Adding Logistic Regression class implementation & utils

.github/workflows/python-package.yml

@@ -41,7 +41,7 @@ jobs:
     - name: Test with pytest
       run: |
         python3 -m pytest
-        python3 -m coverage run --source=./RadClass/ -m pytest
+        python3 -m coverage run --source=./RadClass/,./models/,./scripts/ -m pytest
         python3 -m coverage report
         python3 -m coverage html
         COVERALLS_REPO_TOKEN=${{ secrets.COVERALLS_REPO_TOKEN }} python3 -m coveralls --service=github

README.md

@@ -25,7 +25,13 @@ Versions 3.6-3.9 are currently supported by tests. The following Python packages
 * h5py
 * numpy
 * progressbar2
+* matplotlib
+* seaborn
 * scipy
+* sklearn
+* hyperopt
+* torch
+* shadow-ssml
 
 Modules can be imported from the repository directory (e.g. `from RadClass.H0 import H0`) or `RadClass` can be installed using pip:
 

models/LogReg.py

@@ -0,0 +1,167 @@
+# For hyperopt (parameter optimization)
+from hyperopt import STATUS_OK
+# sklearn models
+from sklearn import linear_model
+# diagnostics
+from sklearn.metrics import balanced_accuracy_score
+from scripts.utils import run_hyperopt
+import joblib
+
+
+class LogReg:
+    '''
+    Methods for deploying sklearn's logistic regression
+    implementation with hyperparameter optimization.
+    Data agnostic (i.e. user supplied data inputs).
+    TODO: Currently only supports binary classification.
+        Add multinomial functions and unit tests.
+        Add functionality for regression(?)
+    Inputs:
+    params: dictionary of logistic regression input functions.
+        keys max_iter, tol, and C supported.
+    random_state: int/float for reproducible intiailization.
+    '''
+
+    # only binary so far
+    def __init__(self, params=None, random_state=0):
+        keys = ['max_iter', 'tol', 'C']
+        # defaults to a fixed value for reproducibility
+        self.random_state = random_state
+        # dictionary of parameters for logistic regression model
+        self.params = params
+        if self.params is None:
+            self.model = linear_model.LogisticRegression(
+                            random_state=self.random_state
+                        )
+        else:
+            if all(key in params.keys() for key in keys):
+                self.model = linear_model.LogisticRegression(
+                                random_state=self.random_state,
+                                max_iter=params['max_iter'],
+                                tol=params['tol'],
+                                C=params['C']
+                            )
+            else:
+                missing = [key for key in keys if key not in params.keys()]
+                raise ValueError('Values for {} not in params'.format(missing))
+
+    def fresh_start(self, params, data_dict):
+        '''
+        Required method for hyperopt optimization.
+        Trains and tests a fresh logistic regression model
+        with given input parameters.
+        This method does not overwrite self.model (self.optimize() does).
+        Inputs:
+        params: dictionary of logistic regression input functions.
+            keys max_iter, tol, and C supported.
+        data_dict: compact data representation with the four requisite
+            data structures used for training and testing a model.
+            keys trainx, trainy, testx, and testy required.
+        '''
+
+        # unpack data
+        trainx = data_dict['trainx']
+        trainy = data_dict['trainy']
+        testx = data_dict['testx']
+        testy = data_dict['testy']
+
+        # supervised logistic regression
+        clf = LogReg(params=params, random_state=self.random_state)
+        # train and test model
+        clf.train(trainx, trainy)
+        # uses balanced_accuracy accounts for class imbalanced data
+        clf_pred, acc = clf.predict(testx, testy)
+
+        # loss function minimizes misclassification
+        return {'loss': 1-acc,
+                'status': STATUS_OK,
+                'model': clf.model,
+                'params': params,
+                'accuracy': acc}
+
+    def optimize(self, space, data_dict, max_evals=50, verbose=True):
+        '''
+        Wrapper method for using hyperopt (see utils.run_hyperopt
+        for more details). After hyperparameter optimization, results
+        are stored, the best model -overwrites- self.model, and the
+        best params -overwrite- self.params.
+        Inputs:
+        space: a hyperopt compliant dictionary with defined optimization
+            spaces. For example:
+                # quniform returns float, some parameters require int;
+                # use this to force int
+                space = {'max_iter': scope.int(hp.quniform('max_iter',
+                                                           10,
+                                                           10000,
+                                                           10)),
+                        'tol'      : hp.loguniform('tol', 1e-5, 1e-1),
+                        'C'        : hp.uniform('C', 0.001,1000.0)
+                        }
+            See hyperopt docs for more information.
+        data_dict: compact data representation with the four requisite
+            data structures used for training and testing a model.
+            keys trainx, trainy, testx, testy required.
+        max_evals: the number of epochs for hyperparameter optimization.
+            Each iteration is one set of hyperparameters trained
+            and tested on a fresh model. Convergence for simpler
+            models like logistic regression typically happens well
+            before 50 epochs, but can increase as more complex models,
+            more hyperparameters, and a larger hyperparameter space is tested.
+        verbose: boolean. If true, print results of hyperopt.
+            If false, print only the progress bar for optimization.
+        '''
+
+        best, worst = run_hyperopt(space=space,
+                                   model=self.fresh_start,
+                                   data_dict=data_dict,
+                                   max_evals=max_evals,
+                                   verbose=verbose)
+
+        # save the results of hyperparameter optimization
+        self.best = best
+        self.model = best['model']
+        self.params = best['params']
+        self.worst = worst
+
+    def train(self, trainx, trainy):
+        '''
+        Wrapper method for sklearn's logisitic regression training method.
+        Inputs:
+        trainx: nxm feature vector/matrix for training model.
+        trainy: nxk class label vector/matrix for training model.
+        '''
+
+        # supervised logistic regression
+        self.model.fit(trainx, trainy)
+
+    def predict(self, testx, testy=None):
+        '''
+        Wrapper method for sklearn's logistic regression predict method.
+        Inputs:
+        testx: nxm feature vector/matrix for testing model.
+        testy: nxk class label vector/matrix for training model.
+            optional: if included, the predicted classes -and-
+            the resulting classification accuracy will be returned.
+        '''
+
+        pred = self.model.predict(testx)
+
+        acc = None
+        if testy is not None:
+            # uses balanced_accuracy_score to account for class imbalance
+            acc = balanced_accuracy_score(testy, pred)
+
+        return pred, acc
+
+    def save(self, filename):
+        '''
+        Save class instance to file using joblib.
+        Inputs:
+        filename: string filename to save object to file under.
+            The file must be saved with extension .joblib.
+            Added to filename if not included as input.
+        '''
+
+        if filename[-7:] != '.joblib':
+            filename += '.joblib'
+        joblib.dump(self, filename)

requirements.txt

@@ -2,3 +2,10 @@ numpy
 h5py
 progressbar2
 scipy>=1.7.0
+scikit-learn
+hyperopt
+matplotlib
+seaborn
+joblib
+torch
+shadow-ssml