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Luc Giffon
bolsonaro
Commits
b56e4254
Commit
b56e4254
authored
5 years ago
by
Léo Bouscarrat
Browse files
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Plain Diff
Hardcoded metric functions for comparison
parent
7330792e
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1 merge request
!9
Resolve "Experiment pipeline"
Changes
3
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3 changed files
code/bolsonaro/models/model_raw_results.py
+11
-11
11 additions, 11 deletions
code/bolsonaro/models/model_raw_results.py
code/bolsonaro/models/omp_forest.py
+3
-0
3 additions, 0 deletions
code/bolsonaro/models/omp_forest.py
code/bolsonaro/trainer.py
+43
-12
43 additions, 12 deletions
code/bolsonaro/trainer.py
with
57 additions
and
23 deletions
code/bolsonaro/models/model_raw_results.py
+
11
−
11
View file @
b56e4254
...
...
@@ -8,8 +8,8 @@ class ModelRawResults(object):
def
__init__
(
self
,
model_object
,
training_time
,
datetime
,
train_score
,
dev_score
,
test_score
,
score_metric
,
train_score_
regressor
,
dev_score_
regressor
,
test_score_
regressor
):
score_metric
,
train_score_
base
,
dev_score_
base
,
test_score_
base
):
self
.
_model_object
=
model_object
self
.
_training_time
=
training_time
...
...
@@ -18,9 +18,9 @@ class ModelRawResults(object):
self
.
_dev_score
=
dev_score
self
.
_test_score
=
test_score
self
.
_score_metric
=
score_metric
self
.
_train_score_
regressor
=
train_score_
regressor
self
.
_dev_score_
regressor
=
dev_score_
regressor
self
.
_test_score_
regressor
=
test_score_
regressor
self
.
_train_score_
base
=
train_score_
base
self
.
_dev_score_
base
=
dev_score_
base
self
.
_test_score_
base
=
test_score_
base
@property
def
model_object
(
self
):
...
...
@@ -51,16 +51,16 @@ class ModelRawResults(object):
return
self
.
_score_metric
@property
def
train_score_
regressor
(
self
):
return
self
.
_train_score_
regressor
def
train_score_
base
(
self
):
return
self
.
_train_score_
base
@property
def
dev_score_
regressor
(
self
):
return
self
.
_dev_score_
regressor
def
dev_score_
base
(
self
):
return
self
.
_dev_score_
base
@property
def
test_score_
regressor
(
self
):
return
self
.
_test_score_
regressor
def
test_score_
base
(
self
):
return
self
.
_test_score_
base
def
save
(
self
,
models_dir
):
save_obj_to_pickle
(
models_dir
+
os
.
sep
+
'
model_raw_results.pickle
'
,
...
...
This diff is collapsed.
Click to expand it.
code/bolsonaro/models/omp_forest.py
+
3
−
0
View file @
b56e4254
...
...
@@ -17,6 +17,9 @@ class OmpForest(BaseEstimator, metaclass=ABCMeta):
def
models_parameters
(
self
):
return
self
.
_models_parameters
def
predict_base_estimator
(
self
,
X
):
return
self
.
_base_forest_estimator
.
predict
(
X
)
def
score_base_estimator
(
self
,
X
,
y
):
return
self
.
_base_forest_estimator
.
score
(
X
,
y
)
...
...
This diff is collapsed.
Click to expand it.
code/bolsonaro/trainer.py
+
43
−
12
View file @
b56e4254
from
bolsonaro.models.model_raw_results
import
ModelRawResults
from
bolsonaro.models.omp_forest_regressor
import
OmpForestRegressor
from
bolsonaro.models.omp_forest_classifier
import
OmpForestBinaryClassifier
,
OmpForestMulticlassClassifier
from
bolsonaro.error_handling.logger_factory
import
LoggerFactory
from
.
import
LOG_PATH
from
sklearn.ensemble
import
RandomForestRegressor
,
RandomForestClassifier
from
sklearn.metrics
import
mean_squared_error
,
accuracy_score
import
time
import
datetime
import
numpy
as
np
...
...
@@ -68,32 +71,60 @@ class Trainer(object):
)
self
.
_end_time
=
time
.
time
()
def
__score_func
(
self
,
model
,
X
,
y_true
):
if
type
(
model
)
==
OmpForestRegressor
:
y_pred
=
model
.
predict
(
X
)
result
=
mean_squared_error
(
y_true
,
y_pred
)
elif
type
(
model
)
in
[
OmpForestBinaryClassifier
,
OmpForestMulticlassClassifier
]:
y_pred
=
model
.
predict
(
X
)
result
=
accuracy_score
(
y_true
,
y_pred
)
else
:
y_pred
=
model
.
predict
(
X
)
result
=
model
.
score
(
y_true
,
y_pred
)
return
result
def
__score_func_base
(
self
,
model
,
X
,
y_true
):
if
type
(
model
)
==
OmpForestRegressor
:
y_pred
=
model
.
predict_base_estimator
(
X
)
result
=
mean_squared_error
(
y_true
,
y_pred
)
elif
type
(
model
)
in
[
OmpForestBinaryClassifier
,
OmpForestMulticlassClassifier
]:
y_pred
=
model
.
predict_base_estimator
(
X
)
result
=
accuracy_score
(
y_true
,
y_pred
)
else
:
y_pred
=
model
.
predict_base_estimator
(
X
)
result
=
model
.
score
(
y_true
,
y_pred
)
return
result
def
compute_results
(
self
,
model
,
models_dir
):
"""
:param model: Object with
:param models_dir: Where the results will be saved
"""
score_func
=
model
.
score
if
type
(
model
)
in
[
RandomForestRegressor
,
RandomForestClassifier
]
\
else
model
.
score_base_estimator
results
=
ModelRawResults
(
model_object
=
model
,
training_time
=
self
.
_end_time
-
self
.
_begin_time
,
datetime
=
datetime
.
datetime
.
now
(),
train_score
=
model
.
score
(
self
.
_dataset
.
X_train
,
self
.
_dataset
.
y_train
),
dev_score
=
model
.
score
(
self
.
_dataset
.
X_dev
,
self
.
_dataset
.
y_dev
),
test_score
=
model
.
score
(
self
.
_dataset
.
X_test
,
self
.
_dataset
.
y_test
),
train_score
=
self
.
__score_func
(
model
,
self
.
_dataset
.
X_train
,
self
.
_dataset
.
y_train
),
dev_score
=
self
.
__score_func
(
model
,
self
.
_dataset
.
X_dev
,
self
.
_dataset
.
y_dev
),
test_score
=
self
.
__score_func
(
model
,
self
.
_dataset
.
X_test
,
self
.
_dataset
.
y_test
),
train_score_base
=
self
.
__score_func_base
(
model
,
self
.
_dataset
.
X_train
,
self
.
_dataset
.
y_train
),
dev_score_base
=
self
.
__score_func_base
(
model
,
self
.
_dataset
.
X_dev
,
self
.
_dataset
.
y_dev
),
test_score_base
=
self
.
__score_func_base
(
model
,
self
.
_dataset
.
X_test
,
self
.
_dataset
.
y_test
),
score_metric
=
'
mse
'
if
type
(
model
)
in
[
RandomForestRegressor
,
RandomForestClassifier
]
\
else
model
.
DEFAULT_SCORE_METRIC
,
# TODO: resolve the used metric in a proper way
train_score_regressor
=
score_func
(
self
.
_dataset
.
X_train
,
self
.
_dataset
.
y_train
),
dev_score_regressor
=
score_func
(
self
.
_dataset
.
X_dev
,
self
.
_dataset
.
y_dev
),
test_score_regressor
=
score_func
(
self
.
_dataset
.
X_test
,
self
.
_dataset
.
y_test
)
else
model
.
DEFAULT_SCORE_METRIC
,
# TODO: resolve the used metric in a proper way
)
results
.
save
(
models_dir
)
self
.
_logger
.
info
(
"
Base performance on test: {}
"
.
format
(
results
.
test_score_
regressor
))
self
.
_logger
.
info
(
"
Base performance on test: {}
"
.
format
(
results
.
test_score_
base
))
self
.
_logger
.
info
(
"
Performance on test: {}
"
.
format
(
results
.
test_score
))
self
.
_logger
.
info
(
"
Base performance on train: {}
"
.
format
(
results
.
train_score_
regressor
))
self
.
_logger
.
info
(
"
Base performance on train: {}
"
.
format
(
results
.
train_score_
base
))
self
.
_logger
.
info
(
"
Performance on train: {}
"
.
format
(
results
.
train_score
))
self
.
_logger
.
info
(
"
Base performance on dev: {}
"
.
format
(
results
.
dev_score_
regressor
))
self
.
_logger
.
info
(
"
Base performance on dev: {}
"
.
format
(
results
.
dev_score_
base
))
self
.
_logger
.
info
(
"
Performance on dev: {}
"
.
format
(
results
.
dev_score
))
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