update dependency and formatting

Author: edwinnglabs

orbit/diagnostics/plot.py

@@ -549,7 +549,7 @@ def plot_bt_predictions2(
             )
         if fix_xylim and ylim is not None:
             ax.set_xlim(xlim)
-            ax.set_ylim(*ylim) # unpack the tuple
+            ax.set_ylim(*ylim)  # unpack the tuple
 
         ax.legend()
         plt.suptitle(title, fontsize=fontsize)

orbit/template/dlt.py

@@ -751,9 +751,10 @@ def predict(
                         global_trend_level + global_trend_slope * idx * self._time_delta
                     )
                 elif self.global_trend_option == GlobalTrendOption.loglinear.name:
-                    full_global_trend[:, idx] = (
-                        global_trend_level
-                        + global_trend_slope * np.log(1 + idx * self._time_delta)
+                    full_global_trend[
+                        :, idx
+                    ] = global_trend_level + global_trend_slope * np.log(
+                        1 + idx * self._time_delta
                     )
                 elif self.global_trend_option == GlobalTrendOption.logistic.name:
                     full_global_trend[:, idx] = self.global_floor + (

orbit/template/ktrlite.py

@@ -189,9 +189,9 @@ def set_init_values(self):
         init_values = None
         if len(self._seasonality) > 1 and self.num_of_regressors > 0:
             init_values = dict()
-            init_values[RegressionSamplingParameters.COEFFICIENTS_KNOT.value] = (
-                np.zeros((self.num_of_regressors, self.num_knots_coefficients))
-            )
+            init_values[
+                RegressionSamplingParameters.COEFFICIENTS_KNOT.value
+            ] = np.zeros((self.num_of_regressors, self.num_knots_coefficients))
             self._init_values = init_values
 
     def _set_default_args(self):
@@ -496,9 +496,9 @@ def predict(
                 seas_regression = np.sum(
                     seas_coef * seasonal_regressor_matrix.transpose(1, 0), axis=-2
                 )
-                seas_decomp["seasonality_{}".format(self._seasonality[idx])] = (
-                    seas_regression
-                )
+                seas_decomp[
+                    "seasonality_{}".format(self._seasonality[idx])
+                ] = seas_regression
                 pos += len(cols)
                 total_seas_regression += seas_regression
         if include_error:

orbit/template/lgt.py

@@ -231,9 +231,9 @@ def set_init_values(self):
                     -1.0,
                     1.0,
                 )
-                init_values[LatentSamplingParameters.INITIAL_SEASONALITY.value] = (
-                    init_sea
-                )
+                init_values[
+                    LatentSamplingParameters.INITIAL_SEASONALITY.value
+                ] = init_sea
             if self.num_of_positive_regressors > 0:
                 x = np.clip(
                     np.random.normal(

orbit/utils/simulation.py

@@ -1,8 +1,6 @@
 import numpy as np
-import pandas as pd
 from statsmodels.tsa.arima_process import ArmaProcess
 import math
-from math import pi
 from orbit.exceptions import IllegalArgument
 
 

requirements.txt

@@ -3,12 +3,12 @@ cmdstanpy>=1.2.1
 importlib_resources
 ipywidgets
 matplotlib>=3.3.2
-numpy>=1.18
-pandas>=1.0.3
+numpy>=1.26.4
+pandas>=2.3.0
 pyro-ppl>=1.4.0
 scipy>=1.4.1
 seaborn>=0.10.0
 setuptools>=42
-statsmodels>=0.11.1
-torch>=1.11.0
+statsmodels>=0.14.4
+torch
 tqdm>=4.36.1