Merge pull request #8 from autonomio/initial_release

#2 Initial Release Loose-Ends Tracker

.github/workflows/ci.yml

@@ -35,6 +35,10 @@ jobs:
       with:
         name: "config.json"
         json: ${{ secrets.CONFIG_JSON }}
+    - name: Add Key
+      run: |
+        echo "${{ secrets.AUTONOMIO_DEV_PEM }}" > autonomio-dev.pem
+        chmod 0400 autonomio-dev.pem
     - name: Tests
       run: |
         pip install tensorflow>=2.0

docs/DistributedScan.md

@@ -0,0 +1,316 @@
+# DistributedScan
+
+The experiment is configured and started through the `DistributedScan()` command. All of the options effecting the experiment, other than the hyperparameters themselves, are configured through the Scan arguments. The most common use-case is where ~10 arguments are invoked.
+
+## Minimal Example
+
+```python
+jako.DistributedScan(x='x', y='y', params=p, model=input_model,config='config.json')
+
+```
+
+## DistributedScan Arguments
+
+`x`, `y`, `params`, `model` and `config` are the only required arguments to start the experiment, all other are optional.</aside>
+
+Argument | Input | Description
+--------- | ------- | -----------
+`x` | array or list of arrays | prediction features
+`y` | array or list of arrays | prediction outcome variable
+`params` | dict or ParamSpace object | the parameter dictionary or the ParamSpace object after splitting
+`model` | function | the Keras model as a function
+`experiment_name` | str | Used for creating the experiment logging folder
+`x_val` | array or list of arrays | validation data for x
+`y_val` | array or list of arrays | validation data for y
+`val_split` | float | validation data split ratio
+`random_method` | str | the random method to be used
+`seed` | float | Seed for random states
+`performance_target` | list | A result at which point to end experiment
+`fraction_limit` | float | The fraction of permutations to be processed
+`round_limit` | int | Maximum number of permutations in the experiment
+`time_limit` | datetime | Time limit for experiment in format `%Y-%m-%d %H:%M`
+`boolean_limit` | function | Limit permutations based on a lambda function
+`reduction_method` | str | Type of reduction optimizer to be used used
+`reduction_interval` | int | Number of permutations after which reduction is applied
+`reduction_window` | int | the lookback window for reduction process
+`reduction_threshold` | float | The threshold at which reduction is applied
+`reduction_metric` | str | The metric to be used for reduction
+`minimize_loss` | bool | `reduction_metric` is a loss
+`disable_progress_bar` | bool | Disable live updating progress bar
+`print_params` | bool | Print each permutation hyperparameters
+`clear_session` | bool | Clear backend session between permutations
+`save_weights` | bool | Keep model weights (increases memory pressure for large models)
+`config` | str or dict | Configuration containing information about machines to distribute and database to upload the data.
+
+
+## DistributedScan Object Properties
+
+Once the `DistributedScan()` procedures are completed, an object with several useful properties is returned.The namespace is strictly kept clean, so all the properties consist of meaningful contents.
+
+In the case conducted the following experiment, we can access the properties in `distributed_scan_object` which is a python class object.
+
+```python
+distributed_scan_object = jako.DistributedScan(x, y, model=iris_model, params=p, fraction_limit=0.1, config='config.json')
+```
+<hr>
+
+**`best_model`** picks the best model based on a given metric and returns the index number for the model.
+
+```python
+distributed_scan_object.best_model(metric='f1score', asc=False)
+```
+NOTE: `metric` has to be one of the metrics used in the experiment, and `asc` has to be True for the case where the metric is something to be minimized.
+
+<hr>
+
+**`data`** returns a pandas DataFrame with the results for the experiment together with the hyperparameter permutation details.
+
+```python
+distributed_scan_object.data
+```
+
+<hr>
+
+**`details`** returns a pandas Series with various meta-information about the experiment.
+
+```python
+distributed_scan_object.details
+```
+
+<hr>
+
+**`evaluate_models`** creates a new column in `distributed_scan_object.data` with result from kfold cross-evaluation.
+
+```python
+distributed_scan_object.evaluate_models(x_val=x_val,
+                            y_val=y_val,
+                            n_models=10,
+                            metric='f1score',
+                            folds=5,
+                            shuffle=True,
+                            average='binary',
+                            asc=False)
+```
+
+Argument | Description
+-------- | -----------
+`distributed_scan_object` | The class object returned by DistributedScan() upon completion of the experiment.
+`x_val` | Input data (features) in the same format as used in DistributedScan(), but should not be the same data (or it will not be much of validation).
+`y_val` | Input data (labels) in the same format as used in DistributedScan(), but should not be the same data (or it will not be much of validation).
+`n_models` | The number of models to be evaluated. If set to 10, then 10 models with the highest metric value are evaluated. See below.
+`metric` | The metric to be used for picking the models to be evaluated.
+`folds` | The number of folds to be used in the evaluation.
+`shuffle` | If the data is to be shuffled or not. Set always to False for timeseries but keep in mind that you might get periodical/seasonal bias.
+`average` |One of the supported averaging methods: 'binary', 'micro', or 'macro'
+`asc` |Set to True if the metric is to be minimized.
+`saved` | bool | if a model saved on local machine should be used
+`custom_objects` | dict | if the model has a custom object, pass it here
+
+<hr>
+
+**`learning_entropy`** returns a pandas DataFrame with entropy measure for each permutation in terms of how much there is variation between results of each epoch in the permutation.
+
+```python
+distributed_scan_object.learning_entropy
+```
+
+<hr>
+
+**`params`** returns a dictionary with the original input parameter ranges for the experiment.
+
+```python
+distributed_scan_object.params
+```
+
+<hr>
+
+**`round_times`** returns a pandas DataFrame with the time when each permutation started, ended, and how many seconds it took.
+
+```python
+distributed_scan_object.round_times
+```
+
+<hr>
+
+<hr>
+
+**`round_history`** returns epoch-by-epoch data for each model in a dictionary.
+
+```python
+distributed_scan_object.round_history
+```
+
+<hr>
+
+**`saved_models`** returns the JSON (dictionary) for each model.
+
+```python
+distributed_scan_object.saved_models
+```
+
+<hr>
+
+**`saved_weights`** returns the weights for each model.
+
+```python
+distributed_scan_object.saved_weights
+```
+
+<hr>
+
+**`x`** returns the input data (features).
+
+```python
+distributed_scan_object.x
+```
+
+<hr>
+
+**`y`** returns the input data (labels).
+
+```python
+distributed_scan_object.y
+```
+
+## Input Model
+
+The input model is any Keras or tf.keras model. It's the model that Jako will use as the basis for the hyperparameter experiment.
+
+#### A minimal example
+
+```python
+def input_model(x_train, y_train, x_val, y_val, params):
+
+    model.add(Dense(12, input_dim=8, activation=params['activation']))
+    model.add(Dense(1, activation='sigmoid'))
+    model.compile(loss='binary_crossentropy', params['optimizer'])
+    out = model.fit(x=x_train,
+                    y=y_train,
+                    validation_data=[x_val, y_val])
+
+    return out, model
+```
+See specific details about defining the model [here](Examples_Typical?id=defining-the-model).
+
+#### Models with multiple inputs or outputs (list of arrays)
+
+For both cases, `DistributedScan(... x_val, y_val ...)` must be explicitly set i.e. you split the data yourself before passing it into Jako. Using the above minimal example as a reference.
+
+For **multi-input** change `model.fit()` as highlighted below:
+
+```python
+out = model.fit(x=[x_train_a, x_train_b],
+                y=y_train,
+                validation_data=[[x_val_a, x_val_b], y_val])
+```
+
+For **multi-output** the same structure is expected but instead of changing the `x` argument values, now change `y`:
+
+```python
+    out = model.fit(x=x_train,
+                    y=[y_train_a, y_train_b],
+                    validation_data=[x_val, [y_val_a, y_val_b]])
+```
+For the case where its both **multi-input** and **multi-output** now both `x` and `y` argument values follow the same structure:
+
+```python
+    out = model.fit(x=[x_train_a, x_train_b],
+                    y=[y_train_a, y_train_b],
+                    validation_data=[[x_val_a, x_val_b], [y_val_a, y_val_b]])
+```
+
+
+## Parameter Dictionary
+
+The first step in an experiment is to decide the hyperparameters you want to use in the optimization process.
+
+#### A minimal example
+
+```python
+p = {
+    'first_neuron': [12, 24, 48],
+    'activation': ['relu', 'elu'],
+    'batch_size': [10, 20, 30]
+}
+```
+
+#### Supported Input Formats
+
+Parameters may be inputted either in a list or tuple.
+
+As a set of discreet values in a list:
+
+```python
+p = {'first_neuron': [12, 24, 48]}
+```
+As a range of values `(min, max, steps)`:
+
+```python
+p = {'first_neuron': (12, 48, 2)}
+```
+
+For the case where a static value is preferred, but it's still useful to include it in in the parameters dictionary, use list:
+
+```python
+p = {'first_neuron': [48]}
+```
+
+## DistributedScan Config file
+
+A config file has all the information regarding connection to remote machines. The config file will also use one of the remote machines as the central datastore. A sample config file will look like the following:
+
+```
+{
+  "run_central_node": true, 
+  "machines": [
+    {
+      "machine_id": 1,
+      "JAKO_IP_ADDRESS": "machine_1_ip_address",
+      "JAKO_PORT": machine_1_port,
+      "JAKO_USER": "machine_1_username",
+      "JAKO_PASSWORD": "machine_1_password"
+    },
+    {
+      "machine_id": 2,
+      "JAKO_IP_ADDRESS": "machine_2_ip_address",
+      "JAKO_PORT": machine_2_port,
+      "JAKO_USER": "machine_2_username",
+      "JAKO_KEY_FILENAME": "machine_2_key_file_path"
+    }
+  ],
+  "database": {
+    "DB_HOST_MACHINE_ID": 1, #the id for machine which is the datastore
+    "DB_USERNAME": "database_username",
+    "DB_PASSWORD": "database_password",
+    "DB_TYPE": "database_type",
+    "DATABASE_NAME": "database_name",
+    "DB_PORT": database_port,
+    "DB_ENCODING": "LATIN1",
+    "DB_UPDATE_INTERVAL": 5
+  }
+}
+```
+
+### DistributeScan config arguments
+
+Argument | Input | Description
+--------- | ------- | -----------
+`run_central_node` | bool | if set to true, the central machine where the script runs will also be included in distributed run.       
+`machines` | list of dict | list of machine configurations    
+`machine_id` | int | id for each machine in ascending order.   
+`JAKO_IP_ADDRESS` | str | ip address for the remote machine   
+`JAKO_PORT` | int | port number for the remote machine   
+`JAKO_USER` | str | username for the remote machine   
+`JAKO_PASSWORD` | str | password for the remote machine   
+`JAKO_KEY_FILENAME` | str | if password not available, the path to RSA private key of the machine could be supplied to this argument.      
+`database` | dict | configuration parameters for central datastore   
+`DB_HOST_MACHINE_ID` | int | The machine id to one of the remote machines that can be used as the host where the database resides.   
+`DB_USERNAME` | str | database username   
+`DB_PASSWORD` | str | database_password   
+`DB_TYPE` | str | database_type. Default is `postgres`.The available options are `postgres`, `mysql` and `sqlite`      
+`DATABASE_NAME` | str | database name   
+`DB_PORT` | int | database_port   
+`DB_ENCODING` | str | Defaults to `LATIN1`   
+`DB_UPDATE_INTERVAL` | int | The frequency with which database update happens. The value is in seconds.Defaults to `5`.    
+
+

docs/Install_Options.md

@@ -0,0 +1,57 @@
+# Install Options
+
+Before installing jako, it is recommended to first setup and start the following:
+* A python or conda environment.
+* A postgresql database setup in one of the machines. This will be used as the central datastore.
+
+
+## Installing Jako 
+
+#### Creating a python virtual environment
+```python
+virtualenv -p python3 jako_env
+source jako_env/bin/activate
+```
+
+#### Creating a conda virtual environment
+```python
+conda create --name jako_env
+conda activate jako_env
+```
+
+#### Install latest from PyPi
+```python
+pip install jako
+```
+
+#### Install a specific version from PyPi
+```python
+pip install jako==0.1
+```
+
+#### Upgrade installation from PyPi
+```python
+pip install -U --no-deps jako
+```
+
+#### Install from monthly
+```python
+pip install --upgrade --no-deps --force-reinstall git+https://github.com/autonomio/jako
+```
+
+#### Install from weekly
+```python
+pip install --upgrade --no-deps --force-reinstall git+https://github.com/autonomio/jako@dev
+```
+
+#### Install from daily
+```python
+pip install --upgrade --no-deps --force-reinstall git+https://github.com/autonomio/jako@daily-dev
+```
+
+## Installing a postgres database
+
+To enable postgres in your central datastore, follow the steps in this 
+* Postgres for ubuntu machine: [link](https://blog.logrocket.com/setting-up-a-remote-postgres-database-server-on-ubuntu-18-04/)   
+* Postgres for Mac Machines: [link](https://www.sqlshack.com/setting-up-a-postgresql-database-on-mac/)
+