Merge branch 'master' into update_versions_from_sktime

shinroo · Oct 10, 2022 · 0f79eb8 · 0f79eb8
2 parents 280513a + 021cafc
commit 0f79eb8
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Showing 4 changed files with 52 additions and 26 deletions.
diff --git a/pycaret/containers/models/time_series.py b/pycaret/containers/models/time_series.py
@@ -284,7 +284,7 @@ def __init__(self, experiment) -> None:
         `sp` is hard coded to 1 irrespective of the `sp` value or whether
         seasonality is detected or not.
         """
-        get_logger()
+        self.logger = get_logger()
         np.random.seed(experiment.seed)
         self.gpu_imported = False
 
@@ -345,7 +345,7 @@ def __init__(self, experiment) -> None:
         `sp` is hard coded to 1 irrespective of the `sp` value or whether
         seasonality is detected or not.
         """
-        get_logger()
+        self.logger = get_logger()
         np.random.seed(experiment.seed)
         self.gpu_imported = False
 
@@ -406,7 +406,7 @@ def __init__(self, experiment) -> None:
         If sp = 1, this model is disabled.
         If sp != 1, model is enabled even when seasonality is not detected.
         """
-        get_logger()
+        self.logger = get_logger()
         np.random.seed(experiment.seed)
         self.gpu_imported = False
 
@@ -466,7 +466,7 @@ class PolyTrendContainer(TimeSeriesContainer):
     model_type = TSModelTypes.BASELINE
 
     def __init__(self, experiment) -> None:
-        get_logger()
+        self.logger = get_logger()
         np.random.seed(experiment.seed)
         self.gpu_imported = False
 
@@ -518,7 +518,7 @@ class ArimaContainer(TimeSeriesContainer):
     model_type = TSModelTypes.CLASSICAL
 
     def __init__(self, experiment) -> None:
-        get_logger()
+        self.logger = get_logger()
         random.seed(experiment.seed)
         np.random.seed(experiment.seed)
         self.gpu_imported = False
@@ -759,7 +759,7 @@ class ExponentialSmoothingContainer(TimeSeriesContainer):
     model_type = TSModelTypes.CLASSICAL
 
     def __init__(self, experiment) -> None:
-        get_logger()
+        self.logger = get_logger()
         np.random.seed(experiment.seed)
         self.gpu_imported = False
 
@@ -892,7 +892,7 @@ class CrostonContainer(TimeSeriesContainer):
     model_type = TSModelTypes.CLASSICAL
 
     def __init__(self, experiment) -> None:
-        get_logger()
+        self.logger = get_logger()
         np.random.seed(experiment.seed)
         self.gpu_imported = False
 
@@ -937,7 +937,7 @@ class ETSContainer(TimeSeriesContainer):
     model_type = TSModelTypes.CLASSICAL
 
     def __init__(self, experiment) -> None:
-        get_logger()
+        self.logger = get_logger()
         np.random.seed(experiment.seed)
         self.gpu_imported = False
 
@@ -1013,7 +1013,7 @@ class ThetaContainer(TimeSeriesContainer):
     model_type = TSModelTypes.CLASSICAL
 
     def __init__(self, experiment) -> None:
-        get_logger()
+        self.logger = get_logger()
         np.random.seed(experiment.seed)
         self.gpu_imported = False
 
@@ -1100,7 +1100,7 @@ class TBATSContainer(TimeSeriesContainer):
     model_type = TSModelTypes.CLASSICAL
 
     def __init__(self, experiment) -> None:
-        get_logger()
+        self.logger = get_logger()
         np.random.seed(experiment.seed)
         self.gpu_imported = False
 
@@ -1163,7 +1163,7 @@ class BATSContainer(TimeSeriesContainer):
     model_type = TSModelTypes.CLASSICAL
 
     def __init__(self, experiment) -> None:
-        get_logger()
+        self.logger = get_logger()
         np.random.seed(experiment.seed)
         self.gpu_imported = False
 
@@ -1225,7 +1225,7 @@ class ProphetContainer(TimeSeriesContainer):
     model_type = TSModelTypes.LINEAR
 
     def __init__(self, experiment) -> None:
-        get_logger()
+        self.logger = get_logger()
         np.random.seed(experiment.seed)
         self.gpu_imported = False
 
@@ -1313,7 +1313,7 @@ class CdsDtContainer(TimeSeriesContainer):
     model_type = None
 
     def __init__(self, experiment) -> None:
-        get_logger()
+        self.logger = get_logger()
         self.seed = experiment.seed
         np.random.seed(self.seed)
 
@@ -2749,7 +2749,7 @@ class EnsembleTimeSeriesContainer(TimeSeriesContainer):
     model_type = "ensemble"
 
     def __init__(self, experiment) -> None:
-        get_logger()
+        self.logger = get_logger()
         np.random.seed(experiment.seed)
         self.gpu_imported = False
 

diff --git a/pycaret/internal/distributions.py b/pycaret/internal/distributions.py
@@ -75,10 +75,20 @@ def get_skopt(self):
     def get_optuna(self):
         import optuna
 
+        optuna_version = int(optuna.__version__[0])
+
         if self.log:
-            return optuna.distributions.LogUniformDistribution(self.lower, self.upper)
+            return (
+                optuna.distributions.FloatDistribution(self.lower, self.upper, log=True)
+                if optuna_version >= 3
+                else optuna.distributions.LogUniformDistribution(self.lower, self.upper)
+            )
         else:
-            return optuna.distributions.UniformDistribution(self.lower, self.upper)
+            return (
+                optuna.distributions.FloatDistribution(self.lower, self.upper)
+                if optuna_version >= 3
+                else optuna.distributions.UniformDistribution(self.lower, self.upper)
+            )
 
     def get_hyperopt(self, label):
         from hyperopt import hp
@@ -145,12 +155,22 @@ def get_skopt(self):
     def get_optuna(self):
         import optuna
 
+        optuna_version = int(optuna.__version__[0])
+
         if self.log:
-            return optuna.distributions.IntLogUniformDistribution(
-                self.lower, self.upper
+            return (
+                optuna.distributions.IntDistribution(self.lower, self.upper, log=True)
+                if optuna_version >= 3
+                else optuna.distributions.IntLogUniformDistribution(
+                    self.lower, self.upper
+                )
             )
         else:
-            return optuna.distributions.IntUniformDistribution(self.lower, self.upper)
+            return (
+                optuna.distributions.IntDistribution(self.lower, self.upper)
+                if optuna_version >= 3
+                else optuna.distributions.IntUniformDistribution(self.lower, self.upper)
+            )
 
     def get_hyperopt(self, label):
         from hyperopt import hp
@@ -256,8 +276,14 @@ def get_skopt(self):
     def get_optuna(self):
         import optuna
 
-        return optuna.distributions.DiscreteUniformDistribution(
-            self.lower, self.upper, self.q
+        optuna_version = int(optuna.__version__[0])
+
+        return (
+            optuna.distributions.FloatDistribution(self.lower, self.upper, step=self.q)
+            if optuna_version >= 3
+            else optuna.distributions.DiscreteUniformDistribution(
+                self.lower, self.upper, self.q
+            )
         )
 
     def get_hyperopt(self, label):

diff --git a/pycaret/internal/plots/time_series.py b/pycaret/internal/plots/time_series.py
@@ -767,7 +767,7 @@ def plot_predictions_with_confidence(
         if isinstance(upper_interval.index, pd.PeriodIndex)
         else upper_interval.index
     )
-    upper_bound = go.Scattergl(
+    upper_bound = go.Scatter(
         name=f"Prediction Interval | {model_label}",  # Changed since we use only 1 legend
         x=x,
         y=upper_interval,
@@ -784,7 +784,7 @@ def plot_predictions_with_confidence(
         if isinstance(preds.index, pd.PeriodIndex)
         else preds.index
     )
-    mean = go.Scattergl(
+    mean = go.Scatter(
         name=f"Forecast | {model_label}",
         x=x,
         y=preds,
@@ -799,7 +799,7 @@ def plot_predictions_with_confidence(
         if isinstance(data.index, pd.PeriodIndex)
         else data.index
     )
-    original = go.Scattergl(
+    original = go.Scatter(
         name="Original",
         x=x,
         y=data,
@@ -813,7 +813,7 @@ def plot_predictions_with_confidence(
         if isinstance(lower_interval.index, pd.PeriodIndex)
         else lower_interval.index
     )
-    lower_bound = go.Scattergl(
+    lower_bound = go.Scatter(
         name="Lower Interval",
         x=x,
         y=lower_interval,

diff --git a/requirements-optional.txt b/requirements-optional.txt
@@ -21,7 +21,7 @@ tune-sklearn>=0.2.1; python_version < '3.10' or platform_system != 'Windows'
 protobuf<4.0.0; python_version < '3.10' or platform_system != 'Windows'  # broken in Ray <1.13
 ray[tune]>=1.0.0; python_version < '3.10' or platform_system != 'Windows'
 hyperopt>=0.2.7
-optuna>=2.2.0
+optuna>=3.0.0
 scikit-optimize>=0.9.0
 
 # MLOps