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55 lines
1.8 KiB
Python
55 lines
1.8 KiB
Python
# Copyright 2020, OpenTelemetry Authors
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import numpy as np
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from sklearn.datasets import load_iris
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from sklearn.decomposition import PCA, TruncatedSVD
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.model_selection import train_test_split
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from sklearn.pipeline import FeatureUnion, Pipeline
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from sklearn.preprocessing import Normalizer, StandardScaler
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X, y = load_iris(return_X_y=True)
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X_train, X_test, y_train, y_test = train_test_split(X, y)
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def pipeline():
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"""A dummy model that has a bunch of components that we can test."""
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model = Pipeline(
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[
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("scaler", StandardScaler()),
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("normal", Normalizer()),
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(
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"union",
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FeatureUnion(
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[
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("pca", PCA(n_components=1)),
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("svd", TruncatedSVD(n_components=2)),
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],
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n_jobs=1, # parallelized components won't generate spans
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),
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),
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("class", RandomForestClassifier(n_estimators=10)),
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]
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)
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model.fit(X_train, y_train)
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return model
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def random_input():
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"""A random record from the feature set."""
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rows = X.shape[0]
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random_row = np.random.choice(rows, size=1)
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return X[random_row, :]
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