korkridake

korkridake

import numpy as np
import matplotlib.pyplot as plt
import azureml.core
# Display the core SDK version number
print("Azure ML SDK Version: ", azureml.core.VERSION)

# Define an environment
from azureml.core.environment import Environment
from azureml.core.conda_dependencies import CondaDependencies

korkridake

import numpy as np
import matplotlib.pyplot as plt
import azureml.core
# Display the core SDK version number
print("Azure ML SDK Version: ", azureml.core.VERSION)

# Define an environment
from azureml.core.environment import Environment
from azureml.core.conda_dependencies import CondaDependencies

korkridake

%%writefile score.py
import json
import numpy as np
import os
import pickle
import joblib

def init():
    global model
    ##################################################################################################

korkridake

# Import libraries
import json
import numpy as np
from azureml.core.model import Model
import joblib

# Load model
model_path = Model.get_model_path(model_name="sklearn_regression_model.pkl")
model = joblib.load(model_path)

korkridake

# Get hold of the current run
run = Run.get_context()

# Experiment parameters
args = {
    "n_estimators": 120
}

reg_model = RandomForestRegressor(**args)
reg_model.fit(data["train"]["X"], data["train"]["y"])

korkridake

# Load data in  Pandas
df = dataset.to_pandas_dataframe()
print(df.shape)
df.head()

# Preprocess data
df = df.drop_duplicates()
df = df.drop(["dateCrawled","dateCreated","lastSeen", "seller", "name", "postalCode"] , axis = 1)
df["notRepairedDamage"] = df["notRepairedDamage"].fillna("nein")
df["fuelType"] = df["fuelType"].fillna("benzin")

korkridake

# Install H2O packages
if ("package:h2o" %in% search()) { detach("package:h2o", unload=TRUE) }
if ("h2o" %in% rownames(installed.packages())) { remove.packages("h2o") }
pkgs <- c("RCurl","jsonlite")
for (pkg in pkgs) {
  if (! (pkg %in% rownames(installed.packages()))) { install.packages(pkg) }
}
install.packages("h2o", type="source", repos=(c("http://h2o-release.s3.amazonaws.com/h2o/latest_stable_R")))
library(h2o)
localH2O = h2o.init()

korkridake

# Get the datastore to upload prepared data
datastore = ws.get_default_datastore()

# Upload the local file from src_dir to the target_path in datastore
datastore.upload(src_dir='../aml-kaggle/datasets/', target_path='ds_regression')

# Create a dataset referencing the cloud location
dataset = Dataset.Tabular.from_delimited_files(datastore.path('ds_regression/autos.csv'))

# Register a dataset

korkridake

# Create an experiment
experiment_name = 'experiment-regression'
experiment = Experiment(workspace = ws, name = experiment_name)

# Choose a name for your CPU cluster
cpu_cluster_name = "kyledevpc1"

# Verify that cluster does not exist already
try:
    cpu_cluster = ComputeTarget(workspace=ws, name=cpu_cluster_name)