├── README.md ├── requirements.txt ├── random_forest_classifier.py ├── logistic_regression.py ├── papermill_s3.py ├── .gitignore ├── template.yaml ├── matplotlib_image.py └── LICENSE /README.md: -------------------------------------------------------------------------------- 1 | # analyzing-data-aws-lambda -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | numpy 2 | scipy 3 | scikit-learn 4 | panda 5 | matplotlib 6 | ipython 7 | jupyter 8 | jupyterlab 9 | papermill 10 | -------------------------------------------------------------------------------- /random_forest_classifier.py: -------------------------------------------------------------------------------- 1 | from sklearn.ensemble import RandomForestClassifier 2 | 3 | import json 4 | 5 | def lambda_handler(event, context): 6 | clf = RandomForestClassifier(random_state=0) 7 | X = [[ 1, 2, 3], [11, 12, 13]] # 2 samples, 3 features 8 | y = [0, 1] # classes of each sample 9 | clf.fit(X, y) # fitting the classifier 10 | A = [[4, 5, 6], [14, 15, 16], [3, 2, 1], [17, 15, 13]] 11 | result = { 12 | 'type': 'RandomForestClassifier', 13 | 'predict({})'.format(X): '{}'.format(clf.predict(X)), 14 | 'predict({})'.format(A): '{}'.format(clf.predict(A)) 15 | } 16 | return { 17 | 'statusCode': 200, 18 | 'body': json.dumps(result) 19 | } 20 | -------------------------------------------------------------------------------- /logistic_regression.py: -------------------------------------------------------------------------------- 1 | from sklearn.preprocessing import StandardScaler 2 | from sklearn.linear_model import LogisticRegression 3 | from sklearn.pipeline import make_pipeline 4 | from sklearn.datasets import load_iris 5 | from sklearn.model_selection import train_test_split 6 | from sklearn.metrics import accuracy_score 7 | 8 | import json 9 | 10 | # create a pipeline object 11 | pipe = make_pipeline( 12 | StandardScaler(), 13 | LogisticRegression(random_state=0) 14 | ) 15 | 16 | # load the iris dataset and split it into train and test sets 17 | X, y = load_iris(return_X_y=True) 18 | X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) 19 | 20 | # fit the whole pipeline 21 | pipe.fit(X_train, y_train) 22 | # we can now use it like any other estimator 23 | ###accuracy_score(pipe.predict(X_test), y_test) 24 | 25 | def lambda_handler(event, context): 26 | result = { 27 | 'accuracy_score': accuracy_score(pipe.predict(X_test), y_test) 28 | } 29 | return { 30 | 'statusCode': 200, 31 | 'body': json.dumps(result) 32 | } 33 | -------------------------------------------------------------------------------- /papermill_s3.py: -------------------------------------------------------------------------------- 1 | import papermill as pm 2 | import jupyter 3 | import sys 4 | import os 5 | import json 6 | import boto3 7 | from urllib.parse import unquote_plus 8 | 9 | import os 10 | 11 | OUTPUT_BUCKET = os.environ['OUTPUT_BUCKET'] 12 | 13 | sys.path.append("/opt/bin") 14 | sys.path.append("/opt/python") 15 | os.environ["IPYTHONDIR"]='/tmp/ipythondir' 16 | 17 | s3_client = boto3.client('s3') 18 | 19 | def lambda_handler(event, context): 20 | 21 | result = { "output_notebooks": [] } 22 | 23 | for record in event['Records']: 24 | bucket = record['s3']['bucket']['name'] 25 | key = unquote_plus(record['s3']['object']['key']) 26 | 27 | print("bucket = {}".format(bucket)) 28 | print("key = {}".format(key)) 29 | 30 | input_notebook = 's3://{}/{}'.format(bucket, key) 31 | output_notebook = 's3://{}/{}'.format(OUTPUT_BUCKET, key) 32 | 33 | print("input_notebook = {}".format(input_notebook)) 34 | print("output_notebook = {}".format(output_notebook)) 35 | 36 | response = s3_client.head_object( 37 | Bucket=bucket, 38 | Key=key 39 | ) 40 | parameters = response['Metadata'] 41 | 42 | # Convert values to int 43 | for key, value in parameters.items(): 44 | try: 45 | parameters[key] = int(value) 46 | except ValueError: 47 | pass 48 | 49 | print("parameters = {}".format(parameters)) 50 | 51 | pm.execute_notebook( 52 | input_notebook, 53 | output_notebook, 54 | parameters = parameters 55 | ) 56 | 57 | result["output_notebooks"].append(output_notebook) 58 | 59 | print("result = {}".format(result)) 60 | 61 | return { 62 | 'statusCode': 200, 63 | 'body': json.dumps(result) 64 | } 65 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | pip-wheel-metadata/ 24 | share/python-wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | 30 | # PyInstaller 31 | # Usually these files are written by a python script from a template 32 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 33 | *.manifest 34 | *.spec 35 | 36 | # Installer logs 37 | pip-log.txt 38 | pip-delete-this-directory.txt 39 | 40 | # Unit test / coverage reports 41 | htmlcov/ 42 | .tox/ 43 | .nox/ 44 | .coverage 45 | .coverage.* 46 | .cache 47 | nosetests.xml 48 | coverage.xml 49 | *.cover 50 | *.py,cover 51 | .hypothesis/ 52 | .pytest_cache/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | target/ 76 | 77 | # Jupyter Notebook 78 | .ipynb_checkpoints 79 | 80 | # IPython 81 | profile_default/ 82 | ipython_config.py 83 | 84 | # pyenv 85 | .python-version 86 | 87 | # pipenv 88 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 89 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 90 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 91 | # install all needed dependencies. 92 | #Pipfile.lock 93 | 94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 95 | __pypackages__/ 96 | 97 | # Celery stuff 98 | celerybeat-schedule 99 | celerybeat.pid 100 | 101 | # SageMath parsed files 102 | *.sage.py 103 | 104 | # Environments 105 | .env 106 | .venv 107 | env/ 108 | venv/ 109 | ENV/ 110 | env.bak/ 111 | venv.bak/ 112 | 113 | # Spyder project settings 114 | .spyderproject 115 | .spyproject 116 | 117 | # Rope project settings 118 | .ropeproject 119 | 120 | # mkdocs documentation 121 | /site 122 | 123 | # mypy 124 | .mypy_cache/ 125 | .dmypy.json 126 | dmypy.json 127 | 128 | # Pyre type checker 129 | .pyre/ 130 | -------------------------------------------------------------------------------- /template.yaml: -------------------------------------------------------------------------------- 1 | AWSTemplateFormatVersion: '2010-09-09' 2 | Transform: 'AWS::Serverless-2016-10-31' 3 | Description: Using an EFS file system to manage data science dependencies. 4 | Globals: 5 | Function: 6 | Runtime: python3.8 7 | CodeUri: . 8 | MemorySize: 3008 9 | Timeout: 900 10 | AutoPublishAlias: live 11 | DeploymentPreference: 12 | Type: AllAtOnce 13 | ProvisionedConcurrencyConfig: 14 | ProvisionedConcurrentExecutions: 1 15 | VpcConfig: 16 | SecurityGroupIds: 17 | - sg-00ea7924c8238d129 18 | SubnetIds: 19 | - subnet-0fede24d7b5b2531e 20 | - subnet-0b4ff175f314e4346 21 | FileSystemConfigs: 22 | - Arn: arn:aws:elasticfilesystem:eu-west-1::access-point/fsap-0108924b681847906 23 | LocalMountPath: /mnt/DataScience 24 | Environment: 25 | Variables: 26 | PYTHONPATH: /mnt/DataScience/lib 27 | OUTPUT_BUCKET: !Ref OutputBucket 28 | Resources: 29 | InputBucket: 30 | Type: 'AWS::S3::Bucket' 31 | Properties: 32 | BucketName: !Sub "${AWS::StackName}-input" 33 | OutputBucket: 34 | Type: 'AWS::S3::Bucket' 35 | RandomForestClassifier: 36 | Type: 'AWS::Serverless::Function' 37 | Properties: 38 | Handler: random_forest_classifier.lambda_handler 39 | Events: 40 | Api: 41 | Type: HttpApi 42 | Properties: 43 | Path: /rfc 44 | Method: GET 45 | LogisticRegression: 46 | Type: 'AWS::Serverless::Function' 47 | Properties: 48 | Handler: logistic_regression.lambda_handler 49 | Events: 50 | Api: 51 | Type: HttpApi 52 | Properties: 53 | Path: /lr 54 | Method: GET 55 | MatplotlibImage: 56 | Type: 'AWS::Serverless::Function' 57 | Properties: 58 | Handler: matplotlib_image.lambda_handler 59 | Policies: 60 | - S3WritePolicy: 61 | BucketName: !Ref OutputBucket 62 | Events: 63 | Api: 64 | Type: HttpApi 65 | Properties: 66 | Path: /mi 67 | Method: GET 68 | PapermillS3: 69 | Type: 'AWS::Serverless::Function' 70 | Properties: 71 | Handler: papermill_s3.lambda_handler 72 | Policies: 73 | - S3ReadPolicy: 74 | BucketName: !Sub "${AWS::StackName}-input" 75 | - S3WritePolicy: 76 | BucketName: !Ref OutputBucket 77 | Events: 78 | BucketEvent: 79 | Type: S3 80 | Properties: 81 | Bucket: 82 | Ref: InputBucket 83 | Events: 84 | - 's3:ObjectCreated:*' 85 | -------------------------------------------------------------------------------- /matplotlib_image.py: -------------------------------------------------------------------------------- 1 | # Author: Alexandre Gramfort 2 | # Albert Thomas 3 | # Adapeted as a Lambda function by Danilo Poccia 4 | # License: BSD 3 clause 5 | 6 | import numpy as np 7 | import matplotlib 8 | import matplotlib.pyplot as plt 9 | 10 | from sklearn import svm 11 | from sklearn.datasets import make_moons, make_blobs 12 | from sklearn.covariance import EllipticEnvelope 13 | from sklearn.ensemble import IsolationForest 14 | from sklearn.neighbors import LocalOutlierFactor 15 | 16 | import io 17 | import time 18 | 19 | import boto3 20 | 21 | import os 22 | 23 | OUTPUT_BUCKET = os.environ['OUTPUT_BUCKET'] 24 | OUTPUT_KEY = 'matplotlib_image.png' 25 | 26 | matplotlib.rcParams['contour.negative_linestyle'] = 'solid' 27 | 28 | image_url = 'https://{}.s3.amazonaws.com/{}'.format(OUTPUT_BUCKET, OUTPUT_KEY) 29 | 30 | def upload_image(): 31 | # Example settings 32 | n_samples = 300 33 | outliers_fraction = 0.15 34 | n_outliers = int(outliers_fraction * n_samples) 35 | n_inliers = n_samples - n_outliers 36 | 37 | # define outlier/anomaly detection methods to be compared 38 | anomaly_algorithms = [ 39 | ("Robust covariance", EllipticEnvelope(contamination=outliers_fraction)), 40 | ("One-Class SVM", svm.OneClassSVM(nu=outliers_fraction, kernel="rbf", 41 | gamma=0.1)), 42 | ("Isolation Forest", IsolationForest(contamination=outliers_fraction, 43 | random_state=42)), 44 | ("Local Outlier Factor", LocalOutlierFactor( 45 | n_neighbors=35, contamination=outliers_fraction))] 46 | 47 | # Define datasets 48 | blobs_params = dict(random_state=0, n_samples=n_inliers, n_features=2) 49 | datasets = [ 50 | make_blobs(centers=[[0, 0], [0, 0]], cluster_std=0.5, 51 | **blobs_params)[0], 52 | make_blobs(centers=[[2, 2], [-2, -2]], cluster_std=[0.5, 0.5], 53 | **blobs_params)[0], 54 | make_blobs(centers=[[2, 2], [-2, -2]], cluster_std=[1.5, .3], 55 | **blobs_params)[0], 56 | 4. * (make_moons(n_samples=n_samples, noise=.05, random_state=0)[0] - 57 | np.array([0.5, 0.25])), 58 | 14. * (np.random.RandomState(42).rand(n_samples, 2) - 0.5)] 59 | 60 | # Compare given classifiers under given settings 61 | xx, yy = np.meshgrid(np.linspace(-7, 7, 150), 62 | np.linspace(-7, 7, 150)) 63 | 64 | plt.figure(figsize=(len(anomaly_algorithms) * 2 + 3, 12.5)) 65 | plt.subplots_adjust(left=.02, right=.98, bottom=.001, top=.96, wspace=.05, 66 | hspace=.01) 67 | 68 | plot_num = 1 69 | rng = np.random.RandomState(42) 70 | 71 | for i_dataset, X in enumerate(datasets): 72 | # Add outliers 73 | X = np.concatenate([X, rng.uniform(low=-6, high=6, 74 | size=(n_outliers, 2))], axis=0) 75 | 76 | for name, algorithm in anomaly_algorithms: 77 | t0 = time.time() 78 | algorithm.fit(X) 79 | t1 = time.time() 80 | plt.subplot(len(datasets), len(anomaly_algorithms), plot_num) 81 | if i_dataset == 0: 82 | plt.title(name, size=18) 83 | 84 | # fit the data and tag outliers 85 | if name == "Local Outlier Factor": 86 | y_pred = algorithm.fit_predict(X) 87 | else: 88 | y_pred = algorithm.fit(X).predict(X) 89 | 90 | # plot the levels lines and the points 91 | if name != "Local Outlier Factor": # LOF does not implement predict 92 | Z = algorithm.predict(np.c_[xx.ravel(), yy.ravel()]) 93 | Z = Z.reshape(xx.shape) 94 | plt.contour(xx, yy, Z, levels=[0], linewidths=2, colors='black') 95 | 96 | colors = np.array(['#377eb8', '#ff7f00']) 97 | plt.scatter(X[:, 0], X[:, 1], s=10, color=colors[(y_pred + 1) // 2]) 98 | 99 | plt.xlim(-7, 7) 100 | plt.ylim(-7, 7) 101 | plt.xticks(()) 102 | plt.yticks(()) 103 | plt.text(.99, .01, ('%.2fs' % (t1 - t0)).lstrip('0'), 104 | transform=plt.gca().transAxes, size=15, 105 | horizontalalignment='right') 106 | plot_num += 1 107 | 108 | #plt.show() 109 | 110 | img_data = io.BytesIO() 111 | plt.savefig(img_data, format='png') 112 | img_data.seek(0) 113 | 114 | s3 = boto3.resource('s3') 115 | bucket = s3.Bucket(OUTPUT_BUCKET) 116 | bucket.put_object(Body=img_data, ContentType='image/png', Key=OUTPUT_KEY, ACL='public-read') 117 | 118 | def lambda_handler(event, context): 119 | upload_image() 120 | return { 121 | 'statusCode': 302, 122 | 'headers': { 123 | 'Location': image_url 124 | } 125 | } 126 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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