
 
-def evaluate():
+def evaluation_report(
-    pass
+    estimator: AccuracyEstimator,
+    protocol: AbstractStochasticSeededProtocol,
+    error_metrics: Iterable[Union[str, Callable]] = "all",
+):
+    base_prevs, true_prevs, estim_prevs = estimate(estimator, protocol)
+
+    if error_metrics == "all":
+        error_metrics = ["mae", "rae", "mrae", "kld", "nkld", "f1e"]
+
+    error_funcs = [
+        error.from_name(e) if isinstance(e, str) else e for e in error_metrics
+    ]
+    assert all(hasattr(e, "__call__") for e in error_funcs), "invalid error function"
+    error_names = [e.__name__ for e in error_funcs]
+
+    df_cols = ["base_prev", "true_prev", "estim_prev"] + error_names
+    if "f1e" in df_cols:
+        df_cols.remove("f1e")
+        df_cols.extend(["f1e_true", "f1e_estim"])
+    lst = []
+    for base_prev, true_prev, estim_prev in zip(base_prevs, true_prevs, estim_prevs):
+        series = {
+            "base_prev": base_prev,
+            "true_prev": true_prev,
+            "estim_prev": estim_prev,
+        }
+        for error_name, error_metric in zip(error_names, error_funcs):
+            if error_name == "f1e":
+                series["f1e_true"] = error_metric(true_prev)
+                series["f1e_estim"] = error_metric(estim_prev)
+                continue
+
+            score = error_metric(true_prev, estim_prev)
+            series[error_name] = score
+
+        lst.append(series)
+
+    df = pd.DataFrame(lst, columns=df_cols)
+    return df

quacc/main.py

@@ -2,10 +2,16 @@ import numpy as np
 import quapy as qp
 import scipy.sparse as sp
 from quapy.data import LabelledCollection
+from quapy.method.aggregative import SLD
 from quapy.protocol import APP, AbstractStochasticSeededProtocol
 from sklearn.linear_model import LogisticRegression
 from sklearn.model_selection import cross_val_predict
 
+import quacc.evaluation as eval
+from quacc.estimator import AccuracyEstimator
+
+qp.environ['SAMPLE_SIZE'] = 100
+
 
 # Extended classes
 #
@@ -133,9 +139,9 @@ def test_1(dataset_name):
 
     orig_prevs, true_prevs, estim_prevs, errors = extend_and_quantify(
         LogisticRegression(),
-        qp.method.aggregative.SLD(LogisticRegression()),
+        SLD(LogisticRegression()),
         train,
-        APP(test, sample_size=100, n_prevalences=11, repeats=1),
+        APP(test, n_prevalences=11, repeats=1),
     )
 
     for orig_prev, true_prev, estim_prev, _errors in zip(
@@ -149,6 +155,18 @@ def test_1(dataset_name):
         print()
 
 
+def test_2(dataset_name):
+    train, test = get_dataset(dataset_name)
+    model = LogisticRegression()
+    model.fit(*train.Xy)
+    estimator = AccuracyEstimator(model, SLD(LogisticRegression()))
+    estimator.fit(train)
+    df = eval.evaluation_report(
+        estimator, APP(test, n_prevalences=11, repeats=1)
+    )
+    print(df.to_string())
+
+
 def main():
     for dataset_name in [
         # "hp",
@@ -156,7 +174,7 @@ def main():
         "spambase",
     ]:
         print(dataset_name)
-        test_1(dataset_name)
+        test_2(dataset_name)
         print("*" * 50)