Added a readme with details for the different algorithms

README.md

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+# 751 Project - Parallel Machine Learning
+
+## Setup
+- Install Python 3
+- Clone this repository
+- Run `pip install -r requirements.txt`
+- Note
+    - If you want to run the ASHA algorithm, you will need to modify `tuning/asha.py` to access
+    your own AWS Lambda. The code for this lambda function can be found in
+    `lambda/run_xgboost.py`. You will also need to place your AWS credentials in `~/.aws/credentials`,
+    and set a default region in `~/.aws/config`.
+    - Refer to [here](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/quickstart.html)
+    for more details about the credential and configuration files.
+
+
+## Usage
+- `python main.py -a [algorithm] -p [parameters]`
+
+
+## Algorithms and Parameters
+
+### Grid Search
+- `-a grid`
+#### Parameters
+```
+{
+    "cv": number of folds for cross-validation
+}
+```
+
+#### Example
+`python main.py -a grid -p '{"cv": 3}'`
+
+---
+
+### Random Search
+- `-a random`
+#### Parameters
+```
+{
+    "n_iter": number of random configurations (iterations) to evaluate,
+    "cv": number of folds for cross-validation
+}
+```
+#### Example
+`python main.py -a random -p '{"n_iter": 10, "cv": 3}'`
+
+---
+
+### Successive Halving Algorithm (SHA)
+- `-a sha`
+#### Parameters
+```
+{
+    "n_configs": number of configurations to evaluate,
+    "min_r": minimum resources (boosting rounds) given to each configuration,
+    "max_r": maximum resources (boosting rounds) given to each configuration,
+    "reduction_factor": amount of configurations to be dropped per iteration (2 = reduce by half)
+    "cv": number of folds for cross-validation
+}
+```
+#### Example
+`python main.py -a sha -p '{"n_configs": 32, "min_r": 1, "max_r": 32, "reduction_factor": 2, "cv": 3}'`
+
+---
+
+### Asynchronous Successive Halving Algorithm (ASHA)
+- `-a asha`
+#### Parameters
+```
+{
+    "n_workers": number of workers allocated to evaluate the configurations in parallel,
+    "min_r": minimum resources (boosting rounds) given to each configuration,
+    "max_r": maximum resources (boosting rounds) given to each configuration,
+    "reduction_factor": amount of configurations to be dropped per iteration (2 = reduce by half),
+    "early_stopping_rounds": number of rounds in which the error must decrease
+    "cv": number of folds for cross-validation
+}
+```
+#### Example
+`python main.py -a asha -p '{"n_workers": 100, "min_r": 1, "max_r": 64, "reduction_factor": 4, "cv": 3}'`
+
+---
+
+## Note
+If running the example commands in windows command line, you will need to escape the double quotes, i.e.
+
+`python main.py -a random -p '{"n_iter": 10, "cv": 3}'`
+
+Becomes
+
+`python main.py -a random -p '{\"n_iter\": 10, \"cv\": 3}'`