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.
Py-AutoML is a minimalistic library which not simplifies the machine learning tasks and also makes our work easier.
pip install py-automlNavigate to folder and install requirements:
pip install -r requirements.txt
Importing the package
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.
X = < df.features >
Y = < df.target >
size = < test_size >Encode target variable if non-numerical:
from pyAutoML import *
Y = EncodeCategorical(Y)signature is as follows : 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 0.98ML(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 AlexNetBut we implement this in a single line of code like below using this package.
alexNet_model = model(input_shape= (30,30,4) , arch="alexNet", classify="Mulit" )Similarly we can also implement
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.
# 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
Let's observe the following code for better understanding
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
nn_visualize(model)By default , it returns keras visualization object
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")
This library is so developer friendly that even we declare type with starting letters.
from pyAutoML.model import *
model2 = model(arch="alexNet")
nn_visualize(model2,type="k")