Merge remote-tracking branch 'origin/main' into extend-docs

.vscode/settings.json

@@ -1,3 +1,6 @@
 {
-    "esbonio.sphinx.confDir": ""
+    "sonarlint.connectedMode.project": {
+        "connectionId": "vscode",
+        "projectKey": "KarelZe_tclf"
+    }
 }

README.md

@@ -1,5 +1,8 @@
 # Trade classification for python 🐍
 
+![GitHubActions](https://github.com/karelze/tclf//actions/workflows/tests.yaml/badge.svg)
+![Codecov](https://codecov.io/gh/karlze/tclf/branch/master/graph/badge.svg)
+
 `tclf` is a [`scikit-learn`](https://scikit-learn.org/stable/)-compatible implementation of trade classification algorithms to classify financial markets transactions into buyer- and seller-initiated trades.
 
 The key features are:
@@ -48,7 +51,7 @@ Run your script with
 ```console
 python main.py
 ```
-In this example, input data is available as a pd.DataFrame/Series with columns conforming to our [naming conventions](naming_conventions.md).
+In this example, input data is available as a pd.DataFrame/Series with columns conforming to our [naming conventions](https://karelze.github.io/tclf/naming_conventions/).
 
 The parameter `layers=[("quote", "ex")]` sets the quote rule at the exchange level and `strategy="random"` specifies the fallback strategy for unclassified trades. The true label `y` is not used in classification and only for API consistency by convention.
 
@@ -84,7 +87,7 @@ print(accuracy_score(y_true, y_pred))
 ```
 In this example, input data is available as np.arrays with both exchange (`"ex"`) and nbbo data (`"best"`). We set the layers parameter to `layers=[("quote", "ex"), ("quote", "best")]` to classify trades first on subset `"ex"` and remaining trades on subset `"best"`. Additionally, we have to set `ClassicalClassifier(..., features=features)` to pass column information to the classifier.
 
-Like before, column/feature names must follow our [naming conventions](naming_conventions.md).
+Like before, column/feature names must follow our [naming conventions](https://karelze.github.io/tclf/naming_conventions/).
 
 ## Supported Algorithms
 

sonar-project.properties

@@ -0,0 +1,2 @@
+sonar.sources=src\
+sonar.issue.ignore.multicriteria.e1.ruleKey=python:S117

tests/test_classical_classifier.py

@@ -1,7 +1,4 @@
-"""Tests for the classical classifier.
-
-Use of artificial data to test the classifier.
-"""
+"""Tests for the classical classifier."""
 
 import numpy as np
 import pandas as pd
@@ -25,7 +22,7 @@ def setup(self) -> None:
         Prepares inputs and expected outputs for testing.
         """
         self.x_train = pd.DataFrame(
-            [[1, 2], [3, 4], [1, 2], [3, 4]], columns=["ask_best", "bid_best"]
+            [[1, 1], [1, 1], [1, 1], [1, 1]], columns=["ask_best", "bid_best"]
         )
         self.y_train = pd.Series([1, 1, -1, -1])
         self.x_test = pd.DataFrame(