updated models for cv

ml/src/analysis/test_model_survival.py

 #!/usr/bin/env python
-import numpy as np
 import paths as pt
-from typing import List
-from tools import file_writer, preprocessor, data_loader
-from tools.classifiers import KnnClassifier, SvmClassifier, LrClassifier
-from tools.classifiers import XgbClassifier, RfClassifier, MlpClassifier
-from sklearn.model_selection import cross_validate
+from tools import data_loader
 from sklearn.preprocessing import LabelEncoder
 from utility.settings import load_settings
-from utility.metrics import compute_mean, compute_std
 from sksurv.ensemble import RandomSurvivalForest
-from sklearn.model_selection import StratifiedKFold, KFold
+from sklearn.model_selection import KFold
 from sksurv.metrics import concordance_index_censored
-import pandas as pd
 
 def main():
     data_settings = load_settings(pt.CONFIGS_DIR, "data.yaml")

ml/src/data/load_and_clean_data.py

@@ -14,15 +14,12 @@ def main():
     td = loader.load_training_done(pt.PATHS_2021[2],
                                    pt.PATHS_2021[4],
                                    pt.RAW_DATA_DIR_2021)
-
     sc = loader.load_screening_content(pt.PATHS_2021[2],
                                        pt.PATHS_2021[4],
                                        pt.RAW_DATA_DIR_2021)
-
     tc = loader.load_training_cancelled(pt.PATHS_2021[2],
                                         pt.PATHS_2021[4],
                                         pt.RAW_DATA_DIR_2021)
-
     ss = loader.load_status_set(pt.PATHS_2021[2],
                                 pt.PATHS_2021[4],
                                 pt.RAW_DATA_DIR_2021)

ml/src/tools/classifiers.py

@@ -62,24 +62,19 @@ class SvmClassifier(BaseClassifer):
     """Support vector machine classifier."""
     def make_model(self):
         return SVC(random_state=0,
-                   class_weight="balanced",
                    probability=True)
 
 class LrClassifier(BaseClassifer):
     """Logistic regression classifier."""
     def make_model(self):
         return LogisticRegression(max_iter=1000,
-                                  class_weight="balanced",
                                   random_state=0)
 
 class XgbClassifier(BaseClassifer):
     """XGBoost classifier."""
     def make_model(self):
-        neg, pos = np.bincount(self.y)
-        scale_pos_weight = neg / pos
         params = {"n_estimators": 400,
                   "learning_rate": 0.1,
-                  "scale_pos_weight": scale_pos_weight,
                   "objective": "binary:logistic",
                   "random_state": 0,
                   "use_label_encoder": False,
@@ -90,7 +85,6 @@ class RfClassifier(BaseClassifer):
     """Random Forest classifier."""
     def make_model(self):
         return RandomForestClassifier(n_estimators=800,
-                                      class_weight="balanced",
                                       random_state=0)
 
 class MlpClassifier(BaseClassifer):
@@ -118,7 +112,5 @@ class MlpClassifier(BaseClassifer):
                         optimizer="Adam",
                         metrics=metrics)
             return model
-        neg, pos = np.bincount(self.y)
-        class_weight = preprocessor.get_class_weight(neg, pos)
-        return KerasClassifier(make_keras_model, epochs=20, batch_size=32,
-                               class_weight=class_weight, verbose=False)
+        return KerasClassifier(make_keras_model, epochs=20,
+                               batch_size=64, verbose=False)

ml/src/tools/file_writer.py

@@ -115,7 +115,7 @@ def write_cv_plot(means: List, stds: List, metric: str,
         fig.suptitle(f"{os.path.splitext(title)[0]} {subtitle}")
         plt.setp(axs[-1, :], xlabel='Seed')
         plt.setp(axs[:, 0], ylabel=metric)
-        plt.savefig(outfile, dpi=300, bbox_inches = "tight")
+        plt.savefig(outfile, dpi=300, bbox_inches="tight")
 
 def write_roc_curve(y_true: np.ndarray, results: List,
                     title: str, subtitle: str, outfile: BytesIO):