matplotlib example iris (snippet)

Python matplotlib example 'iris'

Modules used in program:

import matplotlib.pyplot as plt
import pandas
import sklearn
import pandas
import matplotlib
import numpy
import scipy
import sys

python iris

Python matplotlib example: iris


# coding: utf-8

# In[55]:

# Python version
import sys
print('Python: {}'.format(sys.version))
# scipy
import scipy
print('scipy: {}'.format(scipy.__version__))
# numpy
import numpy
print('numpy: {}'.format(numpy.__version__))
# matplotlib
import matplotlib
print('matplotlib: {}'.format(matplotlib.__version__))
# pandas
import pandas
print('pandas: {}'.format(pandas.__version__))
# scikit-learn
import sklearn
print('sklearn: {}'.format(sklearn.__version__))


# In[61]:

import pandas 
from pandas.tools.plotting import scatter_matrix 
import matplotlib.pyplot as plt
from sklearn.metrics import classification_report
from sklearn.cross_validation import train_test_split
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


# In[66]:

# Load dataset
url = "https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"
names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class']
dataset = pandas.read_csv(url,names=names)


# In[67]:

print(dataset.shape)


# In[70]:

print(dataset.head(20))


# In[72]:

print(dataset.describe())


# In[76]:

print(dataset.groupby('class').size())


# In[80]:

dataset.plot(kind='box', subplots=True, layout=(2,2), sharex=False , sharey=False)
plt.show()


# In[82]:

dataset.hist()
plt.show()


# In[84]:

scatter_matrix(dataset)
plt.show()


# In[118]:

from sklearn.cross_validation import train_test_split
array = dataset.values
X = array[:,0:4]
Y = array[:,4]
validation_size = 0.2
seed = 7
X_train, X_validation, Y_train, Y_validation = train_test_split(X, Y, test_size=validation_size, random_state=seed)


# In[101]:

# Test options and evaluation metric
seed = 7
scoring = 'accuracy'


# In[114]:

# 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 = KFold(n=10, random_state=seed)
    cv_results = sklearn.cross_validation.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)


# In[115]:

# Compare Algorithms
fig = plt.figure()
fig.suptitle('Algorithm Comparison')
ax = fig.add_subplot(111)
plt.boxplot(results)
ax.set_xticklabels(names)
plt.show()


# In[116]:

# 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))


# In[ ]:

Python links

Learn Python: https://pythonbasics.org/
Python Tutorial: https://pythonprogramminglanguage.com