Logistic Regression with Many Features

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Hide code cell source 
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import matplotlib_inline
matplotlib_inline.backend_inline.set_matplotlib_formats('svg')
import seaborn as sns
sns.set_context("paper")
sns.set_style("ticks")

import scipy
import scipy.stats as st
import urllib.request
import os

def download(
    url : str,
    local_filename : str = None
):
    """Download a file from a url.
    
    Arguments
    url            -- The url we want to download.
    local_filename -- The filemame to write on. If not
                      specified 
    """
    if local_filename is None:
        local_filename = os.path.basename(url)
    urllib.request.urlretrieve(url, local_filename)

Logistic Regression with Many Features#

Let’s repeat what we did for the HMX example. Instead of using a linear model inside the sigmoid, we will use a quadratic model. That is, the probability of an explosion will be:

\[ p(y=1|x,\mathbf{w}) = \operatorname{sigm}\left(w_0 + w_1 x + w_2 x^2\right). \]

Let’s load the data first:

Hide code cell source 
url = "https://github.com/PredictiveScienceLab/data-analytics-se/raw/master/lecturebook/data/hmx_data.csv"
download(url)

import pandas as pd

data = pd.read_csv('hmx_data.csv')
x = data['Height'].values
label_coding = {'E': 1, 'N': 0}
y = np.array([label_coding[r] for r in data['Result']])
data['y'] = y
data.head()
Height Result y
0 40.5 E 1
1 40.5 E 1
2 40.5 E 1
3 40.5 E 1
4 40.5 E 1

Now let’s train a second degree polynomial model:

from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LogisticRegression

# Design matrix
poly = PolynomialFeatures(2)
Phi = poly.fit_transform(x[:, None])

# Fit
model = LogisticRegression(
    penalty=None,
    fit_intercept=False
).fit(Phi, y)

Here are the model parameters:

model.coef_

array([[-0.02784038, -0.41748133,  0.01335223]])

Let’s plot the predictions:

fig, ax = plt.subplots()
xx = np.linspace(20.0, 45.0, 100)
Phi_xx = poly.fit_transform(xx[:, None])
predictions_xx = model.predict_proba(Phi_xx)
ax.plot(
    xx,
    predictions_xx[:, 0],
    label='Probability of N'
)
ax.plot(
    xx,
    predictions_xx[:, 1],
    label='Probability of E'
)
ax.set_xlabel('$x$ (cm)')
ax.set_ylabel('Probability')
plt.legend(loc='best', frameon=False)
sns.despine(trim=True);
../_images/defb2f6f719a68f1620506360cce046ab692e2c57819be52703cac38514e319b.svg

Questions#

  • Is it worth going to a second-degree model? Can you compare the two models?

  • Rerun the code above with polynomial degrees 3, 4, and 5. What do you observe? Do you trust the results? Why or why not?

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