├── .gitignore ├── LICENSE ├── README.md ├── data └── titanic.csv ├── examples.bash ├── main.py ├── model ├── model.pkl └── model_columns.pkl ├── requirements.txt └── tests.py /.gitignore: -------------------------------------------------------------------------------- 1 | *.idea 2 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2018 Amir Ziai 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Flask API for scikit learn 2 | A simple Flask application that can serve predictions from a scikit-learn model. Reads a pickled sklearn model into memory when the Flask app is started and returns predictions through the /predict endpoint. You can also use the /train endpoint to train/retrain the model. Any sklearn model can be used for prediction. 3 | 4 | Read more in [this blog post](https://medium.com/@amirziai/a-flask-api-for-serving-scikit-learn-models-c8bcdaa41daa). 5 | 6 | ### Dependencies 7 | - scikit-learn 8 | - Flask 9 | - pandas 10 | - numpy 11 | 12 | ``` 13 | pip install -r requirements.txt 14 | ``` 15 | 16 | ### Running API 17 | ``` 18 | python main.py 19 | ``` 20 | 21 | # Endpoints 22 | ### /predict (POST) 23 | Returns an array of predictions given a JSON object representing independent variables. Here's a sample input: 24 | ``` 25 | [ 26 | {"Age": 85, "Sex": "male", "Embarked": "S"}, 27 | {"Age": 24, "Sex": "female", "Embarked": "C"}, 28 | {"Age": 3, "Sex": "male", "Embarked": "C"}, 29 | {"Age": 21, "Sex": "male", "Embarked": "S"} 30 | ] 31 | ``` 32 | 33 | and sample output: 34 | ``` 35 | {"prediction": [0, 1, 1, 0]} 36 | ``` 37 | 38 | 39 | ### /train (GET) 40 | Trains the model. This is currently hard-coded to be a random forest model that is run on a subset of columns of the titanic dataset. 41 | 42 | ### /wipe (GET) 43 | Removes the trained model. 44 | -------------------------------------------------------------------------------- /data/titanic.csv: -------------------------------------------------------------------------------- 1 | PassengerId,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked,Survived 2 | 1,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S,0 3 | 2,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C,1 4 | 3,3,"Heikkinen, Miss. Laina",female,26,0,0,STON/O2. 3101282,7.925,,S,1 5 | 4,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35,1,0,113803,53.1,C123,S,1 6 | 5,3,"Allen, Mr. William Henry",male,35,0,0,373450,8.05,,S,0 7 | 6,3,"Moran, Mr. James",male,,0,0,330877,8.4583,,Q,0 8 | 7,1,"McCarthy, Mr. Timothy J",male,54,0,0,17463,51.8625,E46,S,0 9 | 8,3,"Palsson, Master. Gosta Leonard",male,2,3,1,349909,21.075,,S,0 10 | 9,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27,0,2,347742,11.1333,,S,1 11 | 10,2,"Nasser, Mrs. Nicholas (Adele Achem)",female,14,1,0,237736,30.0708,,C,1 12 | 11,3,"Sandstrom, Miss. Marguerite Rut",female,4,1,1,PP 9549,16.7,G6,S,1 13 | 12,1,"Bonnell, Miss. Elizabeth",female,58,0,0,113783,26.55,C103,S,1 14 | 13,3,"Saundercock, Mr. William Henry",male,20,0,0,A/5. 2151,8.05,,S,0 15 | 14,3,"Andersson, Mr. Anders Johan",male,39,1,5,347082,31.275,,S,0 16 | 15,3,"Vestrom, Miss. Hulda Amanda Adolfina",female,14,0,0,350406,7.8542,,S,0 17 | 16,2,"Hewlett, Mrs. (Mary D Kingcome) ",female,55,0,0,248706,16,,S,1 18 | 17,3,"Rice, Master. Eugene",male,2,4,1,382652,29.125,,Q,0 19 | 18,2,"Williams, Mr. Charles Eugene",male,,0,0,244373,13,,S,1 20 | 19,3,"Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)",female,31,1,0,345763,18,,S,0 21 | 20,3,"Masselmani, Mrs. Fatima",female,,0,0,2649,7.225,,C,1 22 | 21,2,"Fynney, Mr. Joseph J",male,35,0,0,239865,26,,S,0 23 | 22,2,"Beesley, Mr. Lawrence",male,34,0,0,248698,13,D56,S,1 24 | 23,3,"McGowan, Miss. Anna ""Annie""",female,15,0,0,330923,8.0292,,Q,1 25 | 24,1,"Sloper, Mr. William Thompson",male,28,0,0,113788,35.5,A6,S,1 26 | 25,3,"Palsson, Miss. Torborg Danira",female,8,3,1,349909,21.075,,S,0 27 | 26,3,"Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)",female,38,1,5,347077,31.3875,,S,1 28 | 27,3,"Emir, Mr. Farred Chehab",male,,0,0,2631,7.225,,C,0 29 | 28,1,"Fortune, Mr. Charles Alexander",male,19,3,2,19950,263,C23 C25 C27,S,0 30 | 29,3,"O'Dwyer, Miss. 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Catherine Helen ""Carrie""",female,,1,2,W./C. 6607,23.45,,S,0 891 | 890,1,"Behr, Mr. Karl Howell",male,26,0,0,111369,30,C148,C,1 892 | 891,3,"Dooley, Mr. Patrick",male,32,0,0,370376,7.75,,Q,0 893 | -------------------------------------------------------------------------------- /examples.bash: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | # 3 | # Starting API 4 | python2 main.py 9999 & 5 | sleep 2 6 | # 7 | # POST method predict 8 | curl -d '[ 9 | {"Age": 85, "Sex": "male", "Embarked": "S"}, 10 | {"Age": 24, "Sex": "female", "Embarked": "C"}, 11 | {"Age": 3, "Sex": "male", "Embarked": "C"}, 12 | {"Age": 21, "Sex": "male", "Embarked": "S"} 13 | ]' -H "Content-Type: application/json" \ 14 | -X POST http://localhost:9999/predict && \ 15 | echo -e "\n -> predict OK" 16 | 17 | # GET method wipe 18 | curl -X GET http://localhost:9999/wipe && \ 19 | echo -e "\n -> wipe OK" 20 | 21 | # GET method train 22 | curl -X GET http://localhost:9999/train && \ 23 | echo -e "\n -> train OK" 24 | 25 | # kill runing API 26 | for i in $(ps -elf | grep "python2 main.py 9999" | grep -v grep | cut -d " " -f 4); do 27 | kill -9 $i 28 | done 29 | -------------------------------------------------------------------------------- /main.py: -------------------------------------------------------------------------------- 1 | import sys 2 | import os 3 | import shutil 4 | import time 5 | import traceback 6 | 7 | from flask import Flask, request, jsonify 8 | import pandas as pd 9 | from sklearn.externals import joblib 10 | 11 | app = Flask(__name__) 12 | 13 | # inputs 14 | training_data = 'data/titanic.csv' 15 | include = ['Age', 'Sex', 'Embarked', 'Survived'] 16 | dependent_variable = include[-1] 17 | 18 | model_directory = 'model' 19 | model_file_name = '%s/model.pkl' % model_directory 20 | model_columns_file_name = '%s/model_columns.pkl' % model_directory 21 | 22 | # These will be populated at training time 23 | model_columns = None 24 | clf = None 25 | 26 | 27 | @app.route('/predict', methods=['POST']) 28 | def predict(): 29 | if clf: 30 | try: 31 | json_ = request.json 32 | query = pd.get_dummies(pd.DataFrame(json_)) 33 | 34 | # https://github.com/amirziai/sklearnflask/issues/3 35 | # Thanks to @lorenzori 36 | query = query.reindex(columns=model_columns, fill_value=0) 37 | 38 | prediction = list(clf.predict(query)) 39 | 40 | # Converting to int from int64 41 | return jsonify({"prediction": list(map(int, prediction))}) 42 | 43 | except Exception as e: 44 | 45 | return jsonify({'error': str(e), 'trace': traceback.format_exc()}) 46 | else: 47 | print('train first') 48 | return 'no model here' 49 | 50 | 51 | @app.route('/train', methods=['GET']) 52 | def train(): 53 | # using random forest as an example 54 | # can do the training separately and just update the pickles 55 | from sklearn.ensemble import RandomForestClassifier as rf 56 | 57 | df = pd.read_csv(training_data) 58 | df_ = df[include] 59 | 60 | categoricals = [] # going to one-hot encode categorical variables 61 | 62 | for col, col_type in df_.dtypes.items(): 63 | if col_type == 'O': 64 | categoricals.append(col) 65 | else: 66 | df_[col].fillna(0, inplace=True) # fill NA's with 0 for ints/floats, too generic 67 | 68 | # get_dummies effectively creates one-hot encoded variables 69 | df_ohe = pd.get_dummies(df_, columns=categoricals, dummy_na=True) 70 | 71 | x = df_ohe[df_ohe.columns.difference([dependent_variable])] 72 | y = df_ohe[dependent_variable] 73 | 74 | # capture a list of columns that will be used for prediction 75 | global model_columns 76 | model_columns = list(x.columns) 77 | joblib.dump(model_columns, model_columns_file_name) 78 | 79 | global clf 80 | clf = rf() 81 | start = time.time() 82 | clf.fit(x, y) 83 | 84 | joblib.dump(clf, model_file_name) 85 | 86 | message1 = 'Trained in %.5f seconds' % (time.time() - start) 87 | message2 = 'Model training score: %s' % clf.score(x, y) 88 | return_message = 'Success. \n{0}. \n{1}.'.format(message1, message2) 89 | return return_message 90 | 91 | 92 | @app.route('/wipe', methods=['GET']) 93 | def wipe(): 94 | try: 95 | shutil.rmtree('model') 96 | os.makedirs(model_directory) 97 | return 'Model wiped' 98 | 99 | except Exception as e: 100 | print(str(e)) 101 | return 'Could not remove and recreate the model directory' 102 | 103 | 104 | if __name__ == '__main__': 105 | try: 106 | port = int(sys.argv[1]) 107 | except Exception as e: 108 | port = 80 109 | 110 | try: 111 | clf = joblib.load(model_file_name) 112 | print('model loaded') 113 | model_columns = joblib.load(model_columns_file_name) 114 | print('model columns loaded') 115 | 116 | except Exception as e: 117 | print('No model here') 118 | print('Train first') 119 | print(str(e)) 120 | clf = None 121 | 122 | app.run(host='0.0.0.0', port=port, debug=True) 123 | -------------------------------------------------------------------------------- /model/model.pkl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/amirziai/sklearnflask/975509a062561caa8ae1d67f3de123bd9b3f6e5b/model/model.pkl -------------------------------------------------------------------------------- /model/model_columns.pkl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/amirziai/sklearnflask/975509a062561caa8ae1d67f3de123bd9b3f6e5b/model/model_columns.pkl -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | Flask==1.0.2 2 | numpy==1.16.3 3 | pandas==0.24.2 4 | scikit-learn==0.20.3 -------------------------------------------------------------------------------- /tests.py: -------------------------------------------------------------------------------- 1 | import unittest 2 | 3 | 4 | class MyTestCase(unittest.TestCase): 5 | def test_something(self): 6 | self.assertEqual(True, False) 7 | 8 | 9 | if __name__ == '__main__': 10 | unittest.main() 11 | --------------------------------------------------------------------------------