pymlpipe 0.1.4

PyMLpipe is a Python library for ease Machine Learning Model monitering and Deployment.

PyMLpipe

PyMLpipe is a Python library for ease Machine Learning Model monitering and Deployment.

• Simple
• Intuative
• Easy to use

Installation

Use the package manager pip to install PyMLpipe.

pip install pymlpipe

or

pip3 install pymlpipe

Usage

from sklearn.datasets import  load_iris
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score,precision_score,recall_score,f1_score
#import PyMLPipe from tabular 
from pymlpipe.tabular import PyMLPipe


# Initiate the class
mlp=PyMLPipe()
# Set experiment name
mlp.set_experiment("IrisDataV2")
# Set Version name
mlp.set_version(0.2)

iris_data=load_iris()
data=iris_data["data"]
target=iris_data["target"]
df=pd.DataFrame(data,columns=iris_data["feature_names"])
trainx,testx,trainy,testy=train_test_split(df,target)


# to start monitering use mlp.run()
with mlp.run():
    # set tags
    mlp.set_tags(["Classification","test run","logisticRegression"])
    model=LogisticRegression()
    model.fit(trainx, trainy)
    predictions=model.predict(testx)
    # log performace metrics
    mlp.log_matric("Accuracy", accuracy_score(testy,predictions))
    mlp.log_matric("Precision", precision_score(testy,predictions,average='macro'))
    mlp.log_matric("Recall", recall_score(testy,predictions,average='macro'))
    mlp.log_matric("F1", f1_score(testy,predictions,average='macro'))

    # Save train data and test data
    mlp.register_artifact("train", trainx)
    mlp.register_artifact("test", testx,artifact_type="testing")
    # Save the model
    mlp.scikit_learn.register_model("logistic regression", model)

Usage UI

To start the UI

pymlpipeui 

or

from pymlpipe.pymlpipeUI import start_ui


start_ui(host='0.0.0.0', port=8085)

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

License

MIT