https://github.com/prudhvignv/py-automl
A low code machine learning python library.
https://github.com/prudhvignv/py-automl
deep-learning keras machine-learning pip py-automl pypi-package python scikit-learn
Last synced: 11 days ago
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A low code machine learning python library.
- Host: GitHub
- URL: https://github.com/prudhvignv/py-automl
- Owner: PrudhviGNV
- License: mit
- Created: 2020-08-15T10:18:28.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2022-09-30T06:35:47.000Z (almost 3 years ago)
- Last Synced: 2025-06-24T22:50:35.536Z (12 days ago)
- Topics: deep-learning, keras, machine-learning, pip, py-automl, pypi-package, python, scikit-learn
- Language: Python
- Homepage: https://pypi.org/project/py-automl/
- Size: 430 KB
- Stars: 5
- Watchers: 2
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
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README
# Py-AutoML
[](https://github.com/PrudhviGNV/py-automl/blob/master/LICENCE.md)
[](https://GitHub.com/PrudhviGNV/py-automl)
[](https://prudhvignv.github.io/)
[](https://GitHub.com/PrudhviGNV)
[](https://pypi.python.org/pypi/py-automl/)
[](https://pypi.python.org/pypi/py-automl/)
[](https://pypi.python.org/pypi/py-automl/)
[](https://pypi.python.org/pypi/py-automl/)
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# Introduction
## What is Py-AutoML?
Py-AutoML is an open source `low-code` machine learning library in Python that aims to reduce the hypothesis to insights cycle time in a ML experiment. It mainly helps to do our pet projects quickly and efficiently. In comparison with the other open source machine learning libraries, Py-AutoML is an alternative low-code library that can be used to perform complex machine learning tasks with only few lines of code. Py-AutoML is essentially a Python wrapper around several machine learning libraries and frameworks such as `scikit-learn`, 'tensorflow','keras' and many more.
The design and simplicity of Py-AutoML is inspired by the two principles KISS (keep it simple and sweet) and DRY (Don't Repeat Yourself) . We as engineers have to find a way effective way to mitigate this gap and address data related challenges in business setting.
# Modules
Py-AutoML is a minimalistic library which not simplifies the machine learning tasks and also makes our work easier.
Py-AutoML consists of so many functionalities. such as
-----------------
- #### model.py- implementing popular neural networks such as googlenet , vgg16, simple cnn ,basic cnn, lenet5, alexnet, lstm, mlp etc..
- #### checkpoint.py - consists of callbacks function which is used to store metrics
- #### utils.py - consists of some functionalities used to preprocess test images, spliting the data.
- #### preprocess.py - used to preprocess image dataset such as resize, reshape, convert to greyscale, normalisation etc..
- #### ml.py - allow us to implement and check metrics of popular classical machine learning models such as random forest, decision tree, svm , logistic regression and also displays metric reports of every model
- #### visualize.py - allow us to visualize neural networks in pictorial and graphs form.
# ml.py -> Implemented algorithms
------------
- ### Logistic Regression
- ### Support Vector Machine
- ### Decision Tree Classifier
- ### Random Forest Classifier
- ### K-Nearest Neighbors
--------------------------
# model.py -> Implemented popular neural network architectures
------------
- ### GoogleNet
- ### VGG16
- ### AlexNet
- ### Lenet5
- ### Inception
- ### simple & basic cnn
- ### basic_mlp & deep_mlp
- ### lstm
with predefined configurations
--------------------------
# Getting started
-----------------
## Install the package
```bash
pip install py-automl
```
Navigate to folder and install requirements:
```bash
pip install -r requirements.txt
```
## Usage
Importing the package
```python
import pyAutoML
from pyAutoML import *
from pyAutoML.model import *
# like that...
```
Assign the variables X and Y to the desired columns and assign the variable size to the desired test_size.
```python
X = < df.features >
Y = < df.target >
size = < test_size >
```
## Encoding Categorical Data
Encode target variable if non-numerical:
```python
from pyAutoML import *
Y = EncodeCategorical(Y)
```
## Running py-automl
signature is as follows : ML(X, Y, size=0.25, *args)
```python
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))
```
### output
```python
____________________________________________________
.....................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
```
### you can also write as follows
```python
ML(X,Y)
```
### output
```python
____________________________________________________
.....................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
```
## Defining popular neural networks
### implementing alexNet may looks like this
```python
#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
```
But we implement this in a single line of code like below using this package.
```python
alexNet_model = model(input_shape= (30,30,4) , arch="alexNet", classify="Mulit" )
```
Similarly we can also implement
```python
alexNet_model = model("alexNet")
lenet5_model = model("lenet5")
googleNet_model = model("googleNet")
vgg16_model = model("vgg16")
### etc...
```
For more generalization , let's observe following code.
```python
# 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
```
## Visualization
### we can visualize neural networks architecture in different forms with ease.
Let's observe the following code for better understanding
```python
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'))
```
now let's visualise this
```python
nn_visualize(model)
```
By default , it returns keras visualization object
### output:
```python
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")
```
### output
This library is so developer friendly that even we declare type with starting letters.
```python
from pyAutoML.model import *
model2 = model(arch="alexNet")
nn_visualize(model2,type="k")
```
### output:
## This is a minimal documentation about the package.
For more information and understanding, see examples [HERE](https://github.com/PrudhviGNV/py-automl/edit/master/examples)
and source code: [GITHUB](https://github.com/PrudhviGNV/py-automl)
-------
## Author: [Prudhvi GNV](prudhvignv.github.io)
-------
# Contact:
[LinkedIn](https://linkedin.com/in/prudhvignv/)
[Github](https://github.com/PrudhviGNV)
[Instagram](https://instagram.com/prudhvi-gnv)