Bump numpy to v2+

lightwood/__about__.py

@@ -1,6 +1,6 @@
 __title__ = 'lightwood'
 __package_name__ = 'lightwood'
-__version__ = '25.9.1.0'
+__version__ = '25.12.1.0'
 __description__ = "Lightwood is a toolkit for automatic machine learning model building"
 __email__ = "community@mindsdb.com"
 __author__ = 'MindsDB Inc'

lightwood/api/json_ai.py

@@ -572,7 +572,7 @@ def add_implicit_values(json_ai: JsonAI) -> JsonAI:
             mixers[i]["args"]["fit_on_dev"] = mixers[i]["args"].get("fit_on_dev", "True")
             mixers[i]["args"]["use_stl"] = mixers[i]["args"].get("use_stl", "False")
 
-        elif mixers[i]["module"] in ("NHitsMixer", "GluonTSMixer"):
+        elif mixers[i]["module"] in ("NHitsMixer", ):
             mixers[i]["args"]["horizon"] = "$problem_definition.timeseries_settings.horizon"
             mixers[i]["args"]["window"] = "$problem_definition.timeseries_settings.window"
             mixers[i]["args"]["ts_analysis"] = mixers[i]["args"].get(

lightwood/ensemble/best_of.py

@@ -11,9 +11,6 @@
 from lightwood.api.types import PredictionArguments, SubmodelData
 from lightwood.data.encoded_ds import EncodedDs
 
-# special dispatches
-from lightwood.mixer import GluonTSMixer  # imported from base mixer folder as it needs optional dependencies
-
 
 class BestOf(BaseEnsemble):
     """
@@ -25,24 +22,19 @@ class BestOf(BaseEnsemble):
     def __init__(self, target, mixers: List[BaseMixer], data: EncodedDs, accuracy_functions,
                  args: PredictionArguments, ts_analysis: Optional[dict] = None, fit: bool = True) -> None:
         super().__init__(target, mixers, data, fit=False)
-        self.special_dispatch_list = [GluonTSMixer]
 
         if fit:
             score_list = []
             for _, mixer in enumerate(self.mixers):
+                score_dict = evaluate_accuracies(
+                    data.data_frame,
+                    mixer(data, args)['prediction'],
+                    target,
+                    accuracy_functions,
+                    ts_analysis=ts_analysis
+                )
 
-                if type(mixer) in self.special_dispatch_list:
-                    avg_score = self.special_dispatch(mixer, data, args, target, ts_analysis)
-                else:
-                    score_dict = evaluate_accuracies(
-                        data.data_frame,
-                        mixer(data, args)['prediction'],
-                        target,
-                        accuracy_functions,
-                        ts_analysis=ts_analysis
-                    )
-
-                    avg_score = np.mean(list(score_dict.values()))
+                avg_score = np.mean(list(score_dict.values()))
                 log.info(f'Mixer: {type(mixer).__name__} got accuracy: {avg_score}')
 
                 if is_nan_numeric(avg_score):
@@ -81,7 +73,3 @@ def __call__(self, ds: EncodedDs, args: PredictionArguments) -> pd.DataFrame:
                     else:
                         log.warning(f'Unstable mixer {type(mixer).__name__} failed with exception: {e}.\
                         Trying next best')
-
-    def special_dispatch(self, mixer, data, args, target, ts_analysis):
-        if isinstance(mixer, GluonTSMixer):
-            return mixer.model_train_stats.loss_history[-1]

lightwood/helpers/seed.py

@@ -2,18 +2,10 @@
 import torch
 import numpy as np
 
-try:
-    import mxnet as mx
-except Exception:
-    mx = None
-
 
 def seed(seed_nr: int) -> None:
     torch.manual_seed(seed_nr)
     torch.backends.cudnn.deterministic = True
     torch.backends.cudnn.benchmark = False
     np.random.seed(seed_nr)
     random.seed(seed_nr)
-
-    if mx is not None:
-        mx.random.seed(seed_nr)

lightwood/helpers/templating.py

@@ -1,3 +1,4 @@
+import json
 from typing import Callable
 from collections import deque
 
@@ -8,11 +9,23 @@
 
 
 def is_allowed(v):
-    if '(' in str(v):
+    if isinstance(v, dict):
+        # this required for compatability with nuimpy v1 and v2:
+        # v1: str({np.float64(1.5): np.float64(1.5)}) = {1.5: 1.5}
+        # v2: str({np.float64(1.5): np.float64(1.5)}) = {np.float64(1.5): np.float64(1.5)}
+        # v2: json.dumps({np.float64(1.5): np.float64(1.5)}) = {"1.5": 1.5}
+        try:
+            s = json.dumps(v)
+        except Exception:
+            s = str(v)
+    else:
+        s = str(v)
+
+    if '(' in s:
         return False
-    if 'lambda' in str(v):
+    if 'lambda' in s:
         return False
-    if '__' in str(v):
+    if '__' in s:
         return False
 
     return True

lightwood/mixer/__init__.py

@@ -25,11 +25,6 @@
 except Exception:
     ProphetMixer = None
 
-try:
-    from lightwood.mixer.gluonts import GluonTSMixer
-except Exception:
-    GluonTSMixer = None
-
 try:
     from lightwood.mixer.lightgbm import LightGBM
     from lightwood.mixer.lightgbm_array import LightGBMArray
@@ -43,5 +38,5 @@
     TabTransformerMixer = None
 
 __all__ = ['BaseMixer', 'Neural', 'NeuralTs', 'LightGBM', 'RandomForest', 'LightGBMArray', 'Unit', 'Regression',
-           'SkTime', 'QClassic', 'ProphetMixer', 'ETSMixer', 'ARIMAMixer', 'NHitsMixer', 'GluonTSMixer', 'XGBoostMixer',
+           'SkTime', 'QClassic', 'ProphetMixer', 'ETSMixer', 'ARIMAMixer', 'NHitsMixer', 'XGBoostMixer',
            'TabTransformerMixer', 'XGBoostArrayMixer']