This repository has been archived by the owner on Sep 18, 2024. It is now read-only.
-
Notifications
You must be signed in to change notification settings - Fork 1.8k
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Merge pull request #20 from Microsoft/master
sklearn examples (#169)
- Loading branch information
Showing
6 changed files
with
237 additions
and
0 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,20 @@ | ||
authorName: default | ||
experimentName: example_sklearn-classification | ||
trialConcurrency: 1 | ||
maxExecDuration: 1h | ||
maxTrialNum: 100 | ||
#choice: local, remote | ||
trainingServicePlatform: local | ||
searchSpacePath: search_space.json | ||
#choice: true, false | ||
useAnnotation: false | ||
tuner: | ||
#choice: TPE, Random, Anneal, Evolution | ||
builtinTunerName: TPE | ||
classArgs: | ||
#choice: maximize, minimize | ||
optimize_mode: maximize | ||
trial: | ||
command: python3 main.py | ||
codeDir: . | ||
gpuNum: 0 |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,83 @@ | ||
# Copyright (c) Microsoft Corporation | ||
# All rights reserved. | ||
# | ||
# MIT License | ||
# | ||
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated | ||
# documentation files (the "Software"), to deal in the Software without restriction, including without limitation | ||
# the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and | ||
# to permit persons to whom the Software is furnished to do so, subject to the following conditions: | ||
# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. | ||
# | ||
# THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING | ||
# BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND | ||
# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, | ||
# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | ||
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. | ||
|
||
import nni | ||
from sklearn.model_selection import train_test_split | ||
from sklearn.datasets import load_digits | ||
from sklearn.preprocessing import StandardScaler | ||
from sklearn.svm import SVC | ||
import logging | ||
import numpy as np | ||
|
||
|
||
LOG = logging.getLogger('sklearn_classification') | ||
|
||
def load_data(): | ||
'''Load dataset, use 20newsgroups dataset''' | ||
digits = load_digits() | ||
X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target, random_state=99, test_size=0.25) | ||
|
||
ss = StandardScaler() | ||
X_train = ss.fit_transform(X_train) | ||
X_test = ss.transform(X_test) | ||
|
||
return X_train, X_test, y_train, y_test | ||
|
||
def get_default_parameters(): | ||
'''get default parameters''' | ||
params = { | ||
'C': 1.0, | ||
'keral': 'linear', | ||
'degree': 3, | ||
'gamma': 0.01, | ||
'coef0': 0.01 | ||
} | ||
return params | ||
|
||
def get_model(PARAMS): | ||
'''Get model according to parameters''' | ||
model = SVC() | ||
model.C = PARAMS.get('C') | ||
model.keral = PARAMS.get('keral') | ||
model.degree = PARAMS.get('degree') | ||
model.gamma = PARAMS.get('gamma') | ||
model.coef0 = PARAMS.get('coef0') | ||
|
||
return model | ||
|
||
def run(X_train, X_test, y_train, y_test, PARAMS): | ||
'''Train model and predict result''' | ||
model.fit(X_train, y_train) | ||
score = model.score(X_test, y_test) | ||
LOG.debug('score: %s' % score) | ||
nni.report_final_result(score) | ||
|
||
if __name__ == '__main__': | ||
X_train, X_test, y_train, y_test = load_data() | ||
|
||
try: | ||
# get parameters from tuner | ||
RECEIVED_PARAMS = nni.get_parameters() | ||
LOG.debug(RECEIVED_PARAMS) | ||
PARAMS = get_default_parameters() | ||
PARAMS.update(RECEIVED_PARAMS) | ||
LOG.debug(PARAMS) | ||
model = get_model(PARAMS) | ||
run(X_train, X_test, y_train, y_test, model) | ||
except Exception as exception: | ||
LOG.exception(exception) | ||
raise |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,7 @@ | ||
{ | ||
"C": {"_type":"uniform","_value":[0.1, 1]}, | ||
"keral": {"_type":"choice","_value":["linear", "rbf", "poly", "sigmoid"]}, | ||
"degree": {"_type":"choice","_value":[1, 2, 3, 4]}, | ||
"gamma": {"_type":"uniform","_value":[0.01, 0.1]}, | ||
"coef0 ": {"_type":"uniform","_value":[0.01, 0.1]} | ||
} |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,20 @@ | ||
authorName: default | ||
experimentName: example_sklearn-regression | ||
trialConcurrency: 1 | ||
maxExecDuration: 1h | ||
maxTrialNum: 30 | ||
#choice: local, remote | ||
trainingServicePlatform: local | ||
searchSpacePath: search_space.json | ||
#choice: true, false | ||
useAnnotation: false | ||
tuner: | ||
#choice: TPE, Random, Anneal, Evolution | ||
builtinTunerName: TPE | ||
classArgs: | ||
#choice: maximize, minimize | ||
optimize_mode: maximize | ||
trial: | ||
command: python3 main.py | ||
codeDir: . | ||
gpuNum: 0 |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,102 @@ | ||
# Copyright (c) Microsoft Corporation | ||
# All rights reserved. | ||
# | ||
# MIT License | ||
# | ||
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated | ||
# documentation files (the "Software"), to deal in the Software without restriction, including without limitation | ||
# the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and | ||
# to permit persons to whom the Software is furnished to do so, subject to the following conditions: | ||
# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. | ||
# | ||
# THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING | ||
# BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND | ||
# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, | ||
# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | ||
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. | ||
|
||
import nni | ||
from sklearn.datasets import load_boston | ||
from sklearn.model_selection import train_test_split | ||
from sklearn import linear_model | ||
import logging | ||
import numpy as np | ||
from sklearn.metrics import r2_score | ||
from sklearn.preprocessing import StandardScaler | ||
from sklearn.linear_model import LinearRegression | ||
from sklearn.svm import SVR | ||
from sklearn.neighbors import KNeighborsRegressor | ||
from sklearn.tree import DecisionTreeRegressor | ||
|
||
LOG = logging.getLogger('sklearn_regression') | ||
|
||
def load_data(): | ||
'''Load dataset, use boston dataset''' | ||
boston = load_boston() | ||
X_train, X_test, y_train, y_test = train_test_split(boston.data, boston.target, random_state=99, test_size=0.25) | ||
#normalize data | ||
ss_X = StandardScaler() | ||
ss_y = StandardScaler() | ||
|
||
X_train = ss_X.fit_transform(X_train) | ||
X_test = ss_X.transform(X_test) | ||
y_train = ss_y.fit_transform(y_train[:, None])[:,0] | ||
y_test = ss_y.transform(y_test[:, None])[:,0] | ||
|
||
return X_train, X_test, y_train, y_test | ||
|
||
def get_default_parameters(): | ||
'''get default parameters''' | ||
params = { | ||
'model_name': 'LinearRegression' | ||
} | ||
return params | ||
|
||
def get_model(PARAMS): | ||
'''Get model according to parameters''' | ||
model_dict = { | ||
'LinearRegression': LinearRegression(), | ||
'SVR': SVR(), | ||
'KNeighborsRegressor': KNeighborsRegressor(), | ||
'DecisionTreeRegressor': DecisionTreeRegressor() | ||
} | ||
if not model_dict.get(PARAMS['model_name']): | ||
LOG.exception('Not supported model!') | ||
exit(1) | ||
|
||
model = model_dict[PARAMS['model_name']] | ||
|
||
try: | ||
if PARAMS['model_name'] == 'SVR': | ||
model.kernel = PARAMS['svr_kernel'] | ||
elif PARAMS['model_name'] == 'KNeighborsRegressor': | ||
model.weights = PARAMS['knr_weights'] | ||
except Exception as exception: | ||
LOG.exception(exception) | ||
raise | ||
return model | ||
|
||
|
||
def run(X_train, X_test, y_train, y_test, PARAMS): | ||
'''Train model and predict result''' | ||
model.fit(X_train, y_train) | ||
predict_y = model.predict(X_test) | ||
score = r2_score(y_test, predict_y) | ||
LOG.debug('r2 score: %s' % score) | ||
nni.report_final_result(score) | ||
|
||
if __name__ == '__main__': | ||
X_train, X_test, y_train, y_test = load_data() | ||
|
||
try: | ||
# get parameters from tuner | ||
RECEIVED_PARAMS = nni.get_parameters() | ||
LOG.debug(RECEIVED_PARAMS) | ||
PARAMS = get_default_parameters() | ||
PARAMS.update(RECEIVED_PARAMS) | ||
LOG.debug(PARAMS) | ||
model = get_model(PARAMS) | ||
run(X_train, X_test, y_train, y_test, model) | ||
except Exception as exception: | ||
LOG.exception(exception) | ||
raise |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,5 @@ | ||
{ | ||
"model_name":{"_type":"choice","_value":["LinearRegression", "SVR", "KNeighborsRegressor", "DecisionTreeRegressor"]}, | ||
"svr_kernel": {"_type":"choice","_value":["linear", "poly", "rbf"]}, | ||
"knr_weights": {"_type":"choice","_value":["uniform", "distance"]} | ||
} |