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Minor Unix file formatting fixes (#90)
Signed-off-by: Abolfazl Shahbazi <[email protected]>
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,59 +1,59 @@ | ||
| # Copyright (c) 2020 Intel Corporation | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
| # ============================================================================ | ||
| # | ||
| # THIS IS A GENERATED DOCKERFILE. | ||
| # | ||
| # This file was assembled from multiple pieces, whose use is documented | ||
| # throughout. Please refer to the TensorFlow dockerfiles documentation | ||
| # for more information. | ||
|
|
||
| FROM ubuntu:18.04 | ||
|
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| RUN apt-get update --yes \ | ||
| && apt-get install wget --yes && \ | ||
| rm -rf /var/lib/apt/lists/* | ||
|
|
||
| ENV USE_DAAL4PY_SKLEARN=YES | ||
| ENV USER modin | ||
| ENV UID 1000 | ||
| ENV HOME /home/$USER | ||
|
|
||
| RUN adduser --disabled-password \ | ||
| --gecos "Non-root user" \ | ||
| --uid $UID \ | ||
| --home $HOME \ | ||
| $USER | ||
|
|
||
| ENV CONDA_DIR ${HOME}/miniconda | ||
|
|
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| SHELL ["/bin/bash", "--login", "-c"] | ||
|
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||
| RUN wget --quiet https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O /tmp/miniconda3.sh && \ | ||
| bash /tmp/miniconda3.sh -b -p "${CONDA_DIR}" -f -u && \ | ||
| "${CONDA_DIR}/bin/conda" init bash && \ | ||
| rm -f /tmp/miniconda3.sh && \ | ||
| echo ". '${CONDA_DIR}/etc/profile.d/conda.sh'" >> "${HOME}/.profile" | ||
|
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| RUN conda update -n base -c defaults conda -y && \ | ||
| conda create --name intel_sklearn --yes -c intel python=3.7 scikit-learn | ||
|
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| COPY rcv1_svm.py "${HOME}/rcv1_svm.py" | ||
| COPY rcv1_loader.py "${HOME}/rcv1_loader.py" | ||
|
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| RUN conda activate intel_sklearn && python ${HOME}/rcv1_loader.py | ||
|
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| ENTRYPOINT ["/bin/bash", "--login", "-c", "conda run \"$@\"", "/bin/bash", "-n", "intel_sklearn", "/usr/bin/env", "--"] | ||
|
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| CMD ["python", "${HOME}/rcv1_svm.py"] | ||
| # Copyright (c) 2020 Intel Corporation | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
| # ============================================================================ | ||
| # | ||
| # THIS IS A GENERATED DOCKERFILE. | ||
| # | ||
| # This file was assembled from multiple pieces, whose use is documented | ||
| # throughout. Please refer to the TensorFlow dockerfiles documentation | ||
| # for more information. | ||
|
|
||
| FROM ubuntu:18.04 | ||
|
|
||
| RUN apt-get update --yes \ | ||
| && apt-get install wget --yes && \ | ||
| rm -rf /var/lib/apt/lists/* | ||
|
|
||
| ENV USE_DAAL4PY_SKLEARN=YES | ||
| ENV USER modin | ||
| ENV UID 1000 | ||
| ENV HOME /home/$USER | ||
|
|
||
| RUN adduser --disabled-password \ | ||
| --gecos "Non-root user" \ | ||
| --uid $UID \ | ||
| --home $HOME \ | ||
| $USER | ||
|
|
||
| ENV CONDA_DIR ${HOME}/miniconda | ||
|
|
||
| SHELL ["/bin/bash", "--login", "-c"] | ||
|
|
||
| RUN wget --quiet https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O /tmp/miniconda3.sh && \ | ||
| bash /tmp/miniconda3.sh -b -p "${CONDA_DIR}" -f -u && \ | ||
| "${CONDA_DIR}/bin/conda" init bash && \ | ||
| rm -f /tmp/miniconda3.sh && \ | ||
| echo ". '${CONDA_DIR}/etc/profile.d/conda.sh'" >> "${HOME}/.profile" | ||
|
|
||
| RUN conda update -n base -c defaults conda -y && \ | ||
| conda create --name intel_sklearn --yes -c intel python=3.7 scikit-learn | ||
|
|
||
| COPY rcv1_svm.py "${HOME}/rcv1_svm.py" | ||
| COPY rcv1_loader.py "${HOME}/rcv1_loader.py" | ||
|
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| RUN conda activate intel_sklearn && python ${HOME}/rcv1_loader.py | ||
|
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| ENTRYPOINT ["/bin/bash", "--login", "-c", "conda run \"$@\"", "/bin/bash", "-n", "intel_sklearn", "/usr/bin/env", "--"] | ||
|
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| CMD ["python", "${HOME}/rcv1_svm.py"] |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,23 +1,23 @@ | ||
| # Copyright (c) 2020 Intel Corporation | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
| # ============================================================================ | ||
| # | ||
| # THIS IS A GENERATED DOCKERFILE. | ||
| # | ||
| # This file was assembled from multiple pieces, whose use is documented | ||
| # throughout. Please refer to the TensorFlow dockerfiles documentation | ||
| # for more information. | ||
|
|
||
| from sklearn.datasets import fetch_rcv1 | ||
| rcv1 = fetch_rcv1() | ||
| # Copyright (c) 2020 Intel Corporation | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
| # ============================================================================ | ||
| # | ||
| # THIS IS A GENERATED DOCKERFILE. | ||
| # | ||
| # This file was assembled from multiple pieces, whose use is documented | ||
| # throughout. Please refer to the TensorFlow dockerfiles documentation | ||
| # for more information. | ||
|
|
||
| from sklearn.datasets import fetch_rcv1 | ||
| rcv1 = fetch_rcv1() |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,86 +1,86 @@ | ||
| # Copyright (c) 2020 Intel Corporation | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
| # ============================================================================ | ||
| # | ||
| # THIS IS A GENERATED DOCKERFILE. | ||
| # | ||
| # This file was assembled from multiple pieces, whose use is documented | ||
| # throughout. Please refer to the TensorFlow dockerfiles documentation | ||
| # for more information. | ||
|
|
||
| from sklearn.metrics import accuracy_score, f1_score | ||
| from sklearn.multiclass import OneVsRestClassifier | ||
| from sklearn.model_selection import train_test_split | ||
| from sklearn.datasets import fetch_rcv1 | ||
| import timeit | ||
| import os | ||
|
|
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| t0 = timeit.default_timer() | ||
|
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| rcv1 = fetch_rcv1() | ||
|
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| rcv1_data = rcv1.data | ||
| rcv1_target = rcv1.target | ||
|
|
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| t1 = timeit.default_timer() | ||
| time_load = t1 - t0 | ||
|
|
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| t0 = timeit.default_timer() | ||
|
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| x_train, x_test, y_train, y_test = train_test_split( | ||
| rcv1_data, rcv1_target, test_size=0.05, random_state=42) | ||
|
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| from daal4py.sklearn import patch_sklearn | ||
| patch_sklearn() | ||
|
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| from sklearn.svm import SVC | ||
|
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| t1 = timeit.default_timer() | ||
| time_train_test_split = t1 - t0 | ||
|
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| print('[Data] train: {} test: {}'.format(x_train.shape, x_test.shape)) | ||
| print('[Target] train: {} test: {}'.format(y_train.shape, y_test.shape)) | ||
|
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|
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| print('[Time] Load time {} sec'.format(time_load)) | ||
| print('[Time] train_test_split time {} sec'.format(time_train_test_split)) | ||
|
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| t0 = timeit.default_timer() | ||
|
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| clf = SVC(C=100.0, kernel='rbf', cache_size=8*1024) | ||
| svm = OneVsRestClassifier(clf, n_jobs=4) | ||
| svm.fit(x_train, y_train) | ||
|
|
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| t1 = timeit.default_timer() | ||
| time_fit_train_run = t1 - t0 | ||
|
|
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| print('[Time] Fit time {} sec'.format(time_fit_train_run)) | ||
|
|
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| t0 = timeit.default_timer() | ||
| svm_prediction = svm.predict(x_test) | ||
| t1 = timeit.default_timer() | ||
| time_predict_test_run = t1 - t0 | ||
|
|
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| print('[Time] Predict time {} sec'.format(time_predict_test_run)) | ||
|
|
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| t0 = timeit.default_timer() | ||
| print('Accuracy score is {}'.format(accuracy_score(y_test, svm_prediction))) | ||
| print('F1 samples score is {}'.format( | ||
| f1_score(y_test, svm_prediction, average='samples'))) | ||
| print('F1 micro score is {}'.format( | ||
| f1_score(y_test, svm_prediction, average='micro'))) | ||
| t1 = timeit.default_timer() | ||
| time_metric_run = t1 - t0 | ||
|
|
||
| print('[Time] Metric time {} sec'.format(time_metric_run)) | ||
| # Copyright (c) 2020 Intel Corporation | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
| # ============================================================================ | ||
| # | ||
| # THIS IS A GENERATED DOCKERFILE. | ||
| # | ||
| # This file was assembled from multiple pieces, whose use is documented | ||
| # throughout. Please refer to the TensorFlow dockerfiles documentation | ||
| # for more information. | ||
|
|
||
| from sklearn.metrics import accuracy_score, f1_score | ||
| from sklearn.multiclass import OneVsRestClassifier | ||
| from sklearn.model_selection import train_test_split | ||
| from sklearn.datasets import fetch_rcv1 | ||
| import timeit | ||
| import os | ||
|
|
||
| t0 = timeit.default_timer() | ||
|
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| rcv1 = fetch_rcv1() | ||
|
|
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| rcv1_data = rcv1.data | ||
| rcv1_target = rcv1.target | ||
|
|
||
| t1 = timeit.default_timer() | ||
| time_load = t1 - t0 | ||
|
|
||
| t0 = timeit.default_timer() | ||
|
|
||
| x_train, x_test, y_train, y_test = train_test_split( | ||
| rcv1_data, rcv1_target, test_size=0.05, random_state=42) | ||
|
|
||
| from daal4py.sklearn import patch_sklearn | ||
| patch_sklearn() | ||
|
|
||
| from sklearn.svm import SVC | ||
|
|
||
| t1 = timeit.default_timer() | ||
| time_train_test_split = t1 - t0 | ||
|
|
||
| print('[Data] train: {} test: {}'.format(x_train.shape, x_test.shape)) | ||
| print('[Target] train: {} test: {}'.format(y_train.shape, y_test.shape)) | ||
|
|
||
|
|
||
| print('[Time] Load time {} sec'.format(time_load)) | ||
| print('[Time] train_test_split time {} sec'.format(time_train_test_split)) | ||
|
|
||
| t0 = timeit.default_timer() | ||
|
|
||
| clf = SVC(C=100.0, kernel='rbf', cache_size=8*1024) | ||
| svm = OneVsRestClassifier(clf, n_jobs=4) | ||
| svm.fit(x_train, y_train) | ||
|
|
||
| t1 = timeit.default_timer() | ||
| time_fit_train_run = t1 - t0 | ||
|
|
||
| print('[Time] Fit time {} sec'.format(time_fit_train_run)) | ||
|
|
||
| t0 = timeit.default_timer() | ||
| svm_prediction = svm.predict(x_test) | ||
| t1 = timeit.default_timer() | ||
| time_predict_test_run = t1 - t0 | ||
|
|
||
| print('[Time] Predict time {} sec'.format(time_predict_test_run)) | ||
|
|
||
| t0 = timeit.default_timer() | ||
| print('Accuracy score is {}'.format(accuracy_score(y_test, svm_prediction))) | ||
| print('F1 samples score is {}'.format( | ||
| f1_score(y_test, svm_prediction, average='samples'))) | ||
| print('F1 micro score is {}'.format( | ||
| f1_score(y_test, svm_prediction, average='micro'))) | ||
| t1 = timeit.default_timer() | ||
| time_metric_run = t1 - t0 | ||
|
|
||
| print('[Time] Metric time {} sec'.format(time_metric_run)) |
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