breath ...

.gitignore

@@ -4,6 +4,7 @@
 /dist/
 
 # Cache
+.cache/
 .coverage*
 .mypy_cache/
 .pytest_cache/
@@ -21,7 +22,6 @@ poetry.lock
 
 # Project
 /docs/*
-/outputs/*
 !**/.gitkeep
 
 # Python

.pre-commit-config.yaml

@@ -2,7 +2,7 @@
 # See https://pre-commit.com/hooks.html for more hooks
 
 default_language_version:
-  python: python3.11
+  python: python3.12
 repos:
   # commons
   - repo: https://github.com/pre-commit/pre-commit-hooks

.python-version

@@ -1 +1 @@
-3.11
+3.12

Dockerfile

@@ -1,11 +1,11 @@
 # https://docs.docker.com/engine/reference/builder/
 
 # Define
-FROM python:3.11
+FROM python:3.12
 
 # Install
 COPY dist/*.whl .
 RUN pip install --no-cache-dir *.whl
 
 # Execute
-CMD ["wines", "--help"]
+CMD ["bikes", "--help"]

README.md

@@ -94,7 +94,7 @@ This section details the requirements, actions, and next steps to kickstart your
 
 ## Prerequisites
 
-- [Python>=3.11](https://www.python.org/downloads/) (to benefit from [the latest features and performance improvements](https://docs.python.org/3/whatsnew/3.11.html))
+- [Python>=3.12](https://www.python.org/downloads/) (to benefit from [the latest features and performance improvements](https://docs.python.org/3/whatsnew/3.12.html))
 - [Poetry>=1.5.1](https://python-poetry.org/) (to initialize the project [virtual environment](https://docs.python.org/3/library/venv.html) and its dependencies)
 
 ## Installation
@@ -813,7 +813,7 @@ class InputsSchema(Schema):
     proanthocyanins: papd.Series[float] = pa.Field(gt=0, lt=10)
     color_intensity: papd.Series[float] = pa.Field(gt=0, lt=100)
     hue: papd.Series[float] = pa.Field(gt=0, lt=10)
-    od280_od315_of_diluted_wines: papd.Series[float] = pa.Field(gt=0, lt=10)
+    od280_od315_of_diluted_bikes: papd.Series[float] = pa.Field(gt=0, lt=10)
     proline: papd.Series[float] = pa.Field(gt=0, lt=10000)
 ```
 

confs/inference.yaml

@@ -1,9 +1,11 @@
 job:
   KIND: InferenceJob
   inputs:
-    KIND: ParquetDataset
+    KIND: ParquetReader
     path: data/inputs.parquet
-  output:
-    KIND: ParquetDataset
-    path: outputs/output.parquet
-  model_path: outputs/model.joblib
+  outputs:
+    KIND: ParquetWriter
+    path: outputs/outputs.parquet
+  loader:
+    KIND: JoblibLoader
+    model_path: outputs/model.joblib

confs/training.yaml

@@ -1,9 +1,8 @@
 job:
   KIND: TrainingJob
   inputs:
-    KIND: ParquetDataset
+    KIND: ParquetReader
     path: data/inputs.parquet
-  target:
-    KIND: ParquetDataset
-    path: data/target.parquet
-  output_model: outputs/model.joblib
+  saver:
+    KIND: JoblibSaver
+    path: outputs/model.joblib

confs/tuning.yaml

@@ -1,9 +1,8 @@
 job:
   KIND: TuningJob
   inputs:
-    KIND: ParquetDataset
+    KIND: ParquetReader
     path: data/inputs.parquet
-  target:
-    KIND: ParquetDataset
-    path: data/target.parquet
-  output_results: outputs/results.csv
+  outputs:
+    KIND: CSVWriter
+    path: outputs/results.csv

data/Readme.txt

@@ -0,0 +1,112 @@
+==========================================
+Bike Sharing Dataset
+==========================================
+
+Hadi Fanaee-T
+
+Laboratory of Artificial Intelligence and Decision Support (LIAAD), University of Porto
+INESC Porto, Campus da FEUP
+Rua Dr. Roberto Frias, 378
+4200 - 465 Porto, Portugal
+
+https://archive.ics.uci.edu/dataset/275/bike+sharing+dataset
+
+=========================================
+Background
+=========================================
+
+Bike sharing systems are new generation of traditional bike rentals where whole process from membership, rental and return
+back has become automatic. Through these systems, user is able to easily rent a bike from a particular position and return
+back at another position. Currently, there are about over 500 bike-sharing programs around the world which is composed of
+over 500 thousands bicycles. Today, there exists great interest in these systems due to their important role in traffic,
+environmental and health issues.
+
+Apart from interesting real world applications of bike sharing systems, the characteristics of data being generated by
+these systems make them attractive for the research. Opposed to other transport services such as bus or subway, the duration
+of travel, departure and arrival position is explicitly recorded in these systems. This feature turns bike sharing system into
+a virtual sensor network that can be used for sensing mobility in the city. Hence, it is expected that most of important
+events in the city could be detected via monitoring these data.
+
+=========================================
+Data Set
+=========================================
+Bike-sharing rental process is highly correlated to the environmental and seasonal settings. For instance, weather conditions,
+precipitation, day of week, season, hour of the day, etc. can affect the rental behaviors. The core data set is related to
+the two-year historical log corresponding to years 2011 and 2012 from Capital Bikeshare system, Washington D.C., USA which is
+publicly available in http://capitalbikeshare.com/system-data. We aggregated the data on two hourly and daily basis and then
+extracted and added the corresponding weather and seasonal information. Weather information are extracted from http://www.freemeteo.com.
+
+=========================================
+Associated tasks
+=========================================
+
+	- Regression:
+		Predication of bike rental count hourly or daily based on the environmental and seasonal settings.
+
+	- Event and Anomaly Detection:
+		Count of rented bikes are also correlated to some events in the town which easily are traceable via search engines.
+		For instance, query like "2012-10-30 washington d.c." in Google returns related results to Hurricane Sandy. Some of the important events are
+		identified in [1]. Therefore the data can be used for validation of anomaly or event detection algorithms as well.
+
+
+=========================================
+Files
+=========================================
+
+	- Readme.txt
+	- hour.csv : bike sharing counts aggregated on hourly basis. Records: 17379 hours
+	- day.csv - bike sharing counts aggregated on daily basis. Records: 731 days
+
+
+=========================================
+Dataset characteristics
+=========================================
+Both hour.csv and day.csv have the following fields, except hr which is not available in day.csv
+
+	- instant: record index
+	- dteday : date
+	- season : season (1:springer, 2:summer, 3:fall, 4:winter)
+	- yr : year (0: 2011, 1:2012)
+	- mnth : month ( 1 to 12)
+	- hr : hour (0 to 23)
+	- holiday : weather day is holiday or not (extracted from http://dchr.dc.gov/page/holiday-schedule)
+	- weekday : day of the week
+	- workingday : if day is neither weekend nor holiday is 1, otherwise is 0.
+	+ weathersit :
+		- 1: Clear, Few clouds, Partly cloudy, Partly cloudy
+		- 2: Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist
+		- 3: Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds
+		- 4: Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog
+	- temp : Normalized temperature in Celsius. The values are divided to 41 (max)
+	- atemp: Normalized feeling temperature in Celsius. The values are divided to 50 (max)
+	- hum: Normalized humidity. The values are divided to 100 (max)
+	- windspeed: Normalized wind speed. The values are divided to 67 (max)
+	- casual: count of casual users
+	- registered: count of registered users
+	- cnt: count of total rental bikes including both casual and registered
+
+=========================================
+License
+=========================================
+Use of this dataset in publications must be cited to the following publication:
+
+[1] Fanaee-T, Hadi, and Gama, Joao, "Event labeling combining ensemble detectors and background knowledge", Progress in Artificial Intelligence (2013): pp. 1-15, Springer Berlin Heidelberg, doi:10.1007/s13748-013-0040-3.
+
+@article{
+	year={2013},
+	issn={2192-6352},
+	journal={Progress in Artificial Intelligence},
+	doi={10.1007/s13748-013-0040-3},
+	title={Event labeling combining ensemble detectors and background knowledge},
+	url={http://dx.doi.org/10.1007/s13748-013-0040-3},
+	publisher={Springer Berlin Heidelberg},
+	keywords={Event labeling; Event detection; Ensemble learning; Background knowledge},
+	author={Fanaee-T, Hadi and Gama, Joao},
+	pages={1-15}
+}
+
+=========================================
+Contact
+=========================================
+
+For further information about this dataset please contact Hadi Fanaee-T (hadi.fanaee@fe.up.pt)