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Iris classifier.
__title__ = 'iris_classifier'
__author__ = 'Steven Cutting'
__author_email__ = ''
__created_on__ = '06/15/2017'
__copyright__ = "iris_classifier Copyright (C) 2017 Steven Cutting"
from pkg_resources import resource_string, resource_filename
import pickle
import numpy
from sklearn import svm
load_data = numpy.loadtxt
save_data = numpy.savetxt
def mk_svc_model(x, y, kernel="rbf"):
Read docs for sklearn.svm.SVC.__init__ and
return svm.SVC(kernel=kernel).fit(x, y)
def score_model(model, x, y):
return model.score(x, y)
def mk_predictor_func(model):
def predictor_func(sepal_length, sepal_width, petal_length, petal_width):
return model.predict([[sepal_length, sepal_width, petal_length, petal_width], ])[0]
return predictor_func
def save_model(model, filename):
with open(filename, "w") as f:
pickle.dump(model, f, protocol=2)
def load_model(filename):
with open(filename, "r") as f:
return pickle.load(f)
# ------------------------------------------------------------------------------
# Included with the package
def load_pkg_pre_fit_model():
Load the pre-fit model that is included with the package.
return pickle.loads(resource_string(__name__, "data/iris_model.pickle"))
def load_pkg_data():
Load the data included with the package. The data that was used to fit the model.
Returns (x_data, y_data)
return (load_data(resource_filename(__name__, "data/iris_x.csv")),
load_data(resource_filename(__name__, "data/iris_y.csv")))
except IOError as e:
raise IOError("**Data Not Included In Package**: {}".format(e))