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://galaxy.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 scikitlearn–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.
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