Merge pull request #431 from MannLabs/add-two-step-classifier

Add two step classifier

alphadia/constants/default.yaml

@@ -233,6 +233,7 @@ fdr:
   keep_decoys: false
   channel_wise_fdr: false
   inference_strategy: "heuristic"
+  enable_two_step_classifier: false
 
 search_output:
   peptide_level_lfq: false

alphadia/fdrx/models/logistic_regression.py

@@ -0,0 +1,128 @@
+import logging
+
+import numpy as np
+from sklearn.linear_model import LogisticRegression
+from sklearn.preprocessing import StandardScaler
+
+from alphadia.fdrexperimental import Classifier
+
+logger = logging.getLogger()
+
+
+class LogisticRegressionClassifier(Classifier):
+    def __init__(self) -> None:
+        """Binary classifier using a logistic regression model."""
+        self.scaler = StandardScaler()
+        self.model = LogisticRegression()
+        self._fitted = False
+
+    @property
+    def fitted(self) -> bool:
+        return self._fitted
+
+    def fit(self, x: np.ndarray, y: np.ndarray) -> None:
+        """Fit the classifier to the data.
+
+        Parameters
+        ----------
+
+        x : np.array, dtype=float
+            Training data of shape (n_samples, n_features).
+
+        y : np.array, dtype=int
+            Target values of shape (n_samples,) or (n_samples, n_classes).
+
+        """
+        x_scaled = self.scaler.fit_transform(x)
+        self.model.fit(x_scaled, y)
+        self._fitted = True
+
+    def predict(self, x: np.ndarray) -> np.ndarray:
+        """Predict the class of the data.
+
+        Parameters
+        ----------
+
+        x : np.array, dtype=float
+            Data of shape (n_samples, n_features).
+
+        Returns
+        -------
+
+        y : np.array, dtype=float
+            Predicted class probabilities of shape (n_samples, n_classes).
+
+        """
+        x_scaled = self.scaler.transform(x)
+        return self.model.predict(x_scaled)
+
+    def predict_proba(self, x: np.ndarray) -> np.ndarray:
+        """Predict the class probabilities of the data.
+
+        Parameters
+        ----------
+
+        x : np.array, dtype=float
+            Data of shape (n_samples, n_features).
+
+        Returns
+        -------
+
+        y : np.array, dtype=float
+            Predicted class probabilities of shape (n_samples, n_classes).
+
+        """
+        x_scaled = self.scaler.transform(x)
+        return self.model.predict_proba(x_scaled)
+
+    def to_state_dict(self) -> dict:
+        """Return the state of the classifier as a dictionary.
+
+        Returns
+        -------
+
+        dict : dict
+            Dictionary containing the state of the classifier.
+
+        """
+        state_dict = {"_fitted": self._fitted}
+
+        if self._fitted:
+            state_dict.update(
+                {
+                    "scaler_mean": self.scaler.mean_,
+                    "scaler_var": self.scaler.var_,
+                    "scaler_scale": self.scaler.scale_,
+                    "scaler_n_samples_seen": self.scaler.n_samples_seen_,
+                    "model_coef": self.model.coef_,
+                    "model_intercept": self.model.intercept_,
+                    "model_classes": self.model.classes_,
+                    "is_fitted": self._fitted,
+                }
+            )
+
+        return state_dict
+
+    def from_state_dict(self, state_dict: dict) -> None:
+        """Load the state of the classifier from a dictionary.
+
+        Parameters
+        ----------
+
+        dict : dict
+            Dictionary containing the state of the classifier.
+
+        """
+        self._fitted = state_dict["_fitted"]
+
+        if self._fitted:
+            self.scaler = StandardScaler()
+            self.scaler.mean_ = np.array(state_dict["scaler_mean"])
+            self.scaler.var_ = np.array(state_dict["scaler_var"])
+            self.scaler.scale_ = np.array(state_dict["scaler_scale"])
+            self.scaler.n_samples_seen_ = np.array(state_dict["scaler_n_samples_seen"])
+
+            self.model = LogisticRegression()
+            self.model.coef_ = np.array(state_dict["model_coef"])
+            self.model.intercept_ = np.array(state_dict["model_intercept"])
+            self.model.classes_ = np.array(state_dict["model_classes"])