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chenyangkang committed Sep 20, 2023
1 parent f751258 commit 7c2a4d1
Showing 1 changed file with 25 additions and 25 deletions.
50 changes: 25 additions & 25 deletions stemflow/model/AdaSTEM.py
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Original file line number Diff line number Diff line change
Expand Up @@ -174,8 +174,8 @@ def __init__(self,
Dictionary of {stixel_index: trained_model}.
grid_dict (dict):
An array of stixels assigned to each ensemble.
Feature_importances_ (pd.core.frame.DataFrame):
Feature importance dataframe for each stixel.
feature_importances_ (pd.core.frame.DataFrame):
feature importance dataframe for each stixel.
"""
# save base model
Expand Down Expand Up @@ -838,20 +838,20 @@ def score(self,
self.score_dict = score_dict
return self.score_dict

def calculate_Feature_importances(self):
"""A method to generate Feature importance values for each stixel.
def calculate_feature_importances(self):
"""A method to generate feature importance values for each stixel.
Feature importances are saved in self.Feature_importances_.
feature importances are saved in self.feature_importances_.
Attribute dependence:
1. self.ensemble_df
2. self.model_dict
3. self.stixel_specific_x_names
4. The input base model should have attribute `Feature_importances_`
4. The input base model should have attribute `feature_importances_`
"""
# generate Feature importance dict
Feature_importance_list = []
# generate feature importance dict
feature_importance_list = []

for index,ensemble_row in self.ensemble_df.drop('checklist_indexes', axis=1).iterrows():
if ensemble_row['stixel_checklist_count']<self.stixel_training_size_threshold:
Expand All @@ -865,25 +865,25 @@ def calculate_Feature_importances(self):
if isinstance(the_model, dummy_model1):
importance_dict = dict(zip(self.x_names, [1/len(self.x_names)] * len(self.x_names)))
else:
Feature_imp = the_model.Feature_importances_
importance_dict = dict(zip(x_names, Feature_imp))
feature_imp = the_model.feature_importances_
importance_dict = dict(zip(x_names, feature_imp))

importance_dict['stixel_index'] = stixel_index
Feature_importance_list.append(importance_dict)
feature_importance_list.append(importance_dict)

except Exception as e:
continue

self.Feature_importances_ = pd.DataFrame(Feature_importance_list).set_index('stixel_index').reset_index(drop=False).fillna(0)
self.feature_importances_ = pd.DataFrame(feature_importance_list).set_index('stixel_index').reset_index(drop=False).fillna(0)


def assign_Feature_importances_by_points(self,
def assign_feature_importances_by_points(self,
Sample_ST_df: Union[pd.core.frame.DataFrame, None] = None,
verbosity: Union[None, int]=None,
aggregation: str='mean',
njobs: Union[int, None]=1,
) -> pd.core.frame.DataFrame:
"""Assign Feature importance to the input spatio-temporal points
"""Assign feature importance to the input spatio-temporal points
Args:
Sample_ST_df (Union[pd.core.frame.DataFrame, None], optional):
Expand All @@ -901,20 +901,20 @@ def assign_Feature_importances_by_points(self,
verbosity (Union[None, int], optional):
0 to output nothing, everything other wise. Default None set it to the verbosity of AdaSTEM model class.
aggregation (str, optional):
One of 'mean' and 'median' to aggregate Feature importance across ensembles.
One of 'mean' and 'median' to aggregate feature importance across ensembles.
njobs (Union[int, None], optional):
Number of processes used in this task. If None, use the self.njobs. Default to 1.
Raises:
NameError:
Feature_importances_ attribute is not calculated. Try model.calculate_Feature_importances() first.
feature_importances_ attribute is not calculated. Try model.calculate_feature_importances() first.
ValueError:
f'aggregation not one of [\'mean\',\'median\'].'
KeyError:
One of [`self.Spatio1`, `self.Spatio2`, `self.Temporal1`] not found in `Sample_ST_df.columns`
Returns:
DataFrame with Feature importance assigned.
DataFrame with feature importance assigned.
"""
#
if verbosity is None:
Expand All @@ -925,8 +925,8 @@ def assign_Feature_importances_by_points(self,
verbosity = 1

#
if not 'Feature_importances_' in dir(self):
raise NameError(f'Feature_importances_ attribute is not calculated. Try model.calculate_Feature_importances() first.')
if not 'feature_importances_' in dir(self):
raise NameError(f'feature_importances_ attribute is not calculated. Try model.calculate_feature_importances() first.')
#
if not aggregation in ['mean','median']:
raise ValueError(f'aggregation not one of [\'mean\',\'median\'].')
Expand Down Expand Up @@ -967,7 +967,7 @@ def assign_Feature_importances_by_points(self,
self.Temporal1,
self.Spatio1,
self.Spatio2,
self.Feature_importances_
self.feature_importances_
)
round_res_list.append(res_list)

Expand All @@ -981,7 +981,7 @@ def assign_Feature_importances_by_points(self,
repeat(self.Temporal1),
repeat(self.Spatio1),
repeat(self.Spatio2),
repeat(self.Feature_importances_)
repeat(self.feature_importances_)
)
if verbosity>0:
args_iterator = tqdm(plain_args_iterator, total=len(list(self.ensemble_df.ensemble_index.unique())))
Expand All @@ -999,13 +999,13 @@ def assign_Feature_importances_by_points(self,

# aggregate across ensembles
if aggregation=='mean':
mean_Feature_importances_across_ensembles = round_res_df.groupby('sample_index').mean()
mean_feature_importances_across_ensembles = round_res_df.groupby('sample_index').mean()
elif aggregation=='median':
mean_Feature_importances_across_ensembles = round_res_df.groupby('sample_index').median()
mean_feature_importances_across_ensembles = round_res_df.groupby('sample_index').median()

if self.use_temporal_to_train:
mean_Feature_importances_across_ensembles = mean_Feature_importances_across_ensembles.rename(columns={self.Temporal1:f'{self.Temporal1}_predictor'})
out_ = pd.concat([Sample_ST_df, mean_Feature_importances_across_ensembles], axis=1).dropna()
mean_feature_importances_across_ensembles = mean_feature_importances_across_ensembles.rename(columns={self.Temporal1:f'{self.Temporal1}_predictor'})
out_ = pd.concat([Sample_ST_df, mean_feature_importances_across_ensembles], axis=1).dropna()
return out_


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