From f3c4fa73e79a73ddf411904e344cd2f91b8691aa Mon Sep 17 00:00:00 2001 From: BryR0 Date: Tue, 20 Aug 2024 08:40:34 -0500 Subject: [PATCH 1/2] add pd.concat replace pd.append pd.append Deprecated since version 1.4.0 --- auto_ts/models/ar_based/build_arima_base.py | 6 +++++- auto_ts/models/build_ml.py | 13 ++++++++++--- auto_ts/models/build_prophet.py | 5 ++++- 3 files changed, 19 insertions(+), 5 deletions(-) diff --git a/auto_ts/models/ar_based/build_arima_base.py b/auto_ts/models/ar_based/build_arima_base.py index 21ac95b..6c95ff9 100644 --- a/auto_ts/models/ar_based/build_arima_base.py +++ b/auto_ts/models/ar_based/build_arima_base.py @@ -163,7 +163,11 @@ def fit(self, ts_df: pd.DataFrame, target_col: str, cv: Optional[int]=None): else: concatenated = pd.DataFrame(np.c_[ts_test[self.original_target_col].values, y_forecasted], columns=['original', 'predicted'],index=ts_test.index) - extra_concatenated = extra_concatenated.append(concatenated) + try: + extra_concatenated = extra_concatenated.append(concatenated) + except Exception as e: + extra_concatenated = pd.concat([extra_concatenated,concatenated]) + ### for SARIMAX and Auto_ARIMA, you don't have to restore differences since it predicts like actuals.### y_true = concatenated['original'] diff --git a/auto_ts/models/build_ml.py b/auto_ts/models/build_ml.py index 5f22517..92a461e 100644 --- a/auto_ts/models/build_ml.py +++ b/auto_ts/models/build_ml.py @@ -268,9 +268,13 @@ def fit(self, concatenated = pd.DataFrame(np.c_[y_test_fold, y_pred], columns=['original', 'predicted'],index=y_test_fold.index) if fold_number == 0: - extra_concatenated = copy.deepcopy(concatenated) + extra_concatenated = copy.deepcopy(concatenated) else: - extra_concatenated = extra_concatenated.append(concatenated) + try: + extra_concatenated = extra_concatenated.append(concatenated) + except Exception as e: + extra_concatenated = pd.concat([extra_concatenated,concatenated]) + rmse_fold, rmse_norm = print_dynamic_rmse(concatenated['original'].values, concatenated['predicted'].values, concatenated['original'].values) @@ -334,7 +338,10 @@ def fit(self, rmse_folds.append(rmse_fold) norm_rmse_folds.append(rmse_norm) forecast_df_folds.append(y_pred) - extra_concatenated.append(concatenated) + try: + extra_concatenated.append(concatenated) + except Exception as e: + extra_concatenated = pd.concat([extra_concatenated,concatenated]) ####### Now plot feature importances for pandas dataframes ########### try: ##### This is for plotting pandas dataframes only ################ diff --git a/auto_ts/models/build_prophet.py b/auto_ts/models/build_prophet.py index f53bc7b..3b49707 100644 --- a/auto_ts/models/build_prophet.py +++ b/auto_ts/models/build_prophet.py @@ -265,7 +265,10 @@ def fit(self, ts_df: pd.DataFrame, target_col: str, cv: Optional[int], time_col: if fold_number == 0: extra_concatenated = copy.deepcopy(concatenated) else: - extra_concatenated = extra_concatenated.append(concatenated) + try: + extra_concatenated = extra_concatenated.append(concatenated) + except Exception as e: + extra_concatenated = pd.concat([extra_concatenated,concatenated]) rmse_fold, rmse_norm = print_dynamic_rmse(concatenated['original'].values, concatenated['predicted'].values, concatenated['original'].values) From 3b69d148d5e33de219bee27c9cc6e2b74eacd59b Mon Sep 17 00:00:00 2001 From: BryR0 Date: Tue, 20 Aug 2024 08:45:12 -0500 Subject: [PATCH 2/2] append deprecated --- auto_ts/models/build_ml.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/auto_ts/models/build_ml.py b/auto_ts/models/build_ml.py index 92a461e..174766a 100644 --- a/auto_ts/models/build_ml.py +++ b/auto_ts/models/build_ml.py @@ -338,10 +338,8 @@ def fit(self, rmse_folds.append(rmse_fold) norm_rmse_folds.append(rmse_norm) forecast_df_folds.append(y_pred) - try: - extra_concatenated.append(concatenated) - except Exception as e: - extra_concatenated = pd.concat([extra_concatenated,concatenated]) + extra_concatenated.append(concatenated) + ####### Now plot feature importances for pandas dataframes ########### try: ##### This is for plotting pandas dataframes only ################