Merge pull request #33 from BBVA/develop

New version 4.0

Dockerfile

@@ -1,28 +1,7 @@
 FROM tiangolo/uwsgi-nginx-flask:python3.6
 
-RUN apt-get update && apt-get install -y redis-server python3-celery python-celery-common python3-redis
-
-
 COPY ./ /app
-
 COPY ./requirements.txt /tmp/
-RUN pip3 install numpy sqlalchemy
-RUN pip install --upgrade --no-deps statsmodels
-RUN pip3 install --requirement /tmp/requirements.txt
-
-RUN pip install -U numpy
-
-RUN pip3 install pandas scipy patsy matplotlib numdifftools seaborn
-RUN pip3 install pyflux
-RUN pip3 install pyramid-arima
-RUN pip3 install tensorflow==1.14.0
-RUN pip3 install -U statsmodels
-RUN pip3 install tbats
-RUN pip3 install celery
-RUN pip install redis
-RUN service redis-server start
-RUN pip install h5py
-
-
 COPY ./config/timeout.conf /etc/nginx/conf.d/
-RUN chmod -R g=u /etc/passwd /app
+
+RUN pip install mxnet==1.4.1 gluonts && apt-get update && apt-get install -y redis-server python3-celery python-celery-common python3-redis && rm -rf /var/lib/apt/lists/* && pip3 install numpy pandas && pip3 install --requirement /tmp/requirements.txt && pip3 install fbprophet &&  chmod -R g=u /etc/passwd /app  && pip install --upgrade pyflux && pip install --upgrade numpy && rm -fr /root/.cache/pip && service redis-server start

README.md

@@ -26,23 +26,49 @@ To use TIMECOP engine you will need to send a univariate or multivariate time se
 
 ![alt text](https://raw.githubusercontent.com/BBVA/timecop/master/doc/img/flow_chart.jpg)
 
-## Installation
-
-To use timecop the easiest way is to deploy the docker image made with the last version.
-The steps to use the docker images are:
-
- 1. Install docker
- 2. Download timecop docker image:
-
- 	**docker pull trakuo/timecop:devel**
- 3. Execute a container with the timecop image:
-
-	**docker run --name timecoprunning -p 3000:80 trakuo/timecop:devel**
- 5. Open web test page to test timecop is running
-
-	 **http://localhost:3000/static/index.html**
-
-
+## Main Features
+- [x] **Future**: Forecast n steps
+- [x] **Past**: Sensibility custom anomaly detection
+- [x] **Now**: Sensibility custom anomaly detection
+
+#### Secondary Features
+- [x] Delta / Full timesteps upload
+- [x] Train / Use best known model in production envirotnment
+- [x] Python API for easy integration in notebooks / python apps 
+- [x] Backend Database 
+
+
+
+## Algorithms
+#### Univariate
+
+- [x] Long Short Term Memory networks
+- [x] Facebook prophet
+- [x] vector autoregression
+- [x] Gluon-ts
+- [x] Holtwinters
+- [x] ARIMA
+- [ ] TBATS
+
+
+#### Multivariate
+- [x] Long Short Term Memory networks
+- [x] vector autoregression
+- [ ] Vector error correction model
+- [ ] SArimaX
+
+## Metrics
+- [x] Mean Absolute Error
+- [x] Mean Squeared Error
+- [x] Root Mean Squeared Error
+- [x] Symmetric mean absolute percentage error
+- [x] Mean Absolute Percentage Error
+
+
+## Advanced features
+- [x] Trend
+- [x] ChangePoints
+- [ ] Confidence interval
 
 ## Web test page
 
@@ -166,6 +192,80 @@ An example response will be:
 }
 
 ```
+## Python API Getting Started with the python API
+
+A python package to invoke Timecop from any python code, using the RESTful Web Server API for you.
+For more information, go to the [pip module](https://pypi.org/project/timecopts/)
+
+###### UNIVARIATE
+
+```
+import timecopts as tc
+TS=[1,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9]
+
+URL = 'URL_timecop_server'
+output_json = tc.timecop_univariate(URL,name='test_timeseries', TS,  train=True, restart=True)
+
+```
+
+###### MULTIVARIATE
+
+
+```
+import timecopts as tc
+TS_main=[1,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9]
+
+TS_timeseries= []
+temp = {}
+temp['data'] = [1,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9]
+TS_timeseries.append(temp)
+temp2 = {}
+temp2['data'] = [1,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9]
+TS_timeseries.append(temp2)
+
+URL = 'URL_timecop_server'
+output_json = tc.timecop_multivariate(URL,name='test_timeseries', TS_main,TS_timeseries,  train=True, restart=True)
+```
+
+
+
+
+
+## Python API Getting Started with the python API
+
+A python package to invoke Timecop from any python code, using the RESTful Web Server API for you.
+For more information, go to the [pip module](https://pypi.org/project/timecopts/)
+
+###### UNIVARIATE
+
+```
+import timecopts as tc
+TS=[1,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9]
+
+URL = 'URL_timecop_server'
+output_json = tc.timecop_univariate(URL,name='test_timeseries', TS,  train=True, restart=True)
+
+```
+
+###### MULTIVARIATE
+
+
+```
+import timecopts as tc
+TS_main=[1,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9]
+
+TS_timeseries= []
+temp = {}
+temp['data'] = [1,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9]
+TS_timeseries.append(temp)
+temp2 = {}
+temp2['data'] = [1,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9,2,3,4,5,6,7,8,9]
+TS_timeseries.append(temp2)
+
+URL = 'URL_timecop_server'
+output_json = tc.timecop_multivariate(URL,name='test_timeseries', TS_main,TS_timeseries,  train=True, restart=True)
+```
+
 
 
 #### Response explained