Pushed from Domino: https://ykb.domino-eval.com/workspace/petter/mlop…

…s-best-practices?executionId=6734940e0b77b232dfd8c697

scripts/.ipynb_checkpoints/sklearn_model_train-checkpoint.py

@@ -1,5 +1,6 @@
 import pandas as pd
 import numpy as np
+import scipy.linalg
 import matplotlib.pyplot as plt
 import seaborn as sns
 import sklearn
@@ -14,6 +15,9 @@
 from mlflow.store.artifact.runs_artifact_repo import RunsArtifactRepository
 from mlflow import MlflowClient
 from datetime import datetime
+
+# Patch scipy.linalg to use np.linalg.pinv instead of pinv2
+scipy.linalg.pinv2 = np.linalg.pinv
 
 #Read in data
 path = str('/mnt/data/{}/WineQualityData.csv'.format(os.environ.get('DOMINO_PROJECT_NAME')))
@@ -45,7 +49,6 @@
 #mlflow.set_experiment(experiment_name=os.environ.get('DOMINO_PROJECT_NAME') + " " + os.environ.get('DOMINO_STARTING_USERNAME'))
 mlflow.set_experiment(experiment_name=os.environ.get('DOMINO_PROJECT_NAME') + " " + os.environ.get('DOMINO_STARTING_USERNAME') + " " + os.environ.get('MLFLOW_NAME'))
 
-
 with mlflow.start_run():
     # Set MLFlow tag to differenciate the model approaches
     mlflow.set_tag("Model_Type", "sklearn")
@@ -55,7 +58,10 @@
 
     #initiate and fit Gradient Boosted Classifier
     print('Training model...')
-    gbr = GradientBoostingRegressor(loss='squared_error',learning_rate = 0.15, n_estimators=75, criterion = 'squared_error')
+    #gbr = GradientBoostingRegressor(loss='squared_error',learning_rate = 0.15, n_estimators=75, criterion = 'squared_error')
+    #gbr = GradientBoostingRegressor(loss='ls', learning_rate=0.15, n_estimators=75, criterion='squared_error')
+    gbr = GradientBoostingRegressor(loss='ls', learning_rate=0.15, n_estimators=75, criterion='friedman_mse')
+
     gbr.fit(X_train,y_train)
 
     #Predict test set

scripts/sklearn_model_train.py

@@ -60,7 +60,7 @@
     print('Training model...')
     #gbr = GradientBoostingRegressor(loss='squared_error',learning_rate = 0.15, n_estimators=75, criterion = 'squared_error')
     #gbr = GradientBoostingRegressor(loss='ls', learning_rate=0.15, n_estimators=75, criterion='squared_error')
-    gbr = GradientBoostingRegressor(loss='ls', learning_rate=0.15, n_estimators=75, criterion='mse')
+    gbr = GradientBoostingRegressor(loss='ls', learning_rate=0.15, n_estimators=75, criterion='friedman_mse')
 
     gbr.fit(X_train,y_train)