from sklearn import datasets
iris = datasets.load_iris()

from sklearn.naive_bayes import GaussianNB
gnb = GaussianNB()
y_pred = gnb.fit(iris.data, iris.target).predict(iris.data)
print("Number of mislabeled points out of a total %d points : %d"
      % (iris.data.shape[0],(iris.target != y_pred).sum()))

iris.data.shape[0]

iris.target

y_pred

(iris.target != y_pred)

(iris.target != y_pred).sum()

"""
Explanation: Citation- http://scikit-learn.org/stable/modules/naive_bayes.html
End of explanation
"""


from sklearn import datasets
iris = datasets.load_iris()
digits = datasets.load_digits()

from sklearn import svm
clf = svm.SVC(gamma=0.001, C=100.)

digits.data

digits.target

iris.data

iris.target

clf.fit(digits.data[:-1], digits.target[:-1]) 

clf.predict(digits.data[-1:])

clf = svm.SVC()
iris = datasets.load_iris()
X, y = iris.data, iris.target
clf.fit(X, y) 

import pickle
s = pickle.dumps(clf)
clf2 = pickle.loads(s)
clf2.predict(X[0:1]) 

y[0] 

y

clf2.predict(X)

(clf2.predict(X) != y).sum()

from sklearn import tree
from sklearn.datasets import load_iris

iris.target

clf = tree.DecisionTreeClassifier()
clf = clf.fit(iris.data, iris.target)

clf

clf.predict(X)

iris.target

(clf.predict(X)!=iris.target).sum()

! pip install pydotplus

import pydotplus as pydot

from IPython.display import Image

from sklearn.externals.six import StringIO

"""
Explanation: http://archive.ics.uci.edu/ml/datasets/Pen-Based+Recognition+of+Handwritten+Digits
We create a digit database by collecting 250 samples from 44 writers.
End of explanation
"""


import matplotlib.pyplot as plt
%matplotlib  inline
from collections import OrderedDict
from sklearn.datasets import make_classification
from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier

# Author: Kian Ho <hui.kian.ho@gmail.com>
#         Gilles Louppe <g.louppe@gmail.com>
#         Andreas Mueller <amueller@ais.uni-bonn.de>
#
# License: BSD 3 Clause

print(__doc__)

RANDOM_STATE = 123

# Generate a binary classification dataset.
X, y = make_classification(n_samples=500, n_features=25,
                           n_clusters_per_class=1, n_informative=15,
                           random_state=RANDOM_STATE)

# NOTE: Setting the `warm_start` construction parameter to `True` disables
# support for parallelized ensembles but is necessary for tracking the OOB
# error trajectory during training.
ensemble_clfs = [
    ("RandomForestClassifier, max_features='sqrt'",
        RandomForestClassifier(warm_start=True, oob_score=True,
                               max_features="sqrt",
                               random_state=RANDOM_STATE)),
    ("RandomForestClassifier, max_features='log2'",
        RandomForestClassifier(warm_start=True, max_features='log2',
                               oob_score=True,
                               random_state=RANDOM_STATE)),
    ("RandomForestClassifier, max_features=None",
        RandomForestClassifier(warm_start=True, max_features=None,
                               oob_score=True,
                               random_state=RANDOM_STATE))
]

# Map a classifier name to a list of (<n_estimators>, <error rate>) pairs.
error_rate = OrderedDict((label, []) for label, _ in ensemble_clfs)

# Range of `n_estimators` values to explore.
min_estimators = 15
max_estimators = 175

for label, clf in ensemble_clfs:
    for i in range(min_estimators, max_estimators + 1):
        clf.set_params(n_estimators=i)
        clf.fit(X, y)

        # Record the OOB error for each `n_estimators=i` setting.
        oob_error = 1 - clf.oob_score_
        error_rate[label].append((i, oob_error))

# Generate the "OOB error rate" vs. "n_estimators" plot.
for label, clf_err in error_rate.items():
    xs, ys = zip(*clf_err)

plt.plot(xs, ys, label=label)
plt.xlim(min_estimators, max_estimators)
plt.xlabel("n_estimators")
plt.ylabel("OOB error rate")
plt.legend(loc="upper right")
plt.show()

"""
Explanation: Other examples 
https://nbviewer.jupyter.org/gist/decisionstats/8b762caa7b7deebb68e3f275daf02a9d
https://nbviewer.jupyter.org/gist/decisionstats/47a2324b14ebfd22657b40ec1ae5b480
End of explanation
"""


