atarjus/MLflow

1import mlflow2import pandas as pd3from sklearn.datasets import load_iris4from sklearn.linear_model import LogisticRegression5from sklearn.model_selection import train_test_split6 7# Load Iris dataset8iris = load_iris()9 10# Split dataset into X features and Target variable11X = pd.DataFrame(data = iris["data"], columns= iris["feature_names"])12y = pd.Series(data = iris["target"], name="target")13 14# Split our training set and our test set 15X_train, X_test, y_train, y_test = train_test_split(X, y)16 17# Visualize dataset 18X_train.head()19 20# Set your variables for your environment21EXPERIMENT_NAME="my-first-mlflow-experiment"22 23# Set tracking URI to your Heroku application24mlflow.set_tracking_uri("https://huggingface.co/spaces/atarjus/MLflow/")25 26# Set experiment's info 27mlflow.set_experiment(EXPERIMENT_NAME)28 29# Get our experiment info30experiment = mlflow.get_experiment_by_name(EXPERIMENT_NAME)31 32# Call mlflow autolog33mlflow.sklearn.autolog()34 35with mlflow.start_run(experiment_id = experiment.experiment_id):36 37    # Instanciate and fit the model 38    lr = LogisticRegression()39    lr.fit(X_train.values, y_train.values)40 41    # Store metrics 42    predicted_qualities = lr.predict(X_test.values)43    accuracy = lr.score(X_test.values, y_test.values)44 45    # Print results 46    print("LogisticRegression model")47    print("Accuracy: {}".format(accuracy))48