Skip to content

add pd.concat replace pd.append #114

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
wants to merge 2 commits into
base: master
Choose a base branch
from
Open

add pd.concat replace pd.append #114

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
f3c4fa7
add pd.concat replace pd.append
BryR0 Aug 20, 2024
3b69d14
append deprecated
BryR0 Aug 20, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
6 changes: 5 additions & 1 deletion auto_ts/models/ar_based/build_arima_base.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -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']
Expand Down
9 changes: 7 additions & 2 deletions auto_ts/models/build_ml.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -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)
Expand Down Expand Up @@ -335,6 +339,7 @@ def fit(self,
norm_rmse_folds.append(rmse_norm)
forecast_df_folds.append(y_pred)
extra_concatenated.append(concatenated)

####### Now plot feature importances for pandas dataframes ###########
try:
##### This is for plotting pandas dataframes only ################
Expand Down
5 changes: 4 additions & 1 deletion auto_ts/models/build_prophet.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -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)
Expand Down