Machine Learning hello world
# Load libraries
import pandas
from pandas.plotting import scatter_matrix
import matplotlib.pyplot as plt
from sklearn import model_selection
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC# Load dataset
url = "teste.data"
names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class']
dataset = pandas.read_csv(url, names=names)dataset
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| sepal-length | sepal-width | petal-length | petal-width | class | |
|---|---|---|---|---|---|
| 0 | 5.1 | 3.5 | 1.4 | 0.2 | Iris-setosa |
| 1 | 4.9 | 3.0 | 1.4 | 0.2 | Iris-setosa |
| 2 | 4.7 | 3.2 | 1.3 | 0.2 | Iris-setosa |
| 3 | 4.6 | 3.1 | 1.5 | 0.2 | Iris-setosa |
| 4 | 5.0 | 3.6 | 1.4 | 0.2 | Iris-setosa |
| 5 | 5.4 | 3.9 | 1.7 | 0.4 | Iris-setosa |
| 6 | 4.6 | 3.4 | 1.4 | 0.3 | Iris-setosa |
| 7 | 5.0 | 3.4 | 1.5 | 0.2 | Iris-setosa |
| 8 | 4.4 | 2.9 | 1.4 | 0.2 | Iris-setosa |
| 9 | 4.9 | 3.1 | 1.5 | 0.1 | Iris-setosa |
| 10 | 5.4 | 3.7 | 1.5 | 0.2 | Iris-setosa |
| 11 | 4.8 | 3.4 | 1.6 | 0.2 | Iris-setosa |
| 12 | 4.8 | 3.0 | 1.4 | 0.1 | Iris-setosa |
| 13 | 4.3 | 3.0 | 1.1 | 0.1 | Iris-setosa |
| 14 | 5.8 | 4.0 | 1.2 | 0.2 | Iris-setosa |
| 15 | 5.7 | 4.4 | 1.5 | 0.4 | Iris-setosa |
| 16 | 5.4 | 3.9 | 1.3 | 0.4 | Iris-setosa |
| 17 | 5.1 | 3.5 | 1.4 | 0.3 | Iris-setosa |
| 18 | 5.7 | 3.8 | 1.7 | 0.3 | Iris-setosa |
| 19 | 5.1 | 3.8 | 1.5 | 0.3 | Iris-setosa |
| 20 | 5.4 | 3.4 | 1.7 | 0.2 | Iris-setosa |
| 21 | 5.1 | 3.7 | 1.5 | 0.4 | Iris-setosa |
| 22 | 4.6 | 3.6 | 1.0 | 0.2 | Iris-setosa |
| 23 | 5.1 | 3.3 | 1.7 | 0.5 | Iris-setosa |
| 24 | 4.8 | 3.4 | 1.9 | 0.2 | Iris-setosa |
| 25 | 5.0 | 3.0 | 1.6 | 0.2 | Iris-setosa |
| 26 | 5.0 | 3.4 | 1.6 | 0.4 | Iris-setosa |
| 27 | 5.2 | 3.5 | 1.5 | 0.2 | Iris-setosa |
| 28 | 5.2 | 3.4 | 1.4 | 0.2 | Iris-setosa |
| 29 | 4.7 | 3.2 | 1.6 | 0.2 | Iris-setosa |
| ... | ... | ... | ... | ... | ... |
| 120 | 6.9 | 3.2 | 5.7 | 2.3 | Iris-virginica |
| 121 | 5.6 | 2.8 | 4.9 | 2.0 | Iris-virginica |
| 122 | 7.7 | 2.8 | 6.7 | 2.0 | Iris-virginica |
| 123 | 6.3 | 2.7 | 4.9 | 1.8 | Iris-virginica |
| 124 | 6.7 | 3.3 | 5.7 | 2.1 | Iris-virginica |
| 125 | 7.2 | 3.2 | 6.0 | 1.8 | Iris-virginica |
| 126 | 6.2 | 2.8 | 4.8 | 1.8 | Iris-virginica |
| 127 | 6.1 | 3.0 | 4.9 | 1.8 | Iris-virginica |
| 128 | 6.4 | 2.8 | 5.6 | 2.1 | Iris-virginica |
| 129 | 7.2 | 3.0 | 5.8 | 1.6 | Iris-virginica |
| 130 | 7.4 | 2.8 | 6.1 | 1.9 | Iris-virginica |
| 131 | 7.9 | 3.8 | 6.4 | 2.0 | Iris-virginica |
| 132 | 6.4 | 2.8 | 5.6 | 2.2 | Iris-virginica |
| 133 | 6.3 | 2.8 | 5.1 | 1.5 | Iris-virginica |
| 134 | 6.1 | 2.6 | 5.6 | 1.4 | Iris-virginica |
| 135 | 7.7 | 3.0 | 6.1 | 2.3 | Iris-virginica |
| 136 | 6.3 | 3.4 | 5.6 | 2.4 | Iris-virginica |
| 137 | 6.4 | 3.1 | 5.5 | 1.8 | Iris-virginica |
| 138 | 6.0 | 3.0 | 4.8 | 1.8 | Iris-virginica |
| 139 | 6.9 | 3.1 | 5.4 | 2.1 | Iris-virginica |
| 140 | 6.7 | 3.1 | 5.6 | 2.4 | Iris-virginica |
| 141 | 6.9 | 3.1 | 5.1 | 2.3 | Iris-virginica |
| 142 | 5.8 | 2.7 | 5.1 | 1.9 | Iris-virginica |
| 143 | 6.8 | 3.2 | 5.9 | 2.3 | Iris-virginica |
| 144 | 6.7 | 3.3 | 5.7 | 2.5 | Iris-virginica |
| 145 | 6.7 | 3.0 | 5.2 | 2.3 | Iris-virginica |
| 146 | 6.3 | 2.5 | 5.0 | 1.9 | Iris-virginica |
| 147 | 6.5 | 3.0 | 5.2 | 2.0 | Iris-virginica |
| 148 | 6.2 | 3.4 | 5.4 | 2.3 | Iris-virginica |
| 149 | 5.9 | 3.0 | 5.1 | 1.8 | Iris-virginica |
150 rows × 5 columns
# shape
print(dataset.shape)(150, 5)
# get header
print(dataset.head(20)) sepal-length sepal-width petal-length petal-width class
0 5.1 3.5 1.4 0.2 Iris-setosa
1 4.9 3.0 1.4 0.2 Iris-setosa
2 4.7 3.2 1.3 0.2 Iris-setosa
3 4.6 3.1 1.5 0.2 Iris-setosa
4 5.0 3.6 1.4 0.2 Iris-setosa
5 5.4 3.9 1.7 0.4 Iris-setosa
6 4.6 3.4 1.4 0.3 Iris-setosa
7 5.0 3.4 1.5 0.2 Iris-setosa
8 4.4 2.9 1.4 0.2 Iris-setosa
9 4.9 3.1 1.5 0.1 Iris-setosa
10 5.4 3.7 1.5 0.2 Iris-setosa
11 4.8 3.4 1.6 0.2 Iris-setosa
12 4.8 3.0 1.4 0.1 Iris-setosa
13 4.3 3.0 1.1 0.1 Iris-setosa
14 5.8 4.0 1.2 0.2 Iris-setosa
15 5.7 4.4 1.5 0.4 Iris-setosa
16 5.4 3.9 1.3 0.4 Iris-setosa
17 5.1 3.5 1.4 0.3 Iris-setosa
18 5.7 3.8 1.7 0.3 Iris-setosa
19 5.1 3.8 1.5 0.3 Iris-setosa
print(dataset.describe()) sepal-length sepal-width petal-length petal-width
count 150.000000 150.000000 150.000000 150.000000
mean 5.843333 3.054000 3.758667 1.198667
std 0.828066 0.433594 1.764420 0.763161
min 4.300000 2.000000 1.000000 0.100000
25% 5.100000 2.800000 1.600000 0.300000
50% 5.800000 3.000000 4.350000 1.300000
75% 6.400000 3.300000 5.100000 1.800000
max 7.900000 4.400000 6.900000 2.500000
# class
print(dataset.groupby('class').size())class
Iris-setosa 50
Iris-versicolor 50
Iris-virginica 50
dtype: int64
# box and whisker plots
dataset.plot(kind='box', subplots=True, layout=(2,2), sharex=False, sharey=False)
plt.show()
print(dataset.hist())
plt.show()[[<matplotlib.axes._subplots.AxesSubplot object at 0x1a2376da58>
<matplotlib.axes._subplots.AxesSubplot object at 0x1a23be02b0>]
[<matplotlib.axes._subplots.AxesSubplot object at 0x1a23c8fcf8>
<matplotlib.axes._subplots.AxesSubplot object at 0x1a1a789390>]]
scatter_matrix(dataset)
plt.show()
# Split-out validation dataset
array = dataset.values
X = array[:,0:4]
Y = array[:,4]
validation_size = 0.20
seed = 7
X_train, X_validation, Y_train, Y_validation = model_selection.train_test_split(X, Y, test_size=validation_size, random_state=seed)# Test options and evaluation metric
seed = 7
scoring = 'accuracy'# Spot Check Algorithms
models = []
models.append(('LR', LogisticRegression()))
models.append(('LDA', LinearDiscriminantAnalysis()))
models.append(('KNN', KNeighborsClassifier()))
models.append(('CART', DecisionTreeClassifier()))
models.append(('NB', GaussianNB()))
models.append(('SVM', SVC()))
# evaluate each model in turn
results = []
names = []
for name, model in models:
kfold = model_selection.KFold(n_splits=10, random_state=seed)
cv_results = model_selection.cross_val_score(model, X_train, Y_train, cv=kfold, scoring=scoring)
results.append(cv_results)
names.append(name)
msg = "%s: %f (%f)" % (name, cv_results.mean(), cv_results.std())
print(msg)LR: 0.966667 (0.040825)
LDA: 0.975000 (0.038188)
KNN: 0.983333 (0.033333)
CART: 0.975000 (0.038188)
NB: 0.975000 (0.053359)
SVM: 0.991667 (0.025000)
# Compare Algorithms
fig = plt.figure()
fig.suptitle('Algorithm Comparison')
ax = fig.add_subplot(111)
plt.boxplot(results)
ax.set_xticklabels(names)
plt.show()
# Make predictions on validation dataset
knn = KNeighborsClassifier()
knn.fit(X_train, Y_train)
predictions = knn.predict(X_validation)
print(accuracy_score(Y_validation, predictions))
print(confusion_matrix(Y_validation, predictions))
print(classification_report(Y_validation, predictions))0.9
[[ 7 0 0]
[ 0 11 1]
[ 0 2 9]]
precision recall f1-score support
Iris-setosa 1.00 1.00 1.00 7
Iris-versicolor 0.85 0.92 0.88 12
Iris-virginica 0.90 0.82 0.86 11
avg / total 0.90 0.90 0.90 30
knn.predict([ [3.5,1.3,4.1,4.6]] )