Merge pull request #12 from pralab/fix_docs

update changelog and tutorials

Author: maurapintor

CHANGELOG.md

@@ -1,3 +1,10 @@
+## v0.15.1 (16/09/2021)
+- #11 Fixed install instructions in tutorial notebooks
+- #8 Fixed bug with foolbox CW Attack
+
+## v0.15 (26/11/2021)
+- Migration to www.github.com
+
 ## v0.14.1 (22/04/2021)
 - This version brings fixes for a few issues with the optimizers and related classes, along with improvements to documentation for all attacks, optimizers, and related classes.
 

tutorials/01-Training.ipynb

@@ -3,22 +3,15 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": true,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
+    "collapsed": true
    },
    "source": [
     "# Training of Classifiers and Visualization of Results"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
+   "metadata": {},
    "source": [
     "In this first tutorial we aim to show some basic functionality of SecML.\n",
     "\n",
@@ -29,11 +22,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "%%capture --no-stderr --no-display\n",
@@ -42,16 +31,12 @@
     "try:\n",
     "  import secml\n",
     "except ImportError:\n",
-    "  %pip install git+https://gitlab.com/secml/secml"
+    "  %pip install git+https://github.com/pralab/secml"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
+   "metadata": {},
    "source": [
     "## Creation and visualization of a simple 2D dataset\n",
     "\n",
@@ -66,11 +51,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "random_state = 999\n",
@@ -99,11 +80,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "n_tr = 1000  # Number of training set samples\n",
@@ -124,11 +101,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
+   "metadata": {},
    "source": [
     "Let's visualize the dataset in a 2D plane.\n",
     "\n",
@@ -138,11 +111,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -174,8 +143,7 @@
    "cell_type": "markdown",
    "metadata": {
     "pycharm": {
-     "is_executing": false,
-     "name": "#%% md\n"
+     "is_executing": false
     }
    },
    "source": [
@@ -193,11 +161,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -253,11 +217,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
+   "metadata": {},
    "source": [
     "## Visualization of the decision regions of the classifiers\n",
     "\n",
@@ -268,11 +228,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -304,11 +260,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
+   "metadata": {},
    "source": [
     "## Training other classifiers\n",
     "\n",
@@ -325,9 +277,6 @@
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
     "scrolled": true
    },
    "outputs": [
@@ -482,4 +431,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 1
-}
+}

tutorials/02-NeuralNetworks.ipynb

@@ -3,10 +3,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": true,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
+    "collapsed": true
    },
    "source": [
     "# Neural Networks with PyTorch\n",
@@ -31,11 +28,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "%%capture --no-stderr --no-display\n",
@@ -45,16 +38,12 @@
     "  import secml\n",
     "  import torch\n",
     "except ImportError:\n",
-    "  %pip install git+https://gitlab.com/secml/secml#egg=secml[pytorch]"
+    "  %pip install git+https://github.com/pralab/secml#egg=secml[pytorch]"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
+   "metadata": {},
    "source": [
     "## Classifying blobs\n",
     "\n",
@@ -95,11 +84,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
+   "metadata": {},
    "source": [
     "We will use a 5D dataset composed with 3 gaussians. We can use 4k samples for \n",
     "training and 1k for testing. We can divide the sets in batches so that they can \n",
@@ -149,11 +134,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
+   "metadata": {},
    "source": [
     "Now we can create an instance of the PyTorch model and then wrap it in the \n",
     "specific class that will link it to our library functionalities."
@@ -164,8 +145,7 @@
    "execution_count": 4,
    "metadata": {
     "pycharm": {
-     "is_executing": false,
-     "name": "#%%\n"
+     "is_executing": false
     }
    },
    "outputs": [],
@@ -192,11 +172,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
+   "metadata": {},
    "source": [
     "We can simply use the loaded `CDataset` and pass it to the `fit` method. The \n",
     "wrapper will handle batch processing and train the network for the number of epochs \n",
@@ -208,8 +184,7 @@
    "execution_count": 5,
    "metadata": {
     "pycharm": {
-     "is_executing": true,
-     "name": "#%%\n"
+     "is_executing": true
     }
    },
    "outputs": [
@@ -231,11 +206,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
+   "metadata": {},
    "source": [
     "Using the model in \"predict\" mode is just as easy. We can use the method `predict` \n",
     "defined in our wrapper, and pass in the data. We can evaluate the accuracy with \n",
@@ -247,8 +218,7 @@
    "execution_count": 6,
    "metadata": {
     "pycharm": {
-     "is_executing": true,
-     "name": "#%%\n"
+     "is_executing": true
     }
    },
    "outputs": [
@@ -291,4 +261,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 1
-}
+}