housing-package 0.0.2
housing model
Requires Python: >=3.6

# Median housing value prediction

The housing data can be downloaded from https://raw.githubusercontent.com/ageron/handson-ml/master/. The script has codes to download the data. We have modelled the median house value on given housing data.

The following techniques have been used:

 - Linear regression
 - Decision Tree
 - Random Forest

## Steps performed
 - We prepar and clean the data. We check and imput for missing values.
 - Features are generated and the variables are checked for correlation.
 - Multiple sampling techinuqies are evaluated. The data set is split into train and test.
 - All the above said modelling techniques are tried and evaluated. The final metric used to evaluate is mean squared error.

## To run the code
 - We have to create an environment from env.yml file which is attached in this repository .
 - Command to create environment - `conda env create -f env.yml`
 - Command tp see newly created environment - `conda env list`
 - Command to activate the environment - `conda activate mle-dev`
 - Now execute the python script .

 ## To execute the python script
 - We have to open the mlflow ui . my mlflow listening at - http://127.0.0.1:5000
 - you can check your mlflow uri by `mlflow ui` command .
 - If your mlflow is listening at different uri then need to change the mlflowrun.py line 8 remote_server_uri .
 - Now run the python script .
 - command to run the scripts - `python scripts/mlflowrun.py`
OR

 - We can run individual file from src -> py_packages folder
 - python src/py_packages/ingest.py <--path , --log_level, --log_path --no_console_log>
   python src/py_packages/train.py <--train_folder,--output_folder,--log_level,--log_path,--no_consoler_log>
   python src/py_packages/score.py <--model-folder,--data_folder,--log_level,--log_path,--no_consoler_log>


1) ingest_data : To download the data and split into train and test (arguments can be passed to give download path)
    - While executing the script to terminal , data will get stored as per the default settings
    - Arguments that can be passed : <--path , --log_level, --log_path --no_console_log>
       * --path :: Specify the path for the training dataset
       * --log_level :: Specify the level of log required
       * --log_path :: Specify the location where logs to be created (If not specified no logs will be created)
       * --no_console_log :: Specify the log to console


 2) train : To fit and train different models based on the training data provided
    - Argumentd that can be passed : <--train_folder,--output_folder,--log_level,--log_path,--no_consoler_log>
       * --train_folder :: Specify path with file name where the train data is present.
       * --output_folder :: Specify the location where the trained models pkl files to save.
       * --log_level :: Specify the level of log required
       * --log_path :: Specify the location where logs to be created (If not specified no logs will be created)
       * --no_console_log :: Specify the log to console



 3) score : To check the scores of model on the new data passed.
    - Argumentd that can be passed : <--model-folder,--output-folder,--log_level,--log_path,--no_consoler_log>
      * --model_folder :: Specify the location where the models pkl file saved.
      * --data_folder :: Specify the location where test datasets are present.
      * --log_level :: Specify the level of log required
      * --log_path :: Specify the location where logs to be created (If not specified no logs will be created)
      * --no_console_log :: Specify the log to console


 ## To execute the test python script
   - Command to execute the test script of data ingestion module- `python test/test_data_ingestion.py`
   - Command to execute the test script of train module - `python test/test_train.py`
   - Command to execute the test script of score module - `python test/test_score.py`
   - Command to execute the test script to check the installation - `python test/test_installation.py`
   OR
   - We can directly run all 4 test script using - `pytest` command .

## To create html in docs using sphinx
   - To document the housing package , We need to create a folder named 'docs'.
   - Command to go to docs folder -   `cd docs`
   - Command to generates a complete documentation directory along with a make.bat file, which will be used later to generate HTML - `sphinx-quickstart`
   - After running the command, accept the defaults.
   - Now ,go to conf.py file. Add 'os.path.abspath('..')' above project information. Here, it will tell sphinx that the code is residing outside of the current docs folder.
   - Next, go to the extensions part and add the extensions - ["sphinx.ext.autodoc", "sphinx.ext.viewcode", "sphinx.ext.napoleon"]
   - Lastly, go to themes and replace ‘alabaster’ with ‘sphinx_rtd_theme’.
   - Now , go to parent folder - `cd ..`
   - Run command - `sphinx-apidoc -o docs src/housing_package/`
   - It will generate housing_package.rst and modules.rst files .
   - Now, include the generated modules.rst file in your index.rst
   - Go inside the docs folder and run the command - `make html`
   - We can see HTML files generated inside _build folder in docs .