optimized supervised imports statements, unused imports removed.

supervised/algorithms/baseline.py

@@ -1,5 +1,4 @@
 import logging
-import os
 
 import sklearn
 from sklearn.base import ClassifierMixin, RegressorMixin

supervised/algorithms/catboost.py

@@ -1,6 +1,5 @@
 import copy
 import logging
-import os
 import time
 
 import numpy as np
@@ -28,7 +27,7 @@
 logger.setLevel(LOG_LEVEL)
 
 import catboost
-from catboost import CatBoost, CatBoostClassifier, CatBoostRegressor, Pool
+from catboost import CatBoostClassifier, CatBoostRegressor, Pool
 
 
 def catboost_eval_metric(ml_task, eval_metric):

supervised/algorithms/decision_tree.py

@@ -7,7 +7,6 @@
 from sklearn.base import BaseEstimator, ClassifierMixin, RegressorMixin
 from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
 
-from supervised.algorithms.algorithm import BaseAlgorithm
 from supervised.algorithms.registry import (
     BINARY_CLASSIFICATION,
     MULTICLASS_CLASSIFICATION,

supervised/algorithms/extra_trees.py

@@ -1,11 +1,9 @@
 import logging
-import os
 
 import sklearn
 from sklearn.base import ClassifierMixin, RegressorMixin
 from sklearn.ensemble import ExtraTreesClassifier, ExtraTreesRegressor
 
-from supervised.algorithms.algorithm import BaseAlgorithm
 from supervised.algorithms.registry import (
     BINARY_CLASSIFICATION,
     MULTICLASS_CLASSIFICATION,

supervised/algorithms/knn.py

@@ -1,13 +1,10 @@
 import logging
-import os
 
-import numpy as np
 import sklearn
 from sklearn.base import ClassifierMixin, RegressorMixin
 from sklearn.model_selection import train_test_split
 from sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor
 
-from supervised.algorithms.algorithm import BaseAlgorithm
 from supervised.algorithms.registry import (
     BINARY_CLASSIFICATION,
     MULTICLASS_CLASSIFICATION,

supervised/algorithms/lightgbm.py

@@ -2,7 +2,6 @@
 import copy
 import logging
 import os
-import time
 
 import lightgbm as lgb
 import numpy as np

supervised/algorithms/linear.py

@@ -1,15 +1,12 @@
 import logging
 import os
-import warnings
 
 import numpy as np
 import pandas as pd
 import sklearn
 from sklearn.base import ClassifierMixin, RegressorMixin
-from sklearn.exceptions import ConvergenceWarning
 from sklearn.linear_model import LinearRegression, LogisticRegression
 
-from supervised.algorithms.algorithm import BaseAlgorithm
 from supervised.algorithms.registry import (
     BINARY_CLASSIFICATION,
     MULTICLASS_CLASSIFICATION,

supervised/algorithms/nn.py

@@ -7,7 +7,6 @@
 from sklearn.base import ClassifierMixin, RegressorMixin
 from sklearn.neural_network import MLPClassifier, MLPRegressor
 
-from supervised.algorithms.algorithm import BaseAlgorithm
 from supervised.algorithms.registry import (
     BINARY_CLASSIFICATION,
     MULTICLASS_CLASSIFICATION,

supervised/algorithms/random_forest.py

@@ -1,12 +1,9 @@
 import logging
-import os
 
-import numpy as np
 import sklearn
 from sklearn.base import ClassifierMixin, RegressorMixin
 from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
 
-from supervised.algorithms.algorithm import BaseAlgorithm
 from supervised.algorithms.registry import (
     BINARY_CLASSIFICATION,
     MULTICLASS_CLASSIFICATION,

supervised/algorithms/registry.py

@@ -64,16 +64,4 @@ def get_eval_metric(algorithm_name, ml_task, automl_eval_metric):
 
         return automl_eval_metric
 
-
-import supervised.algorithms.baseline
-import supervised.algorithms.catboost
-import supervised.algorithms.decision_tree
-import supervised.algorithms.extra_trees
-import supervised.algorithms.knn
-import supervised.algorithms.lightgbm
-import supervised.algorithms.linear
-import supervised.algorithms.nn
-
 # Import algorithm to be registered
-import supervised.algorithms.random_forest
-import supervised.algorithms.xgboost

supervised/algorithms/xgboost.py

@@ -1,8 +1,5 @@
 import copy
 import logging
-import os
-import time
-from inspect import signature
 
 import numpy as np
 import pandas as pd
@@ -31,8 +28,6 @@
 logger = logging.getLogger(__name__)
 logger.setLevel(LOG_LEVEL)
 
-import tempfile
-
 
 class XgbAlgorithmException(Exception):
     def __init__(self, message):

supervised/automl.py

@@ -8,7 +8,6 @@
 warnings.filterwarnings("ignore", message=".*The 'nopython' keyword.*")
 
 from collections.abc import Iterable
-from re import U
 
 # libraries for type hints
 from typing import List, Optional, Union

supervised/base_automl.py

@@ -1,10 +1,7 @@
-import copy
-import gc
 import json
 import logging
 import os
 import shutil
-import sys
 import time
 import types
 from abc import ABC
@@ -25,33 +22,30 @@
     AlgorithmsRegistry,
 )
 from supervised.callbacks.early_stopping import EarlyStopping
-from supervised.callbacks.learner_time_constraint import LearnerTimeConstraint
-from supervised.callbacks.metric_logger import MetricLogger
 from supervised.callbacks.total_time_constraint import TotalTimeConstraint
 from supervised.ensemble import Ensemble
 from supervised.exceptions import AutoMLException, NotTrainedException
 from supervised.model_framework import ModelFramework
 from supervised.preprocessing.exclude_missing_target import ExcludeRowsMissingTarget
-
 # disable EDA
 # from supervised.preprocessing.eda import EDA
 from supervised.preprocessing.preprocessing_utils import PreprocessingUtils
 from supervised.tuner.data_info import DataInfo
 from supervised.tuner.mljar_tuner import MljarTuner
 from supervised.tuner.time_controller import TimeController
 from supervised.utils.automl_plots import AutoMLPlots
-from supervised.utils.config import LOG_LEVEL, mem
+from supervised.utils.config import LOG_LEVEL
 from supervised.utils.data_validation import (
     check_bool,
     check_greater_than_zero_integer,
     check_greater_than_zero_integer_or_float,
     check_integer,
     check_positive_integer,
 )
+from supervised.utils.jsonencoder import MLJSONEncoder
 from supervised.utils.leaderboard_plots import LeaderboardPlots
 from supervised.utils.metric import Metric, UserDefinedEvalMetric
 from supervised.utils.utils import dump_data, load_data
-from supervised.utils.jsonencoder import MLJSONEncoder
 
 try:
     import matplotlib.font_manager as font_manager

supervised/callbacks/early_stopping.py

@@ -11,8 +11,6 @@
 logger = logging.getLogger(__name__)
 logger.setLevel(LOG_LEVEL)
 
-from supervised.utils.config import mem
-
 
 class EarlyStopping(Callback):
     def __init__(self, params):

supervised/callbacks/max_iters_constraint.py

@@ -1,6 +1,3 @@
-import numpy as np
-import pandas as pd
-
 from supervised.callbacks.callback import Callback
 
 

supervised/callbacks/metric_logger.py

@@ -2,8 +2,6 @@
 
 log = logging.getLogger(__name__)
 
-import numpy as np
-
 from supervised.callbacks.callback import Callback
 from supervised.utils.metric import Metric
 

supervised/ensemble.py

@@ -1,15 +1,13 @@
 import copy
 import json
 import logging
-import operator
 import os
 import time
 import uuid
 
 import numpy as np
 import pandas as pd
 
-from supervised.algorithms.algorithm import BaseAlgorithm
 from supervised.algorithms.registry import (
     BINARY_CLASSIFICATION,
     MULTICLASS_CLASSIFICATION,
@@ -19,13 +17,12 @@
 from supervised.model_framework import ModelFramework
 from supervised.utils.additional_metrics import AdditionalMetrics
 from supervised.utils.config import LOG_LEVEL
-from supervised.utils.metric import Metric
 from supervised.utils.jsonencoder import MLJSONEncoder
+from supervised.utils.metric import Metric
 
 logger = logging.getLogger(__name__)
 logger.setLevel(LOG_LEVEL)
 
-import matplotlib.pyplot as plt
 from tabulate import tabulate
 
 from supervised.utils.learning_curves import LearningCurves

supervised/fairness/metrics.py

@@ -1,21 +1,10 @@
-import os
-
 import numpy as np
 import pandas as pd
-from matplotlib import pyplot as plt
 from sklearn.metrics import (
-    accuracy_score,
-    classification_report,
-    confusion_matrix,
-    f1_score,
-    matthews_corrcoef,
     mean_absolute_error,
     mean_absolute_percentage_error,
     mean_squared_error,
-    precision_score,
     r2_score,
-    recall_score,
-    roc_auc_score,
 )
 
 from supervised.fairness.optimization import FairnessOptimization

supervised/fairness/optimization.py

@@ -1,8 +1,4 @@
-import os
-
 import numpy as np
-import pandas as pd
-from matplotlib import pyplot as plt
 
 
 class FairnessOptimization:

supervised/model_framework.py

@@ -3,10 +3,8 @@
 import json
 import logging
 import os
-import sys
 import time
 import uuid
-import zipfile
 
 import numpy as np
 import pandas as pd
@@ -20,22 +18,19 @@
 )
 from supervised.callbacks.callback_list import CallbackList
 from supervised.exceptions import AutoMLException
-from supervised.preprocessing.exclude_missing_target import ExcludeRowsMissingTarget
 from supervised.preprocessing.preprocessing import Preprocessing
 from supervised.utils.additional_metrics import AdditionalMetrics
 from supervised.utils.config import LOG_LEVEL
+from supervised.utils.jsonencoder import MLJSONEncoder
 from supervised.utils.metric import Metric
 from supervised.validation.validation_step import ValidationStep
-from supervised.utils.jsonencoder import MLJSONEncoder
 
 logger = logging.getLogger(__name__)
 logger.setLevel(LOG_LEVEL)
 
 import joblib
-import optuna
 
 from supervised.tuner.optuna.tuner import OptunaTuner
-from supervised.utils.config import mem
 from supervised.utils.learning_curves import LearningCurves
 
 

supervised/preprocessing/datetime_transformer.py

@@ -1,6 +1,3 @@
-import datetime
-import json
-
 import numpy as np
 import pandas as pd
 

supervised/preprocessing/eda.py

@@ -1,6 +1,5 @@
 import logging
 import os
-import re
 import warnings
 from collections import defaultdict
 

supervised/preprocessing/exclude_missing_target.py

@@ -1,6 +1,4 @@
-import json
 import logging
-import os
 import warnings
 
 import numpy as np

supervised/preprocessing/goldenfeatures_transformer.py

@@ -1,4 +1,3 @@
-import datetime
 import itertools
 import json
 import os

supervised/preprocessing/kmeans_transformer.py

@@ -1,11 +1,8 @@
-import datetime
-import json
 import os
 import time
 
 import joblib
 import numpy as np
-import pandas as pd
 from sklearn.cluster import MiniBatchKMeans
 from sklearn.preprocessing import StandardScaler
 

supervised/preprocessing/label_binarizer.py

@@ -1,5 +1,3 @@
-import json
-
 import numpy as np
 
 

supervised/preprocessing/label_encoder.py

@@ -1,4 +1,3 @@
-import copy
 import logging
 from decimal import Decimal
 

supervised/preprocessing/loo_encoder.py

@@ -1,10 +1,7 @@
-import copy
 import json
 import logging
 import warnings
-from decimal import Decimal
 
-import numpy as np
 import pandas as pd
 from category_encoders.leave_one_out import LeaveOneOutEncoder
 

supervised/preprocessing/preprocessing.py

@@ -1,14 +1,11 @@
-import copy
 import logging
-import warnings
 
 import numpy as np
 import pandas as pd
 
 from supervised.algorithms.registry import (
     BINARY_CLASSIFICATION,
     MULTICLASS_CLASSIFICATION,
-    REGRESSION,
 )
 from supervised.exceptions import AutoMLException
 from supervised.preprocessing.datetime_transformer import DateTimeTransformer
@@ -21,7 +18,6 @@
 from supervised.preprocessing.label_encoder import LabelEncoder
 from supervised.preprocessing.preprocessing_categorical import PreprocessingCategorical
 from supervised.preprocessing.preprocessing_missing import PreprocessingMissingValues
-from supervised.preprocessing.preprocessing_utils import PreprocessingUtils
 from supervised.preprocessing.scale import Scale
 from supervised.preprocessing.text_transformer import TextTransformer
 from supervised.utils.config import LOG_LEVEL