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import os
import modal
LOCAL=True
if LOCAL == False:
stub = modal.Stub()
image = modal.Image.debian_slim().apt_install(["libgomp1"]).pip_install(["hopsworks", "seaborn", "joblib", "scikit-learn"])
@stub.function(image=image, schedule=modal.Period(days=1), secret=modal.Secret.from_name("HOPSWORKS_API_KEY"))
def f():
g()
def g():
import hopsworks
import pandas as pd
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report
import seaborn as sns
from matplotlib import pyplot
from hsml.schema import Schema
from hsml.model_schema import ModelSchema
import joblib
# You have to set the environment variable 'HOPSWORKS_API_KEY' for login to succeed
project = hopsworks.login()
# fs is a reference to the Hopsworks Feature Store
fs = project.get_feature_store()
# The feature view is the input set of features for your model. The features can come from different feature groups.
# You can select features from different feature groups and join them together to create a feature view
try:
feature_view = fs.get_feature_view(name="iris_modal", version=1)
except:
iris_fg = fs.get_feature_group(name="iris_modal", version=1)
query = iris_fg.select_all()
feature_view = fs.create_feature_view(name="iris_modal",
version=1,
description="Read from Iris flower dataset",
labels=["variety"],
query=query)
# You can read training data, randomly split into train/test sets of features (X) and labels (y)
X_train, X_test, y_train, y_test = feature_view.train_test_split(0.2)
# Train our model with the Scikit-learn K-nearest-neighbors algorithm using our features (X_train) and labels (y_train)
model = KNeighborsClassifier(n_neighbors=2)
model.fit(X_train, y_train.values.ravel())
# Evaluate model performance using the features from the test set (X_test)
y_pred = model.predict(X_test)
# Compare predictions (y_pred) with the labels in the test set (y_test)
metrics = classification_report(y_test, y_pred, output_dict=True)
results = confusion_matrix(y_test, y_pred)
# Create the confusion matrix as a figure, we will later store it as a PNG image file
df_cm = pd.DataFrame(results, ['True Setosa', 'True Versicolor', 'True Virginica'],
['Pred Setosa', 'Pred Versicolor', 'Pred Virginica'])
cm = sns.heatmap(df_cm, annot=True)
fig = cm.get_figure()
# We will now upload our model to the Hopsworks Model Registry. First get an object for the model registry.
mr = project.get_model_registry()
# The contents of the 'iris_model' directory will be saved to the model registry. Create the dir, first.
model_dir="iris_model"
if os.path.isdir(model_dir) == False:
os.mkdir(model_dir)
# Save both our model and the confusion matrix to 'model_dir', whose contents will be uploaded to the model registry
joblib.dump(model, model_dir + "/iris_model.pkl")
fig.savefig(model_dir + "/confusion_matrix.png")
# Specify the schema of the model's input/output using the features (X_train) and labels (y_train)
input_schema = Schema(X_train)
output_schema = Schema(y_train)
model_schema = ModelSchema(input_schema, output_schema)
# Create an entry in the model registry that includes the model's name, desc, metrics
iris_model = mr.python.create_model(
name="iris_modal",
metrics={"accuracy" : metrics['accuracy']},
model_schema=model_schema,
description="Iris Flower Predictor"
)
# Upload the model to the model registry, including all files in 'model_dir'
iris_model.save(model_dir)
if __name__ == "__main__":
if LOCAL == True :
g()
else:
with stub.run():
f()
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