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ravinkohliravinkohli
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fix flake and mypy after rebase
1 parent 80cda62 commit 9d36aad

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3 files changed

+53
-47
lines changed

3 files changed

+53
-47
lines changed

‎autoPyTorch/pipeline/components/preprocessing/tabular_preprocessing/feature_preprocessing/ExtraTreesPreprocessorClassification.py

Lines changed: 20 additions & 17 deletions
Original file line numberDiff line numberDiff line change
@@ -1,6 +1,4 @@
1-
from functools import partial
2-
from math import ceil, floor
3-
from typing import Any, Callable, Dict, List, Optional, Union
1+
from typing import Any, Dict, Optional, Union
42

53
from ConfigSpace.configuration_space import ConfigurationSpace
64
from ConfigSpace.hyperparameters import (
@@ -11,14 +9,14 @@
119

1210
import numpy as np
1311

12+
from sklearn.base import BaseEstimator
1413
from sklearn.ensemble import ExtraTreesClassifier
1514
from sklearn.feature_selection import SelectFromModel
16-
from sklearn.base import BaseEstimator
1715

1816
from autoPyTorch.datasets.base_dataset import BaseDatasetPropertiesType
1917
from autoPyTorch.pipeline.components.preprocessing.tabular_preprocessing.feature_preprocessing. \
2018
base_feature_preprocessor import autoPyTorchFeaturePreprocessingComponent
21-
from autoPyTorch.utils.common import HyperparameterSearchSpace, add_hyperparameter, check_none, subsampler
19+
from autoPyTorch.utils.common import HyperparameterSearchSpace, add_hyperparameter, check_none
2220

2321

2422
class ExtraTreesPreprocessorClassification(autoPyTorchFeaturePreprocessingComponent):
@@ -27,8 +25,8 @@ def __init__(self, bootstrap: bool = True, n_estimators: int = 10,
2725
max_depth: Optional[Union[str, int]] = 5, min_samples_split: int = 2,
2826
min_samples_leaf: int = 1, min_weight_fraction_leaf: float = 0,
2927
max_leaf_nodes: Optional[Union[str, int]] = "none",
30-
min_impurity_decrease: float = 0, oob_score=False,
31-
verbose=0,
28+
min_impurity_decrease: float = 0, oob_score: bool = False,
29+
verbose: int = 0,
3230
random_state: Optional[np.random.RandomState] = None):
3331
self.bootstrap = bootstrap
3432
self.n_estimators = n_estimators
@@ -52,13 +50,19 @@ def fit(self, X: Dict[str, Any], y: Any = None) -> BaseEstimator:
5250

5351
if check_none(self.max_leaf_nodes):
5452
self.max_leaf_nodes = None
55-
else:
53+
elif isinstance(self.max_leaf_nodes, int):
5654
self.max_leaf_nodes = int(self.max_leaf_nodes)
55+
else:
56+
raise ValueError(f"Expected `max_leaf_nodes` to be either "
57+
f"in ('None', 'none', None) or an integer, got {self.max_leaf_nodes}")
5758

5859
if check_none(self.max_depth):
5960
self.max_depth = None
60-
else:
61+
elif isinstance(self.max_depth, int):
6162
self.max_depth = int(self.max_depth)
63+
else:
64+
raise ValueError(f"Expected `max_depth` to be either "
65+
f"in ('None', 'none', None) or an integer, got {self.max_depth}")
6266

6367
# TODO: add class_weights
6468
estimator = ExtraTreesClassifier(
@@ -97,13 +101,13 @@ def get_hyperparameter_search_space(
97101
default_value="none",
98102
),
99103
max_features: HyperparameterSearchSpace = HyperparameterSearchSpace(hyperparameter='max_features',
100-
value_range=(0, 1),
101-
default_value=0.5,
102-
),
103-
min_impurity_decrease: HyperparameterSearchSpace = HyperparameterSearchSpace(hyperparameter='min_impurity_decrease',
104-
value_range=(0,),
105-
default_value=0,
106-
),
104+
value_range=(0, 1),
105+
default_value=0.5,
106+
),
107+
min_impurity_decrease: HyperparameterSearchSpace = HyperparameterSearchSpace(
108+
hyperparameter='min_impurity_decrease',
109+
value_range=(0,),
110+
default_value=0),
107111
criterion: HyperparameterSearchSpace = HyperparameterSearchSpace(hyperparameter='criterion',
108112
value_range=("gini", "entropy"),
109113
default_value="gini",
@@ -140,7 +144,6 @@ def get_hyperparameter_search_space(
140144

141145
return cs
142146

143-
144147
@staticmethod
145148
def get_properties(dataset_properties: Optional[Dict[str, BaseDatasetPropertiesType]] = None) -> Dict[str, Any]:
146149
return {'shortname': 'ETC',

‎autoPyTorch/pipeline/components/preprocessing/tabular_preprocessing/feature_preprocessing/ExtraTreesPreprocessorRegression.py

Lines changed: 14 additions & 11 deletions
Original file line numberDiff line numberDiff line change
@@ -1,6 +1,4 @@
1-
from functools import partial
2-
from math import ceil, floor
3-
from typing import Any, Callable, Dict, List, Optional, Union
1+
from typing import Any, Dict, List, Optional, Union
42

53
from ConfigSpace.configuration_space import ConfigurationSpace
64
from ConfigSpace.hyperparameters import (
@@ -11,9 +9,9 @@
119

1210
import numpy as np
1311

12+
from sklearn.base import BaseEstimator
1413
from sklearn.ensemble import ExtraTreesRegressor
1514
from sklearn.feature_selection import SelectFromModel
16-
from sklearn.base import BaseEstimator
1715

1816
from autoPyTorch.datasets.base_dataset import BaseDatasetPropertiesType
1917
from autoPyTorch.pipeline.components.preprocessing.tabular_preprocessing.feature_preprocessing. \
@@ -27,7 +25,7 @@ def __init__(self, bootstrap: bool = True, n_estimators: int = 10,
2725
max_depth: Optional[Union[str, int]] = 5, min_samples_split: int = 2,
2826
min_samples_leaf: int = 1, min_weight_fraction_leaf: float = 0,
2927
max_leaf_nodes: Optional[Union[str, int]] = "none",
30-
oob_score=False, verbose=0,
28+
oob_score: bool = False, verbose: int = 0,
3129
random_state: Optional[np.random.RandomState] = None):
3230
self.bootstrap = bootstrap
3331
self.n_estimators = n_estimators
@@ -55,13 +53,19 @@ def fit(self, X: Dict[str, Any], y: Any = None) -> BaseEstimator:
5553

5654
if check_none(self.max_leaf_nodes):
5755
self.max_leaf_nodes = None
58-
else:
56+
elif isinstance(self.max_leaf_nodes, int):
5957
self.max_leaf_nodes = int(self.max_leaf_nodes)
58+
else:
59+
raise ValueError(f"Expected `max_leaf_nodes` to be either "
60+
f"in ('None', 'none', None) or an integer, got {self.max_leaf_nodes}")
6061

6162
if check_none(self.max_depth):
6263
self.max_depth = None
63-
else:
64+
elif isinstance(self.max_depth, int):
6465
self.max_depth = int(self.max_depth)
66+
else:
67+
raise ValueError(f"Expected `max_depth` to be either "
68+
f"in ('None', 'none', None) or an integer, got {self.max_depth}")
6569

6670
num_features = len(X['dataset_properties']['numerical_columns'])
6771
max_features = int(
@@ -106,9 +110,9 @@ def get_hyperparameter_search_space(
106110
default_value="none",
107111
),
108112
max_features: HyperparameterSearchSpace = HyperparameterSearchSpace(hyperparameter='max_features',
109-
value_range=(0.1, 1),
110-
default_value=1,
111-
),
113+
value_range=(0.1, 1),
114+
default_value=1,
115+
),
112116
criterion: HyperparameterSearchSpace = HyperparameterSearchSpace(hyperparameter='criterion',
113117
value_range=('mse', 'friedman_mse', 'mae'),
114118
default_value="mse",
@@ -144,7 +148,6 @@ def get_hyperparameter_search_space(
144148

145149
return cs
146150

147-
148151
@staticmethod
149152
def get_properties(dataset_properties: Optional[Dict[str, BaseDatasetPropertiesType]] = None) -> Dict[str, Any]:
150153
return {'shortname': 'ETR',

‎autoPyTorch/pipeline/components/preprocessing/tabular_preprocessing/feature_preprocessing/LibLinearSVCPreprocessor.py

Lines changed: 19 additions & 19 deletions
Original file line numberDiff line numberDiff line change
@@ -1,4 +1,4 @@
1-
from typing import Any, Dict, Optional, Union
1+
from typing import Any, Dict, Optional
22

33
from ConfigSpace.configuration_space import ConfigurationSpace
44
from ConfigSpace.hyperparameters import (
@@ -9,14 +9,15 @@
99

1010
import numpy as np
1111

12-
from sklearn.svm import LinearSVC
13-
from sklearn.feature_selection import SelectFromModel
1412
from sklearn.base import BaseEstimator
13+
from sklearn.feature_selection import SelectFromModel
14+
from sklearn.svm import LinearSVC
15+
1516

1617
from autoPyTorch.datasets.base_dataset import BaseDatasetPropertiesType
1718
from autoPyTorch.pipeline.components.preprocessing.tabular_preprocessing.feature_preprocessing. \
1819
base_feature_preprocessor import autoPyTorchFeaturePreprocessingComponent
19-
from autoPyTorch.utils.common import HyperparameterSearchSpace, add_hyperparameter, check_none
20+
from autoPyTorch.utils.common import HyperparameterSearchSpace, add_hyperparameter
2021

2122

2223
class LibLinearSVCPreprocessor(autoPyTorchFeaturePreprocessingComponent):
@@ -56,7 +57,6 @@ def fit(self, X: Dict[str, Any], y: Any = None) -> BaseEstimator:
5657
prefit=False)
5758
return self
5859

59-
6060
@staticmethod
6161
def get_properties(dataset_properties: Optional[Dict[str, BaseDatasetPropertiesType]] = None) -> Dict[str, Any]:
6262
return {'shortname': 'LinearSVC Preprocessor',
@@ -70,9 +70,9 @@ def get_properties(dataset_properties: Optional[Dict[str, BaseDatasetPropertiesT
7070
def get_hyperparameter_search_space(
7171
dataset_properties: Optional[Dict[str, BaseDatasetPropertiesType]] = None,
7272
penalty: HyperparameterSearchSpace = HyperparameterSearchSpace(hyperparameter='penalty',
73-
value_range=("l1",),
74-
default_value="l1",
75-
),
73+
value_range=("l1",),
74+
default_value="l1",
75+
),
7676
loss: HyperparameterSearchSpace = HyperparameterSearchSpace(hyperparameter='loss',
7777
value_range=("hinge", "squared_hinge"),
7878
default_value="squared_hinge",
@@ -82,22 +82,22 @@ def get_hyperparameter_search_space(
8282
default_value=False,
8383
),
8484
tol: HyperparameterSearchSpace = HyperparameterSearchSpace(hyperparameter='tol',
85-
value_range=(1e-5, 1e-1),
86-
default_value=1e-4,
87-
log=True
88-
),
85+
value_range=(1e-5, 1e-1),
86+
default_value=1e-4,
87+
log=True
88+
),
8989
C: HyperparameterSearchSpace = HyperparameterSearchSpace(hyperparameter='C',
90-
value_range=(0.03125, 32768),
91-
default_value=1,
92-
log=True
93-
),
90+
value_range=(0.03125, 32768),
91+
default_value=1,
92+
log=True
93+
),
9494
multi_class: HyperparameterSearchSpace = HyperparameterSearchSpace(hyperparameter='multi_class',
9595
value_range=("ovr",),
9696
default_value="ovr"),
9797
fit_intercept: HyperparameterSearchSpace = HyperparameterSearchSpace(hyperparameter='fit_intercept',
98-
value_range=(True,),
99-
default_value=True,
100-
),
98+
value_range=(True,),
99+
default_value=True,
100+
),
101101
intercept_scaling: HyperparameterSearchSpace = HyperparameterSearchSpace(hyperparameter='intercept_scaling',
102102
value_range=(1,),
103103
default_value=1,

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