Running Jupyter with SciKit-learn

Scikit-learn is an open source data analysis library, and the gold standard for Machine Learning (ML) in the Python ecosystem. Algorithmic decision-making methods, including:Classification,Regression, and Clustering.

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

First start your Jupyter server using the short process:

1. Open https://ondemand.hpc.fau.edu
2. Login
3. Click Interactive Apps -> Jupyter Notebook
4. Enter the requirements for your job
5. Click “Launch”
6. Wait for allocation to a node, once available click "Connect to Jupyter"
7. Create a new notebook by clicking: New -> Python 3 or open an existing notebook  
8. Enter "pip install --user -U scikit-learn “
   1. You may wish to update your pip if you wish. This is not required.
   2. Enter “pip install upgrade --user pip”
9. Enter "pip install --user matplotlib"
10. Wait for this to complete.

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 and you will be presented with the graph confirming Sci-kit is working in Jupyter.