modified: metrics.py

modified: phenotyping.py
autonlab · Mar 29, 2022 · 26793bc · 26793bc
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Showing 2 changed files with 18 additions and 19 deletions.
diff --git a/auton_survival/metrics.py b/auton_survival/metrics.py
@@ -47,11 +47,11 @@ def survival_diff_metric(metric, outcomes, treatment_indicator,
   metric : str
     The metric to evalute for comparing survival outcomes. 
     Options include:
-      - 'median'
-      - 'time_to'
-      - 'hazard_ratio'
-      - 'restricted_mean'
-      - 'survival_at'
+      - `median`
+      - `time_to`
+      - `hazard_ratio`
+      - `restricted_mean`
+      - `survival_at`
   outcomes : pd.DataFrame
       A pandas dataframe with rows corresponding to individual samples and columns 'time' and 'event'.
   treatment_indicator : np.array
@@ -145,10 +145,10 @@ def survival_regression_metric(metric, predictions, outcomes, times,
   metric: string
       Measure used to assess the survival regression model performance.
       Options include: 
-      - 'brs' : brier score
-      - 'ibs' : integrated brier score
-      - 'auc': cumulative dynamic area under the curve
-      - 'ctd' : concordance index inverse probability of censoring weights (ipcw)
+      - `brs` : brier score
+      - `ibs` : integrated brier score
+      - `auc`: cumulative dynamic area under the curve
+      - `ctd` : concordance index inverse probability of censoring weights (ipcw)
   predictions: np.array
       A numpy array of survival time predictions for the samples.
   outcomes : pd.DataFrame
@@ -221,9 +221,9 @@ def phenotype_purity(phenotypes, outcomes,
       A pandas dataframe with rows corresponding to individual samples and columns 'time' and 'event'.
   strategy: string, default='instantaneous'
       Options include: 
-      - 'instantaneous': Predict the Kaplan Meier survival estimate at a certain point in time and compute
+      - `instantaneous` : Predict the Kaplan Meier survival estimate at a certain point in time and compute
           the brier score.
-      - 'integrated' : Predict the Kaplan Meier survival estimate at all unique times points and compute 
+      - `integrated` : Predict the Kaplan Meier survival estimate at all unique times points and compute 
           the integrated brier score.
   folds: pd.DataFrame, default=None
       A pandas dataframe of train and test folds.

diff --git a/auton_survival/phenotyping.py b/auton_survival/phenotyping.py
@@ -25,7 +25,6 @@
 characteristics."""
 
 from random import random
-from re import I
 import numpy as np
 import pandas as pd
 
@@ -239,13 +238,13 @@ class ClusteringPhenotyper(Phenotyper):
       Additional arguments for dimensionality reduction and clustering
       Please include dictionary key and item pairs specified by the following scikit-learn modules:
 
-      - 'pca' : sklearn.decomposition.PCA
-      - 'nnmf' : sklearn.decomposition.NMF
-      - 'kpca' : sklearn.decomposition.KernelPCA
-      - 'kmeans' : sklearn.cluster.KMeans
-      - 'dbscan' : sklearn.cluster.DBSCAN
-      - 'gmm' : sklearn.mixture.GaussianMixture
-      - 'hierarchical' : sklearn.cluster.AgglomerativeClustering
+      - `pca` : sklearn.decomposition.PCA
+      - `nnmf` : sklearn.decomposition.NMF
+      - `kpca` : sklearn.decomposition.KernelPCA
+      - `kmeans` : sklearn.cluster.KMeans
+      - `dbscan` : sklearn.cluster.DBSCAN
+      - `gmm` : sklearn.mixture.GaussianMixture
+      - `hierarchical` : sklearn.cluster.AgglomerativeClustering
 
   """