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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib
matplotlib.style.use('ggplot') # Look Pretty
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def plotDecisionBoundary(model, X, y):
fig = plt.figure()
ax = fig.add_subplot(111)
padding = 0.6
resolution = 0.0025
colors = ['royalblue','forestgreen','ghostwhite']
# Calculate the boundaris
x_min, x_max = X[:, 0].min(), X[:, 0].max()
y_min, y_max = X[:, 1].min(), X[:, 1].max()
x_range = x_max - x_min
y_range = y_max - y_min
x_min -= x_range * padding
y_min -= y_range * padding
x_max += x_range * padding
y_max += y_range * padding
# Create a 2D Grid Matrix. The values stored in the matrix
# are the predictions of the class at at said location
xx, yy = np.meshgrid(np.arange(x_min, x_max, resolution),
np.arange(y_min, y_max, resolution))
# What class does the classifier say?
Z = model.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
# Plot the contour map
cs = plt.contourf(xx, yy, Z, cmap=plt.cm.terrain)
# Plot the test original points as well...
for label in range(len(np.unique(y))):
indices = np.where(y == label)
plt.scatter(X[indices, 0], X[indices, 1], c=colors[label], label=str(label), alpha=0.8)
p = model.get_params()
plt.axis('tight')
plt.title('K = ' + str(p['n_neighbors']))
Load up the dataset into a variable called X. Check .head and dtypes to make sure you're loading your data properly--don't fail on the 1st step!
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# .. your code here ..
Copy the wheat_type series slice out of X, and into a series called y. Then drop the original wheat_type column from the X:
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# .. your code here ..
Do a quick, "ordinal" conversion of y. In actuality our classification isn't ordinal, but just as an experiment...
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# .. your code here ..
Do some basic nan munging. Fill each row's nans with the mean of the feature:
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# .. your code here ..
Split X into training and testing data sets using train_test_split(). Use 0.33 test size, and use random_state=1. This is important so that your answers are verifiable. In the real world, you wouldn't specify a random_state:
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# .. your code here ..
Create an instance of SKLearn's Normalizer class and then train it using its .fit() method against your training data. The reason you only fit against your training data is because in a real-world situation, you'll only have your training data to train with! In this lab setting, you have both train+test data; but in the wild, you'll only have your training data, and then unlabeled data you want to apply your models to.
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# .. your code here ..
With your trained pre-processor, transform both your training AND testing data. Any testing data has to be transformed with your preprocessor that has ben fit against your training data, so that it exist in the same feature-space as the original data used to train your models.
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# .. your code here ..
Just like your preprocessing transformation, create a PCA transformation as well. Fit it against your training data, and then project your training and testing features into PCA space using the PCA model's .transform() method. This has to be done because the only way to visualize the decision boundary in 2D would be if your KNN algo ran in 2D as well:
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# .. your code here ..
Create and train a KNeighborsClassifier. Start with K=9 neighbors. Be sure train your classifier against the pre-processed, PCA- transformed training data above! You do not, of course, need to transform your labels.
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# .. your code here ..
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# I hope your KNeighbors classifier model from earlier was named 'knn'
# If not, adjust the following line:
plotDecisionBoundary(knn, X_train, y_train)
Display the accuracy score of your test data/labels, computed by your KNeighbors model. You do NOT have to run .predict before calling .score, since .score will take care of running your predictions for you automatically.
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# .. your code here ..
Instead of the ordinal conversion, try and get this assignment working with a proper Pandas get_dummies for feature encoding. You might have to update some of the plotDecisionBoundary() code.
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plt.show()
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