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V3 scoring model, add prom monitoring

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This commit is contained in:
Jackson Harper
2024-07-04 17:49:02 +08:00
parent ca7800f1c1
commit e1fe4237de
5 changed files with 90 additions and 142 deletions
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49
ml/digest-score/app.py
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@ -1,5 +1,6 @@
import logging
from flask import Flask, request, jsonify
from prometheus_client import start_http_server, Histogram, Summary, Counter, generate_latest
from typing import List
from timeit import default_timer as timer
@ -17,17 +18,31 @@ from datetime import datetime
import dateutil.parser
from google.cloud import storage
from features.user_history import FEATURE_COLUMNS
from treeinterpreter import treeinterpreter as ti
app = Flask(__name__)
logging.basicConfig(level=logging.INFO, stream=sys.stdout)
USER_HISTORY_PATH = 'user_features.pkl'
MODEL_PIPELINE_PATH = 'predict_read_pipeline-v002.pkl'
MODEL_PIPELINE_PATH = 'predict_read_model-v003.pkl'
pipeline = None
user_features = None
# these buckets are used for reporting scores, we want to make sure
# there is decent diversity in the returned scores.
score_bucket_ranges = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]
score_buckets = {
f'score_bucket_{int(b * 10)}': Counter(f'inference_score_bucket_{int(b * 10)}', f'Number of scores in the range {b - 0.1:.1f} to {b:.1f}')
for b in score_bucket_ranges
}
def observe_score(score):
for b in score_bucket_ranges:
if b - 0.1 < score <= b:
score_buckets[f'score_bucket_{int(b * 10)}'].inc()
break
def download_from_gcs(bucket_name, gcs_path, destination_path):
storage_client = storage.Client()
bucket = storage_client.bucket(bucket_name)
@ -72,14 +87,20 @@ def merge_dicts(dict1, dict2):
dict1[key] = value
return dict1
def predict_proba_wrapper(X):
return pipeline.predict_proba(X)
def refresh_data():
start = timer()
global pipeline
global explainer
global user_features
if os.getenv('LOAD_LOCAL_MODEL') != None:
if os.getenv('LOAD_LOCAL_MODEL') == None:
print(f"loading data from {os.getenv('GCS_BUCKET')}")
gcs_bucket_name = os.getenv('GCS_BUCKET')
download_from_gcs(gcs_bucket_name, f'data/features/user_features.pkl', USER_HISTORY_PATH)
download_from_gcs(gcs_bucket_name, f'data/models/predict_read_pipeline-v002.pkl', MODEL_PIPELINE_PATH)
download_from_gcs(gcs_bucket_name, f'data/features/{USER_HISTORY_PATH}', USER_HISTORY_PATH)
download_from_gcs(gcs_bucket_name, f'data/models/{MODEL_PIPELINE_PATH}', MODEL_PIPELINE_PATH)
pipeline = load_pipeline(MODEL_PIPELINE_PATH)
user_features = load_user_features(USER_HISTORY_PATH)
end = timer()
@ -90,6 +111,7 @@ def refresh_data():
def compute_score(user_id, item_features):
interaction_score = compute_interaction_score(user_id, item_features)
observe_score(interaction_score)
return {
'score': interaction_score,
'interaction_score': interaction_score,
@ -97,6 +119,7 @@ def compute_score(user_id, item_features):
def compute_interaction_score(user_id, item_features):
start = timer()
original_url_host = urlparse(item_features.get('original_url')).netloc
df_test = pd.DataFrame([{
'user_id': user_id,
@ -134,21 +157,27 @@ def compute_interaction_score(user_id, item_features):
else:
print("skipping feature: ", name)
continue
df_test = pd.merge(df_test, df, on=merge_keys, how='left')
df_test = df_test.fillna(0)
df_predict = df_test[FEATURE_COLUMNS]
end = timer()
print('time to compute score (in seconds):', end - start)
interaction_score = pipeline.predict_proba(df_predict)
print('score', interaction_score, 'item_features', df_test[df_test != 0].stack())
print("INTERACTION SCORE: ", interaction_score)
print('item_features:\n', df_predict[df_predict != 0].stack())
return interaction_score[0][1]
return np.float64(interaction_score[0][1])
@app.route('/_ah/health', methods=['GET'])
def ready():
return jsonify({'OK': 'yes'}), 200
@app.route('/metrics')
def metrics():
return generate_latest(), 200, {'Content-Type': 'text/plain; charset=utf-8'}
@app.route('/refresh', methods=['GET'])
def refresh():
@ -158,6 +187,7 @@ def refresh():
@app.route('/users/<user_id>/features', methods=['GET'])
def get_user_features(user_id):
print("user_features", user_features)
result = {}
df_user = pd.DataFrame([{
'user_id': user_id,
@ -193,6 +223,7 @@ def predict():
@app.route('/batch', methods=['POST'])
def batch():
start = timer()
try:
result = {}
data = request.get_json()
@ -210,6 +241,8 @@ def batch():
library_item_id = item['library_item_id']
result[library_item_id] = compute_score(user_id, item)
end = timer()
print(f'time to compute batch of {len(items)} items (in seconds): {end - start}')
return jsonify(result)
except Exception as e:
app.logger.error(f"exception in batch endpoint: {request.get_json()}\n{e}")

6
ml/digest-score/features.py
View File

@ -15,13 +15,17 @@ from google.cloud import storage
from features.extract import extract_and_upload_raw_data
from features.user_history import generate_and_upload_user_history
from datetime import datetime, timezone
def main():
execution_date = os.getenv('EXECUTION_DATE')
num_days_history = os.getenv('NUM_DAYS_HISTORY')
gcs_bucket_name = os.getenv('GCS_BUCKET')
current_date_utc = datetime.now(timezone.utc)
execution_date = current_date_utc.strftime("%Y-%m-%d")
print(f'updating features using execution date: {execution_date}')
extract_and_upload_raw_data(execution_date, num_days_history, gcs_bucket_name)
generate_and_upload_user_history(execution_date, gcs_bucket_name)

83
ml/digest-score/features/user_history.py
View File

@ -17,65 +17,30 @@ import pyarrow.feather as feather
from google.cloud import storage
FEATURE_COLUMNS=[
# targets
# 'user_clicked', 'user_read', 'user_long_read',
# item attributes / user setup attributes
'item_word_count','item_has_site_icon', 'is_subscription',
'inbox_folder', 'has_author',
# how the user has setup the subscription
'is_newsletter', 'is_feed', 'days_since_subscribed',
'subscription_count', 'subscription_auto_add_to_library',
'subscription_fetch_content',
# user/item interaction history
'user_original_url_host_saved_count_week_1',
'user_original_url_host_interaction_count_week_1',
'user_original_url_host_rate_week_1',
'user_original_url_host_proportion_week_1',
'user_original_url_host_saved_count_week_2',
'user_original_url_host_interaction_count_week_2',
'user_original_url_host_rate_week_2',
'user_original_url_host_proportion_week_2',
'user_original_url_host_saved_count_week_3',
'user_original_url_host_interaction_count_week_3',
'user_original_url_host_rate_week_3',
'user_original_url_host_proportion_week_3',
'user_original_url_host_saved_count_week_4',
'user_original_url_host_interaction_count_week_4',
'user_original_url_host_rate_week_4',
'user_original_url_host_proportion_week_4',
'user_subscription_saved_count_week_1',
'user_subscription_interaction_count_week_1',
'user_subscription_rate_week_1', 'user_subscription_proportion_week_1',
'user_site_saved_count_week_3', 'user_site_interaction_count_week_3',
'user_site_rate_week_3', 'user_site_proportion_week_3',
'user_site_saved_count_week_2', 'user_site_interaction_count_week_2',
'user_site_rate_week_2', 'user_site_proportion_week_2',
'user_subscription_saved_count_week_2',
'user_subscription_interaction_count_week_2',
'user_subscription_rate_week_2', 'user_subscription_proportion_week_2',
'user_site_saved_count_week_1', 'user_site_interaction_count_week_1',
'user_site_rate_week_1', 'user_site_proportion_week_1',
'user_subscription_saved_count_week_3',
'user_subscription_interaction_count_week_3',
'user_subscription_rate_week_3', 'user_subscription_proportion_week_3',
'user_author_saved_count_week_4',
'user_author_interaction_count_week_4', 'user_author_rate_week_4',
'user_author_proportion_week_4', 'user_author_saved_count_week_1',
'user_author_interaction_count_week_1', 'user_author_rate_week_1',
'user_author_proportion_week_1', 'user_site_saved_count_week_4',
'user_site_interaction_count_week_4', 'user_site_rate_week_4',
'user_site_proportion_week_4', 'user_author_saved_count_week_2',
'user_author_interaction_count_week_2', 'user_author_rate_week_2',
'user_author_proportion_week_2', 'user_author_saved_count_week_3',
'user_author_interaction_count_week_3', 'user_author_rate_week_3',
'user_author_proportion_week_3', 'user_subscription_saved_count_week_4',
'user_subscription_interaction_count_week_4',
'user_subscription_rate_week_4', 'user_subscription_proportion_week_4'
'user_subscription_rate_week_1',
'user_subscription_proportion_week_1',
'user_site_rate_week_3',
'user_site_proportion_week_3',
'user_site_rate_week_2',
'user_site_proportion_week_2',
'user_subscription_rate_week_2',
'user_subscription_proportion_week_2',
'user_site_rate_week_1',
'user_site_proportion_week_1',
'user_subscription_rate_week_3',
'user_subscription_proportion_week_3',
'user_author_rate_week_4',
'user_author_proportion_week_4',
'user_author_rate_week_1',
'user_author_proportion_week_1',
'user_site_rate_week_4',
'user_site_proportion_week_4',
'user_author_rate_week_2',
'user_author_proportion_week_2',
'user_author_rate_week_3',
'user_author_proportion_week_3',
'user_subscription_rate_week_4',
'user_subscription_proportion_week_4'
]
def parquet_to_dataframe(file_path):

5
ml/digest-score/requirements.txt
View File

@ -6,5 +6,8 @@ google-cloud-storage
flask
pydantic
sklearn2pmml
sqlalchemy
sqlalchemy
pyarrow
prometheus_client
treeinterpreter
xgboost==2.1.0

89
ml/digest-score/train.py
View File

@ -1,17 +1,11 @@
import pandas as pd
import os
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
from sklearn.linear_model import SGDClassifier
from sklearn.ensemble import RandomForestClassifier, VotingClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, classification_report, confusion_matrix
from sklearn.utils import shuffle
import xgboost as xgb
from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split
from sklearn2pmml import PMMLPipeline, sklearn2pmml
from google.cloud import storage
from google.cloud.exceptions import PreconditionFailed
@ -23,13 +17,6 @@ import pyarrow.feather as feather
from features.user_history import FEATURE_COLUMNS
DB_PARAMS = {
'dbname': os.getenv('DB_NAME') or 'omnivore',
'user': os.getenv('DB_USER'),
'password': os.getenv('DB_PASSWORD'),
'host': os.getenv('DB_HOST') or 'localhost',
'port': os.getenv('DB_PORT') or '5432'
}
def parquet_to_dataframe(file_path):
table = pq.read_table(file_path)
@ -122,58 +109,15 @@ def prepare_data(df):
return X, Y
def train_random_forest_model(X, Y):
model = RandomForestClassifier(
class_weight={0: 1, 1: 10},
n_estimators=10,
max_depth=10,
random_state=42
)
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
def train_xgb_model(X, Y):
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=42)
model = xgb.XGBClassifier(max_depth=7, n_estimators=5)
model.fit(X_train, y_train)
X_train, X_test, Y_train, Y_test = train_test_split(X_scaled, Y, test_size=0.3, random_state=42)
pipeline = PMMLPipeline([
("scaler", scaler),
("classifier", model)
])
pipeline.fit(X_train, Y_train)
Y_pred = pipeline.predict(X_test)
print_classification_report(Y_test, Y_pred)
print_feature_importance(X, model)
return pipeline
def print_feature_importance(X, rf):
# Get feature importances
importances = rf.feature_importances_
# Get the indices of the features sorted by importance
indices = np.argsort(importances)[::-1]
# Print the feature ranking
print("Feature ranking:")
for f in range(X.shape[1]):
print(f"{f + 1}. feature {indices[f]} ({importances[indices[f]]:.4f}) - {X.columns[indices[f]]}")
def print_classification_report(Y_test, Y_pred):
report = classification_report(Y_test, Y_pred, target_names=['Not Clicked', 'Clicked'], output_dict=True)
print("Classification Report:")
print(f"Accuracy: {report['accuracy']:.4f}")
print(f"Precision (Not Clicked): {report['Not Clicked']['precision']:.4f}")
print(f"Recall (Not Clicked): {report['Not Clicked']['recall']:.4f}")
print(f"F1-Score (Not Clicked): {report['Not Clicked']['f1-score']:.4f}")
print(f"Precision (Clicked): {report['Clicked']['precision']:.4f}")
print(f"Recall (Clicked): {report['Clicked']['recall']:.4f}")
print(f"F1-Score (Clicked): {report['Clicked']['f1-score']:.4f}")
y_pred = model.predict(X_test)
print(classification_report(y_test, y_pred))
return model
def main():
@ -181,24 +125,23 @@ def main():
num_days_history = os.getenv('NUM_DAYS_HISTORY')
gcs_bucket_name = os.getenv('GCS_BUCKET')
raw_data_path = f'raw_library_items_${execution_date}.parquet'
raw_data_path = f'raw_library_items_{execution_date}.parquet'
user_history_path = 'features_user_features.pkl'
pipeline_path = 'predict_read_pipeline-v002.pkl'
model_path = 'predict_read_model-v003.pkl'
download_from_gcs(gcs_bucket_name, f'data/features/user_features.pkl', user_history_path)
download_from_gcs(gcs_bucket_name, f'data/raw/library_items_{execution_date}.parquet', raw_data_path)
sampled_raw_df = load_and_sample_library_items_from_parquet(raw_data_path, 0.10)
sampled_raw_df = load_and_sample_library_items_from_parquet(raw_data_path, 0.95)
user_history = load_dataframes_from_pickle(user_history_path)
merged_df = merge_user_preference_data(sampled_raw_df, user_history)
print("created merged data", merged_df.columns)
X, Y = prepare_data(merged_df)
random_forest_pipeline = train_random_forest_model(X, Y)
save_to_pickle(random_forest_pipeline, pipeline_path)
upload_to_gcs(gcs_bucket_name, pipeline_path, f'data/models/{pipeline_path}')
xgb_model = train_xgb_model(X, Y)
save_to_pickle(xgb_model, model_path)
upload_to_gcs(gcs_bucket_name, model_path, f'data/models/{model_path}')
if __name__ == "__main__":