migrate to hatch

.github/PULL_REQUEST_TEMPLATE.md

@@ -9,7 +9,6 @@ the below items:
 - [ ] Code is [PEP8](https://www.python.org/dev/peps/pep-0008/)
 - [ ] I'm making the PR to `master`
 - [ ] I've updated the versions based on [Semantic Versioning](https://semver.org/)
-  - [ ] `setup.py`
   - [ ] `talos/__init__.py`
   - [ ] `docs/index.html`
   - [ ] `docs/_coverpage.md`

.github/workflows/ci.yml

@@ -9,7 +9,7 @@ jobs:
     strategy:
       max-parallel: 9
       matrix:
-        python-version: [3.7, 3.8]
+        python-version: ['3.9', '3.10', '3.11']
         os: [ubuntu-latest, macos-latest]
 
     steps:
@@ -33,7 +33,7 @@ jobs:
     - name: Tests
       run: |
         export MPLBACKEND=agg
-        pip install tensorflow>=2.0
+        pip install tensorflow==2.14.1
         pip install coveralls
         coverage run --source=talos ./test-ci.py
     - name: Coverage

.github/workflows/deploy.yml

@@ -16,11 +16,11 @@ jobs:
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip
-        pip install setuptools wheel twine
+        pip install hatch
     - name: Build and publish
       env:
-        TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }}
-        TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }}
+        PYPI_API_TOKEN: ${{ secrets.PYPI_API_TOKEN }}
       run: |
-        python setup.py sdist bdist_wheel
-        twine upload dist/*
+        hatch build
+        hatch publish --user __token__ --auth $PYPI_API_TOKEN
+

.gitignore

@@ -14,4 +14,5 @@ talos.egg-info
 *.npy
 test.sh
 .vscode
-.test_*
+test_*/
+test/

docs/Examples_Generator.md

@@ -83,8 +83,8 @@ return out, model
 ### Parameter Dictionary
 ```python
 p = {'activation':['relu', 'elu'],
-     'optimizer': ['AdaDelta'],
-     'losses': ['logcosh'],
+     'optimizer': ['Adam'],
+     'losses': ['LogCosh'],
      'shapes': ['brick'],
      'first_neuron': [32],
      'dropout': [.2, .3],

docs/Examples_Generator_Code.md

@@ -36,8 +36,8 @@ def mnist_model(x_train, y_train, x_val, y_val, params):
     return out, model
 
 p = {'activation':['relu', 'elu'],
-     'optimizer': ['AdaDelta'],
-     'losses': ['logcosh'],
+     'optimizer': ['Adam'],
+     'losses': ['LogCosh'],
      'shapes': ['brick'],
      'first_neuron': [32],
      'dropout': [.2, .3],

docs/Examples_PyTorch.md

@@ -138,8 +138,8 @@ return net, net.parameters()
 
 ```python
 p = {'activation':['relu', 'elu'],
-       'optimizer': ['Nadam', 'Adam'],
-       'losses': ['logcosh'],
+       'optimizer': ['Adagrad', 'Adam'],
+       'losses': ['LogCosh'],
        'hidden_layers':[0, 1, 2],
        'batch_size': (20, 50, 5),
        'epochs': [10, 20]}

docs/Examples_PyTorch_Code.md

@@ -106,8 +106,8 @@ def breast_cancer(x_train, y_train, x_val, y_val, params):
 
 
 p = {'activation':['relu', 'elu'],
-       'optimizer': ['Nadam', 'Adam'],
-       'losses': ['logcosh'],
+       'optimizer': ['Adagrad', 'Adam'],
+       'losses': ['LogCosh'],
        'hidden_layers':[0, 1, 2],
        'batch_size': (20, 50, 5),
        'epochs': [10, 20]}

docs/Examples_Typical.md

@@ -55,8 +55,8 @@ return out, model
 ### Parameter Dictionary
 ```python
 p = {'activation':['relu', 'elu'],
-     'optimizer': ['Nadam', 'Adam'],
-     'losses': ['logcosh'],
+     'optimizer': ['Adagrad', 'Adam'],
+     'losses': ['LogCosh'],
      'hidden_layers':[0, 1, 2],
      'batch_size': (20, 50, 5),
      'epochs': [10, 20]}

docs/Examples_Typical_Code.md

@@ -31,7 +31,7 @@ def iris_model(x_train, y_train, x_val, y_val, params):
 
 # set the parameter space boundaries
 p = {'activation':['relu', 'elu'],
-     'optimizer': ['Nadam', 'Adam'],
+     'optimizer': ['Adagrad', 'Adam'],
      'losses': ['categorical_crossentropy'],
      'epochs': [100, 200],
      'batch_size': [4, 6, 8]}

docs/Hidden_Layers.md

@@ -18,8 +18,8 @@ When hidden layers are used, `dropout`, `shapes`, `hidden_layers`, and `first_ne
 ```python
 
     p = {'activation':['relu', 'elu'],
-         'optimizer': ['Nadam', 'Adam'],
-         'losses': ['logcosh'],
+         'optimizer': ['Adagrad', 'Adam'],
+         'losses': ['LogCosh'],
          'shapes': ['brick'],          # <<< required
          'first_neuron': [32, 64],     # <<< required
          'hidden_layers':[0, 1, 2],    # <<< required

docs/_coverpage.md

@@ -1,6 +1,6 @@
 ![logo](_media/talos_logo_bg.png)
 
-## v1.3.3
+## v1.4
 
 > Hyperparameter Experiments with Tensorflow, PyTorch and Keras
 

docs/index.html

@@ -16,7 +16,7 @@
   <div id="app"></div>
   <script>
     window.$docsify = {
-      name: 'Talos 1.3.3',
+      name: 'Talos 1.4',
       repo: 'https://github.com/autonomio/talos',
       coverpage: true,
       loadSidebar: true,

examples/Functional Model Hyperparameter Optimization.ipynb

@@ -125,7 +125,7 @@
     "     'epochs': [100],\n",
     "     'dropout': [0],\n",
     "     'kernel_initializer': ['uniform','normal'],\n",
-    "     'optimizer': ['Nadam', 'Adam'],\n",
+    "     'optimizer': ['Adagrad', 'Adam'],\n",
     "     'losses': ['binary_crossentropy'],\n",
     "     'activation':['relu', 'elu'],\n",
     "     'last_activation': ['sigmoid']}"

examples/Hyperparameter Optimization on Keras with Breast Cancer Data.ipynb

@@ -2,20 +2,21 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "<img src='https://raw.githubusercontent.com/autonomio/hyperio/master/logo.png' width=250px>"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "This notebook is a compliment to the *Hyperparameter Optimization on Keras* article. "
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## Overview\n",
     "\n",
@@ -28,19 +29,20 @@
     "3) Defining the Parameter Space Boundaries \n",
     "\n",
     "4) Running the Experiment"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## 1. The Required Inputs and Data"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "from tensorflow.keras.models import Sequential\n",
     "from tensorflow.keras.layers import Dropout, Dense\n",
@@ -50,33 +52,33 @@
     "import sys\n",
     "sys.path.insert(0, '/Users/mikko/Documents/GitHub/talos')\n",
     "import talos"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# then we load the dataset\n",
     "x, y = talos.templates.datasets.breast_cancer()\n",
     "\n",
     "# and normalize every feature to mean 0, std 1\n",
     "x = talos.utils.rescale_meanzero(x)"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## 2. Creating the Keras Model"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# first we have to make sure to input data and params into the function\n",
     "def breast_cancer_model(x_train, y_train, x_val, y_val, params):\n",
@@ -106,20 +108,20 @@
     "                        verbose=0)\n",
     "\n",
     "    return history, model"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## 3. Defining the Parameter Space Boundary"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# then we can go ahead and set the parameter space\n",
     "p = {'first_neuron':[9, 10, 11],\n",
@@ -128,24 +130,26 @@
     "     'epochs': [100],\n",
     "     'dropout': [0],\n",
     "     'kernel_initializer': ['uniform','normal'],\n",
-    "     'optimizer': ['Nadam', 'Adam'],\n",
+    "     'optimizer': ['Adagrad', 'Adam'],\n",
     "     'losses': ['binary_crossentropy'],\n",
     "     'activation':['relu', 'elu'],\n",
     "     'last_activation': ['sigmoid']}"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## 4. Starting the Experiment"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
    "source": [
     "# and run the experiment\n",
     "t = talos.Scan(x=x,\n",
@@ -155,11 +159,7 @@
     "               experiment_name='breast_cancer',\n",
     "               round_limit=50,\n",
     "               disable_progress_bar=True)"
-   ],
-   "outputs": [],
-   "metadata": {
-    "collapsed": true
-   }
+   ]
   }
  ],
  "metadata": {
@@ -183,4 +183,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}

examples/Hyperparameter Optimization with Keras for the Iris Prediction.ipynb

@@ -145,16 +145,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from tensorflow.keras.optimizers import Adam, Nadam\n",
+    "from tensorflow.keras.optimizers.legacy import Adam, Adagrad\n",
     "from tensorflow.keras.activations import softmax\n",
-    "from tensorflow.keras.losses import categorical_crossentropy, logcosh\n",
+    "from tensorflow.keras.losses import categorical_crossentropy, LogCosh\n",
     "\n",
     "p = {'lr': (0.1, 10, 10),\n",
     "     'first_neuron':[4, 8, 16, 32, 64, 128],\n",
     "     'batch_size': [2, 3, 4],\n",
     "     'epochs': [200],\n",
     "     'dropout': (0, 0.40, 10),\n",
-    "     'optimizer': [Adam, Nadam],\n",
+    "     'optimizer': [Adam, Adagrad],\n",
     "     'loss': ['categorical_crossentropy'],\n",
     "     'last_activation': ['softmax'],\n",
     "     'weight_regulizer': [None]}"