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usap_demo.py
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usap_demo.py
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"""
deodel - demo
Module provides examplea of usage for the deodel module.
Tested with Winpython64-3.10.5.0
"""
# c4pub@git 2023
#
# Latest version available at: https://github.com/c4pub/deodel
#
import datetime
import random
import deodel
import deodel2
# >- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# >- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
def AccuracyEval(x_data, y_target, classifier, iterations = 1, random_seed = None) :
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
print("- - - - - - - - - ")
print("- - - - Prediction accuracy test")
print()
print("- - - - - - classifier:", classifier)
print("- - - - - - iterations:", iterations)
print("- - - - - - random_seed:", random_seed)
print()
begin_time_ref = datetime.datetime.now()
crt_time_ref = datetime.datetime.now()
test_fraction = 1.0/3
cumulate_acc = 0
print(" ", end='')
crt_rand_seed = random_seed
for crt_idx in range(iterations) :
if not random_seed == None :
crt_rand_seed = random_seed + crt_idx
ret_tuple = train_test_split(x_data, y_target, test_size = test_fraction, random_state = crt_rand_seed)
x_train, x_test, y_train, y_test = ret_tuple
classifier.fit(x_train, y_train)
predictions = classifier.predict(x_test)
accuracy = accuracy_score(y_test, predictions)
cumulate_acc += accuracy
print('.', end='')
print(" .")
new_time_ref = datetime.datetime.now()
delta = new_time_ref - crt_time_ref
delta_secs = delta.total_seconds()
print("- - - - - - delta_secs:", delta_secs)
crt_time_ref = new_time_ref
avg_accuracy = (cumulate_acc * 1.0) / iterations
print("- - - - - - avg_accuracy:", avg_accuracy)
print()
return avg_accuracy
# >- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
def Demo() :
# >- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Trivial example
# >- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
print("- - - - - - - - - ")
print("- - - - - - - - - ")
print("- - - - Trivial example")
print()
X = [[0], [1], [2], [3]]
y = [0, 0, 1, 1]
dd_classif = deodel.DeodataDelangaClassifier()
dd_classif.fit(X, y)
print(dd_classif.predict([[1.1]]))
# >- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Example of using data formated as list of list tables.
# >- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
print("- - - - - - - - - ")
print("- - - - - - - - - ")
print("- - - - Usage woth mixed attributes as lists of lists")
print()
train_X = [ ['a', 1.01, 'az', 'e' ],
['b', "3.01", 3, 'd' ],
['d', "4", 5, 'd' ],
['b', 2, 3.0, None],
['c', '3.01', 'az', 'e' ]]
train_y = [ 'TA',
'TB',
'TB',
'TC',
'TA' ]
query_entry = ['a', 0.9, 5.7, 'd']
query_test = [query_entry]
expected_result = ['TB']
classf = deodel.DeodataDelangaClassifier()
classf.fit(train_X, train_y)
query_predict = classf.predict(query_test)
print("- - - - query_predict:", query_predict)
print("- - - - expected_result:", expected_result)
set_eval = ( query_predict == expected_result )
if set_eval :
print("- - - - test ok")
else :
print("- - - - test failed")
print("- - - - invalid test_result")
print()
# >- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Example of using the deodel classifier in a similar way to
# other sklearn classfiers.
# >- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
print("- - - - - - - - - ")
print("- - - - - - - - - ")
print("- - - - Usage with sklearn facilities")
print()
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn import neighbors
from sklearn import tree
print("- - - - - - - - - ")
print()
data_set = datasets.load_wine()
x_data = data_set.data
y_target = data_set.target
x_train, x_test, y_train, y_test = train_test_split(x_data, y_target, test_size=0.5, random_state=42)
###
classifier = neighbors.KNeighborsClassifier()
print("- - - - - - - - - ")
print("- - - - classifier:", classifier)
classifier.fit(x_train, y_train)
predictions = classifier.predict(x_test)
accuracy = accuracy_score(y_test, predictions)
print("- - - - accuracy:", accuracy)
print()
###
classifier = tree.DecisionTreeClassifier()
print("- - - - - - - - - ")
print("- - - - classifier:", classifier)
classifier.fit(x_train, y_train)
predictions = classifier.predict(x_test)
accuracy = accuracy_score(y_test, predictions)
print("- - - - accuracy:", accuracy)
print()
###
classifier = deodel.DeodataDelangaClassifier()
print("- - - - - - - - - ")
print("- - - - classifier:", classifier)
classifier.fit(x_train, y_train)
predictions = classifier.predict(x_test)
accuracy = accuracy_score(y_test, predictions)
print("- - - - accuracy:", accuracy)
print()
# >- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Example for comparing the average accuracy for a set of
# classifiers on a toy data set
# >- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
print("- - - - - - - - - ")
print("- - - - - - - - - ")
print("- - - - average accuracy test")
print()
data_set = datasets.load_breast_cancer()
data_len = len(data_set.target)
print("- - - - data_len:", data_len)
print()
x_data = data_set.data
y_target = data_set.target
iter_no = 30
random_seed = 42
random.seed(random_seed)
classifier_lst = [
neighbors.KNeighborsClassifier(),
tree.DecisionTreeClassifier(),
deodel.DeodataDelangaClassifier(),
deodel2.DeodelSecond(),
]
for crt_classif in classifier_lst :
accuracy = AccuracyEval(x_data, y_target, crt_classif, iterations=iter_no, random_seed=random_seed)
print("- - - - - - - - - ")
# >- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
def CrtTimeStamp() :
import datetime
in_time_stamp = datetime.datetime.now()
time_str = in_time_stamp.strftime("%Y-%m-%d %H:%M:%S")
out_str = "time_stamp: %s" % (time_str)
print(out_str)
return out_str
# >- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# >- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
print()
# >- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
CrtTimeStamp()
# >- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
print()
print("- - - - - - - - - ")
print()
Demo()
print()
print("- - - - - - - - - ")
print()
# >- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
CrtTimeStamp()
# >- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
print()
# >-----------------------------------------------------------------------------