py automl
1.0.0
Py-Automl هي مكتبة تعليمية آلية low-code في Python تهدف إلى تقليل الفرضية إلى وقت دورة الرؤى في تجربة ML. إنه يساعد بشكل أساسي على القيام بمشاريع الحيوانات الأليفة بسرعة وكفاءة. بالمقارنة مع مكتبات التعلم الآلي مفتوحة المصدر الأخرى ، فإن PY-Automl هي مكتبة بديلة منخفضة الرمز يمكن استخدامها لأداء مهام التعلم الآلي المعقدة مع أسطر قليلة فقط من التعليمات البرمجية. Py-Automl هو في الأساس غلاف Python حول العديد من مكتبات التعلم الآلي وأطر مثل scikit-learn و "Tensorflow" و "Keras" وغيرها الكثير.
إن تصميم وبساطة PY-Automl مستوحى من مبدأين قبلة (تبقيه بسيطًا وحلوًا) وجافًا (لا تكرر نفسك). يجب علينا كمهندسين إيجاد طريقة فعالة للتخفيف من هذه الفجوة ومعالجة التحديات المتعلقة بالبيانات في إعداد الأعمال.
Py-Automl هي مكتبة الحد الأدنى التي لا تبسط مهام التعلم الآلي وتجعل عملنا أسهل.
pip install py-automlانتقل إلى المجلد وتثبيت المتطلبات:
pip install -r requirements.txt
استيراد الحزمة
import pyAutoML
from pyAutoML import *
from pyAutoML . model import *
# like that...قم بتعيين المتغيرات x و y للأعمدة المطلوبة وتعيين الحجم المتغير إلى test_size المطلوب.
X = < df . features >
Y = < df . target >
size = < test_size > تشفير المتغير الهدف إذا لم يكن numerical:
from pyAutoML import *
Y = EncodeCategorical ( Y )التوقيع كما يلي: ML (x ، y ، size = 0.25 ، *args)
from pyAutoML . ml import ML , ml , EncodeCategorical
import pandas as pd
import numpy as np
from sklearn . ensemble import RandomForestClassifier
from sklearn . tree import DecisionTreeClassifier
from sklearn . neighbors import KNeighborsClassifier
from sklearn . linear_model import LogisticRegression
from sklearn . svm import SVC
from sklearn import datasets
##reading the Iris dataset into the code
df = datasets . load_iris ()
##assigning the desired columns to X and Y in preparation for running fastML
X = df . data [:, : 4 ]
Y = df . target
##running the EncodeCategorical function from fastML to handle the process of categorial encoding of data
Y = EncodeCategorical ( Y )
size = 0.33
ML ( X , Y , size , SVC (), RandomForestClassifier (), DecisionTreeClassifier (), KNeighborsClassifier (), LogisticRegression ( max_iter = 7000 )) ____________________________________________________
..................... Py - AutoML ......................
____________________________________________________
SVC ______________________________
Accuracy Score for SVC is
0.98
Confusion Matrix for SVC is
[[ 16 0 0 ]
[ 0 18 1 ]
[ 0 0 15 ]]
Classification Report for SVC is
precision recall f1 - score support
0 1.00 1.00 1.00 16
1 1.00 0.95 0.97 19
2 0.94 1.00 0.97 15
accuracy 0.98 50
macro avg 0.98 0.98 0.98 50
weighted avg 0.98 0.98 0.98 50
____________________________________________________
RandomForestClassifier ______________________________
Accuracy Score for RandomForestClassifier is
0.96
Confusion Matrix for RandomForestClassifier is
[[ 16 0 0 ]
[ 0 18 1 ]
[ 0 1 14 ]]
Classification Report for RandomForestClassifier is
precision recall f1 - score support
0 1.00 1.00 1.00 16
1 0.95 0.95 0.95 19
2 0.93 0.93 0.93 15
accuracy 0.96 50
macro avg 0.96 0.96 0.96 50
weighted avg 0.96 0.96 0.96 50
____________________________________________________
DecisionTreeClassifier ______________________________
Accuracy Score for DecisionTreeClassifier is
0.98
Confusion Matrix for DecisionTreeClassifier is
[[ 16 0 0 ]
[ 0 18 1 ]
[ 0 0 15 ]]
Classification Report for DecisionTreeClassifier is
precision recall f1 - score support
0 1.00 1.00 1.00 16
1 1.00 0.95 0.97 19
2 0.94 1.00 0.97 15
accuracy 0.98 50
macro avg 0.98 0.98 0.98 50
weighted avg 0.98 0.98 0.98 50
____________________________________________________
KNeighborsClassifier ______________________________
Accuracy Score for KNeighborsClassifier is
0.98
Confusion Matrix for KNeighborsClassifier is
[[ 16 0 0 ]
[ 0 18 1 ]
[ 0 0 15 ]]
Classification Report for KNeighborsClassifier is
precision recall f1 - score support
0 1.00 1.00 1.00 16
1 1.00 0.95 0.97 19
2 0.94 1.00 0.97 15
accuracy 0.98 50
macro avg 0.98 0.98 0.98 50
weighted avg 0.98 0.98 0.98 50
____________________________________________________
LogisticRegression ______________________________
Accuracy Score for LogisticRegression is
0.98
Confusion Matrix for LogisticRegression is
[[ 16 0 0 ]
[ 0 18 1 ]
[ 0 0 15 ]]
Classification Report for LogisticRegression is
precision recall f1 - score support
0 1.00 1.00 1.00 16
1 1.00 0.95 0.97 19
2 0.94 1.00 0.97 15
accuracy 0.98 50
macro avg 0.98 0.98 0.98 50
weighted avg 0.98 0.98 0.98 50
Model Accuracy
0 SVC 0.98
1 RandomForestClassifier 0.96
2 DecisionTreeClassifier 0.98
3 KNeighborsClassifier 0.98
4 LogisticRegression 0.98 ML ( X , Y ) ____________________________________________________
..................... Py - AutoML ......................
____________________________________________________
SVC ______________________________
Accuracy Score for SVC is
0.9736842105263158
Confusion Matrix for SVC is
[[ 13 0 0 ]
[ 0 15 1 ]
[ 0 0 9 ]]
Classification Report for SVC is
precision recall f1 - score support
0 1.00 1.00 1.00 13
1 1.00 0.94 0.97 16
2 0.90 1.00 0.95 9
accuracy 0.97 38
macro avg 0.97 0.98 0.97 38
weighted avg 0.98 0.97 0.97 38
____________________________________________________
RandomForestClassifier ______________________________
Accuracy Score for RandomForestClassifier is
0.9736842105263158
Confusion Matrix for RandomForestClassifier is
[[ 13 0 0 ]
[ 0 15 1 ]
[ 0 0 9 ]]
Classification Report for RandomForestClassifier is
precision recall f1 - score support
0 1.00 1.00 1.00 13
1 1.00 0.94 0.97 16
2 0.90 1.00 0.95 9
accuracy 0.97 38
macro avg 0.97 0.98 0.97 38
weighted avg 0.98 0.97 0.97 38
____________________________________________________
DecisionTreeClassifier ______________________________
Accuracy Score for DecisionTreeClassifier is
0.9736842105263158
Confusion Matrix for DecisionTreeClassifier is
[[ 13 0 0 ]
[ 0 15 1 ]
[ 0 0 9 ]]
Classification Report for DecisionTreeClassifier is
precision recall f1 - score support
0 1.00 1.00 1.00 13
1 1.00 0.94 0.97 16
2 0.90 1.00 0.95 9
accuracy 0.97 38
macro avg 0.97 0.98 0.97 38
weighted avg 0.98 0.97 0.97 38
____________________________________________________
KNeighborsClassifier ______________________________
Accuracy Score for KNeighborsClassifier is
0.9736842105263158
Confusion Matrix for KNeighborsClassifier is
[[ 13 0 0 ]
[ 0 15 1 ]
[ 0 0 9 ]]
Classification Report for KNeighborsClassifier is
precision recall f1 - score support
0 1.00 1.00 1.00 13
1 1.00 0.94 0.97 16
2 0.90 1.00 0.95 9
accuracy 0.97 38
macro avg 0.97 0.98 0.97 38
weighted avg 0.98 0.97 0.97 38
____________________________________________________
LogisticRegression ______________________________
Accuracy Score for LogisticRegression is
0.9736842105263158
Confusion Matrix for LogisticRegression is
[[ 13 0 0 ]
[ 0 15 1 ]
[ 0 0 9 ]]
Classification Report for LogisticRegression is
precision recall f1 - score support
0 1.00 1.00 1.00 13
1 1.00 0.94 0.97 16
2 0.90 1.00 0.95 9
accuracy 0.97 38
macro avg 0.97 0.98 0.97 38
weighted avg 0.98 0.97 0.97 38
Model Accuracy
0 SVC 0.9736842105263158
1 RandomForestClassifier 0.9736842105263158
2 DecisionTreeClassifier 0.9736842105263158
3 KNeighborsClassifier 0.9736842105263158
4 LogisticRegression 0.9736842105263158 #Instantiation
AlexNet = Sequential ()
#1st Convolutional Layer
AlexNet . add ( Conv2D ( filters = 96 , input_shape = input_shape , kernel_size = ( 11 , 11 ), strides = ( 4 , 4 ), padding = 'same' ))
AlexNet . add ( BatchNormalization ())
AlexNet . add ( Activation ( 'relu' ))
AlexNet . add ( MaxPooling2D ( pool_size = ( 2 , 2 ), strides = ( 2 , 2 ), padding = 'same' ))
#2nd Convolutional Layer
AlexNet . add ( Conv2D ( filters = 256 , kernel_size = ( 5 , 5 ), strides = ( 1 , 1 ), padding = 'same' ))
AlexNet . add ( BatchNormalization ())
AlexNet . add ( Activation ( 'relu' ))
AlexNet . add ( MaxPooling2D ( pool_size = ( 2 , 2 ), strides = ( 2 , 2 ), padding = 'same' ))
#3rd Convolutional Layer
AlexNet . add ( Conv2D ( filters = 384 , kernel_size = ( 3 , 3 ), strides = ( 1 , 1 ), padding = 'same' ))
AlexNet . add ( BatchNormalization ())
AlexNet . add ( Activation ( 'relu' ))
#4th Convolutional Layer
AlexNet . add ( Conv2D ( filters = 384 , kernel_size = ( 3 , 3 ), strides = ( 1 , 1 ), padding = 'same' ))
AlexNet . add ( BatchNormalization ())
AlexNet . add ( Activation ( 'relu' ))
#5th Convolutional Layer
AlexNet . add ( Conv2D ( filters = 256 , kernel_size = ( 3 , 3 ), strides = ( 1 , 1 ), padding = 'same' ))
AlexNet . add ( BatchNormalization ())
AlexNet . add ( Activation ( 'relu' ))
AlexNet . add ( MaxPooling2D ( pool_size = ( 2 , 2 ), strides = ( 2 , 2 ), padding = 'same' ))
#Passing it to a Fully Connected layer
AlexNet . add ( Flatten ())
# 1st Fully Connected Layer
AlexNet . add ( Dense ( 4096 , input_shape = ( 32 , 32 , 3 ,)))
AlexNet . add ( BatchNormalization ())
AlexNet . add ( Activation ( 'relu' ))
# Add Dropout to prevent overfitting
AlexNet . add ( Dropout ( 0.4 ))
#2nd Fully Connected Layer
AlexNet . add ( Dense ( 4096 ))
AlexNet . add ( BatchNormalization ())
AlexNet . add ( Activation ( 'relu' ))
#Add Dropout
AlexNet . add ( Dropout ( 0.4 ))
#3rd Fully Connected Layer
AlexNet . add ( Dense ( 1000 ))
AlexNet . add ( BatchNormalization ())
AlexNet . add ( Activation ( 'relu' ))
#Add Dropout
AlexNet . add ( Dropout ( 0.4 ))
#Output Layer
AlexNet . add ( Dense ( 10 ))
AlexNet . add ( BatchNormalization ())
AlexNet . add ( Activation ( classifier_function ))
AlexNet . compile ( 'adam' , loss_function , metrics = [ 'acc' ])
return AlexNetلكننا نقوم بتنفيذ هذا في سطر واحد من التعليمات البرمجية كما يلي باستخدام هذه الحزمة.
alexNet_model = model ( input_shape = ( 30 , 30 , 4 ) , arch = "alexNet" , classify = "Mulit" )وبالمثل يمكننا التنفيذ أيضًا
alexNet_model = model ( "alexNet" )
lenet5_model = model ( "lenet5" )
googleNet_model = model ( "googleNet" )
vgg16_model = model ( "vgg16" )
### etc...لمزيد من التعميم ، دعونا نلاحظ الكود التالي.
# Lets take all models that are defined in the py_automl and which are implemented in a signle line of code
models = [ "simple_cnn" , "basic_cnn" , "googleNet" , "inception" , "vgg16" , "lenet5" , "alexNet" , "basic_mlp" , "deep_mlp" , "basic_lstm" , "deep_lstm" ]
d = {}
for i in models :
d [ i ] = model ( i ) # assigning all architectures to its model names using dictionary
دعونا نلاحظ الكود التالي لفهم أفضل
import keras
from keras import layers
model = keras . Sequential ()
model . add ( layers . Conv2D ( filters = 6 , kernel_size = ( 3 , 3 ), activation = 'relu' , input_shape = ( 32 , 32 , 1 )))
model . add ( layers . AveragePooling2D ())
model . add ( layers . Conv2D ( filters = 16 , kernel_size = ( 3 , 3 ), activation = 'relu' ))
model . add ( layers . AveragePooling2D ())
model . add ( layers . Flatten ())
model . add ( layers . Dense ( units = 120 , activation = 'relu' ))
model . add ( layers . Dense ( units = 84 , activation = 'relu' ))
model . add ( layers . Dense ( units = 10 , activation = 'softmax' ))الآن دعونا نتصور هذا
nn_visualize ( model )بشكل افتراضي ، يعيد كائن التصور Keras
from keras . models import Sequential
from keras . layers import Dense
import numpy
# fix random seed for reproducibility
numpy . random . seed ( 7 )
# load pima indians dataset
dataset = numpy . loadtxt ( "pima-indians-diabetes.csv" , delimiter = "," )
# split into input (X) and output (Y) variables
X = dataset [:, 0 : 8 ]
Y = dataset [:, 8 ]
# create model
model = Sequential ()
model . add ( Dense ( 12 , input_dim = 8 , activation = 'relu' ))
model . add ( Dense ( 8 , activation = 'relu' ))
model . add ( Dense ( 1 , activation = 'sigmoid' ))
# Compile model
model . compile ( loss = 'binary_crossentropy' , optimizer = 'adam' , metrics = [ 'accuracy' ])
# Fit the model
model . fit ( X , Y , epochs = 150 , batch_size = 10 )
# evaluate the model
scores = model . evaluate ( X , Y )
print ( " n %s: %.2f%%" % ( model . metrics_names [ 1 ], scores [ 1 ] * 100 ))
#Neural network visualization
nn_visualize ( model , type = "graphviz" )
هذه المكتبة صديقة للمطورين لدرجة أننا نعلن النوع مع بدء الحروف.
from pyAutoML . model import *
model2 = model ( arch = "alexNet" )
nn_visualize ( model2 , type = "k" )
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