from sklearn.datasets import load_digits

from sklearn.model_selection import train_test_split

from sklearn.model_selection import GridSearchCV

from sklearn.metrics import classification_report

from sklearn.svm import SVC

import numpy as np

import pandas as pd

digits = load_digits()

X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target,

test_size=0.25, random_state=33)

tuned_parameters = [{'kernel': ['rbf'], 'gamma': np.logspace(-4,1,6),

'C': np.logspace(-1,4,6)}]

gs = GridSearchCV(SVC(), tuned_parameters, cv=5)

gs.fit(X_train, y_train)

print("Best parameters set found on development set:")

print()

print(gs.best_params_)

print("Grid scores on development set:")

print()

means = gs.cv_results_['mean_test_score']

stds = gs.cv_results_['std_test_score']

for mean, std, params in zip(means, stds, gs.cv_results_['params']):

print("%0.3f (+/-%0.03f) for %r" % (mean, std * 2, params))

print()

print("Detailed classification report:")

print()

print("The model is trained on the full development set.")

print("The scores are computed on the full evaluation set.")

print()

y_true, y_pred = y_test, gs.predict(X_test)

print(classification_report(y_true, y_pred))

print()