py automl下载 - py automl源代码下载

py-automl

介绍

什么是py-automl？

PY-AUTOML是Python中的一个开源low-code机学习库，旨在将假设减少到ML实验中的见解周期时间。它主要有助于快速有效地进行宠物项目。与其他开源机器学习库相比，PY-AUTOML是一个替代性低代码库，可用于仅使用几行代码执行复杂的机器学习任务。 PY-AUTOML本质上是围绕几个机器学习库和框架（例如scikit-learn ，“ Tensorflow”，“ Keras”等）的Python包装。

PY-AUTOML的设计和简单性灵感来自两个原理接吻（保持简单而甜美）和干燥（不要重复自己）。作为工程师，我们必须找到一种有效的方法来减轻这一差距并解决业务环境中与数据相关的挑战。

模块

PY-AUTOML是一个简约的库，不能简化机器学习任务，也可以使我们的工作更轻松。

PY-AUTOML由许多功能组成。例如

模型。实施流行的神经网络，例如Googlenet，VGG16，Simple CNN，Basic CNN，Lenet5，Alexnet，LSTM，MLP等。
checkpoint.py-由用于存储指标的回调函数组成
utils.py-由一些功能组成，用于预处理测试图像，拼写数据。
preprocess.py-用于预处理图像数据集，例如调整大小，重塑，转换为灰度，归一化等。
ml.py-允许我们实施和检查流行的经典机器学习模型的指标，例如随机森林，决策树，SVM，逻辑回归，还显示每个模型的度量报告
可视化。

ml.py->实现的算法

逻辑回归
支持向量机
决策树分类器
随机森林分类器
k-near最邻居

模型。Py->实施流行的神经网络体系结构

Googlenet
VGG16
Alexnet
Lenet5
成立
简单和基本的CNN
basic_mlp＆deep_mlp
LSTM

具有预定义的配置

入门

安装软件包

pip install py-automl

导航到文件夹并安装要求：

pip install -r requirements.txt

用法

导入软件包

 import pyAutoML
from pyAutoML import *
from pyAutoML . model import *
# like that...

将变量x和y分配给所需的列，并将变量大小分配给所需的test_size。

 X = < df . features >
Y = < df . target >
size = < test_size >

编码分类数据

编码目标变量如果非数字：

 from pyAutoML import *
Y = EncodeCategorical ( Y )

运行py-automl

签名如下：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 ))

输出

 ____________________________________________________
..................... 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

您也可以写如下

 ML ( X , Y )

输出

 ____________________________________________________
..................... 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

定义流行的神经网络

实施Alexnet可能看起来像这样

 #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 AlexNet

但是，我们使用此软件包在下面的一行代码中实现了这一点。

 alexNet_model = model ( input_shape = ( 30 , 30 , 4 ) , arch = "alexNet" , classify = "Mulit" )

同样，我们也可以实施

 alexNet_model = model ( "alexNet" )

lenet5_model = model ( "lenet5" )

googleNet_model = model ( "googleNet" )

vgg16_model = model ( "vgg16" )

### etc...

有关更多概括，让我们观察以下代码。

 # 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

可视化

我们可以轻松地以不同形式可视化神经网络架构。

让我们观察以下代码以更好地理解

 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' ))

现在让我们想象一下

 nn_visualize ( model )

默认情况下，它返回Keras可视化对象

输出：

 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" )

输出

1_gtwmrlh1aylzaymylhgieg

这个库是如此友好，即使我们声明使用开始字母的类型也是如此。

 from pyAutoML . model import *
model2 = model ( arch = "alexNet" )

nn_visualize ( model2 , type = "k" )

py automl

py-automl

介绍

什么是py-automl？

模块

PY-AUTOML由许多功能组成。例如

模型。实施流行的神经网络，例如Googlenet，VGG16，Simple CNN，Basic CNN，Lenet5，Alexnet，LSTM，MLP等。

checkpoint.py-由用于存储指标的回调函数组成

utils.py-由一些功能组成，用于预处理测试图像，拼写数据。

preprocess.py-用于预处理图像数据集，例如调整大小，重塑，转换为灰度，归一化等。

ml.py-允许我们实施和检查流行的经典机器学习模型的指标，例如随机森林，决策树，SVM，逻辑回归，还显示每个模型的度量报告

可视化。

ml.py->实现的算法

逻辑回归

支持向量机

决策树分类器

随机森林分类器

k-near最邻居

模型。Py->实施流行的神经网络体系结构

Googlenet

VGG16

Alexnet

Lenet5

成立

简单和基本的CNN

basic_mlp＆deep_mlp

LSTM