Running Jupyter with SciKit-learn

For more information on Scikit check out (https://scikit-learn.org/)

First start your Jupyter server using the short process:

1. Open https://notebooks.hpc.fau.edu
2. Click “Login”
3. Click “Login to Jupyter”
4. Enter your username and password.
5. Click “Login”
6. Click “New Server”
7. Click “New” -> “Terminal”
8. Enter “pip install –U scikit–learn–user “
   1. You may wish to update your pip if you wish. This is not required.
   2. Enter “pip install upgrade –user pip”
9. Wait for this to complete.
10. Exit the Jupyter server and log back in.
11. You should now have scikit-learn installed.

Running Scikit-learn code:

1. Click “New”
2. “Click Python 3”
3. Enter the following demo code from (https://scikit-learn.org/stable/auto_examples/plot_isotonic_regression.html#sphx-glr-auto-examples-plot-isotonic-regression-py)

   print(__doc__)
   
   # Author: Nelle Varoquaux <nelle.varoquaux@gmail.com>
   #         Alexandre Gramfort <alexandre.gramfort@inria.fr>
   # License: BSD
   
   import numpy as np
   import matplotlib.pyplot as plt
   from matplotlib.collections import LineCollection
   
   from sklearn.linear_model import LinearRegression
   from sklearn.isotonic import IsotonicRegression
   from sklearn.utils import check_random_state
   
   n = 100
   x = np.arange(n)
   rs = check_random_state(0)
   y = rs.randint(-50, 50, size=(n,)) + 50. * np.log1p(np.arange(n))
   
   # #############################################################################
   # Fit IsotonicRegression and LinearRegression models
   
   ir = IsotonicRegression()
   
   y_ = ir.fit_transform(x, y)
   
   lr = LinearRegression()
   lr.fit(x[:, np.newaxis], y)  # x needs to be 2d for LinearRegression
   
   # #############################################################################
   # Plot result
   
   segments = [[[i, y[i]], [i, y_[i]]] for i in range(n)]
   lc = LineCollection(segments, zorder=0)
   lc.set_array(np.ones(len(y)))
   lc.set_linewidths(np.full(n, 0.5))
   
   fig = plt.figure()
   plt.plot(x, y, 'r.', markersize=12)
   plt.plot(x, y_, 'g.-', markersize=12)
   plt.plot(x, lr.predict(x[:, np.newaxis]), 'b-')
   plt.gca().add_collection(lc)
   plt.legend(('Data', 'Isotonic Fit', 'Linear Fit'), loc='lower right')
   plt.title('Isotonic regression')
   plt.show()
   

4. Click Run (possibly twice) and you will be presented with the graph confirming Sci-kit is working in Jupyter.