CV updates

auton_survival/models/cmhe/__init__.py

@@ -82,7 +82,7 @@
 from .cmhe_utilities import train_cmhe, predict_survival
 from .cmhe_utilities import predict_latent_phi, predict_latent_z
 
-from auton_survival.preprocessing import _dataframe_to_array
+from auton_survival.utils import _dataframe_to_array
 
 
 class DeepCoxMixturesHeterogenousEffects:

auton_survival/models/cph/__init__.py

@@ -29,7 +29,7 @@
 from .dcph_torch import DeepCoxPHTorch, DeepRecurrentCoxPHTorch
 from .dcph_utilities import train_dcph, predict_survival
 
-from auton_survival.preprocessing import _dataframe_to_array
+from auton_survival.utils import _dataframe_to_array
 from auton_survival.models.dsm.utilities import _get_padded_features
 from auton_survival.models.dsm.utilities import _get_padded_targets
 

auton_survival/models/dcm/__init__.py

@@ -56,7 +56,7 @@
 from .dcm_torch import DeepCoxMixturesTorch
 from .dcm_utilities import train_dcm, predict_survival, predict_latent_z
 
-from auton_survival.preprocessing import _dataframe_to_array
+from auton_survival.utils import _dataframe_to_array
 
 
 class DeepCoxMixtures:

auton_survival/models/dsm/__init__.py

@@ -170,7 +170,7 @@
 from .utilities import _get_padded_features, _get_padded_targets
 from .utilities import _reshape_tensor_with_nans
 
-from auton_survival.preprocessing import _dataframe_to_array
+from auton_survival.utils import _dataframe_to_array
 
 
 __pdoc__ = {}

auton_survival/phenotyping.py

@@ -483,7 +483,8 @@ def __init__(self,
 
     self.random_seed = random_seed
 
-  def fit(self, features, outcomes, interventions, horizon):
+  def fit(self, features, outcomes, interventions, metric, 
+          horizon, cat_feats, num_feats):
 
     """Fit a counterfactual model and regress the difference of the estimated
     counterfactual Restricted Mean Survival Time using a Random Forest regressor.
@@ -502,6 +503,10 @@ def fit(self, features, outcomes, interventions, horizon):
     horizon : np.float
         The event horizon at which to compute the counterfacutal RMST for
         regression.
+    cat_feats: list
+        List of categorical features.
+    num_feats: list
+        List of numerical/continuous features. 
 
     Returns
     -----------
@@ -512,7 +517,8 @@ def fit(self, features, outcomes, interventions, horizon):
     cf_model = CounterfactualSurvivalRegressionCV(model=self.cf_method,
                                     hyperparam_grid=self.cf_hyperparams)
 
-    self.cf_model = cf_model.fit(features, outcomes, interventions)
+    self.cf_model = cf_model.fit(features, outcomes, interventions,
+                                 metric, cat_feats, num_feats)
 
     times = np.unique(outcomes.time.values)
     cf_predictions = self.cf_model.predict_counterfactual_survival(features,

auton_survival/preprocessing.py

@@ -376,9 +376,3 @@ def fit_transform(self, data, cat_feats, num_feats,
       output = pd.get_dummies(output, dummy_na=False, drop_first=True)
 
     return output
-
-def _dataframe_to_array(data):
-  if isinstance(data, (pd.Series, pd.DataFrame)):
-    return data.to_numpy()
-  else:
-    return data

auton_survival/utils.py

@@ -1,4 +1,5 @@
 import inspect
+import pandas as pd
 
 def _get_method_kwargs(method, kwargs):
 
@@ -11,3 +12,9 @@ def _get_method_kwargs(method, kwargs):
   method_kwargs = {k: kwargs[k] for k in method_params}
 
   return method_kwargs
+
+def _dataframe_to_array(data):
+  if isinstance(data, (pd.Series, pd.DataFrame)):
+    return data.to_numpy()
+  else:
+    return data

examples/CV Survival Regression on SUPPORT Dataset.ipynb

@@ -9,11 +9,6 @@
     "`auton-survival` offers a simple to use API to train Survival Regression Models that performs cross validation model selection by minimizing integrated brier score. In this notebook we demonstrate the use of `auton-survival` to train survival models on the *SUPPORT* dataset in cross validation fashion."
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -37,10 +32,8 @@
     "\n",
     "cat_feats = ['sex', 'dzgroup', 'dzclass', 'income', 'race', 'ca']\n",
     "num_feats = ['age', 'num.co', 'meanbp', 'wblc', 'hrt', 'resp', \n",
-    "\t     'temp', 'pafi', 'alb', 'bili', 'crea', 'sod', 'ph', \n",
-    "             'glucose', 'bun', 'urine', 'adlp', 'adls']\n",
-    "\n",
-    "features = Preprocessor().fit_transform(features, cat_feats=cat_feats, num_feats=num_feats)"
+    "             'temp', 'pafi', 'alb', 'bili', 'crea', 'sod', 'ph', \n",
+    "             'glucose', 'bun', 'urine', 'adlp', 'adls']"
    ]
   },
   {
@@ -59,25 +52,23 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "from auton_survival.experiments import SurvivalRegressionCV\n",
-    "\n",
-    "param_grid = {'k' : [3],\n",
-    "              'distribution' : ['Weibull'],\n",
-    "              'learning_rate' : [ 1e-4, 1e-3],\n",
-    "              'layers' : [[], [100]]}\n",
-    "\n",
-    "experiment = SurvivalRegressionCV(model='dsm', cv_folds=5, hyperparam_grid=param_grid, random_seed=0)\n",
-    "model = experiment.fit(features, outcomes)\n"
-   ]
+   "source": []
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "experiment.folds"
+    "from auton_survival.experiments import SurvivalRegressionCV\n",
+    "\n",
+    "param_grid = {'k' : [3],\n",
+    "              'distribution' : ['Weibull'],\n",
+    "              'learning_rate' : [1e-4, 1e-3],\n",
+    "              'layers' : [[100]]}\n",
+    "\n",
+    "experiment = SurvivalRegressionCV(model='dsm', num_folds=3, hyperparam_grid=param_grid, random_seed=0)\n",
+    "model = experiment.fit(features, outcomes, cat_feats=cat_feats, num_feats=num_feats, one_hot=True)\n"
    ]
   },
   {
@@ -86,8 +77,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "out_risk = model.predict_risk(features, times)\n",
-    "out_survival = model.predict_survival(features, times)"
+    "experiment.folds"
    ]
   },
   {
@@ -96,7 +86,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from sksurv.metrics import concordance_index_ipcw, brier_score, cumulative_dynamic_auc"
+    "from auton_survival.preprocessing import Preprocessor\n",
+    "\n",
+    "preprocessor = Preprocessor(cat_feat_strat='replace', num_feat_strat='median',\n",
+    "                                scaling_strategy='standard', one_hot=True)\n",
+    "features_preprocessed = preprocessor.fit_transform(features, cat_feats=cat_feats, \n",
+    "                                   num_feats=num_feats, fill_value=-1)\n",
+    "\n",
+    "out_risk = model.predict_risk(features_preprocessed, times)\n",
+    "out_survival = model.predict_survival(features_preprocessed, times)"
    ]
   },
   {