Add py311, remove py37

.github/workflows/pythonpackage.yml

@@ -11,7 +11,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ['3.7', '3.8', '3.9', '3.10']
+        python-version: ['3.8', '3.9', '3.10', '3.11']
 
     steps:
       - uses: actions/checkout@v2

examples/experiments/survival_exp.py

@@ -24,7 +24,9 @@
 def Y_join(T, E):
     col_event = "Event"
     col_time = "Time"
-    y = np.empty(dtype=[(col_event, np.bool), (col_time, np.float64)], shape=T.shape[0])
+    y = np.empty(
+        dtype=[(col_event, np.bool_), (col_time, np.float64)], shape=T.shape[0]
+    )
     y[col_event] = E.values
     y[col_time] = T.values
     return y

ngboost/distns/categorical.py

@@ -1,5 +1,5 @@
 """The NGBoost categorial distribution and scores"""
-# pylint: disable=invalid-unary-operand-type, unused-argument, no-self-use
+# pylint: disable=invalid-unary-operand-type, unused-argument
 import numpy as np
 import scipy as sp
 

ngboost/helpers.py

@@ -17,7 +17,7 @@ def Y_from_censored(T, E=None):
     else:
         E = check_array(E, ensure_2d=False)
         E = E.reshape(E.shape[0])
-    Y = np.empty(dtype=[("Event", np.bool), ("Time", np.float64)], shape=T.shape[0])
-    Y["Event"] = E.astype(np.bool)
+    Y = np.empty(dtype=[("Event", np.bool_), ("Time", np.float64)], shape=T.shape[0])
+    Y["Event"] = E.astype(np.bool_)
     Y["Time"] = T.astype(np.float64)
     return Y

ngboost/ngboost.py

@@ -2,7 +2,7 @@
 # pylint: disable=line-too-long,too-many-instance-attributes,too-many-arguments
 # pylint: disable=unused-argument,too-many-locals,too-many-branches,too-many-statements
 # pylint: disable=unused-variable,invalid-unary-operand-type,attribute-defined-outside-init
-# pylint: disable=redundant-keyword-arg,protected-access
+# pylint: disable=redundant-keyword-arg,protected-access,unnecessary-lambda-assignment
 import numpy as np
 from sklearn.base import clone
 from sklearn.model_selection import train_test_split
@@ -290,7 +290,7 @@ def fit(
             best_val_loss = np.inf
 
         if not train_loss_monitor:
-            train_loss_monitor = lambda D, Y, W: D.total_score(  # NOQA
+            train_loss_monitor = lambda D, Y, W: D.total_score(  # noqa
                 Y, sample_weight=W
             )
 

pyproject.toml

@@ -13,13 +13,12 @@ classifiers = [
 license = "Apache License 2.0"
 
 [tool.poetry.dependencies]
-python = ">=3.7.1, <3.11"
+python = ">=3.8, <3.12"
 scikit-learn = ">=1.0.2"
 numpy = ">=1.21.2"
 scipy = ">=1.7.2"
 tqdm = ">=4.3"
 lifelines = ">=0.25"
-pandas = ">=1.3.5"
 
 [tool.poetry.dev-dependencies]
 pytest = "^6.1.2"

tests/test_score.py

@@ -1,3 +1,4 @@
+# pylint: disable=unnecessary-lambda-assignment
 from typing import List, Tuple
 
 import numpy as np