AutoML in nnetsauce (randomized and quasi-randomized nnetworks)
Want to share your content on python-bloggers? click here.
Content:
- Installing nnetsauce for Python
- Classification
- Regression
Disclaimer: I have no affiliation with the lazypredict
project.
A few days ago, I stumbled accross a cool Python package called lazypredict
. Pretty well-designed, working, and relying on scikit-learn
’s design.
With lazypredict
, you can rapidly have an idea of which scikit-learn model (can also work with xgboost
’s and lightgbm
’s scikit-learn
-like interfaces) performs best on a given data set, with a little preprocessing, and without hyperparameters’ tuning (this is important to note).
I thought something similar could be beneficial to nnetsauce’s classes CustomClassifier
, CustomRegressor
(see detailed examples below, and interact with the graphs) and MTS
. For now.
So far, in nnetsauce
(Python version), I adapted the lazy prediction feature to regression (CustomRegressor
) and classification (CustomClassifier
). Not for univariate and multivariate time series forecasting (MTS
) yet. You can try it from a GitHub branch.
2 – Installation
!pip install git+https://github.com/Techtonique/nnetsauce.git@lazy-predict
2 – Classification
2 – 1 Loading the Dataset
import nnetsauce as ns from sklearn.datasets import load_breast_cancer data = load_breast_cancer() X = data.data y= data.target
2 – 2 Building the classification model using LazyPredict
from sklearn.model_selection import train_test_split # split the data X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123) # build the lazyclassifier clf = ns.LazyClassifier(verbose=0, ignore_warnings=True, custom_metric=None, n_hidden_features=10, col_sample=0.9) # fit it models, predictions = clf.fit(X_train, X_test, y_train, y_test)
100%|██████████| 27/27 [00:09<00:00, 2.71it/s]
# print the best models display(models)
Accuracy | Balanced Accuracy | ROC AUC | F1 Score | Time Taken | |
---|---|---|---|---|---|
Model | |||||
LogisticRegression | 0.99 | 0.99 | 0.99 | 0.99 | 0.69 |
LinearSVC | 0.98 | 0.98 | 0.98 | 0.98 | 0.33 |
SGDClassifier | 0.98 | 0.98 | 0.98 | 0.98 | 0.19 |
Perceptron | 0.98 | 0.98 | 0.98 | 0.98 | 0.15 |
LabelPropagation | 0.98 | 0.98 | 0.98 | 0.98 | 0.33 |
LabelSpreading | 0.98 | 0.98 | 0.98 | 0.98 | 0.43 |
SVC | 0.98 | 0.98 | 0.98 | 0.98 | 0.16 |
RandomForestClassifier | 0.98 | 0.98 | 0.98 | 0.98 | 0.66 |
ExtraTreesClassifier | 0.98 | 0.98 | 0.98 | 0.98 | 0.40 |
KNeighborsClassifier | 0.98 | 0.98 | 0.98 | 0.98 | 0.34 |
DecisionTreeClassifier | 0.97 | 0.97 | 0.97 | 0.97 | 0.53 |
PassiveAggressiveClassifier | 0.97 | 0.97 | 0.97 | 0.97 | 0.21 |
LinearDiscriminantAnalysis | 0.97 | 0.96 | 0.96 | 0.97 | 0.19 |
CalibratedClassifierCV | 0.97 | 0.96 | 0.96 | 0.97 | 0.24 |
AdaBoostClassifier | 0.96 | 0.96 | 0.96 | 0.96 | 1.31 |
BaggingClassifier | 0.95 | 0.95 | 0.95 | 0.95 | 0.63 |
RidgeClassifier | 0.96 | 0.94 | 0.94 | 0.96 | 0.27 |
RidgeClassifierCV | 0.96 | 0.94 | 0.94 | 0.96 | 0.18 |
QuadraticDiscriminantAnalysis | 0.95 | 0.94 | 0.94 | 0.95 | 0.81 |
ExtraTreeClassifier | 0.94 | 0.93 | 0.93 | 0.94 | 0.12 |
NuSVC | 0.94 | 0.91 | 0.91 | 0.94 | 0.29 |
GaussianNB | 0.93 | 0.91 | 0.91 | 0.93 | 0.17 |
BernoulliNB | 0.92 | 0.90 | 0.90 | 0.92 | 0.31 |
NearestCentroid | 0.92 | 0.89 | 0.89 | 0.92 | 0.24 |
DummyClassifier | 0.64 | 0.50 | 0.50 | 0.50 | 0.27 |
model_dictionary = clf.provide_models(X_train, X_test, y_train, y_test)
model_dictionary['LogisticRegression']
Pipeline(steps=[('preprocessor', ColumnTransformer(transformers=[('numeric', Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', StandardScaler())]), Int64Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29], dtype='int64')), ('categorical_low', Pipeline(steps=[('imputer', SimpleImputer(fill_value='missing', strategy='c... OneHotEncoder(handle_unknown='ignore', sparse=False))]), Int64Index([], dtype='int64')), ('categorical_high', Pipeline(steps=[('imputer', SimpleImputer(fill_value='missing', strategy='constant')), ('encoding', OrdinalEncoder())]), Int64Index([], dtype='int64'))])), ('classifier', CustomClassifier(col_sample=0.9, n_hidden_features=10, obj=LogisticRegression(random_state=42)))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
Pipeline(steps=[('preprocessor', ColumnTransformer(transformers=[('numeric', Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', StandardScaler())]), Int64Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29], dtype='int64')), ('categorical_low', Pipeline(steps=[('imputer', SimpleImputer(fill_value='missing', strategy='c... OneHotEncoder(handle_unknown='ignore', sparse=False))]), Int64Index([], dtype='int64')), ('categorical_high', Pipeline(steps=[('imputer', SimpleImputer(fill_value='missing', strategy='constant')), ('encoding', OrdinalEncoder())]), Int64Index([], dtype='int64'))])), ('classifier', CustomClassifier(col_sample=0.9, n_hidden_features=10, obj=LogisticRegression(random_state=42)))])
ColumnTransformer(transformers=[('numeric', Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', StandardScaler())]), Int64Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29], dtype='int64')), ('categorical_low', Pipeline(steps=[('imputer', SimpleImputer(fill_value='missing', strategy='constant')), ('encoding', OneHotEncoder(handle_unknown='ignore', sparse=False))]), Int64Index([], dtype='int64')), ('categorical_high', Pipeline(steps=[('imputer', SimpleImputer(fill_value='missing', strategy='constant')), ('encoding', OrdinalEncoder())]), Int64Index([], dtype='int64'))])
Int64Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29], dtype='int64')
SimpleImputer()
StandardScaler()
Int64Index([], dtype='int64')
SimpleImputer(fill_value='missing', strategy='constant')
OneHotEncoder(handle_unknown='ignore', sparse=False)
Int64Index([], dtype='int64')
SimpleImputer(fill_value='missing', strategy='constant')
OrdinalEncoder()
CustomClassifier(col_sample=0.9, n_hidden_features=10, obj=LogisticRegression(random_state=42))
LogisticRegression(random_state=42)
LogisticRegression(random_state=42)
model_dictionary['LogisticRegression'].get_params()
{'memory': None, 'steps': [('preprocessor', ColumnTransformer(transformers=[('numeric', Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', StandardScaler())]), Int64Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29], dtype='int64')), ('categorical_low', Pipeline(steps=[('imputer', SimpleImputer(fill_value='missing', strategy='constant')), ('encoding', OneHotEncoder(handle_unknown='ignore', sparse=False))]), Int64Index([], dtype='int64')), ('categorical_high', Pipeline(steps=[('imputer', SimpleImputer(fill_value='missing', strategy='constant')), ('encoding', OrdinalEncoder())]), Int64Index([], dtype='int64'))])), ('classifier', CustomClassifier(col_sample=0.9, n_hidden_features=10, obj=LogisticRegression(random_state=42)))], 'verbose': False, 'preprocessor': ColumnTransformer(transformers=[('numeric', Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', StandardScaler())]), Int64Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29], dtype='int64')), ('categorical_low', Pipeline(steps=[('imputer', SimpleImputer(fill_value='missing', strategy='constant')), ('encoding', OneHotEncoder(handle_unknown='ignore', sparse=False))]), Int64Index([], dtype='int64')), ('categorical_high', Pipeline(steps=[('imputer', SimpleImputer(fill_value='missing', strategy='constant')), ('encoding', OrdinalEncoder())]), Int64Index([], dtype='int64'))]), 'classifier': CustomClassifier(col_sample=0.9, n_hidden_features=10, obj=LogisticRegression(random_state=42)), 'preprocessor__n_jobs': None, 'preprocessor__remainder': 'drop', 'preprocessor__sparse_threshold': 0.3, 'preprocessor__transformer_weights': None, 'preprocessor__transformers': [('numeric', Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', StandardScaler())]), Int64Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29], dtype='int64')), ('categorical_low', Pipeline(steps=[('imputer', SimpleImputer(fill_value='missing', strategy='constant')), ('encoding', OneHotEncoder(handle_unknown='ignore', sparse=False))]), Int64Index([], dtype='int64')), ('categorical_high', Pipeline(steps=[('imputer', SimpleImputer(fill_value='missing', strategy='constant')), ('encoding', OrdinalEncoder())]), Int64Index([], dtype='int64'))], 'preprocessor__verbose': False, 'preprocessor__verbose_feature_names_out': True, 'preprocessor__numeric': Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', StandardScaler())]), 'preprocessor__categorical_low': Pipeline(steps=[('imputer', SimpleImputer(fill_value='missing', strategy='constant')), ('encoding', OneHotEncoder(handle_unknown='ignore', sparse=False))]), 'preprocessor__categorical_high': Pipeline(steps=[('imputer', SimpleImputer(fill_value='missing', strategy='constant')), ('encoding', OrdinalEncoder())]), 'preprocessor__numeric__memory': None, 'preprocessor__numeric__steps': [('imputer', SimpleImputer()), ('scaler', StandardScaler())], 'preprocessor__numeric__verbose': False, 'preprocessor__numeric__imputer': SimpleImputer(), 'preprocessor__numeric__scaler': StandardScaler(), 'preprocessor__numeric__imputer__add_indicator': False, 'preprocessor__numeric__imputer__copy': True, 'preprocessor__numeric__imputer__fill_value': None, 'preprocessor__numeric__imputer__keep_empty_features': False, 'preprocessor__numeric__imputer__missing_values': nan, 'preprocessor__numeric__imputer__strategy': 'mean', 'preprocessor__numeric__imputer__verbose': 'deprecated', 'preprocessor__numeric__scaler__copy': True, 'preprocessor__numeric__scaler__with_mean': True, 'preprocessor__numeric__scaler__with_std': True, 'preprocessor__categorical_low__memory': None, 'preprocessor__categorical_low__steps': [('imputer', SimpleImputer(fill_value='missing', strategy='constant')), ('encoding', OneHotEncoder(handle_unknown='ignore', sparse=False))], 'preprocessor__categorical_low__verbose': False, 'preprocessor__categorical_low__imputer': SimpleImputer(fill_value='missing', strategy='constant'), 'preprocessor__categorical_low__encoding': OneHotEncoder(handle_unknown='ignore', sparse=False), 'preprocessor__categorical_low__imputer__add_indicator': False, 'preprocessor__categorical_low__imputer__copy': True, 'preprocessor__categorical_low__imputer__fill_value': 'missing', 'preprocessor__categorical_low__imputer__keep_empty_features': False, 'preprocessor__categorical_low__imputer__missing_values': nan, 'preprocessor__categorical_low__imputer__strategy': 'constant', 'preprocessor__categorical_low__imputer__verbose': 'deprecated', 'preprocessor__categorical_low__encoding__categories': 'auto', 'preprocessor__categorical_low__encoding__drop': None, 'preprocessor__categorical_low__encoding__dtype': numpy.float64, 'preprocessor__categorical_low__encoding__handle_unknown': 'ignore', 'preprocessor__categorical_low__encoding__max_categories': None, 'preprocessor__categorical_low__encoding__min_frequency': None, 'preprocessor__categorical_low__encoding__sparse': False, 'preprocessor__categorical_low__encoding__sparse_output': True, 'preprocessor__categorical_high__memory': None, 'preprocessor__categorical_high__steps': [('imputer', SimpleImputer(fill_value='missing', strategy='constant')), ('encoding', OrdinalEncoder())], 'preprocessor__categorical_high__verbose': False, 'preprocessor__categorical_high__imputer': SimpleImputer(fill_value='missing', strategy='constant'), 'preprocessor__categorical_high__encoding': OrdinalEncoder(), 'preprocessor__categorical_high__imputer__add_indicator': False, 'preprocessor__categorical_high__imputer__copy': True, 'preprocessor__categorical_high__imputer__fill_value': 'missing', 'preprocessor__categorical_high__imputer__keep_empty_features': False, 'preprocessor__categorical_high__imputer__missing_values': nan, 'preprocessor__categorical_high__imputer__strategy': 'constant', 'preprocessor__categorical_high__imputer__verbose': 'deprecated', 'preprocessor__categorical_high__encoding__categories': 'auto', 'preprocessor__categorical_high__encoding__dtype': numpy.float64, 'preprocessor__categorical_high__encoding__encoded_missing_value': nan, 'preprocessor__categorical_high__encoding__handle_unknown': 'error', 'preprocessor__categorical_high__encoding__unknown_value': None, 'classifier__a': 0.01, 'classifier__activation_name': 'relu', 'classifier__backend': 'cpu', 'classifier__bias': True, 'classifier__cluster_encode': True, 'classifier__col_sample': 0.9, 'classifier__direct_link': True, 'classifier__dropout': 0, 'classifier__n_clusters': 2, 'classifier__n_hidden_features': 10, 'classifier__nodes_sim': 'sobol', 'classifier__obj__C': 1.0, 'classifier__obj__class_weight': None, 'classifier__obj__dual': False, 'classifier__obj__fit_intercept': True, 'classifier__obj__intercept_scaling': 1, 'classifier__obj__l1_ratio': None, 'classifier__obj__max_iter': 100, 'classifier__obj__multi_class': 'auto', 'classifier__obj__n_jobs': None, 'classifier__obj__penalty': 'l2', 'classifier__obj__random_state': 42, 'classifier__obj__solver': 'lbfgs', 'classifier__obj__tol': 0.0001, 'classifier__obj__verbose': 0, 'classifier__obj__warm_start': False, 'classifier__obj': LogisticRegression(random_state=42), 'classifier__row_sample': 1, 'classifier__seed': 123, 'classifier__type_clust': 'kmeans', 'classifier__type_scaling': ('std', 'std', 'std')}
3 – Regression
from sklearn.datasets import load_diabetes from sklearn.model_selection import train_test_split
data = load_diabetes() X = data.data y= data.target X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .2, random_state = 123) regr = ns.LazyRegressor(verbose=0, ignore_warnings=True, custom_metric=None) models, predictions = regr.fit(X_train, X_test, y_train, y_test) model_dictionary = regr.provide_models(X_train, X_test, y_train, y_test)
100%|██████████| 40/40 [00:03<00:00, 12.38it/s]
display(models)
Adjusted R-Squared | R-Squared | RMSE | Time Taken | |
---|---|---|---|---|
Model | ||||
LassoLarsIC | 0.53 | 0.59 | 51.11 | 0.03 |
SGDRegressor | 0.53 | 0.58 | 51.24 | 0.03 |
HuberRegressor | 0.53 | 0.58 | 51.26 | 0.05 |
Ridge | 0.53 | 0.58 | 51.37 | 0.03 |
KernelRidge | 0.53 | 0.58 | 51.37 | 0.03 |
RidgeCV | 0.53 | 0.58 | 51.37 | 0.03 |
Lasso | 0.52 | 0.58 | 51.52 | 0.03 |
LassoLars | 0.52 | 0.58 | 51.52 | 0.03 |
LassoCV | 0.52 | 0.58 | 51.58 | 0.12 |
LassoLarsCV | 0.52 | 0.58 | 51.58 | 0.05 |
TransformedTargetRegressor | 0.52 | 0.58 | 51.62 | 0.03 |
LinearRegression | 0.52 | 0.58 | 51.62 | 0.03 |
OrthogonalMatchingPursuitCV | 0.52 | 0.58 | 51.69 | 0.05 |
BayesianRidge | 0.52 | 0.57 | 51.77 | 0.03 |
LinearSVR | 0.51 | 0.57 | 52.04 | 0.02 |
ElasticNetCV | 0.51 | 0.56 | 52.49 | 0.08 |
LarsCV | 0.50 | 0.56 | 52.79 | 0.05 |
PassiveAggressiveRegressor | 0.49 | 0.55 | 53.39 | 0.03 |
GradientBoostingRegressor | 0.48 | 0.54 | 54.00 | 0.26 |
ElasticNet | 0.46 | 0.52 | 54.92 | 0.03 |
BaggingRegressor | 0.46 | 0.52 | 54.92 | 0.07 |
RandomForestRegressor | 0.46 | 0.52 | 55.07 | 0.37 |
HistGradientBoostingRegressor | 0.45 | 0.51 | 55.42 | 0.20 |
ExtraTreesRegressor | 0.44 | 0.51 | 55.71 | 0.24 |
AdaBoostRegressor | 0.44 | 0.51 | 55.75 | 0.14 |
MLPRegressor | 0.43 | 0.50 | 56.38 | 0.45 |
TweedieRegressor | 0.42 | 0.48 | 57.03 | 0.03 |
RANSACRegressor | 0.42 | 0.48 | 57.14 | 0.16 |
KNeighborsRegressor | 0.31 | 0.39 | 62.10 | 0.05 |
OrthogonalMatchingPursuit | 0.31 | 0.38 | 62.27 | 0.04 |
GaussianProcessRegressor | 0.19 | 0.28 | 67.13 | 0.05 |
ExtraTreeRegressor | 0.15 | 0.24 | 69.09 | 0.03 |
SVR | 0.12 | 0.22 | 69.98 | 0.04 |
NuSVR | 0.12 | 0.22 | 70.14 | 0.04 |
DummyRegressor | -0.13 | -0.00 | 79.39 | 0.03 |
DecisionTreeRegressor | -0.26 | -0.11 | 83.75 | 0.03 |
Lars | -1.95 | -1.61 | 128.28 | 0.14 |
model_dictionary["LassoLarsIC"]
Pipeline(steps=[('preprocessor', ColumnTransformer(transformers=[('numeric', Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', StandardScaler())]), Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype='int64')), ('categorical_low', Pipeline(steps=[('imputer', SimpleImputer(fill_value='missing', strategy='constant')), ('encoding', OneHotEncoder(handle_unknown='ignore', sparse=False))]), Int64Index([], dtype='int64')), ('categorical_high', Pipeline(steps=[('imputer', SimpleImputer(fill_value='missing', strategy='constant')), ('encoding', OrdinalEncoder())]), Int64Index([], dtype='int64'))])), ('regressor', CustomRegressor(obj=LassoLarsIC()))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
Pipeline(steps=[('preprocessor', ColumnTransformer(transformers=[('numeric', Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', StandardScaler())]), Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype='int64')), ('categorical_low', Pipeline(steps=[('imputer', SimpleImputer(fill_value='missing', strategy='constant')), ('encoding', OneHotEncoder(handle_unknown='ignore', sparse=False))]), Int64Index([], dtype='int64')), ('categorical_high', Pipeline(steps=[('imputer', SimpleImputer(fill_value='missing', strategy='constant')), ('encoding', OrdinalEncoder())]), Int64Index([], dtype='int64'))])), ('regressor', CustomRegressor(obj=LassoLarsIC()))])
ColumnTransformer(transformers=[('numeric', Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', StandardScaler())]), Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype='int64')), ('categorical_low', Pipeline(steps=[('imputer', SimpleImputer(fill_value='missing', strategy='constant')), ('encoding', OneHotEncoder(handle_unknown='ignore', sparse=False))]), Int64Index([], dtype='int64')), ('categorical_high', Pipeline(steps=[('imputer', SimpleImputer(fill_value='missing', strategy='constant')), ('encoding', OrdinalEncoder())]), Int64Index([], dtype='int64'))])
Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype='int64')
SimpleImputer()
StandardScaler()
Int64Index([], dtype='int64')
SimpleImputer(fill_value='missing', strategy='constant')
OneHotEncoder(handle_unknown='ignore', sparse=False)
Int64Index([], dtype='int64')
SimpleImputer(fill_value='missing', strategy='constant')
OrdinalEncoder()
CustomRegressor(obj=LassoLarsIC())
LassoLarsIC()
LassoLarsIC()
model_dictionary["LassoLarsIC"].get_params()
{'memory': None, 'steps': [('preprocessor', ColumnTransformer(transformers=[('numeric', Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', StandardScaler())]), Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype='int64')), ('categorical_low', Pipeline(steps=[('imputer', SimpleImputer(fill_value='missing', strategy='constant')), ('encoding', OneHotEncoder(handle_unknown='ignore', sparse=False))]), Int64Index([], dtype='int64')), ('categorical_high', Pipeline(steps=[('imputer', SimpleImputer(fill_value='missing', strategy='constant')), ('encoding', OrdinalEncoder())]), Int64Index([], dtype='int64'))])), ('regressor', CustomRegressor(obj=LassoLarsIC()))], 'verbose': False, 'preprocessor': ColumnTransformer(transformers=[('numeric', Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', StandardScaler())]), Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype='int64')), ('categorical_low', Pipeline(steps=[('imputer', SimpleImputer(fill_value='missing', strategy='constant')), ('encoding', OneHotEncoder(handle_unknown='ignore', sparse=False))]), Int64Index([], dtype='int64')), ('categorical_high', Pipeline(steps=[('imputer', SimpleImputer(fill_value='missing', strategy='constant')), ('encoding', OrdinalEncoder())]), Int64Index([], dtype='int64'))]), 'regressor': CustomRegressor(obj=LassoLarsIC()), 'preprocessor__n_jobs': None, 'preprocessor__remainder': 'drop', 'preprocessor__sparse_threshold': 0.3, 'preprocessor__transformer_weights': None, 'preprocessor__transformers': [('numeric', Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', StandardScaler())]), Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype='int64')), ('categorical_low', Pipeline(steps=[('imputer', SimpleImputer(fill_value='missing', strategy='constant')), ('encoding', OneHotEncoder(handle_unknown='ignore', sparse=False))]), Int64Index([], dtype='int64')), ('categorical_high', Pipeline(steps=[('imputer', SimpleImputer(fill_value='missing', strategy='constant')), ('encoding', OrdinalEncoder())]), Int64Index([], dtype='int64'))], 'preprocessor__verbose': False, 'preprocessor__verbose_feature_names_out': True, 'preprocessor__numeric': Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', StandardScaler())]), 'preprocessor__categorical_low': Pipeline(steps=[('imputer', SimpleImputer(fill_value='missing', strategy='constant')), ('encoding', OneHotEncoder(handle_unknown='ignore', sparse=False))]), 'preprocessor__categorical_high': Pipeline(steps=[('imputer', SimpleImputer(fill_value='missing', strategy='constant')), ('encoding', OrdinalEncoder())]), 'preprocessor__numeric__memory': None, 'preprocessor__numeric__steps': [('imputer', SimpleImputer()), ('scaler', StandardScaler())], 'preprocessor__numeric__verbose': False, 'preprocessor__numeric__imputer': SimpleImputer(), 'preprocessor__numeric__scaler': StandardScaler(), 'preprocessor__numeric__imputer__add_indicator': False, 'preprocessor__numeric__imputer__copy': True, 'preprocessor__numeric__imputer__fill_value': None, 'preprocessor__numeric__imputer__keep_empty_features': False, 'preprocessor__numeric__imputer__missing_values': nan, 'preprocessor__numeric__imputer__strategy': 'mean', 'preprocessor__numeric__imputer__verbose': 'deprecated', 'preprocessor__numeric__scaler__copy': True, 'preprocessor__numeric__scaler__with_mean': True, 'preprocessor__numeric__scaler__with_std': True, 'preprocessor__categorical_low__memory': None, 'preprocessor__categorical_low__steps': [('imputer', SimpleImputer(fill_value='missing', strategy='constant')), ('encoding', OneHotEncoder(handle_unknown='ignore', sparse=False))], 'preprocessor__categorical_low__verbose': False, 'preprocessor__categorical_low__imputer': SimpleImputer(fill_value='missing', strategy='constant'), 'preprocessor__categorical_low__encoding': OneHotEncoder(handle_unknown='ignore', sparse=False), 'preprocessor__categorical_low__imputer__add_indicator': False, 'preprocessor__categorical_low__imputer__copy': True, 'preprocessor__categorical_low__imputer__fill_value': 'missing', 'preprocessor__categorical_low__imputer__keep_empty_features': False, 'preprocessor__categorical_low__imputer__missing_values': nan, 'preprocessor__categorical_low__imputer__strategy': 'constant', 'preprocessor__categorical_low__imputer__verbose': 'deprecated', 'preprocessor__categorical_low__encoding__categories': 'auto', 'preprocessor__categorical_low__encoding__drop': None, 'preprocessor__categorical_low__encoding__dtype': numpy.float64, 'preprocessor__categorical_low__encoding__handle_unknown': 'ignore', 'preprocessor__categorical_low__encoding__max_categories': None, 'preprocessor__categorical_low__encoding__min_frequency': None, 'preprocessor__categorical_low__encoding__sparse': False, 'preprocessor__categorical_low__encoding__sparse_output': True, 'preprocessor__categorical_high__memory': None, 'preprocessor__categorical_high__steps': [('imputer', SimpleImputer(fill_value='missing', strategy='constant')), ('encoding', OrdinalEncoder())], 'preprocessor__categorical_high__verbose': False, 'preprocessor__categorical_high__imputer': SimpleImputer(fill_value='missing', strategy='constant'), 'preprocessor__categorical_high__encoding': OrdinalEncoder(), 'preprocessor__categorical_high__imputer__add_indicator': False, 'preprocessor__categorical_high__imputer__copy': True, 'preprocessor__categorical_high__imputer__fill_value': 'missing', 'preprocessor__categorical_high__imputer__keep_empty_features': False, 'preprocessor__categorical_high__imputer__missing_values': nan, 'preprocessor__categorical_high__imputer__strategy': 'constant', 'preprocessor__categorical_high__imputer__verbose': 'deprecated', 'preprocessor__categorical_high__encoding__categories': 'auto', 'preprocessor__categorical_high__encoding__dtype': numpy.float64, 'preprocessor__categorical_high__encoding__encoded_missing_value': nan, 'preprocessor__categorical_high__encoding__handle_unknown': 'error', 'preprocessor__categorical_high__encoding__unknown_value': None, 'regressor__a': 0.01, 'regressor__activation_name': 'relu', 'regressor__backend': 'cpu', 'regressor__bias': True, 'regressor__cluster_encode': True, 'regressor__col_sample': 1, 'regressor__direct_link': True, 'regressor__dropout': 0, 'regressor__n_clusters': 2, 'regressor__n_hidden_features': 5, 'regressor__nodes_sim': 'sobol', 'regressor__obj__copy_X': True, 'regressor__obj__criterion': 'aic', 'regressor__obj__eps': 2.220446049250313e-16, 'regressor__obj__fit_intercept': True, 'regressor__obj__max_iter': 500, 'regressor__obj__noise_variance': None, 'regressor__obj__normalize': 'deprecated', 'regressor__obj__positive': False, 'regressor__obj__precompute': 'auto', 'regressor__obj__verbose': False, 'regressor__obj': LassoLarsIC(), 'regressor__row_sample': 1, 'regressor__seed': 123, 'regressor__type_clust': 'kmeans', 'regressor__type_scaling': ('std', 'std', 'std')}
Want to share your content on python-bloggers? click here.
Copyright © 2024 | MH Corporate basic by MH Themes