sklearn examples

examples/trials/sklearn/classification/config.yml

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+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

examples/trials/sklearn/classification/main.py

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+# 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

examples/trials/sklearn/classification/search_space.json

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+{
+    "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]}
+}

examples/trials/sklearn/regression/config.yml

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+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

examples/trials/sklearn/regression/main.py

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+# 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

examples/trials/sklearn/regression/search_space.json

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+{
+    "model_name":{"_type":"choice","_value":["LinearRegression", "SVR", "KNeighborsRegressor", "DecisionTreeRegressor"]},
+    "svr_kernel": {"_type":"choice","_value":["linear", "poly", "rbf"]},
+    "knr_weights": {"_type":"choice","_value":["uniform", "distance"]}
+}