Exploration Notebook by: Nathan Danielsen Prema Damodaran
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# read the iris data into a DataFrame
import pandas as pd
url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data'
col_names = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species']
iris = pd.read_csv(url, header=None, names=col_names)
In [2]:
iris.head()
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# map each iris species to a number
iris['species_num'] = iris.species.map({'Iris-setosa':0, 'Iris-versicolor':1, 'Iris-virginica':2})
In [128]:
import yellowbrick as yb
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams['figure.figsize'] = (12, 8)
from yellowbrick.features.rankd import Rank2D
from yellowbrick.features.radviz import RadViz
from yellowbrick.features.pcoords import ParallelCoordinates
In [26]:
# Specify the features of interest and the classes of the target
features = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width']
classes = ['Iris-setosa', 'Iris-versicolor', 'Iris-virginica']
# Extract the numpy arrays from the data frame
X = iris[features].as_matrix()
y = iris.species_num.as_matrix()
In [32]:
visualizer = RadViz(classes=classes, features=features)
visualizer.fit(X, y) # Fit the data to the visualizer
visualizer.transform(X) # Transform the data
visualizer.show() # Draw/show/show the data
Setosas tend to have the largest septal-width. This can could be a great predictor.
Then, let's remove setosa from the training set and see fi we can find any differentiation between veriscolor and virginica.
In [141]:
# Specify the features of interest and the classes of the target
iris_subset = iris[iris.species_num!=0]
features = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width']
classes = ['Iris-setosa','Iris-versicolor', 'Iris-virginica'] # but have to leave in more than two classes
# Extract the numpy arrays from the data frame
X = iris_subset[features].as_matrix()
y = iris_subset.species_num.as_matrix()
assert y.shape[0] == X.shape[0]
In [142]:
visualizer = RadViz(classes=classes, features=features)
visualizer.fit(X, y) # Fit the data to the visualizer
visualizer.transform(X) # Transform the data
visualizer.show() # Draw/show/show the data
In [144]:
# Specify the features of interest and the classes of the target
features = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width']
classes = ['Iris-setosa', 'Iris-versicolor', 'Iris-virginica']
# Extract the numpy arrays from the data frame
X = iris[features].as_matrix()
y = iris.species_num.as_matrix()
visualizer = Rank2D(features=features, algorithm='covariance')
visualizer.fit(X, y) # Fit the data to the visualizer
visualizer.transform(X) # Transform the data
visualizer.show() # Draw/show/show the data
This covariance chart is not intereptatble as they don't have labels. Also there shouldn't be half numbers in labels.
In [63]:
features = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width']
classes = ['Iris-setosa', 'Iris-versicolor', 'Iris-virginica']
# Extract the numpy arrays from the data frame
X = iris[features].as_matrix()
y = iris.species_num.values
assert y.shape[0] == X.shape[0]
In [64]:
visualizer = ParallelCoordinates(classes=classes, features=features)
visualizer.fit(X, y) # Fit the data to the visualizer
visualizer.transform(X) # Transform the data
visualizer.show() # Draw/show/show the data
This clearly demonstrates the separation between features - especially petal_length and petal_width. One concern is that this demonstraction data might be obsured by the scaling of the features and add noise to the intepretation.
In [65]:
from sklearn import preprocessing
In [66]:
features = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width']
classes = ['Iris-setosa', 'Iris-versicolor', 'Iris-virginica']
# Extract the numpy arrays from the data frame
X = iris[features].as_matrix()
X_scaled = preprocessing.scale(X)
y = iris.species_num.values
assert y.shape[0] == X.shape[0]
In [67]:
visualizer = ParallelCoordinates(classes=classes, features=features)
visualizer.fit(X_scaled, y) # Fit the data to the visualizer
visualizer.transform(X) # Transform the data
visualizer.show()
The scaled dataset makes it easier to see the separation between classes for each of the features.
*TODO - Add scaling option to PararalCordinates and potentially other visualizers
From the feature selection phase, we determined that petal_length and petal_width seem to have the best separation.
In [145]:
# Classifier Evaluation Imports
from sklearn.naive_bayes import GaussianNB
from sklearn.model_selection import train_test_split
from yellowbrick.classifier import ClassificationReport, ClassBalance
In [146]:
features = ['petal_length', 'petal_width']
classes = ['Iris-setosa', 'Iris-versicolor', 'Iris-virginica']
# Extract the numpy arrays from the data frame
X = iris[features].as_matrix()
y = iris.species_num.as_matrix()
assert y.shape[0] == X.shape[0]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
assert y_train.shape[0] == X_train.shape[0]
assert y_test.shape[0] == X_test.shape[0]
In [147]:
# Instantiate the classification model and visualizer
bayes = GaussianNB()
visualizer = ClassificationReport(bayes, classes=classes)
visualizer.fit(X_train, y_train) # Fit the training data to the visualizer
visualizer.score(X_test, y_test) # Evaluate the model on the test data
g = visualizer.show() # Draw/show/show the data
In [96]:
visualizer
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Note: There seems to be some sort of bug in the draw/ fit methods.
In [148]:
# Classifier Evaluation Imports
from sklearn.naive_bayes import MultinomialNB
from sklearn.model_selection import train_test_split
from yellowbrick.classifier import ClassificationReport, ClassBalance
In [149]:
features = ['petal_length', 'petal_width']
classes = ['Iris-setosa', 'Iris-versicolor', 'Iris-virginica']
# Extract the numpy arrays from the data frame
X = iris[features].as_matrix()
y = iris.species_num.as_matrix()
assert y.shape[0] == X.shape[0]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
assert y_train.shape[0] == X_train.shape[0]
assert y_test.shape[0] == X_test.shape[0]
In [151]:
# Instantiate the classification model and visualizer
bayes = MultinomialNB()
visualizer = ClassificationReport(bayes)# classes=classes)
visualizer.fit(X_train, y_train) # Fit the training data to the visualizer
visualizer.score(X_test, y_test) # Evaluate the model on the test data
g = visualizer.show() # Draw/show/show the data
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In [108]:
features = ['petal_length', 'petal_width']
classes = ['Iris-setosa', 'Iris-versicolor', 'Iris-virginica']
# Extract the numpy arrays from the data frame
X = iris[features].as_matrix()
y = iris.species_num.as_matrix()
assert y.shape[0] == X.shape[0]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)
assert y_train.shape[0] == X_train.shape[0]
assert y_test.shape[0] == X_test.shape[0]
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test = pd.DataFrame(y_test, columns=['species'])
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test.species.value_counts() # The test train split provides unbalanced classes
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In [132]:
from sklearn.ensemble import RandomForestClassifier
from yellowbrick.classifier import ClassificationReport
# Instantiate the classification model and visualizer
forest = RandomForestClassifier()
visualizer = ClassBalance(forest, classes=classes)
visualizer.fit(X_train, y_train) # Fit the training data to the visualizer
visualizer.score(X_test, y_test) # Evaluate the model on the test data
g = visualizer.show() # Draw/show/show the data
Note: The visualization works, but the example provides an unbalanced training/ testing set that provides a misleading for a Randomforest classifier. A more powerful example would train with balanced classes.
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