finalize sorting method, add tests #290 #286

src/pytimetk/core/anomalize.py

@@ -357,8 +357,7 @@ def anomalize(
 
     if reduce_memory:
         result = reduce_memory_usage(result)
-
-    result.index = idx_unsorted
+
     result = result.sort_index()
 
     return result

src/pytimetk/feature_engineering/diffs.py

@@ -137,13 +137,17 @@ def augment_diffs(
         ret = _augment_diffs_pandas(data, date_column, value_column, periods, normalize=normalize)
     elif engine == 'polars':
         ret = _augment_diffs_polars(data, date_column, value_column, periods, normalize=normalize)
+
+        # Polars Index to Match Pandas
+        ret.index = idx_unsorted
+
     else:
         raise ValueError("Invalid engine. Use 'pandas' or 'polars'.")
 
     if reduce_memory:
         ret = reduce_memory_usage(ret)
 
-    ret.index = idx_unsorted
+    # Sort index
     ret = ret.sort_index()
 
     return ret

src/pytimetk/feature_engineering/expanding.py

@@ -254,13 +254,16 @@ def augment_expanding(
             min_periods, 
             **kwargs
         )
+
+        # Polars Index to Match Pandas
+        ret.index = idx_unsorted
+
     else:
         raise ValueError("Invalid engine. Use 'pandas' or 'polars'.")
 
     if reduce_memory:
         ret = reduce_memory_usage(ret)
 
-    ret.index = idx_unsorted
     ret = ret.sort_index()
 
     return ret

src/pytimetk/feature_engineering/fourier.py

@@ -134,11 +134,13 @@ def augment_fourier(
         ret = _augment_fourier_pandas(data, date_column, periods, max_order)
     else:
         ret = _augment_fourier_polars(data, date_column, periods, max_order)
+
+        # Polars Index to Match Pandas
+        ret.index = idx_unsorted
 
     if reduce_memory:
         ret = reduce_memory_usage(ret)
 
-    ret.index = idx_unsorted
     ret = ret.sort_index()
 
     return ret

src/pytimetk/feature_engineering/hilbert.py

@@ -165,13 +165,14 @@ def augment_hilbert(
         ret = _augment_hilbert_pandas(data, date_column, value_column)
     elif engine == 'polars':
         ret = _augment_hilbert_polars(data, date_column, value_column)
+        # Polars Index to Match Pandas
+        ret.index = idx_unsorted
     else:
         raise ValueError("Invalid engine. Use 'pandas' or 'polars'.")
 
     if reduce_memory:
         ret = reduce_memory_usage(ret)
 
-    ret.index = idx_unsorted
     ret = ret.sort_index()
 
     return ret

src/pytimetk/feature_engineering/holiday_signature.py

@@ -212,13 +212,14 @@ def augment_holiday_signature(
         ret = _augment_holiday_signature_pandas(data, date_column, country_name)
     elif engine == 'polars':
         ret = _augment_holiday_signature_polars(data, date_column, country_name)
+        # Polars Index to Match Pandas
+        ret.index = idx_unsorted
     else:
         raise ValueError("Invalid engine. Use 'pandas' or 'polars'.")
 
     if reduce_memory:
         ret = reduce_memory_usage(ret)
 
-    ret.index = idx_unsorted
     ret = ret.sort_index()
 
     return ret

src/pytimetk/feature_engineering/lags.py

@@ -131,13 +131,14 @@ def augment_lags(
         ret = _augment_lags_pandas(data, date_column, value_column, lags)
     elif engine == 'polars':
         ret = _augment_lags_polars(data, date_column, value_column, lags)
+        # Polars Index to Match Pandas
+        ret.index = idx_unsorted
     else:
         raise ValueError("Invalid engine. Use 'pandas' or 'polars'.")
 
     if reduce_memory:
         ret = reduce_memory_usage(ret)
 
-    ret.index = idx_unsorted
     ret = ret.sort_index()
 
     return ret

src/pytimetk/feature_engineering/leads.py

@@ -132,13 +132,14 @@ def augment_leads(
         ret = _augment_leads_pandas(data, date_column, value_column, leads)
     elif engine == 'polars':
         ret = _augment_leads_polars(data, date_column, value_column, leads)
+        # Polars Index to Match Pandas
+        ret.index = idx_unsorted
     else:
         raise ValueError("Invalid engine. Use 'pandas' or 'polars'.")
 
     if reduce_memory:
         ret = reduce_memory_usage(ret)
 
-    ret.index = idx_unsorted
     ret = ret.sort_index()
 
     return ret

src/pytimetk/feature_engineering/rolling.py

@@ -250,13 +250,16 @@ def augment_rolling(
             show_progress,
             **kwargs
         )
+
+        # Polars Index to Match Pandas
+        ret.index = idx_unsorted
+
     else:
         raise ValueError("Invalid engine. Use 'pandas' or 'polars'.")
 
     if reduce_memory:
         ret = reduce_memory_usage(ret)
 
-    ret.index = idx_unsorted
     ret = ret.sort_index()
 
     return ret

src/pytimetk/feature_engineering/wavelet.py

@@ -247,7 +247,6 @@ def _apply_cwt(df):
     if reduce_memory:
         ret = reduce_memory_usage(ret)
 
-    ret.index = idx_unsorted
     ret = ret.sort_index()
 
     return ret

src/pytimetk/finance/atr.py

@@ -166,13 +166,14 @@ def augment_atr(
         ret = _augment_atr_pandas(data, date_column, high_column, low_column, close_column, periods, normalize)
     elif engine == 'polars':
         ret = _augment_atr_polars(data, date_column, high_column, low_column, close_column, periods, normalize)
+        # Polars Index to Match Pandas
+        ret.index = idx_unsorted
     else:
         raise ValueError("Invalid engine. Use 'pandas' or 'polars'.")
 
     if reduce_memory:
         ret = reduce_memory_usage(ret)
 
-    ret.index = idx_unsorted
     ret = ret.sort_index()
 
     return ret

src/pytimetk/finance/bbands.py

@@ -168,13 +168,14 @@ def augment_bbands(
         ret = _augment_bbands_pandas(data, date_column, close_column, periods, std_dev)
     elif engine == 'polars':
         ret = _augment_bbands_polars(data, date_column, close_column, periods, std_dev)
+        # Polars Index to Match Pandas
+        ret.index = idx_unsorted
     else:
         raise ValueError("Invalid engine. Use 'pandas' or 'polars'.")
 
     if reduce_memory:
         ret = reduce_memory_usage(ret)
 
-    ret.index = idx_unsorted
     ret = ret.sort_index()
 
     return ret

src/pytimetk/finance/cmo.py

@@ -172,13 +172,14 @@ def augment_cmo(
         ret = _augment_cmo_pandas(data, date_column, close_column, periods)
     elif engine == 'polars':
         ret = _augment_cmo_polars(data, date_column, close_column, periods)
+        # Polars Index to Match Pandas
+        ret.index = idx_unsorted
     else:
         raise ValueError("Invalid engine. Use 'pandas' or 'polars'.")
 
     if reduce_memory:
         ret = reduce_memory_usage(ret)
 
-    ret.index = idx_unsorted
     ret = ret.sort_index()
 
     return ret

src/pytimetk/finance/macd.py

@@ -136,13 +136,14 @@ def augment_macd(
         ret = _augment_macd_pandas(data, date_column, close_column, fast_period, slow_period, signal_period)
     elif engine == 'polars':
         ret = _augment_macd_polars(data, date_column, close_column, fast_period, slow_period, signal_period)
+        # Polars Index to Match Pandas
+        ret.index = idx_unsorted
     else:
         raise ValueError("Invalid engine. Use 'pandas' or 'polars'.")
 
     if reduce_memory:
         ret = reduce_memory_usage(ret)
 
-    ret.index = idx_unsorted
     ret = ret.sort_index()
 
     return ret

src/pytimetk/finance/ppo.py

@@ -126,13 +126,14 @@ def augment_ppo(
         ret = _augment_ppo_pandas(data, date_column, close_column, fast_period, slow_period)
     elif engine == 'polars':
         ret = _augment_ppo_polars(data, date_column, close_column, fast_period, slow_period)
+        # Polars Index to Match Pandas
+        ret.index = idx_unsorted
     else:
         raise ValueError("Invalid engine. Use 'pandas' or 'polars'.")
 
     if reduce_memory:
         ret = reduce_memory_usage(ret)
 
-    ret.index = idx_unsorted
     ret = ret.sort_index()
 
     return ret

src/pytimetk/finance/qsmomentum.py

@@ -181,13 +181,14 @@ def f(close):
                         )
 
                         ret.rename(columns={ret.columns[-1]: f'{close_column}_qsmom_{fp}_{sp}_{np}'}, inplace=True)
-
-
 
+    # if engine == 'polars':
+    #     # Polars Index to Match Pandas
+    #     ret.index = idx_unsorted     
+
     if reduce_memory:
         ret = reduce_memory_usage(ret)
 
-    ret.index = idx_unsorted
     ret = ret.sort_index()
 
     return ret

src/pytimetk/finance/roc.py

@@ -151,13 +151,14 @@ def augment_roc(
         ret = _augment_roc_pandas(data, date_column, close_column, periods, start_index=start_index)
     elif engine == 'polars':
         ret = _augment_roc_polars(data, date_column, close_column, periods, start_index=start_index)
+        # Polars Index to Match Pandas
+        ret.index = idx_unsorted
     else:
         raise ValueError("Invalid engine. Use 'pandas' or 'polars'.")
 
     if reduce_memory:
         ret = reduce_memory_usage(ret)
 
-    ret.index = idx_unsorted
     ret = ret.sort_index()
 
     return ret

src/pytimetk/finance/rsi.py

@@ -157,13 +157,14 @@ def augment_rsi(
         ret = _augment_rsi_pandas(data, date_column, close_column, periods)
     elif engine == 'polars':
         ret = _augment_rsi_polars(data, date_column, close_column, periods)
+        # Polars Index to Match Pandas
+        ret.index = idx_unsorted
     else:
         raise ValueError("Invalid engine. Use 'pandas' or 'polars'.")
 
     if reduce_memory:
         ret = reduce_memory_usage(ret)
 
-    ret.index = idx_unsorted
     ret = ret.sort_index()
 
     return ret

tests/test_rolling.py

@@ -165,6 +165,7 @@ def test_example_1_polars():
                 window = [2,7],  # Specifying multiple window sizes
                 window_func = [
                     'mean',  # Built-in mean function
+                    # 'std'
                     ('std', lambda x: x.std())  # Lambda function to compute standard deviation
                 ],
                 engine = 'polars'  # Using pandas engine
@@ -211,6 +212,8 @@ def test_sort_pandas_lambda():
     stocks_df = tk.load_dataset("stocks_daily")
     stocks_df['date'] = pd.to_datetime(stocks_df['date'])
 
+    # stocks_df = stocks_df.sort_values('date').reset_index()
+
     rolled_df_pandas_fast = stocks_df[['symbol','date','adjusted']] \
         .groupby('symbol') \
         .augment_rolling(