Add bosthon housing

Author: raman-nbg

.gitignore

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+.idea/
+.ipynb_checkpoints/
+.vscode/
+.venv/

README.md

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+# ML Aide tutorial
+
+## Setup
+
+To run this tutorial we use [pyenv](https://github.com/pyenv/pyenv) and 
+[virtualenv](https://virtualenv.pypa.io/en/latest/) to create a virtual 
+environment. All dependencies will be installed in the virtual environment.
+
+1. Create virtual environment
+```
+virtualenv venv
+```
+
+2. Activate virtual environment
+```
+source venv/bin/activate
+```
+
+3. Install dependencies
+```
+pip install -r requirements.txt
+```
+
+## Start
+```
+cd boston-house-prcing
+python app.py
+```

boston-house-pricing/app.py

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+from mlaide import client
+from mlaide.model import ArtifactRef
+import pandas as pd
+from sklearn import metrics
+from sklearn.linear_model import Lasso, LinearRegression
+from sklearn.model_selection import cross_val_score, train_test_split
+from sklearn.pipeline import Pipeline
+from sklearn.preprocessing import StandardScaler
+import numpy as np
+
+# create connection
+options = client.MvcOptions(
+ mvc_server_url='http://localhost:8881/api/v1',
+ api_key='NTIxYmUxOWMtNTJkMi00NDQ0LTljYjUtMTU1ZWZhMDFjYWFmOuKCqDdBPMKw4oKjwrjCsOKCsHTigrFj'
+)
+mlaide_client = client.MvcClient(project_key='usa-housing', options=options)
+
+# start tracking
+run_data_preparation = mlaide_client.start_new_run(experiment_key='linear-regression', run_name='data preparation')
+
+# read data
+housing_data = pd.read_csv('data/housing.csv')
+
+# add dataset as artifact
+artifact = run_data_preparation.create_artifact(name="USA housing dataset", artifact_type="dataset", metadata={})
+run_data_preparation.add_artifact_file(artifact, 'data/housing.csv')
+run_data_preparation.set_completed_status()
+
+
+# create run with an reference to the dataset artifact
+artifact_ref = ArtifactRef(name="USA housing dataset", version=1)
+run_pipeline_setup = mlaide_client.start_new_run(experiment_key='linear-regression', 
+ run_name='pipeline setup', 
+ used_artifacts=[artifact_ref])
+
+X = housing_data[['Avg. Area Income', 'Avg. Area House Age', 'Avg. Area Number of Rooms',
+ 'Avg. Area Number of Bedrooms', 'Area Population']]
+y = housing_data['Price']
+
+test_size=0.3
+random_state=42
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=random_state)
+
+run_pipeline_setup.log_parameter('test_size', test_size)
+run_pipeline_setup.log_parameter('random_state', random_state)
+
+# create pipeline with standard scaler and store the pipeline in ML Aide
+pipeline = Pipeline([
+ ('std_scalar', StandardScaler())
+])
+
+X_train = pipeline.fit_transform(X_train)
+X_test = pipeline.transform(X_test)
+
+run_pipeline_setup.log_model(pipeline, model_name="pipeline")
+run_pipeline_setup.set_completed_status()
+
+# Linear Regression
+dataset_artifact_ref = ArtifactRef(name="USA housing dataset", version=1)
+pipeline_artifact_ref = ArtifactRef(name="pipeline", version=1)
+run_linear_regression = mlaide_client.start_new_run(experiment_key='linear-regression',
+ run_name='linear regression',
+ used_artifacts=[dataset_artifact_ref, pipeline_artifact_ref])
+
+lin_reg = LinearRegression(normalize=True)
+lin_reg.fit(X_train,y_train)
+
+run_linear_regression.log_model(lin_reg, 'linear regression')
+
+test_pred = lin_reg.predict(X_test)
+train_pred = lin_reg.predict(X_train)
+
+mae = metrics.mean_absolute_error(y_test, test_pred)
+mse = metrics.mean_squared_error(y_test, test_pred)
+rmse = np.sqrt(metrics.mean_squared_error(y_test, test_pred))
+r2 = metrics.r2_score(y_test, test_pred)
+cross_validation = cross_val_score(LinearRegression(), X, y, cv=10).mean()
+
+run_linear_regression.log_metric('mae', mae)
+run_linear_regression.log_metric('mse', mse)
+run_linear_regression.log_metric('rmse', rmse)
+run_linear_regression.log_metric('r2', r2)
+run_linear_regression.log_metric('cross validation', cross_validation)
+
+run_linear_regression.set_completed_status()
+
+# Lasso Regression
+dataset_artifact_ref = ArtifactRef(name="USA housing dataset", version=1)
+pipeline_artifact_ref = ArtifactRef(name="pipeline", version=1)
+run_lasso = mlaide_client.start_new_run(experiment_key='lasso-regression',
+ run_name='lasso regression',
+ used_artifacts=[dataset_artifact_ref, pipeline_artifact_ref])
+
+alpha = 0.1
+precompute = True
+positive = True
+selection = 'random'
+random_state = 42
+
+run_lasso.log_parameter('alpha', alpha)
+run_lasso.log_parameter('precompute', precompute)
+run_lasso.log_parameter('positive', positive)
+run_lasso.log_parameter('selection', selection)
+run_lasso.log_parameter('random state', random_state)
+ 
+model = Lasso(alpha=alpha, 
+ precompute=precompute, 
+ positive=positive, 
+ selection=selection,
+ random_state=random_state)
+model.fit(X_train, y_train)
+
+run_lasso.log_model(model, 'lasso')
+
+test_pred = model.predict(X_test)
+train_pred = model.predict(X_train)
+
+mae = metrics.mean_absolute_error(y_test, test_pred)
+mse = metrics.mean_squared_error(y_test, test_pred)
+rmse = np.sqrt(metrics.mean_squared_error(y_test, test_pred))
+r2 = metrics.r2_score(y_test, test_pred)
+cross_validation = cross_val_score(Lasso(), X, y, cv=10).mean()
+
+run_lasso.log_metric('mae', mae)
+run_lasso.log_metric('mse', mse)
+run_lasso.log_metric('rmse', rmse)
+run_lasso.log_metric('r2', r2)
+run_lasso.log_metric('cross validation', cross_validation)
+
+run_lasso.set_completed_status()
+