Release/2.13.9rc1

ads/opctl/operator/lowcode/common/utils.py

@@ -11,6 +11,7 @@
 import tempfile
 from typing import List, Union
 
+import cloudpickle
 import fsspec
 import oracledb
 import pandas as pd
@@ -126,7 +127,26 @@ def load_data(data_spec, storage_options=None, **kwargs):
     return data
 
 
+def _safe_write(fn, **kwargs):
+    try:
+        fn(**kwargs)
+    except Exception:
+        logger.warning(f'Failed to write file {kwargs.get("filename", "UNKNOWN")}')
+
+
 def write_data(data, filename, format, storage_options=None, index=False, **kwargs):
+    return _safe_write(
+        fn=_write_data,
+        data=data,
+        filename=filename,
+        format=format,
+        storage_options=storage_options,
+        index=index,
+        **kwargs,
+    )
+
+
+def _write_data(data, filename, format, storage_options=None, index=False, **kwargs):
     disable_print()
     if not format:
         _, format = os.path.splitext(filename)
@@ -143,11 +163,24 @@ def write_data(data, filename, format, storage_options=None, index=False, **kwar
 
 
 def write_json(json_dict, filename, storage_options=None):
+    return _safe_write(
+        fn=_write_json,
+        json_dict=json_dict,
+        filename=filename,
+        storage_options=storage_options,
+    )
+
+
+def _write_json(json_dict, filename, storage_options=None):
     with fsspec.open(filename, mode="w", **storage_options) as f:
         f.write(json.dumps(json_dict))
 
 
 def write_simple_json(data, path):
+    return _safe_write(fn=_write_simple_json, data=data, path=path)
+
+
+def _write_simple_json(data, path):
     if ObjectStorageDetails.is_oci_path(path):
         storage_options = default_signer()
     else:
@@ -156,6 +189,60 @@ def write_simple_json(data, path):
         json.dump(data, f, indent=4)
 
 
+def write_file(local_filename, remote_filename, storage_options, **kwargs):
+    return _safe_write(
+        fn=_write_file,
+        local_filename=local_filename,
+        remote_filename=remote_filename,
+        storage_options=storage_options,
+        **kwargs,
+    )
+
+
+def _write_file(local_filename, remote_filename, storage_options, **kwargs):
+    with open(local_filename) as f1:
+        with fsspec.open(
+            remote_filename,
+            "w",
+            **storage_options,
+        ) as f2:
+            f2.write(f1.read())
+
+
+def load_pkl(filepath):
+    return _safe_write(fn=_load_pkl, filepath=filepath)
+
+
+def _load_pkl(filepath):
+    storage_options = {}
+    if ObjectStorageDetails.is_oci_path(filepath):
+        storage_options = default_signer()
+
+    with fsspec.open(filepath, "rb", **storage_options) as f:
+        return cloudpickle.load(f)
+    return None
+
+
+def write_pkl(obj, filename, output_dir, storage_options):
+    return _safe_write(
+        fn=_write_pkl,
+        obj=obj,
+        filename=filename,
+        output_dir=output_dir,
+        storage_options=storage_options,
+    )
+
+
+def _write_pkl(obj, filename, output_dir, storage_options):
+    pkl_path = os.path.join(output_dir, filename)
+    with fsspec.open(
+        pkl_path,
+        "wb",
+        **storage_options,
+    ) as f:
+        cloudpickle.dump(obj, f)
+
+
 def merge_category_columns(data, target_category_columns):
     result = data.apply(
         lambda x: "__".join([str(x[col]) for col in target_category_columns]), axis=1
@@ -290,4 +377,8 @@ def disable_print():
 
 # Restore
 def enable_print():
+    try:
+        sys.stdout.close()
+    except Exception:
+        pass
     sys.stdout = sys.__stdout__

ads/opctl/operator/lowcode/forecast/model/automlx.py

@@ -38,6 +38,7 @@ def __init__(self, config: ForecastOperatorConfig, datasets: ForecastDatasets):
         super().__init__(config, datasets)
         self.global_explanation = {}
         self.local_explanation = {}
+        self.explainability_kwargs = {}
 
     def set_kwargs(self):
         model_kwargs_cleaned = self.spec.model_kwargs
@@ -54,6 +55,9 @@ def set_kwargs(self):
             self.spec.preprocessing.enabled
             or model_kwargs_cleaned.get("preprocessing", True)
         )
+        sample_ratio = model_kwargs_cleaned.pop("sample_to_feature_ratio", None)
+        if sample_ratio is not None:
+            self.explainability_kwargs = {"sample_to_feature_ratio": sample_ratio}
         return model_kwargs_cleaned, time_budget
 
     def preprocess(self, data, series_id):  # TODO: re-use self.le for explanations
@@ -445,6 +449,7 @@ def explain_model(self):
                         else None,
                         pd.DataFrame(data_i[self.spec.target_column]),
                         task="forecasting",
+                        **self.explainability_kwargs,
                     )
 
                     # Generate explanations for the forecast
@@ -518,7 +523,9 @@ def get_validation_score_and_metric(self, model):
         model_params = model.selected_model_params_
         if len(trials) > 0:
             score_col = [col for col in trials.columns if "Score" in col][0]
-            validation_score = trials[trials.Hyperparameters == model_params][score_col].iloc[0]
+            validation_score = trials[trials.Hyperparameters == model_params][
+                score_col
+            ].iloc[0]
         else:
             validation_score = 0
         return -1 * validation_score
@@ -531,8 +538,12 @@ def generate_train_metrics(self) -> pd.DataFrame:
         for s_id in self.forecast_output.list_series_ids():
             try:
                 metrics = {self.spec.metric.upper(): self.models[s_id]["score"]}
-                metrics_df = pd.DataFrame.from_dict(metrics, orient="index", columns=[s_id])
-                logger.warning("AutoMLX failed to generate training metrics. Recovering validation loss instead")
+                metrics_df = pd.DataFrame.from_dict(
+                    metrics, orient="index", columns=[s_id]
+                )
+                logger.warning(
+                    "AutoMLX failed to generate training metrics. Recovering validation loss instead"
+                )
                 total_metrics = pd.concat([total_metrics, metrics_df], axis=1)
             except Exception as e:
                 logger.debug(

ads/opctl/operator/lowcode/forecast/model/base_model.py

@@ -11,7 +11,6 @@
 from abc import ABC, abstractmethod
 from typing import Tuple
 
-import fsspec
 import numpy as np
 import pandas as pd
 import report_creator as rc
@@ -25,10 +24,13 @@
     disable_print,
     enable_print,
     human_time_friendly,
+    load_pkl,
     merged_category_column_name,
     seconds_to_datetime,
     write_data,
+    write_file,
     write_json,
+    write_pkl,
 )
 from ads.opctl.operator.lowcode.forecast.utils import (
     _build_metrics_df,
@@ -38,8 +40,6 @@
     evaluate_train_metrics,
     get_auto_select_plot,
     get_forecast_plots,
-    load_pkl,
-    write_pkl,
 )
 
 from ..const import (
@@ -493,13 +493,11 @@ def _save_report(
                 enable_print()
 
                 report_path = os.path.join(unique_output_dir, self.spec.report_filename)
-                with open(report_local_path) as f1:
-                    with fsspec.open(
-                        report_path,
-                        "w",
-                        **storage_options,
-                    ) as f2:
-                        f2.write(f1.read())
+                write_file(
+                    local_filename=report_local_path,
+                    remote_filename=report_path,
+                    storage_options=storage_options,
+                )
 
         # forecast csv report
         # todo: add test data into forecast.csv
@@ -576,7 +574,9 @@ def _save_report(
                     # Round to 4 decimal places before writing
                     global_expl_rounded = self.formatted_global_explanation.copy()
                     global_expl_rounded = global_expl_rounded.apply(
-                        lambda col: np.round(col, 4) if np.issubdtype(col.dtype, np.number) else col
+                        lambda col: np.round(col, 4)
+                        if np.issubdtype(col.dtype, np.number)
+                        else col
                     )
                     if self.spec.generate_explanation_files:
                         write_data(
@@ -598,7 +598,9 @@ def _save_report(
                     # Round to 4 decimal places before writing
                     local_expl_rounded = self.formatted_local_explanation.copy()
                     local_expl_rounded = local_expl_rounded.apply(
-                        lambda col: np.round(col, 4) if np.issubdtype(col.dtype, np.number) else col
+                        lambda col: np.round(col, 4)
+                        if np.issubdtype(col.dtype, np.number)
+                        else col
                     )
                     if self.spec.generate_explanation_files:
                         write_data(

ads/opctl/operator/lowcode/forecast/model/neuralprophet.py

@@ -19,12 +19,10 @@
 from ads.opctl.operator.lowcode.common.utils import (
     disable_print,
     enable_print,
-)
-from ads.opctl.operator.lowcode.forecast.utils import (
-    _select_plot_list,
     load_pkl,
     write_pkl,
 )
+from ads.opctl.operator.lowcode.forecast.utils import _select_plot_list
 
 from ..const import DEFAULT_TRIALS, SupportedModels
 from ..operator_config import ForecastOperatorConfig
@@ -159,20 +157,18 @@ def _train_model(self, i, s_id, df, model_kwargs):
                 upper_bound=self.get_horizon(forecast[upper_bound_col_name]).values,
                 lower_bound=self.get_horizon(forecast[lower_bound_col_name]).values,
             )
-            core_columns = set(forecast.columns) - set(
-                [
-                    "y",
-                    "yhat1",
-                    upper_bound_col_name,
-                    lower_bound_col_name,
-                    "future_regressors_additive",
-                    "future_regressors_multiplicative",
-                ]
-            )
+            core_columns = set(forecast.columns) - {
+                "y",
+                "yhat1",
+                upper_bound_col_name,
+                lower_bound_col_name,
+                "future_regressors_additive",
+                "future_regressors_multiplicative",
+            }
             exog_variables = set(
                 filter(lambda x: x.startswith("future_regressor_"), list(core_columns))
             )
-            combine_terms = list(core_columns - exog_variables - set(["ds"]))
+            combine_terms = list(core_columns - exog_variables - {"ds"})
             temp_df = (
                 forecast[list(core_columns)]
                 .rename({"ds": "Date"}, axis=1)

ads/opctl/operator/lowcode/forecast/utils.py

@@ -1,14 +1,12 @@
 #!/usr/bin/env python
 
-# Copyright (c) 2023, 2024 Oracle and/or its affiliates.
+# Copyright (c) 2023, 2025 Oracle and/or its affiliates.
 # Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/
 
 import logging
 import os
 from typing import Set
 
-import cloudpickle
-import fsspec
 import numpy as np
 import pandas as pd
 import report_creator as rc
@@ -21,7 +19,6 @@
     r2_score,
 )
 
-from ads.common.object_storage_details import ObjectStorageDetails
 from ads.dataset.label_encoder import DataFrameLabelEncoder
 from ads.opctl import logger
 from ads.opctl.operator.lowcode.forecast.const import ForecastOutputColumns
@@ -170,26 +167,6 @@ def _build_metrics_per_horizon(
     return metrics_df
 
 
-def load_pkl(filepath):
-    storage_options = {}
-    if ObjectStorageDetails.is_oci_path(filepath):
-        storage_options = default_signer()
-
-    with fsspec.open(filepath, "rb", **storage_options) as f:
-        return cloudpickle.load(f)
-    return None
-
-
-def write_pkl(obj, filename, output_dir, storage_options):
-    pkl_path = os.path.join(output_dir, filename)
-    with fsspec.open(
-        pkl_path,
-        "wb",
-        **storage_options,
-    ) as f:
-        cloudpickle.dump(obj, f)
-
-
 def _build_metrics_df(y_true, y_pred, series_id):
     if len(y_true) == 0 or len(y_pred) == 0:
         return pd.DataFrame()
@@ -251,7 +228,10 @@ def evaluate_train_metrics(output):
 
 
 def _select_plot_list(fn, series_ids, target_category_column):
-    blocks = [rc.Widget(fn(s_id=s_id), label=s_id if target_category_column else None) for s_id in series_ids]
+    blocks = [
+        rc.Widget(fn(s_id=s_id), label=s_id if target_category_column else None)
+        for s_id in series_ids
+    ]
     return rc.Select(blocks=blocks) if len(blocks) > 1 else blocks[0]
 
 
@@ -264,8 +244,10 @@ def get_auto_select_plot(backtest_results):
     back_test_csv_columns = backtest_results.columns.tolist()
     back_test_column = "backtest"
     metric_column = "metric"
-    models = [x for x in back_test_csv_columns if x not in [back_test_column, metric_column]]
-    for i, column in enumerate(models):
+    models = [
+        x for x in back_test_csv_columns if x not in [back_test_column, metric_column]
+    ]
+    for column in models:
         fig.add_trace(
             go.Scatter(
                 x=backtest_results[back_test_column],
@@ -283,7 +265,7 @@ def get_forecast_plots(
     horizon,
     test_data=None,
     ci_interval_width=0.95,
-    target_category_column=None
+    target_category_column=None,
 ):
     def plot_forecast_plotly(s_id):
         fig = go.Figure()
@@ -380,7 +362,9 @@ def plot_forecast_plotly(s_id):
         )
         return fig
 
-    return _select_plot_list(plot_forecast_plotly, forecast_output.list_series_ids(), target_category_column)
+    return _select_plot_list(
+        plot_forecast_plotly, forecast_output.list_series_ids(), target_category_column
+    )
 
 
 def convert_target(target: str, target_col: str):