добавить в блокнот research часть про фильтрацию признаков с помощью SequentialFeatureSelector

iis_project/mlxtend_utils/feature_selection.py

@@ -0,0 +1,3 @@
+SEQUENTIAL_FEATURE_SELECTOR_PARAMS_COMMON_INCLUDE = [
+    'k_features', 'forward', 'floating', 'scoring', 'cv', 'fixed_features', 'feature_groups',
+]

requirements/requirements-isolated-research-model.txt.unused

@@ -1 +1,2 @@
+mlxtend ~=0.23.4
 scikit-learn >=1.7.2,<2

requirements/requirements-research.txt

@@ -1,2 +1,3 @@
-mlflow>=2.16,<2.22
+mlflow >=2.16,<2.22
+mlxtend ~=0.23.4
 scikit-learn >=1.7.2,<2

research/research.py

@@ -48,17 +48,23 @@ mlflow_baseline_run_name: str = 'Baseline model'
 # Имя ноговго прогона MLFlow для baseline модели.
 mlflow_feateng_run_name: str = 'Model with engineered features'
 # Имя ноговго прогона MLFlow для модели, использующей дополнительные признаки
+mlflow_feateng_filtered_run_name: str = 'Model with filtered engineered features'
+# Имя ноговго прогона MLFlow для модели, использующей дополнительные признаки и фильтрацию признаков
 
 # %%
+from collections.abc import Sequence
 import os
 import pathlib
 import pickle
 import sys
 
 # %%
+import matplotlib
 import mlflow
 import mlflow.models
 import mlflow.sklearn
+import mlxtend.feature_selection
+import mlxtend.plotting
 import sklearn.compose
 import sklearn.ensemble
 import sklearn.metrics
@@ -74,6 +80,7 @@ CODE_PATH = BASE_PATH
 sys.path.insert(0, str(CODE_PATH.resolve()))
 
 # %%
+from iis_project.mlxtend_utils.feature_selection import SEQUENTIAL_FEATURE_SELECTOR_PARAMS_COMMON_INCLUDE
 from iis_project.sklearn_utils import filter_params
 from iis_project.sklearn_utils.compose import COLUMN_TRANSFORMER_PARAMS_COMMON_INCLUDE
 from iis_project.sklearn_utils.ensemble import RANDOM_FOREST_REGRESSOR_PARAMS_COMMON_EXCLUDE
@@ -99,6 +106,16 @@ DATA_PATH = (
     else (BASE_PATH / 'data')
 )
 
+
+# %%
+def build_sequential_feature_selector(*args, **kwargs):
+    return mlxtend.feature_selection.SequentialFeatureSelector(*args, **kwargs)
+
+def plot_sequential_feature_selection(feature_selector, *args_rest, **kwargs):
+    metric_dict = feature_selector.get_metric_dict()
+    return mlxtend.plotting.plot_sequential_feature_selection(metric_dict, *args_rest, **kwargs)
+
+
 # %% [markdown]
 # ## Загрузка и обзор данных
 
@@ -389,11 +406,12 @@ mlflow_log_model(
 features_to_extend_as_polynomial = ('selling_price', 'driven_kms')
 features_to_extend_as_spline = ('age',)
 
-# %%
-assert set(features_to_extend_as_polynomial) <= {*features_to_scale_to_standard_columns}
-assert set(features_to_extend_as_spline) <= {*features_to_scale_to_standard_columns}
 
-preprocess_transformer = sklearn.compose.ColumnTransformer(
+# %%
+def build_preprocess_transformer():
+    assert set(features_to_extend_as_polynomial) <= {*features_to_scale_to_standard_columns}
+    assert set(features_to_extend_as_spline) <= {*features_to_scale_to_standard_columns}
+    return sklearn.compose.ColumnTransformer(
         [
             (
                 'extend_features_as_polynomial',
@@ -425,24 +443,28 @@ preprocess_transformer = sklearn.compose.ColumnTransformer(
             ),
         ],
         remainder='drop',
-)
+    )
+
+preprocess_transformer = build_preprocess_transformer()
 preprocess_transformer
 
 # %% [markdown]
 # Демонстрация предобработки данных:
 
 # %%
-df_tfd_features_matrix_test = preprocess_transformer.fit_transform(df_orig_features_test, df_target_test.iloc[:, 0])
+preprocess_transformer_tmp = build_preprocess_transformer()
-df_tfd_features_test = pandas_dataframe_from_transformed_artifacts(df_tfd_features_matrix_test, preprocess_transformer)
+df_augd_features_matrix_train = preprocess_transformer_tmp.fit_transform(df_orig_features_train, df_target_train.iloc[:, 0])
+df_augd_features_train = pandas_dataframe_from_transformed_artifacts(df_augd_features_matrix_train, preprocess_transformer_tmp)
+del preprocess_transformer_tmp
 
 # %% [markdown]
 # Обзор предобработанного датасета:
 
 # %%
-df_tfd_features_test.info()
+df_augd_features_train.info()
 
 # %%
-df_tfd_features_test.head(0x8)
+df_augd_features_train.head(0x8)
 
 # %%
 regressor = build_regressor(random_state=0x3AEF)
@@ -524,4 +546,134 @@ mlflow_log_model(
     ),
 )
 
+# %% [markdown]
+# ### Модель с дополнительными и отфильтрованными признаками
+
+# %%
+regressor = build_regressor(random_state=0x8EDD)
+regressor
+
+# %% [markdown]
+# Выбор признаков среди дополненного набора по минимизации MAPE:
+
+# %%
+len(df_augd_features_train.columns)
+
+# %%
+FILTERED_FEATURES_NUM = (4, 8)
+
+# %%
+feature_selector = build_sequential_feature_selector(
+    regressor, k_features=FILTERED_FEATURES_NUM, forward=True, floating=True, cv=4, scoring='neg_mean_absolute_percentage_error',
+    verbose=1,
+)
+feature_selector
+
+# %%
+_ = feature_selector.fit(df_augd_features_train, df_target_train.iloc[:, 0])
+
+# %% [markdown]
+# Имена выбранных признаков:
+
+# %%
+feature_selector.k_feature_names_
+
+# %% [markdown]
+# MAPE в зависимости от количества выбранных признаков (указан регион выбора, ограниченный `FILTERED_FEATURES_NUM`):
+
+# %%
+fig, ax = plot_sequential_feature_selection(feature_selector, kind='std_dev')
+ax.grid(True)
+if isinstance(FILTERED_FEATURES_NUM, Sequence):
+    _ = ax.axvspan(min(FILTERED_FEATURES_NUM), max(FILTERED_FEATURES_NUM), color=matplotlib.colormaps.get_cmap('tab10')(6), alpha=0.15)
+# хотелось бы поставить верхнюю границу `len(df_augd_features_train.columns)`, но SequentialFeatureSelector до неё не досчитывает-то
+_ = ax.set_xlim((1, (max(FILTERED_FEATURES_NUM) if isinstance(FILTERED_FEATURES_NUM, Sequence) else FILTERED_FEATURES_NUM)))
+_ = ax.set_ylim((None, 0.))
+
+# %% [markdown]
+# Составной пайплайн:
+
+# %%
+pipeline = sklearn.pipeline.Pipeline([
+    ('preprocess', preprocess_transformer),
+    ('select_features', feature_selector),
+    ('regress', regressor),
+])
+pipeline
+
+# %%
+model_params = filter_params(
+    pipeline.get_params(),
+    include={
+        'preprocess': (
+            False,
+            {
+                **{k: True for k in COLUMN_TRANSFORMER_PARAMS_COMMON_INCLUDE},
+                'extend_features_as_polynomial': {
+                    'extend_features': True,
+                    'scale_to_standard': True,
+                },
+                'extend_features_as_spline': True,
+                'scale_to_standard': True,
+                'encode_categorical_wrt_target': True,
+            },
+        ),
+        'select_features': (
+            False,
+            {
+                **{k: True for k in SEQUENTIAL_FEATURE_SELECTOR_PARAMS_COMMON_INCLUDE},
+                'estimator': False,
+            },
+        ),
+        'regress': (False, True),
+    },
+    exclude={
+        'preprocess': {
+            'extend_features_as_polynomial': {
+                'scale_to_standard': STANDARD_SCALER_PARAMS_COMMON_EXCLUDE,
+            },
+            'scale_to_standard': STANDARD_SCALER_PARAMS_COMMON_EXCLUDE,
+        },
+        'select_features': (),  # TODO: ай-яй-яй
+        'regress': RANDOM_FOREST_REGRESSOR_PARAMS_COMMON_EXCLUDE,
+    },
+)
+model_params
+
+# %% [markdown]
+# Обучение модели:
+
+# %%
+# XXX: SequentialFeatureSelector обучается опять!?
+_ = pipeline.fit(df_orig_features_train, df_target_train.iloc[:, 0])
+
+# %% [markdown]
+# Оценка качества:
+
+# %%
+target_test_predicted = pipeline.predict(df_orig_features_test)
+
+# %% [markdown]
+# Метрики качества (MAPE, а также MSE, MAE):
+
+# %%
+metrics = score_predictions(df_target_test, target_test_predicted)
+metrics
+
+# %%
+mlflow_log_model(
+    pipeline,
+    model_params=model_params,
+    metrics={k: float(v) for k, v in metrics.items()},
+    run_name=mlflow_feateng_filtered_run_name,
+    model_signature=mlflow_model_signature,
+    input_example=df_orig_features.head(MODEL_INOUT_EXAMPLE_SIZE),
+    #pip_requirements=str(MODEL_PIP_REQUIREMENTS_PATH),
+    comment_file_path=(
+        model_comment_path
+        if model_comment_path is not None
+        else (BASE_PATH / 'research' / model_comment_relpath)
+    ),
+)
+
 # %%