Update apis

.gitignore

@@ -59,6 +59,6 @@ target/
 # IPython
 .ipynb_checkpoints
 notebooks/.ipynb_checkpoints
-
+/.idea
 # Emacs
 *~

README.md

@@ -28,12 +28,20 @@ Once this is installed, the following command will install all required packages
 Original install (2015)
 $ conda install numpy scipy matplotlib scikit-learn ipython-notebook seaborn
 
-Or for current versions of Anaconda (Mar 2018)
+For older version Anaconda (Mar 2018)
 
 $ conda create -n skl_tut python=3.4.5 ipywidgets=5.2.2 numpy scipy matplotlib scikit-learn ipython-notebook seaborn pillow
+```
 
-$ activate skl_tut
+**For current (2023) version of the project:** 
+```
+$ conda create -n skl_tut python=3.10.8 --file requirements.txt
+```
 
+```
+$ activate skl_tut
+```
+```
 $ jupyter notebook --notebook-dir='<tutorial folder>'
 ```
 

notebooks/01-Preliminaries.ipynb

@@ -115,9 +115,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "IPython: 8.10.0\n",
+      "numpy: 1.23.5\n",
+      "scipy: 1.10.0\n",
+      "matplotlib: 3.6.2\n",
+      "scikit-learn: 1.2.1\n",
+      "seaborn: 0.12.2\n"
+     ]
+    }
+   ],
    "source": [
     "from __future__ import print_function\n",
     "\n",
@@ -134,7 +147,10 @@
     "print('matplotlib:', matplotlib.__version__)\n",
     "\n",
     "import sklearn\n",
-    "print('scikit-learn:', sklearn.__version__)"
+    "print('scikit-learn:', sklearn.__version__)\n",
+    "\n",
+    "import seaborn\n",
+    "print('seaborn:', seaborn.__version__)"
    ]
   },
   {
@@ -156,7 +172,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -170,9 +186,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.0"
+   "version": "3.10.8"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }

notebooks/02.1-Machine-Learning-Intro.ipynb

@@ -71,10 +71,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
-    "\n",
-    "plt.style.use('seaborn')"
+    "plt.style.use(\"seaborn-v0_8-darkgrid\")"
    ]
   },
   {
@@ -194,7 +192,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from IPython.core.display import Image, display\n",
+    "from IPython.display import Image, display\n",
     "display(Image(filename='images/iris_setosa.jpg'))\n",
     "print(\"Iris Setosa\\n\")\n",
     "\n",
@@ -422,7 +420,7 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -436,9 +434,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.0"
+   "version": "3.10.8"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }

notebooks/02.2-Basic-Principles.ipynb

@@ -30,10 +30,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%matplotlib inline\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "plt.style.use('seaborn')"
+    "plt.style.use(\"seaborn-v0_8-darkgrid\")"
    ]
   },
   {
@@ -51,7 +50,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from sklearn.linear_model import LinearRegression"
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.preprocessing import normalize"
    ]
   },
   {
@@ -67,8 +67,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "model = LinearRegression(normalize=True)\n",
-    "print(model.normalize)"
+    "model = LinearRegression() # normalize=True is no longer an argument, not using it does not seem to affect the result here"
    ]
   },
   {
@@ -471,7 +470,7 @@
    "outputs": [],
    "source": [
     "from sklearn.cluster import KMeans\n",
-    "k_means = KMeans(n_clusters=3, random_state=0) # Fixing the RNG in kmeans\n",
+    "k_means = KMeans(n_clusters=3, random_state=0, n_init='auto') # Fixing the RNG in kmeans\n",
     "k_means.fit(X)\n",
     "y_pred = k_means.predict(X)\n",
     "\n",
@@ -740,7 +739,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "iso = Isomap(n_components=2)\n",
+    "iso = Isomap(n_components=2, n_neighbors=7)\n",
     "data_projected = iso.fit_transform(digits.data)"
    ]
   },
@@ -807,8 +806,16 @@
    "outputs": [],
    "source": [
     "from sklearn.linear_model import LogisticRegression\n",
-    "clf = LogisticRegression(penalty='l2')\n",
-    "clf.fit(Xtrain, ytrain)\n",
+    "from sklearn import preprocessing\n",
+    "\n",
+    "scaler = preprocessing.StandardScaler().fit(Xtrain) # Scale data to avoid 'ConvergenceWarning: lbfgs failed to converge (status=1)'\n",
+    "X_scaled = scaler.transform(Xtrain)\n",
+    "\n",
+    "scaler_test = preprocessing.StandardScaler().fit(Xtest)\n",
+    "Xtest = scaler.transform(Xtest)\n",
+    "\n",
+    "clf = LogisticRegression(penalty='l2', max_iter=150) # Increase max_iter from 100 to 150 to avoid 'ConvergenceWarning: lbfgs failed to converge (status=1)'\n",
+    "clf.fit(X_scaled, ytrain)\n",
     "ypred = clf.predict(Xtest)"
    ]
   },
@@ -897,7 +904,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -911,9 +918,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.0"
+   "version": "3.10.8"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }

notebooks/03.1-Classification-SVMs.ipynb

@@ -28,12 +28,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%matplotlib inline\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
     "from scipy import stats\n",
     "\n",
-    "plt.style.use('seaborn')"
+    "plt.style.use(\"seaborn-v0_8-darkgrid\")"
    ]
   },
   {
@@ -58,7 +57,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from sklearn.datasets.samples_generator import make_blobs\n",
+    "from sklearn.datasets import make_blobs\n",
     "X, y = make_blobs(n_samples=50, centers=2,\n",
     "                  random_state=0, cluster_std=0.60)\n",
     "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='spring');"
@@ -240,7 +239,7 @@
     "    plt.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1],\n",
     "                s=200, facecolors='none')\n",
     "    \n",
-    "interact(plot_svm, N=[10, 200], kernel='linear');"
+    "interact(plot_svm, N=(10, 200), kernel='linear');"
    ]
   },
   {
@@ -266,7 +265,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from sklearn.datasets.samples_generator import make_circles\n",
+    "from sklearn.datasets import make_circles\n",
     "X, y = make_circles(100, factor=.1, noise=.1)\n",
     "\n",
     "clf = SVC(kernel='linear').fit(X, y)\n",
@@ -355,7 +354,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -369,9 +368,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.0"
+   "version": "3.10.8"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }

notebooks/03.2-Regression-Forests.ipynb

@@ -28,12 +28,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%matplotlib inline\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
     "from scipy import stats\n",
     "\n",
-    "plt.style.use('seaborn')"
+    "plt.style.use(\"seaborn-v0_8-darkgrid\")"
    ]
   },
   {
@@ -410,7 +409,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -424,9 +423,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.0"
+   "version": "3.10.8"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }

notebooks/04.1-Dimensionality-PCA.ipynb

@@ -26,12 +26,11 @@
    "source": [
     "from __future__ import print_function, division\n",
     "\n",
-    "%matplotlib inline\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
     "from scipy import stats\n",
     "\n",
-    "plt.style.use('seaborn')"
+    "plt.style.use(\"seaborn-v0_8-darkgrid\")"
    ]
   },
   {
@@ -355,7 +354,7 @@
     "                 size=18)\n",
     "    plt.clim(0, 16)\n",
     "    \n",
-    "interact(plot_digits, n_components=[1, 64], nside=[1, 8]);"
+    "interact(plot_digits, n_components=(1, 64), nside=[1, 8]);"
    ]
   },
   {
@@ -388,7 +387,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -402,9 +401,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.0"
+   "version": "3.10.8"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }