From 0b4ac6aa994cb5a4e4a95abd0fb406edf6b5708c Mon Sep 17 00:00:00 2001 From: Dimitris Poulopoulos Date: Fri, 1 Sep 2023 18:36:08 +0300 Subject: [PATCH 01/15] question-answering: Transform raw cell to code cell Signed-off-by: Dimitris Poulopoulos --- E2E-Demos/Question-Answering/01.create-vectorstore.ipynb | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/E2E-Demos/Question-Answering/01.create-vectorstore.ipynb b/E2E-Demos/Question-Answering/01.create-vectorstore.ipynb index c2084156..83876c1f 100644 --- a/E2E-Demos/Question-Answering/01.create-vectorstore.ipynb +++ b/E2E-Demos/Question-Answering/01.create-vectorstore.ipynb @@ -84,11 +84,13 @@ ] }, { - "cell_type": "raw", + "cell_type": "code", + "execution_count": null, "id": "bafbff81-1b65-41fb-9d65-a26e88779d37", "metadata": { "tags": [] }, + "outputs": [], "source": [ "def load_docs(source_dir: str) -> list:\n", " \"\"\"Load all documents in a the given directory.\"\"\"\n", From e3c582fbc463c6777f807fdeb522609409a260e7 Mon Sep 17 00:00:00 2001 From: Dimitris Poulopoulos Date: Fri, 1 Sep 2023 18:38:47 +0300 Subject: [PATCH 02/15] question-answering: Use a set of public images by default Use a set of public docker images by default, since new clusters do not have the permissions to pull images from the gcr.io registry. Signed-off-by: Dimitris Poulopoulos --- E2E-Demos/Question-Answering/02.serve-vectorstore.ipynb | 4 ++-- E2E-Demos/Question-Answering/04.serve-llm.ipynb | 8 ++++---- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/E2E-Demos/Question-Answering/02.serve-vectorstore.ipynb b/E2E-Demos/Question-Answering/02.serve-vectorstore.ipynb index f10bb26e..b10afe60 100644 --- a/E2E-Demos/Question-Answering/02.serve-vectorstore.ipynb +++ b/E2E-Demos/Question-Answering/02.serve-vectorstore.ipynb @@ -137,8 +137,8 @@ "metadata": {}, "outputs": [], "source": [ - "predictor_image = (input(\"Enter the name of the predictor image (default: gcr.io/mapr-252711/ezua-demos/vectorstore:v0.1.0): \")\n", - " or \"gcr.io/mapr-252711/ezua-demos/vectorstore:v0.1.0\")" + "predictor_image = (input(\"Enter the name of the predictor image (default: dpoulopoulos/vectorstore:b98a87f): \")\n", + " or \"dpoulopoulos/vectorstore:b98a87f\")" ] }, { diff --git a/E2E-Demos/Question-Answering/04.serve-llm.ipynb b/E2E-Demos/Question-Answering/04.serve-llm.ipynb index ee03260f..8e770e79 100644 --- a/E2E-Demos/Question-Answering/04.serve-llm.ipynb +++ b/E2E-Demos/Question-Answering/04.serve-llm.ipynb @@ -89,10 +89,10 @@ "metadata": {}, "outputs": [], "source": [ - "PREDICTOR_IMAGE = (input(\"Enter the name of the predictor image (default: gcr.io/mapr-252711/ezua-demos/llm-predictor:v0.1.0): \")\n", - " or \"gcr.io/mapr-252711/ezua-demos/llm-predictor:v0.1.0\")\n", - "TRANSFORMER_IMAGE = (input(\"Enter the name of the transformer image (default: gcr.io/mapr-252711/ezua-demos/llm-transformer:v0.1.0): \")\n", - " or \"gcr.io/mapr-252711/ezua-demos/llm-transformer:v0.1.0\")" + "PREDICTOR_IMAGE = (input(\"Enter the name of the predictor image (default: dpoulopoulos/llm-predictor:b98a87f): \")\n", + " or \"dpoulopoulos/llm-predictor:b98a87f\")\n", + "TRANSFORMER_IMAGE = (input(\"Enter the name of the transformer image (default: dpoulopoulos/llm-transformer:b98a87f): \")\n", + " or \"dpoulopoulos/llm-transformer:b98a87f\")" ] }, { From 8750cb5b7275da2d5078c6c37830f6455f6a7100 Mon Sep 17 00:00:00 2001 From: Dimitris Poulopoulos Date: Fri, 1 Sep 2023 18:43:24 +0300 Subject: [PATCH 03/15] bike-sharing: Call the mlflow library after imports Signed-off-by: Dimitris Poulopoulos --- Data-Science/MLflow/bike-sharing-mlflow.ipynb | 38 +++++++++---------- 1 file changed, 19 insertions(+), 19 deletions(-) diff --git a/Data-Science/MLflow/bike-sharing-mlflow.ipynb b/Data-Science/MLflow/bike-sharing-mlflow.ipynb index 3527652f..71f68a46 100644 --- a/Data-Science/MLflow/bike-sharing-mlflow.ipynb +++ b/Data-Science/MLflow/bike-sharing-mlflow.ipynb @@ -30,25 +30,6 @@ "### Set Expriment" ] }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2022/11/18 19:24:12 INFO mlflow.tracking.fluent: Experiment with name 'bike-sharing-exp' does not exist. Creating a new experiment.\n" - ] - } - ], - "source": [ - "# Set up an experiment with set_exp from ezmllib.mlflow\n", - "experiment_name = 'bike-sharing-exp'\n", - "mlflow.set_experiment(experiment_name)" - ] - }, { "cell_type": "markdown", "metadata": { @@ -125,6 +106,25 @@ "os.mkdir(\"model_artifacts\")" ] }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2022/11/18 19:24:12 INFO mlflow.tracking.fluent: Experiment with name 'bike-sharing-exp' does not exist. Creating a new experiment.\n" + ] + } + ], + "source": [ + "# Set up an experiment with set_exp from ezmllib.mlflow\n", + "experiment_name = 'bike-sharing-exp'\n", + "mlflow.set_experiment(experiment_name)" + ] + }, { "cell_type": "markdown", "metadata": { From dcdce30f01030e903d7e1ff31c43fd57903832cc Mon Sep 17 00:00:00 2001 From: Prasad Adireddi Date: Thu, 7 Sep 2023 14:19:01 +0530 Subject: [PATCH 04/15] Add DAG files for demo --- .../Airflow/Scripts/clean_data.py | 261 ++++++++++++++++++ .../load_s3_csv_to_mariadb_db_czech.py | 132 +++++++++ .../load_s3_csv_to_mariadb_db_germany.py | 132 +++++++++ .../load_s3_csv_to_mariadb_db_swiss.py | 132 +++++++++ 4 files changed, 657 insertions(+) create mode 100644 Data-Engineering/Airflow/Scripts/clean_data.py create mode 100644 Data-Engineering/Airflow/load_s3_csv_to_mariadb_db_czech.py create mode 100644 Data-Engineering/Airflow/load_s3_csv_to_mariadb_db_germany.py create mode 100644 Data-Engineering/Airflow/load_s3_csv_to_mariadb_db_swiss.py diff --git a/Data-Engineering/Airflow/Scripts/clean_data.py b/Data-Engineering/Airflow/Scripts/clean_data.py new file mode 100644 index 00000000..0f2474f5 --- /dev/null +++ b/Data-Engineering/Airflow/Scripts/clean_data.py @@ -0,0 +1,261 @@ +#!/bin/python3 +""" +EZUAF DEMO +created Vincent Charbonnier, 2023 +clean fruit&veg dataset in SQL DBs +""" + +import subprocess + +# List of libraries to install +libraries_to_install = ["fuzzywuzzy", "pycountry", "python-Levenshtein"] + +# Run the pip command to install the libraries +for library in libraries_to_install: + try: + subprocess.run(["pip", "install", library], check=True) + print(f"{library} library installed successfully.") + except subprocess.CalledProcessError: + print(f"Failed to install {library} library.") + +# import the necessary libraries +from fuzzywuzzy import process +import pycountry +import requests +import sys +import argparse +import logging + +# Set up logging +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + +# Parse command line arguments +parser = argparse.ArgumentParser() +parser.add_argument("-db", "--database_type", help="Type of SQL database (e.g. mysql, postgresql)", required=True) +parser.add_argument("-H", "--host", help="SQL hostname", required=True) +parser.add_argument("-u", "--user", help="SQL username", required=True) +parser.add_argument("-p", "--password", help="SQL password", required=True) +parser.add_argument("-P", "--port", help="SQL port", default=3306, type=int) +parser.add_argument("-d", "--database", help="SQL database name", required=True) +parser.add_argument("-t", "--table", help="SQL table name", required=True) +args = parser.parse_args() +if args.database_type != None: db_type=args.database_type +else: + print("hostname required") + sys.exit() +if args.host != None: db_host=args.host +else: + print("hostname required") + sys.exit() +if args.user != None: db_user=args.user +else: + print("username is required") + sys.exit() +if args.password != None: db_password=args.password +else: + print("password is required") + sys.exit() +if args.database != None: db_name=args.database +else: + print("database name is required") + sys.exit() +if args.table != None: table_name=args.table +else: + print("table name is required") + sys.exit() +if args.port != None: db_port=args.port +else: + print("port name is required") + +# Define a minimum match score for fuzzy matching +MIN_MATCH_SCORE = 70 + +# Define a function to validate the country name using fuzzy matching +def validate_country_name_fuzzy(country_name): + countries = [c.name for c in pycountry.countries] + match = process.extractOne(country_name, countries) + if match[1] >= MIN_MATCH_SCORE: + country = pycountry.countries.get(name=match[0]) + return country.alpha_2 + else: + return None + +# Define a function to validate the currency code using fuzzy matching +def validate_currency_code(currency_code): + currencies = [c.alpha_3 for c in pycountry.currencies] + match = process.extractOne(currency_code, currencies) + if match[1] >= MIN_MATCH_SCORE: + return match[0] + else: + return None + +def get_currency_code(country_name): + """ + Given a country name, returns its currency code using pycountry library. + If the country name is not found, returns None. + """ + try: + country_code = pycountry.countries.search_fuzzy(country_name)[0].alpha_3 + if country_code in ['AUT', 'BEL', 'CYP', 'EST', 'FIN', 'FRA', 'DEU', 'GRC', 'IRL', 'ITA', 'LVA', 'LTU', 'LUX', 'MLT', 'NLD', 'PRT', 'SVK', 'SVN', 'ESP']: + return 'EUR' + currency_code = pycountry.currencies.get(numeric=pycountry.countries.get(alpha_3=country_code).numeric).alpha_3 + return currency_code + except LookupError: + print(f"E: Could not find currency for country {country_name}") + return None + +def get_database_connection(database_type): + """ + Given the database type, returns a connection to the database. + If the database type is not supported, returns None. + """ + if database_type == "mysql": + # Define the MySQL database parameters + host = db_host + user = db_user + password = db_password + database = db_name + table = table_name + port = db_port + + # Connect to the MySQL database + print("# Connecting to the MySQL database...") + try: + import mysql.connector + except ImportError: + print("Error: mysql.connector library not found. Please install it using pip.") + sys.exit() + + cnx = mysql.connector.connect(user=user, password=password, + host=host, port=port, database=database) + return cnx + elif database_type == "postgresql": + # Define the PostgreSQL database parameters + host = db_host + user = db_user + password = db_password + database = db_name + table = table_name + port = db_port + + # Connect to the PostgreSQL database + print("# Connecting to the PostgreSQL database...") + try: + import psycopg2 + except ImportError: + print("Error: psycopg2 library not found. Please install it using pip.") + sys.exit() + + cnx = psycopg2.connect(host=host, port=port, user=user, + password=password, database=database) + return cnx + else: + print("Error: Unsupported database type.") + return None + +def execute_query(cursor, query, params=None): + """ + Given a cursor, a query, and optional parameters, executes the query and returns the result. + If an error occurs, prints an error message and returns None. + """ + try: + cursor.execute(query, params) + return cursor.fetchall() + except Exception as e: + print(f"Error: {str(e)}") + return None + +def update_database_table(database_type, database, table): + # Create a connection to the database + cnx = get_database_connection(database_type) + if cnx is None: + return + + # Create a cursor to execute SQL queries + cursor = cnx.cursor() + + # Read the database table + print(f"# Reading the {database_type} database table {database}.{table}...") + query = f"SELECT * FROM {table}" + rows = execute_query(cursor, query) + + # Define the API endpoint for fetching the latest exchange rates + exchange_rate_api_endpoint = "https://api.exchangerate-api.com/v4/latest/EUR" + + # Fetch the latest exchange rates from the API + print("# Fetching the latest exchange rates from the API...") + response = requests.get(exchange_rate_api_endpoint) + if response.status_code == 200: + exchange_rates = response.json().get("rates") + else: + # Handle the case where the API request fails + print("Error: Unable to fetch exchange rates from API") + exit() + + # Iterate over the rows and validate the country names + print("# Analyzing the data...") + updated_rows = [] + for row in rows: + country = str(row[9]) + curr_code = str(row[7]) + # Validate the country name and get the currency code + try: + country_obj = pycountry.countries.search_fuzzy(country)[0] + country_name = country_obj.official_name + currency_code = get_currency_code(country_name) + except LookupError: + print(f"E: Could not find currency for country {country_name}") + continue + + # If the currency code is already EUR, add the row to updated_rows without performing any currency conversion + if curr_code == "EUR": + updated_rows.append((row[0], row[1], row[2], row[3], row[4], row[5], row[6], row[7], row[8], country_name, row[10])) + else: + # Convert the currency value to EUR using the exchange rate + currency_rate = exchange_rates.get(currency_code) + + if currency_rate is not None: + currency_code = "EUR" + + # Convert row[3] and row[5] to float + try: + unitprice = float(row[3]) + quantity = float(row[5]) + except ValueError: + print("Error: Unable to convert to numeric value for multiplication.") + continue # Skip this iteration and move to the next row + + # Calculate the unit price in Euro + unit_euro = unitprice / currency_rate + + # Calculate the total sales and round to two decimal places + totalsales = unit_euro * quantity + + updated_rows.append((row[0], row[1], row[2], unit_euro, row[4], quantity, totalsales, currency_code, row[8], country_name, row[10])) + + # Write the corrected data back to the database table + print(f"# Updating the {database_type} database table {db_name}.{table_name} with validated country names and currencies...") + query = f"TRUNCATE TABLE {table}" + cursor.execute(query) + query = f"INSERT INTO {table} (productid, product, type, unitprice, unit, qty, totalsales, currency, store, country, year) VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)" + cursor.executemany(query, updated_rows) + cnx.commit() + + # Get details from the database cursor + cursor.execute(f"SELECT * FROM {table} LIMIT 10") + column_names = [column[0] for column in cursor.description] + results = cursor.fetchall() + + # Print the results from the database + print(f"# Results from the {database_type} database {db_name}:") + print(column_names) + for result in results: + print(result) + + # Close the database connection + cnx.close() + print(f"# Done updating {db_type} database {db_name}.") + +# Call the function to update the SQL database +update_database_table(db_type, db_name, table_name) diff --git a/Data-Engineering/Airflow/load_s3_csv_to_mariadb_db_czech.py b/Data-Engineering/Airflow/load_s3_csv_to_mariadb_db_czech.py new file mode 100644 index 00000000..163dc14c --- /dev/null +++ b/Data-Engineering/Airflow/load_s3_csv_to_mariadb_db_czech.py @@ -0,0 +1,132 @@ +# This DAG downloads a CSV file from MinIO and imports it into MariaDB +# +# This DAG assumes that you have already created the following connections in Airflow: +# 1. s3_connection: A connection to your MinIO instance +# 2. mysql_connection: A connection to your MariaDB instance +# +# +# created by Dirk Derichsweiler & Vincent Charbonnier & Isabelle Steinhauser +# 2023-08-08 +# + +# Define the AWS credentials and bucket/file information + +from datetime import datetime, timedelta +from airflow import DAG +from airflow.operators.python_operator import PythonOperator +import boto3 +import pandas as pd +import mysql.connector +import logging +import os + +default_args = { + 'owner': 'airflow', + 'depends_on_past': False, + 'start_date': datetime.now(), # Set the start date to the current date and time + 'retries': 1, + 'retry_delay': timedelta(minutes=5), +} + +# Define the op_kwargs dictionary +op_kwargs = { + 'bucket_name': 'ezaf-demo', + 'file_name': 'Czechia_sales_data_2019_2023.csv', + 's3_endpoint': 'https://home.hpe-qa16-ezaf.com:31900', + 'access_key': 'minioadmin', + 'secret_key': 'minioadmin', + 'db_host': '10.227.209.61', + 'db_port': '3306', + 'db_user': 'hpepoc', + 'db_password': 'Admin@12345', + 'db_name': 'czech_mysql_store1', + 'table_name': 'czech' +} + +def read_csv_from_s3(bucket_name, file_name, s3_endpoint, access_key=None, secret_key=None): + s3 = boto3.client( + 's3', + verify=False, + endpoint_url=s3_endpoint, + aws_access_key_id=access_key, + aws_secret_access_key=secret_key + ) + obj = s3.get_object(Bucket=bucket_name, Key=file_name) + df = pd.read_csv(obj['Body']) + return df + +def import_csv_to_mariadb(df, db_host, db_port, db_user, db_password, db_name, table_name): + conn = mysql.connector.connect( + host=db_host, + port=db_port, + user=db_user, + password=db_password, + database=db_name + ) + cursor = conn.cursor() + + # Create table if it doesn't exist + create_table_query = f"CREATE TABLE IF NOT EXISTS {table_name} (" + for column in df.columns: + # Skip the 'PRICE' column + if column == 'UNITPRICE' or column == 'TOTALSALES': + create_table_query += f"{column} FLOAT, " + else: + create_table_query += f"{column} VARCHAR(255), " + create_table_query = create_table_query[:-2] + ")" + cursor.execute(create_table_query) + + # Convert 'PRICE' column to numeric type + df['UNITPRICE'] = df['UNITPRICE'].astype(float) + + # Insert data into the table + insert_query = f"INSERT INTO {table_name} ({', '.join(df.columns)}) VALUES ({', '.join(['%s'] * len(df.columns))})" + cursor.executemany(insert_query, df.values.tolist()) + + conn.commit() + cursor.close() + conn.close() + +def process_csv_file(bucket_name, file_name, s3_endpoint, access_key=None, secret_key=None, + db_host=None, db_port=None, db_user=None, db_password=None, db_name=None, table_name=None): + df = read_csv_from_s3(bucket_name, file_name, s3_endpoint, access_key, secret_key) + + # Output the headers + headers = df.columns.tolist() + logging.info("Headers: %s", headers) + + import_csv_to_mariadb(df, db_host, db_port, db_user, db_password, db_name, table_name) + +# Define the function to run the clean_data.py script +def run_clean_data_script(db_host=None, db_port=None, db_user=None, db_password=None, db_name=None, table_name=None): + import subprocess + + script_path = "/usr/local/airflow/dags/gitdags/Scripts/clean_data.py" # Replace with the actual path to clean_data.py + command = [ + "python", + script_path, + "-db", "mysql", + "-H", db_host, + "-u", db_user, + "-p", db_password, + "-P", db_port, + "-d", db_name, + "-t", table_name + ] + + subprocess.run(command, check=True) + +with DAG('load_s3_csv_to_mysql_db_czech', default_args=default_args, schedule_interval='0 7 * * *') as dag: + process_csv = PythonOperator( + task_id='process_csv', + python_callable=process_csv_file, + op_kwargs=op_kwargs + ) + + clean_data = PythonOperator( + task_id='clean_data', + python_callable=run_clean_data_script, + op_kwargs=op_kwargs + ) + + process_csv >> clean_data diff --git a/Data-Engineering/Airflow/load_s3_csv_to_mariadb_db_germany.py b/Data-Engineering/Airflow/load_s3_csv_to_mariadb_db_germany.py new file mode 100644 index 00000000..df811ca5 --- /dev/null +++ b/Data-Engineering/Airflow/load_s3_csv_to_mariadb_db_germany.py @@ -0,0 +1,132 @@ +# This DAG downloads a CSV file from MinIO and imports it into MariaDB +# +# This DAG assumes that you have already created the following connections in Airflow: +# 1. s3_connection: A connection to your MinIO instance +# 2. mysql_connection: A connection to your MariaDB instance +# +# +# created by Dirk Derichsweiler & Vincent Charbonnier & Isabelle Steinhauser +# 2023-08-08 +# + +# Define the AWS credentials and bucket/file information + +from datetime import datetime, timedelta +from airflow import DAG +from airflow.operators.python_operator import PythonOperator +import boto3 +import pandas as pd +import mysql.connector +import logging +import os + +default_args = { + 'owner': 'airflow', + 'depends_on_past': False, + 'start_date': datetime.now(), # Set the start date to the current date and time + 'retries': 1, + 'retry_delay': timedelta(minutes=5), +} + +# Define the op_kwargs dictionary +op_kwargs = { + 'bucket_name': 'ezaf-demo', + 'file_name': 'Germany_sales_data_2019_2023.csv', + 's3_endpoint': 'https://home.hpe-qa16-ezaf.com:31900', + 'access_key': 'minioadmin', + 'secret_key': 'minioadmin', + 'db_host': '10.227.209.61', + 'db_port': '3306', + 'db_user': 'hpepoc', + 'db_password': 'Admin@12345', + 'db_name': 'german_mysql_store1', + 'table_name': 'germany' +} + +def read_csv_from_s3(bucket_name, file_name, s3_endpoint, access_key=None, secret_key=None): + s3 = boto3.client( + 's3', + verify=False, + endpoint_url=s3_endpoint, + aws_access_key_id=access_key, + aws_secret_access_key=secret_key + ) + obj = s3.get_object(Bucket=bucket_name, Key=file_name) + df = pd.read_csv(obj['Body']) + return df + +def import_csv_to_mariadb(df, db_host, db_port, db_user, db_password, db_name, table_name): + conn = mysql.connector.connect( + host=db_host, + port=db_port, + user=db_user, + password=db_password, + database=db_name + ) + cursor = conn.cursor() + + # Create table if it doesn't exist + create_table_query = f"CREATE TABLE IF NOT EXISTS {table_name} (" + for column in df.columns: + # Skip the 'PRICE' column + if column == 'UNITPRICE' or column == 'TOTALSALES': + create_table_query += f"{column} FLOAT, " + else: + create_table_query += f"{column} VARCHAR(255), " + create_table_query = create_table_query[:-2] + ")" + cursor.execute(create_table_query) + + # Convert 'PRICE' column to numeric type + df['UNITPRICE'] = df['UNITPRICE'].astype(float) + + # Insert data into the table + insert_query = f"INSERT INTO {table_name} ({', '.join(df.columns)}) VALUES ({', '.join(['%s'] * len(df.columns))})" + cursor.executemany(insert_query, df.values.tolist()) + + conn.commit() + cursor.close() + conn.close() + +def process_csv_file(bucket_name, file_name, s3_endpoint, access_key=None, secret_key=None, + db_host=None, db_port=None, db_user=None, db_password=None, db_name=None, table_name=None): + df = read_csv_from_s3(bucket_name, file_name, s3_endpoint, access_key, secret_key) + + # Output the headers + headers = df.columns.tolist() + logging.info("Headers: %s", headers) + + import_csv_to_mariadb(df, db_host, db_port, db_user, db_password, db_name, table_name) + +# Define the function to run the clean_data.py script +def run_clean_data_script(db_host=None, db_port=None, db_user=None, db_password=None, db_name=None, table_name=None): + import subprocess + + script_path = "/usr/local/airflow/dags/gitdags/Scripts/clean_data.py" # Replace with the actual path to clean_data.py + command = [ + "python", + script_path, + "-db", "mysql", + "-H", db_host, + "-u", db_user, + "-p", db_password, + "-P", db_port, + "-d", db_name, + "-t", table_name + ] + + subprocess.run(command, check=True) + +with DAG('load_s3_csv_to_mysql_db_germany', default_args=default_args, schedule_interval='0 7 * * *') as dag: + process_csv = PythonOperator( + task_id='process_csv', + python_callable=process_csv_file, + op_kwargs=op_kwargs + ) + + clean_data = PythonOperator( + task_id='clean_data', + python_callable=run_clean_data_script, + op_kwargs=op_kwargs + ) + + process_csv >> clean_data diff --git a/Data-Engineering/Airflow/load_s3_csv_to_mariadb_db_swiss.py b/Data-Engineering/Airflow/load_s3_csv_to_mariadb_db_swiss.py new file mode 100644 index 00000000..b2742aa3 --- /dev/null +++ b/Data-Engineering/Airflow/load_s3_csv_to_mariadb_db_swiss.py @@ -0,0 +1,132 @@ +# This DAG downloads a CSV file from MinIO and imports it into MariaDB +# +# This DAG assumes that you have already created the following connections in Airflow: +# 1. s3_connection: A connection to your MinIO instance +# 2. mysql_connection: A connection to your MariaDB instance +# +# +# created by Dirk Derichsweiler & Vincent Charbonnier & Isabelle Steinhauser +# 2023-08-08 +# + +# Define the AWS credentials and bucket/file information + +from datetime import datetime, timedelta +from airflow import DAG +from airflow.operators.python_operator import PythonOperator +import boto3 +import pandas as pd +import mysql.connector +import logging +import os + +default_args = { + 'owner': 'airflow', + 'depends_on_past': False, + 'start_date': datetime.now(), # Set the start date to the current date and time + 'retries': 1, + 'retry_delay': timedelta(minutes=5), +} + +# Define the op_kwargs dictionary +op_kwargs = { + 'bucket_name': 'ezaf-demo', + 'file_name': 'Switzerland_sales_data_2019_2023.csv', + 's3_endpoint': 'https://home.hpe-qa16-ezaf.com:31900', + 'access_key': 'minioadmin', + 'secret_key': 'minioadmin', + 'db_host': '10.227.209.65', + 'db_port': '3306', + 'db_user': 'hpepoc', + 'db_password': 'Admin@12345', + 'db_name': 'swiss_mariadb_store1', + 'table_name': 'swiss' +} + +def read_csv_from_s3(bucket_name, file_name, s3_endpoint, access_key=None, secret_key=None): + s3 = boto3.client( + 's3', + verify=False, + endpoint_url=s3_endpoint, + aws_access_key_id=access_key, + aws_secret_access_key=secret_key + ) + obj = s3.get_object(Bucket=bucket_name, Key=file_name) + df = pd.read_csv(obj['Body']) + return df + +def import_csv_to_mariadb(df, db_host, db_port, db_user, db_password, db_name, table_name): + conn = mysql.connector.connect( + host=db_host, + port=db_port, + user=db_user, + password=db_password, + database=db_name + ) + cursor = conn.cursor() + + # Create table if it doesn't exist + create_table_query = f"CREATE TABLE IF NOT EXISTS {table_name} (" + for column in df.columns: + # Skip the 'PRICE' column + if column == 'UNITPRICE' or column == 'TOTALSALES': + create_table_query += f"{column} FLOAT, " + else: + create_table_query += f"{column} VARCHAR(255), " + create_table_query = create_table_query[:-2] + ")" + cursor.execute(create_table_query) + + # Convert 'PRICE' column to numeric type + df['UNITPRICE'] = df['UNITPRICE'].astype(float) + + # Insert data into the table + insert_query = f"INSERT INTO {table_name} ({', '.join(df.columns)}) VALUES ({', '.join(['%s'] * len(df.columns))})" + cursor.executemany(insert_query, df.values.tolist()) + + conn.commit() + cursor.close() + conn.close() + +def process_csv_file(bucket_name, file_name, s3_endpoint, access_key=None, secret_key=None, + db_host=None, db_port=None, db_user=None, db_password=None, db_name=None, table_name=None): + df = read_csv_from_s3(bucket_name, file_name, s3_endpoint, access_key, secret_key) + + # Output the headers + headers = df.columns.tolist() + logging.info("Headers: %s", headers) + + import_csv_to_mariadb(df, db_host, db_port, db_user, db_password, db_name, table_name) + +# Define the function to run the clean_data.py script +def run_clean_data_script(db_host=None, db_port=None, db_user=None, db_password=None, db_name=None, table_name=None): + import subprocess + + script_path = "/usr/local/airflow/dags/gitdags/Scripts/clean_data.py" # Replace with the actual path to clean_data.py + command = [ + "python", + script_path, + "-db", "mysql", + "-H", db_host, + "-u", db_user, + "-p", db_password, + "-P", db_port, + "-d", db_name, + "-t", table_name + ] + + subprocess.run(command, check=True) + +with DAG('load_s3_csv_to_mariadb_swiss', default_args=default_args, schedule_interval='0 7 * * *') as dag: + process_csv = PythonOperator( + task_id='process_csv', + python_callable=process_csv_file, + op_kwargs=op_kwargs + ) + + clean_data = PythonOperator( + task_id='clean_data', + python_callable=run_clean_data_script, + op_kwargs=op_kwargs + ) + + process_csv >> clean_data From 1742250123870d5ac0ebd0ebdfa1815cd7c7fbb4 Mon Sep 17 00:00:00 2001 From: Prasad Adireddi Date: Thu, 7 Sep 2023 14:58:01 +0530 Subject: [PATCH 05/15] Update DAG for the emo --- Data-Engineering/Airflow/load_s3_csv_to_mariadb_db_czech.py | 4 ++-- Data-Engineering/Airflow/load_s3_csv_to_mariadb_db_swiss.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/Data-Engineering/Airflow/load_s3_csv_to_mariadb_db_czech.py b/Data-Engineering/Airflow/load_s3_csv_to_mariadb_db_czech.py index 163dc14c..7bf3972f 100644 --- a/Data-Engineering/Airflow/load_s3_csv_to_mariadb_db_czech.py +++ b/Data-Engineering/Airflow/load_s3_csv_to_mariadb_db_czech.py @@ -16,7 +16,7 @@ from airflow.operators.python_operator import PythonOperator import boto3 import pandas as pd -import mysql.connector +import MySQLdb import logging import os @@ -56,7 +56,7 @@ def read_csv_from_s3(bucket_name, file_name, s3_endpoint, access_key=None, secre return df def import_csv_to_mariadb(df, db_host, db_port, db_user, db_password, db_name, table_name): - conn = mysql.connector.connect( + conn = MySQLdb.connector.connect( host=db_host, port=db_port, user=db_user, diff --git a/Data-Engineering/Airflow/load_s3_csv_to_mariadb_db_swiss.py b/Data-Engineering/Airflow/load_s3_csv_to_mariadb_db_swiss.py index b2742aa3..db0b2d9a 100644 --- a/Data-Engineering/Airflow/load_s3_csv_to_mariadb_db_swiss.py +++ b/Data-Engineering/Airflow/load_s3_csv_to_mariadb_db_swiss.py @@ -16,7 +16,7 @@ from airflow.operators.python_operator import PythonOperator import boto3 import pandas as pd -import mysql.connector +import MySQLdb import logging import os From 3ebe5a96d1c00375ceae785cbcdff90ac11336ab Mon Sep 17 00:00:00 2001 From: Prasad Adireddi Date: Thu, 7 Sep 2023 15:09:05 +0530 Subject: [PATCH 06/15] update dag for demo --- Data-Engineering/Airflow/load_s3_csv_to_mariadb_db_germany.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Data-Engineering/Airflow/load_s3_csv_to_mariadb_db_germany.py b/Data-Engineering/Airflow/load_s3_csv_to_mariadb_db_germany.py index df811ca5..ff46407f 100644 --- a/Data-Engineering/Airflow/load_s3_csv_to_mariadb_db_germany.py +++ b/Data-Engineering/Airflow/load_s3_csv_to_mariadb_db_germany.py @@ -16,7 +16,7 @@ from airflow.operators.python_operator import PythonOperator import boto3 import pandas as pd -import mysql.connector +import MySQLdb import logging import os From 0dfa4812ac5aa160ba7700ac6e3b830c45199102 Mon Sep 17 00:00:00 2001 From: Arkadii Kravchuk <64816141+akravacyber@users.noreply.github.com> Date: Thu, 7 Sep 2023 16:00:06 +0300 Subject: [PATCH 07/15] fix bugs (#42) --- Data-Engineering/Airflow/Scripts/clean_data.py | 8 ++++---- .../Airflow/load_s3_csv_to_mariadb_db_czech.py | 2 +- .../Airflow/load_s3_csv_to_mariadb_db_germany.py | 2 +- .../Airflow/load_s3_csv_to_mariadb_db_swiss.py | 2 +- 4 files changed, 7 insertions(+), 7 deletions(-) diff --git a/Data-Engineering/Airflow/Scripts/clean_data.py b/Data-Engineering/Airflow/Scripts/clean_data.py index 0f2474f5..03aa1d9d 100644 --- a/Data-Engineering/Airflow/Scripts/clean_data.py +++ b/Data-Engineering/Airflow/Scripts/clean_data.py @@ -122,13 +122,13 @@ def get_database_connection(database_type): # Connect to the MySQL database print("# Connecting to the MySQL database...") try: - import mysql.connector + import MySQLdb except ImportError: - print("Error: mysql.connector library not found. Please install it using pip.") + print("Error: MySQLdb library not found. Please install it using pip.") sys.exit() - cnx = mysql.connector.connect(user=user, password=password, - host=host, port=port, database=database) + cnx = MySQLdb.Connection(user=user, password=password, + host=host, port=port, database=database) return cnx elif database_type == "postgresql": # Define the PostgreSQL database parameters diff --git a/Data-Engineering/Airflow/load_s3_csv_to_mariadb_db_czech.py b/Data-Engineering/Airflow/load_s3_csv_to_mariadb_db_czech.py index 7bf3972f..9a3c1bd3 100644 --- a/Data-Engineering/Airflow/load_s3_csv_to_mariadb_db_czech.py +++ b/Data-Engineering/Airflow/load_s3_csv_to_mariadb_db_czech.py @@ -56,7 +56,7 @@ def read_csv_from_s3(bucket_name, file_name, s3_endpoint, access_key=None, secre return df def import_csv_to_mariadb(df, db_host, db_port, db_user, db_password, db_name, table_name): - conn = MySQLdb.connector.connect( + conn = MySQLdb.Connection( host=db_host, port=db_port, user=db_user, diff --git a/Data-Engineering/Airflow/load_s3_csv_to_mariadb_db_germany.py b/Data-Engineering/Airflow/load_s3_csv_to_mariadb_db_germany.py index ff46407f..648c1bd7 100644 --- a/Data-Engineering/Airflow/load_s3_csv_to_mariadb_db_germany.py +++ b/Data-Engineering/Airflow/load_s3_csv_to_mariadb_db_germany.py @@ -56,7 +56,7 @@ def read_csv_from_s3(bucket_name, file_name, s3_endpoint, access_key=None, secre return df def import_csv_to_mariadb(df, db_host, db_port, db_user, db_password, db_name, table_name): - conn = mysql.connector.connect( + conn = MySQLdb.Connection( host=db_host, port=db_port, user=db_user, diff --git a/Data-Engineering/Airflow/load_s3_csv_to_mariadb_db_swiss.py b/Data-Engineering/Airflow/load_s3_csv_to_mariadb_db_swiss.py index db0b2d9a..a74f1892 100644 --- a/Data-Engineering/Airflow/load_s3_csv_to_mariadb_db_swiss.py +++ b/Data-Engineering/Airflow/load_s3_csv_to_mariadb_db_swiss.py @@ -56,7 +56,7 @@ def read_csv_from_s3(bucket_name, file_name, s3_endpoint, access_key=None, secre return df def import_csv_to_mariadb(df, db_host, db_port, db_user, db_password, db_name, table_name): - conn = mysql.connector.connect( + conn = MySQLdb.Connection( host=db_host, port=db_port, user=db_user, From 707aaf99b9c071cb4b35db27cded8772dbae74e2 Mon Sep 17 00:00:00 2001 From: Aravind G <138818991+sip-aravind-g@users.noreply.github.com> Date: Thu, 7 Sep 2023 18:32:20 +0530 Subject: [PATCH 08/15] Ezaf 2419 | News Recommendation System (#37) * EZAF-2419 | ray runtime env validate script * update readme * EZAF-2429 || News Recommendation System --- .../ray-runtime-env-packages-vlidate.ipynb | 243 ++++++++++++++++++ .../Ray/Ray-RuntimeEnv-Proxy/readme.md | 11 +- Data-Science/Ray/news_recommendation.ipynb | 17 +- 3 files changed, 269 insertions(+), 2 deletions(-) create mode 100644 Data-Science/Ray/Ray-RuntimeEnv-Proxy/ray-runtime-env-packages-vlidate.ipynb diff --git a/Data-Science/Ray/Ray-RuntimeEnv-Proxy/ray-runtime-env-packages-vlidate.ipynb b/Data-Science/Ray/Ray-RuntimeEnv-Proxy/ray-runtime-env-packages-vlidate.ipynb new file mode 100644 index 00000000..2e5f6b9e --- /dev/null +++ b/Data-Science/Ray/Ray-RuntimeEnv-Proxy/ray-runtime-env-packages-vlidate.ipynb @@ -0,0 +1,243 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "id": "4c6f181b-2e1f-465d-aabd-e5d0de5781d6", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-18 15:11:16,796\tINFO client_builder.py:237 -- Passing the following kwargs to ray.init() on the server: ignore_reinit_error\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "check1\n", + "check2\n", + "check3\n", + "hello Avi, welcome to Ray !\n", + "check4\n", + "\u001b[2m\u001b[36m(A pid=804, ip=10.224.1.240)\u001b[0m configuration_dtl :: ===== :: {'MINIO_HOST_URL': 'home.hpe-apps-ezaf.com:31900', 'MINIO_ACCESS_KEY': 'minioadmin', 'MINIO_SECRET_KEY': 'minioadmin', 'SOURCE_PATH': '/source/feed-2.csv', 'GENERATED_PATH': '/source/generated-data.csv', 'storage_uri': 's3://experiments/ray/pickels/logisticregression/model', 'protocol_version': 'v2', 'bucket_name': 'experiments', 'EZAF_ENV': 'hpe-staging-ezaf', 'token_url': 'https://keycloak.{0}.com/realms/UA/protocol/openid-connect/token'}\n", + "\u001b[2m\u001b[36m(A pid=804, ip=10.224.1.240)\u001b[0m MINIO_CLIENT \n", + "\u001b[2m\u001b[36m(A pid=804, ip=10.224.1.240)\u001b[0m shape============== [20, 7]\n", + "\u001b[2m\u001b[36m(A pid=804, ip=10.224.1.240)\u001b[0m X.values[0]============== [ 0. 10. 3. 0. 1. 2. 10.59] ======= [0.0, 10.0, 3.0, 0.0, 1.0, 2.0, 10.59]\n", + "( step customer age gender merchant category amount\n", + "0 0 10 3 0 1 2 10.590000\n", + "1 0 7 2 0 1 2 21.920000\n", + "2 0 14 3 1 2 0 44.260000\n", + "3 0 3 3 1 3 0 324.500000\n", + "4 0 15 1 1 0 2 26.070000\n", + "5 0 12 2 0 1 2 10.880000\n", + "6 0 13 4 1 1 2 8.050000\n", + "7 0 1 3 0 1 2 25.590000\n", + "8 0 5 5 0 1 2 15.260000\n", + "9 0 6 3 1 1 2 20.730000\n", + "10 1 0 2 1 1 2 4.650000\n", + "11 1 16 3 1 1 2 5.810000\n", + "12 1 9 3 0 0 2 56.620000\n", + "13 1 8 3 1 1 2 29.320000\n", + "14 1 2 2 0 0 2 24.190000\n", + "15 1 17 2 0 0 2 6.790000\n", + "16 1 18 3 0 1 2 74.530000\n", + "17 1 4 4 0 4 1 255.140000\n", + "18 1 4 4 0 4 1 21.930000\n", + "19 1 11 4 1 1 2 5.360000\n", + "20 0 4 3 0 3 0 23.227007\n", + "21 0 12 3 1 2 0 81.762797\n", + "22 0 3 3 0 3 0 296.833337\n", + "23 0 11 3 0 2 0 37.741261\n", + "24 0 3 3 0 3 0 323.072259\n", + "25 1 4 4 0 4 1 248.122677\n", + "26 0 3 3 0 3 0 312.878222\n", + "27 0 3 3 0 3 0 269.867841\n", + "28 0 5 3 0 3 0 25.990152\n", + "29 0 12 3 0 2 0 82.936343\n", + "30 0 3 3 0 3 0 303.397758\n", + "31 0 9 3 0 2 0 33.647811, fraud\n", + "0 0\n", + "1 0\n", + "2 1\n", + "3 1\n", + "4 0\n", + "5 0\n", + "6 0\n", + "7 0\n", + "8 0\n", + "9 0\n", + "10 0\n", + "11 0\n", + "12 0\n", + "13 0\n", + "14 0\n", + "15 0\n", + "16 0\n", + "17 1\n", + "18 1\n", + "19 0\n", + "20 1\n", + "21 1\n", + "22 1\n", + "23 1\n", + "24 1\n", + "25 1\n", + "26 1\n", + "27 1\n", + "28 1\n", + "29 1\n", + "30 1\n", + "31 1)\n", + "check5\n" + ] + } + ], + "source": [ + "import ray\n", + "'''\n", + "SAMPLE CODE TO VALIDATE RUNTIME_ENV\n", + "'''\n", + "runtime_env = {\n", + " \"eager_install\":True,\n", + " \"pip\": [\"imblearn\",\"minio\", \"urllib3\", \"requests\", \"pandas\", \"scipy\", \"kfp\"], \n", + " \"env_vars\":{\"HTTP_PROXY\": \"http://10.78.90.46:80\", \"HTTPS_PROXY\": \"http://10.78.90.46:80\", \"http_proxy\": \"http://10.78.90.46:80\", \"https_proxy\": \"http://10.78.90.46:80\"},\n", + " \"pip_check\": False\n", + "}\n", + "\n", + "ray.init(\n", + " address=\"ray://kuberay-head-svc.kuberay:10001\", \n", + " ignore_reinit_error=True, \n", + " runtime_env=runtime_env, \n", + " _temp_dir=\"/tmp/ray/\", \n", + " local_mode=False,\n", + " namespace=\"kuberay\")\n", + "\n", + "print(\"check1\")\n", + "@ray.remote(runtime_env=runtime_env)\n", + "# @ray.remote\n", + "class A:\n", + " def __call__(self, *args, **kwargs):\n", + " return \"hello Avi, welcome to Ray !\"\n", + " \n", + " def overspaling_smote(self):\n", + " import json\n", + " from minio import Minio\n", + " from imblearn.over_sampling import SMOTE\n", + " import urllib3\n", + " import uuid\n", + " import requests\n", + " import pandas as pd\n", + " from requests.packages.urllib3.exceptions import InsecureRequestWarning\n", + " requests.packages.urllib3.disable_warnings(InsecureRequestWarning)\n", + "\n", + " configuration_dtl = {\n", + " \"MINIO_HOST_URL\":\"home.hpe-apps-ezaf.com:31900\",\n", + " \"MINIO_ACCESS_KEY\":\"minioadmin\",\n", + " \"MINIO_SECRET_KEY\":\"minioadmin\",\n", + " \"SOURCE_PATH\":\"/source/feed-2.csv\",\n", + " \"GENERATED_PATH\":\"/source/generated-data.csv\",\n", + " \"storage_uri\": \"s3://experiments/ray/pickels/logisticregression/model\", \n", + " \"protocol_version\": \"v2\",\n", + " \"bucket_name\": \"experiments\",\n", + " \"EZAF_ENV\":\"hpe-staging-ezaf\",\n", + " \"token_url\":\"https://keycloak.{0}.com/realms/UA/protocol/openid-connect/token\"\n", + " }\n", + "\n", + " print(\"configuration_dtl :: ===== ::\", json.loads(json.dumps(configuration_dtl)))\n", + " MINIO_CLIENT_INFR = Minio(\n", + " endpoint= configuration_dtl.get('MINIO_HOST_URL'), \n", + " access_key=configuration_dtl.get('MINIO_ACCESS_KEY'), \n", + " secret_key=configuration_dtl.get('MINIO_SECRET_KEY'),\n", + " secure=True,\n", + " http_client = urllib3.PoolManager(cert_reqs='CERT_NONE'))\n", + "\n", + " print(\"MINIO_CLIENT\", MINIO_CLIENT_INFR)\n", + " csv_file = MINIO_CLIENT_INFR.get_object(configuration_dtl.get('bucket_name'), configuration_dtl.get('GENERATED_PATH'))\n", + " data = pd.read_csv(csv_file)\n", + " data.head(5)\n", + " data_reduced = data.drop(['zipcodeOri','zipMerchant'],axis=1)\n", + " data_reduced.loc[:,['customer','merchant','category']].astype('category')\n", + "\n", + " # turning object columns type to categorical for easing the transformation process\n", + " col_categorical = data_reduced.select_dtypes(include= ['object']).columns\n", + " for col in col_categorical:\n", + " data_reduced[col] = data_reduced[col].astype('category')\n", + " data_reduced[col_categorical] = data_reduced[col_categorical].apply(lambda x: x.cat.codes)\n", + " data_reduced.head(5)\n", + "\n", + " # In contrast, model inference is the process of using a trained model to infer a result from live data.\n", + " X = data_reduced.drop(['fraud'], axis=1)\n", + " y = data_reduced['fraud']\n", + " print(\"shape==============\", [len(X.values), len(X.values[0])])\n", + " print(\"X.values[0]==============\", X.values[0], \"=======\", list(X.values[0]))\n", + "\n", + " '''\n", + " 1. Using SMOTE (Synthetic Minority Oversampling Technique) for balancing the dataset. \n", + " 2. Resulted counts show that now we have exact number of class instances (1 and 0).\n", + " '''\n", + " sm = SMOTE(random_state=42,k_neighbors=2)\n", + " X_res, y_res = sm.fit_resample(X, y)\n", + " y_res = pd.DataFrame(y_res)\n", + "\n", + " return X_res,y_res\n", + "\n", + "print(\"check2\")\n", + "a = A.remote()\n", + "print(\"check3\")\n", + "print(ray.get(a.__call__.remote()))\n", + "print(\"check4\")\n", + "print(ray.get(a.overspaling_smote.remote()))\n", + "print(\"check5\")\n", + "ray.shutdown()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c1cf7f7e-ab17-4bfb-939a-da407cce01a3", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8f24bc91-7e68-466e-973c-67861514dc3f", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "raw", + "id": "789d387f-84fa-47f4-b596-96fb7cfef6f5", + "metadata": { + "tags": [] + }, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Data-Science/Ray/Ray-RuntimeEnv-Proxy/readme.md b/Data-Science/Ray/Ray-RuntimeEnv-Proxy/readme.md index 1b167a53..bca16e3a 100644 --- a/Data-Science/Ray/Ray-RuntimeEnv-Proxy/readme.md +++ b/Data-Science/Ray/Ray-RuntimeEnv-Proxy/readme.md @@ -20,4 +20,13 @@ To complete the tutorial follow simple steps below: 1. Login to you EzAF cluster. 2. Create a new notebook server using the `jupyter-data-science` image. 3. Clone the repository locally. -4. Launch the `ray-runtimeenv-workaround.ipynb` notebook file. \ No newline at end of file +4. Launch the `ray-runtimeenv-workaround.ipynb` notebook file. + + +## EXAMPLE-3 +With environmental variables such as (runtime_env, num_cpus, proxy, etc.) +To complete the tutorial follow simple steps below: +1. Login to you EzAF cluster. +2. Create a new notebook server using the `jupyter-data-science` image. +3. Clone the repository locally. +4. Launch the `ray-runtime-env-packages-vlidate.ipynb` notebook file. \ No newline at end of file diff --git a/Data-Science/Ray/news_recommendation.ipynb b/Data-Science/Ray/news_recommendation.ipynb index 6cd14323..d7c733c7 100644 --- a/Data-Science/Ray/news_recommendation.ipynb +++ b/Data-Science/Ray/news_recommendation.ipynb @@ -1,6 +1,7 @@ { "cells": [ { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -192,10 +193,11 @@ "#ray.init(address=\"ray://kuberay-head-svc.kuberay:10001\", runtime_env={\"working_dir\": \"./\"})\n", "\n", "# Run this line, all required packages exist in Ray cluster\n", - "ray.init(address=\"ray://kuberay-head-svc.kuberay:10001\", runtime_env={\"working_dir\": \"./\"]})" + "ray.init(address=\"ray://kuberay-head-svc.kuberay:10001\", runtime_env={\"working_dir\": \"./\"})" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -218,6 +220,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -320,6 +323,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -356,6 +360,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "tags": [] @@ -374,6 +379,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -381,6 +387,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -401,6 +408,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -464,6 +472,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -491,6 +500,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -498,6 +508,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -515,6 +526,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -549,6 +561,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -581,6 +594,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "tags": [] @@ -609,6 +623,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "tags": [] From 731e2ade63979d0ab16f49afaab72abc6db2d6cb Mon Sep 17 00:00:00 2001 From: Arkadii Kravchuk <64816141+akravacyber@users.noreply.github.com> Date: Thu, 7 Sep 2023 17:35:25 +0300 Subject: [PATCH 09/15] Convert port to int param (#43) --- Data-Engineering/Airflow/Scripts/clean_data.py | 2 +- Data-Engineering/Airflow/load_s3_csv_to_mariadb_db_czech.py | 2 +- Data-Engineering/Airflow/load_s3_csv_to_mariadb_db_germany.py | 2 +- Data-Engineering/Airflow/load_s3_csv_to_mariadb_db_swiss.py | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/Data-Engineering/Airflow/Scripts/clean_data.py b/Data-Engineering/Airflow/Scripts/clean_data.py index 03aa1d9d..e4063544 100644 --- a/Data-Engineering/Airflow/Scripts/clean_data.py +++ b/Data-Engineering/Airflow/Scripts/clean_data.py @@ -128,7 +128,7 @@ def get_database_connection(database_type): sys.exit() cnx = MySQLdb.Connection(user=user, password=password, - host=host, port=port, database=database) + host=host, port=int(port), database=database) return cnx elif database_type == "postgresql": # Define the PostgreSQL database parameters diff --git a/Data-Engineering/Airflow/load_s3_csv_to_mariadb_db_czech.py b/Data-Engineering/Airflow/load_s3_csv_to_mariadb_db_czech.py index 9a3c1bd3..36b9e37b 100644 --- a/Data-Engineering/Airflow/load_s3_csv_to_mariadb_db_czech.py +++ b/Data-Engineering/Airflow/load_s3_csv_to_mariadb_db_czech.py @@ -58,7 +58,7 @@ def read_csv_from_s3(bucket_name, file_name, s3_endpoint, access_key=None, secre def import_csv_to_mariadb(df, db_host, db_port, db_user, db_password, db_name, table_name): conn = MySQLdb.Connection( host=db_host, - port=db_port, + port=int(db_port), user=db_user, password=db_password, database=db_name diff --git a/Data-Engineering/Airflow/load_s3_csv_to_mariadb_db_germany.py b/Data-Engineering/Airflow/load_s3_csv_to_mariadb_db_germany.py index 648c1bd7..1455db83 100644 --- a/Data-Engineering/Airflow/load_s3_csv_to_mariadb_db_germany.py +++ b/Data-Engineering/Airflow/load_s3_csv_to_mariadb_db_germany.py @@ -58,7 +58,7 @@ def read_csv_from_s3(bucket_name, file_name, s3_endpoint, access_key=None, secre def import_csv_to_mariadb(df, db_host, db_port, db_user, db_password, db_name, table_name): conn = MySQLdb.Connection( host=db_host, - port=db_port, + port=int(db_port), user=db_user, password=db_password, database=db_name diff --git a/Data-Engineering/Airflow/load_s3_csv_to_mariadb_db_swiss.py b/Data-Engineering/Airflow/load_s3_csv_to_mariadb_db_swiss.py index a74f1892..671e01bd 100644 --- a/Data-Engineering/Airflow/load_s3_csv_to_mariadb_db_swiss.py +++ b/Data-Engineering/Airflow/load_s3_csv_to_mariadb_db_swiss.py @@ -58,7 +58,7 @@ def read_csv_from_s3(bucket_name, file_name, s3_endpoint, access_key=None, secre def import_csv_to_mariadb(df, db_host, db_port, db_user, db_password, db_name, table_name): conn = MySQLdb.Connection( host=db_host, - port=db_port, + port=int(db_port), user=db_user, password=db_password, database=db_name From 0060a0cebe7841cc2f1be1e326f23b1362ef73c3 Mon Sep 17 00:00:00 2001 From: Prasad Adireddi Date: Thu, 7 Sep 2023 20:06:56 +0530 Subject: [PATCH 10/15] update DAG for demo --- Data-Engineering/Airflow/load_s3_csv_to_mariadb_db_czech.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/Data-Engineering/Airflow/load_s3_csv_to_mariadb_db_czech.py b/Data-Engineering/Airflow/load_s3_csv_to_mariadb_db_czech.py index 36b9e37b..9461b2dd 100644 --- a/Data-Engineering/Airflow/load_s3_csv_to_mariadb_db_czech.py +++ b/Data-Engineering/Airflow/load_s3_csv_to_mariadb_db_czech.py @@ -32,7 +32,7 @@ op_kwargs = { 'bucket_name': 'ezaf-demo', 'file_name': 'Czechia_sales_data_2019_2023.csv', - 's3_endpoint': 'https://home.hpe-qa16-ezaf.com:31900', + 's3_endpoint': 'https://home.hpe-qa16-ezaf.com', 'access_key': 'minioadmin', 'secret_key': 'minioadmin', 'db_host': '10.227.209.61', @@ -56,9 +56,9 @@ def read_csv_from_s3(bucket_name, file_name, s3_endpoint, access_key=None, secre return df def import_csv_to_mariadb(df, db_host, db_port, db_user, db_password, db_name, table_name): - conn = MySQLdb.Connection( + conn = MySQLdb.connector.connect( host=db_host, - port=int(db_port), + port=db_port, user=db_user, password=db_password, database=db_name From 15f6f80f3a735e0131644ef799539a72ba3eae90 Mon Sep 17 00:00:00 2001 From: Skand Tandon Date: Fri, 22 Sep 2023 09:56:49 +0530 Subject: [PATCH 11/15] In this notebook, we'll show how you can use whylog with Spark for Data validation and Profiling. (#47) * In this notebook, we'll show how you can use whylog with Spark for Data validation and Profiling * Updated the dataset * Saved the visualization html --- Model-Monitoring/Data Validation.ipynb | 641 +++++++++++++++++++++++ Model-Monitoring/Whylogs Profiling.ipynb | 477 +++++++++++++++++ 2 files changed, 1118 insertions(+) create mode 100644 Model-Monitoring/Data Validation.ipynb create mode 100644 Model-Monitoring/Whylogs Profiling.ipynb diff --git a/Model-Monitoring/Data Validation.ipynb b/Model-Monitoring/Data Validation.ipynb new file mode 100644 index 00000000..74dc7740 --- /dev/null +++ b/Model-Monitoring/Data Validation.ipynb @@ -0,0 +1,641 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "aIS7i5hkEMM-" + }, + "source": [ + "# Data Validation for Spark Dataframes with whylogs" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "imgT_wtKEMNB" + }, + "source": [ + "## About the Dataset - 🛏️ Airbnb Listings in Rio de Janeiro, Brazil\n", + "\n", + "We will read data made available from Airbnb. It's a listing dataset from the city of Rio de Janeiro, Brazil. We'll access data that was adapted from the following location: \"http://data.insideairbnb.com/brazil/rj/rio-de-janeiro/2021-01-26/data/listings.csv.gz\"\n", + "\n", + "In this example, we want to do some basic data validation. Let's define those:\n", + "\n", + "- Completeness Checks\n", + " - `id` (long): should not contain any missing values\n", + " - `listing_url` (string): should not contain any missing values\n", + " - `last_review` (string): should not contain any missing values\n", + "- Consistency Checks\n", + " - `last_review` (string): date should be in the format YYYY-MM-DD\n", + " - `listing_url` (string): should be an url from airbnb (starting with https://www.airbnb.com/rooms/)\n", + " - `latitude` and `longitude` (double): should be within the range of -24 to -22 and -44 to -43 respectively\n", + " - `room_type` (string): frequent strings should be in the set of expected values\n", + "- Statistics Checks\n", + " - `reviews_per_month` (double): standard deviation should be in expected range\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "i7X1jXAaEMNJ", + "tags": [] + }, + "source": [ + "## Initializing a SparkSession\n", + "\n", + "### Execute the cell below and:\n", + "\n", + "- Select Add Endpoint\n", + "- Select Single Sign-On\n", + "- Select Create Session, selecting language as Python and in properties use \"spark.kubernetes.container.image\": \"gcr.io/mapr-252711/spark-3.4.1:202309170405R\" the whylogs -integerated spark image in properties.\n", + "- Click Create Session\n", + "\n", + "When your session is ready the Manage Sessions pane will become active, providing you the session ID. The session state will become idle which means that you are good to go!" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "cf6b87b7803340a089c0e8bee54dfa4b", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "MagicsControllerWidget(children=(Tab(children=(ManageSessionWidget(children=(HTML(value='
'), HBox(childre…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%manage_spark" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import os\n", + "os.environ['HTTP_PROXY'] = \"http://hpeproxy.its.hpecorp.net:443\"\n", + "os.environ['HTTPS_PROXY'] = \"http://hpeproxy.its.hpecorp.net:443\"" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": { + "id": "yWhIyVdmEMNJ" + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pyspark.sql import SparkSession\n", + "\n", + "spark = SparkSession.builder.appName('whylogs-testing').getOrCreate()\n", + "arrow_config_key = \"spark.sql.execution.arrow.pyspark.enabled\"\n", + "spark.conf.set(arrow_config_key, \"true\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HF1aa0iOEMNK" + }, + "source": [ + "## Creating the PySpark dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": { + "id": "dKxGJ8elEMNL", + "outputId": "3ec26cae-8035-42ed-ba96-52e8aea74508" + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pyspark import SparkFiles\n", + "spark_dataframe = spark.read.format('parquet').load(\"file:///mounts/shared-volume/shared/airbnb_listings.parquet\")" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": { + "id": "Nw0DbogyEMNM", + "outputId": "d8779448-4d53-4d63-b218-7f01eac1ed84" + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-RECORD 0--------------------------------------\n", + " name | Very Nice 2Br in ... \n", + " description | Discounts for lon... \n", + " listing_url | https://www.airbn... \n", + " last_review | 2020-12-26 \n", + " number_of_reviews_ltm | 13 \n", + " number_of_reviews_l30d | 0 \n", + " id | 17878 \n", + " latitude | -22.96592 \n", + " longitude | -43.17896 \n", + " availability_365 | 286 \n", + " bedrooms | 2.0 \n", + " bathrooms | null \n", + " reviews_per_month | 2.01 \n", + " room_type | Entire home/apt \n", + "only showing top 1 row" + ] + } + ], + "source": [ + "spark_dataframe.show(n=1, vertical=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": { + "id": "_409gnFJEMNM", + "outputId": "aa2d912e-5c1e-4700-9338-a9b5f78b577f" + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "root\n", + " |-- name: string (nullable = true)\n", + " |-- description: string (nullable = true)\n", + " |-- listing_url: string (nullable = true)\n", + " |-- last_review: string (nullable = true)\n", + " |-- number_of_reviews_ltm: long (nullable = true)\n", + " |-- number_of_reviews_l30d: long (nullable = true)\n", + " |-- id: long (nullable = true)\n", + " |-- latitude: double (nullable = true)\n", + " |-- longitude: double (nullable = true)\n", + " |-- availability_365: long (nullable = true)\n", + " |-- bedrooms: double (nullable = true)\n", + " |-- bathrooms: double (nullable = true)\n", + " |-- reviews_per_month: double (nullable = true)\n", + " |-- room_type: string (nullable = true)" + ] + } + ], + "source": [ + "spark_dataframe.printSchema()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "fb4PrnKXEMNN" + }, + "source": [ + "## Creating the Condition Count Metrics\n", + "\n", + "To create a profile with the standard metrics, we can simply call `collect_dataset_profile_view` from whylog's PySpark extra module. However, if we look at our defined set of constraints, there are two of those that need to checked agains individual values:\n", + "\n", + "- `last_review` (string): date should be in the format YYYY-MM-DD\n", + "- `listing_url` (string): should be an url from airbnb (starting with https://www.airbnb.com/rooms/)\n", + "\n", + "As opposed to the other constraints, that can be checked against aggregate metrics, these two need to be checked against individual values. For that, we will create two condition count metrics. Later on, we will create metric constraints based on these metrics." + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": { + "id": "Q4O5WpVPEMNN" + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import datetime\n", + "from whylogs.core.relations import Predicate\n", + "from typing import Any\n", + "from whylogs.core.metrics.condition_count_metric import Condition\n", + "from whylogs.core.schema import DeclarativeSchema\n", + "from whylogs.core.resolvers import STANDARD_RESOLVER\n", + "from whylogs.core.specialized_resolvers import ConditionCountMetricSpec\n", + "\n", + "def date_format(x: Any) -> bool:\n", + " date_format = '%Y-%m-%d'\n", + " try:\n", + " datetime.datetime.strptime(x, date_format)\n", + " return True\n", + " except ValueError:\n", + " return False\n", + "\n", + "last_review_conditions = {\"is_date_format\": Condition(Predicate().is_(date_format))}\n", + "listing_url_conditions = {\"url_matches_airbnb_domain\": Condition(Predicate().matches(\"^https:\\/\\/www.airbnb.com\\/rooms\"))}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UfcBc7wOEMNN" + }, + "source": [ + "Now that we have the our set of conditions for both columns, we can create the condition count metrics. We can do so by creating a Standard Schema and then extending it by adding the condition count metrics with `add_condition_count_metrics`:" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": { + "id": "C9P8TBx6EMNN" + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "schema = DeclarativeSchema(STANDARD_RESOLVER)\n", + "\n", + "schema.add_resolver_spec(column_name=\"last_review\", metrics=[ConditionCountMetricSpec(last_review_conditions)])\n", + "schema.add_resolver_spec(column_name=\"listing_url\", metrics=[ConditionCountMetricSpec(listing_url_conditions)])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ElWEdtq0EMNO" + }, + "source": [ + "## Profiling the PySpark DataFrame\n", + "\n", + "Now, we can use the schema to pass to our logger through `collect_dataset_profile_view`" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "id": "qRm46DHWEMNO", + "outputId": "539700f3-73e8-44d1-a577-ad01769f5f28" + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from whylogs.api.pyspark.experimental import collect_dataset_profile_view\n", + "\n", + "dataset_profile_view = collect_dataset_profile_view(input_df=spark_dataframe, schema=schema)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UctByqZ2EMNP" + }, + "source": [ + "This will create a profile with the standard metrics, as well as the two condition count metrics that we created. As a sanity check, let's see the metrics for the `last_review` column:" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": { + "id": "t369rIm8EMNP", + "outputId": "3197f9b7-bf8e-48c3-fe4f-696fa514652c" + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['types', 'cardinality', 'counts', 'distribution', 'frequent_items', 'condition_count']" + ] + } + ], + "source": [ + "dataset_profile_view.get_column(\"last_review\").get_metric_names()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "I3kjXR7zEMNQ", + "tags": [] + }, + "source": [ + "## Creating and Visualizing Metric Constraints\n", + "\n", + "We have all that we need to build our set of constraints. We will use out-of-the-box factory constraints to do that:" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": { + "id": "tHOLuyXNEMNQ", + "outputId": "796d08f1-9784-4d93-9efc-e5be4e916809" + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ReportResult(name='last_review meets condition is_date_format', passed=1, failed=0, summary=None), ReportResult(name='last_review has no missing values', passed=0, failed=1, summary=None), ReportResult(name='listing_url meets condition url_matches_airbnb_domain', passed=1, failed=0, summary=None), ReportResult(name='listing_url has no missing values', passed=1, failed=0, summary=None), ReportResult(name='latitude is in range [-24,-22]', passed=1, failed=0, summary=None), ReportResult(name='longitude is in range [-44,-43]', passed=1, failed=0, summary=None), ReportResult(name='id has no missing values', passed=1, failed=0, summary=None), ReportResult(name='reviews_per_month standard deviation between 0.8 and 1.1 (inclusive)', passed=1, failed=0, summary=None), ReportResult(name=\"room_type values in set {'Hotel room', 'Entire home/apt', 'Shared room', 'Private room'}\", passed=1, failed=0, summary=None)]" + ] + } + ], + "source": [ + "from whylogs.core.constraints.factories import condition_meets\n", + "from whylogs.core.constraints import ConstraintsBuilder\n", + "from whylogs.core.constraints.factories import no_missing_values\n", + "from whylogs.core.constraints.factories import is_in_range\n", + "from whylogs.core.constraints.factories import stddev_between_range\n", + "from whylogs.core.constraints.factories import frequent_strings_in_reference_set\n", + "\n", + "builder = ConstraintsBuilder(dataset_profile_view=dataset_profile_view)\n", + "reference_set = {\"Entire home/apt\", \"Private room\", \"Shared room\", \"Hotel room\"}\n", + "\n", + "builder.add_constraint(condition_meets(column_name=\"last_review\", condition_name=\"is_date_format\"))\n", + "builder.add_constraint(condition_meets(column_name=\"listing_url\", condition_name=\"url_matches_airbnb_domain\"))\n", + "builder.add_constraint(no_missing_values(column_name=\"last_review\"))\n", + "builder.add_constraint(no_missing_values(column_name=\"listing_url\"))\n", + "builder.add_constraint(is_in_range(column_name=\"latitude\",lower=-24,upper=-22))\n", + "builder.add_constraint(is_in_range(column_name=\"longitude\",lower=-44,upper=-43))\n", + "builder.add_constraint(no_missing_values(column_name=\"id\"))\n", + "builder.add_constraint(stddev_between_range(column_name=\"reviews_per_month\", lower=0.8, upper=1.1))\n", + "builder.add_constraint(frequent_strings_in_reference_set(column_name=\"room_type\", reference_set=reference_set))\n", + "\n", + "constraints = builder.build()\n", + "constraints.generate_constraints_report()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "m1qxKc90EMNR" + }, + "source": [ + "Now, we can visualize the constraints report using the __Notebook Profile Visualizer__:" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": { + "id": "6rnfh4wBEMNR", + "outputId": "413add67-9400-44df-b7b2-7c5b6fddc0b5" + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "" + ] + } + ], + "source": [ + "from whylogs.viz import NotebookProfileVisualizer\n", + "visualization = NotebookProfileVisualizer()\n", + "visualization.constraints_report(constraints, cell_height=300)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NgqO76WeEMNR" + }, + "source": [ + "Looks like we have some missing values for `last_review`. Other than that, the data looks good!" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "An error was encountered:\n", + "'module' object is not callable\n", + "Traceback (most recent call last):\n", + "TypeError: 'module' object is not callable\n", + "\n" + ] + } + ], + "source": [ + "import os \n", + "os.getcwd() \n", + "visualization.set_profiles(target_profile_view=dataset_profile_view)\n", + "visualization.write( rendered_html=visualization.profile_summary(), html_file_name=\"/mounts/shared-volume/shared/model_monitoring/data_validation\", )" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "You can now load the html from the EZAU platform." + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "PySpark", + "language": "python", + "name": "pysparkkernel" + }, + "language_info": { + "codemirror_mode": { + "name": "python", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "pyspark", + "pygments_lexer": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Model-Monitoring/Whylogs Profiling.ipynb b/Model-Monitoring/Whylogs Profiling.ipynb new file mode 100644 index 00000000..8e1d03af --- /dev/null +++ b/Model-Monitoring/Whylogs Profiling.ipynb @@ -0,0 +1,477 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "64dc520e-42c8-427c-848b-60bfd125ab66", + "metadata": { + "tags": [] + }, + "source": [ + "## Initializing a SparkSession\n", + "\n", + "### Execute the cell below and:\n", + "\n", + "- Select Add Endpoint\n", + "- Select Single Sign-On\n", + "- Select Create Session, selecting language as Python and in properties use \"spark.kubernetes.container.image\": \"gcr.io/mapr-252711/spark-3.4.1:202309170405R\" the whylogs -integerated spark image in properties.\n", + "- Click Create Session\n", + "\n", + "When your session is ready the Manage Sessions pane will become active, providing you the session ID. The session state will become idle which means that you are good to go!" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "a65a34aa-0dfb-4f6f-9d01-75e3f9031cf1", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ec19c5ec53f34b8b884451b859ec6ebe", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "MagicsControllerWidget(children=(Tab(children=(ManageSessionWidget(children=(HTML(value='
'), HTML(value='…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%manage_spark" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "35276080-befc-4c05-9793-bf422c1b7447", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import os\n", + "os.environ['HTTP_PROXY'] = \"http://hpeproxy.its.hpecorp.net:443\"\n", + "os.environ['HTTPS_PROXY'] = \"http://hpeproxy.its.hpecorp.net:443\"" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "da19979d-832d-4090-92b0-9e2c5bd1dfc3", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pyspark.sql import SparkSession\n", + "spark = SparkSession.builder.appName('whylogs-testing').getOrCreate()\n", + "arrow_config_key = \"spark.sql.execution.arrow.pyspark.enabled\"\n", + "spark.conf.set(arrow_config_key, \"true\")" + ] + }, + { + "cell_type": "markdown", + "id": "6ab0829b-ba9f-45e8-a504-c65776faaf36", + "metadata": { + "tags": [] + }, + "source": [ + "For the sake of simplicity, we will read the Wine Quality dataset, available in this URL: \"http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv\"." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "d0e4b205-bd2a-4c4a-a087-176fcec920f7", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-RECORD 0----------------------\n", + " fixed acidity | 7.4 \n", + " volatile acidity | 0.7 \n", + " citric acid | 0 \n", + " residual sugar | 1.9 \n", + " chlorides | 0.076 \n", + " free sulfur dioxide | 11 \n", + " total sulfur dioxide | 34 \n", + " density | 0.9978 \n", + " pH | 3.51 \n", + " sulphates | 0.56 \n", + " alcohol | 9.4 \n", + " quality | 5 \n", + "only showing top 1 row" + ] + } + ], + "source": [ + "from pyspark import SparkFiles\n", + "spark_dataframe= spark.read.format('csv').option(\"header\",\"true\").option(\"delimiter\", \";\").load(\"file:///mounts/shared-volume/shared/winequality-red.csv\")\n", + "\n", + "spark_dataframe.show(n=1, vertical=True)\n" + ] + }, + { + "cell_type": "markdown", + "id": "24cbc8ee-755d-418e-b9d6-952f96fceadb", + "metadata": { + "tags": [] + }, + "source": [ + "### __Profiling the data with whylogs__\n", + "Now that we have a Spark DataFrame in place, let's see how easy it is to profile our data with whylogs." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "f9f5a8c3-f158-4401-aea0-b0c70a4efa50", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import whylogs\n", + "from whylogs.api.pyspark.experimental import collect_column_profile_views\n", + "\n", + "column_views_dict = collect_column_profile_views(spark_dataframe)" + ] + }, + { + "cell_type": "markdown", + "id": "6588f1f8-0042-41b6-b150-525c5fe779d2", + "metadata": { + "tags": [] + }, + "source": [ + "Yeap. It's done. It is that easy.\n", + "\n", + "But what do we get with a column_views_dict?" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "0df41833-77e9-487a-9069-d5b905381c01", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'alcohol': , 'chlorides': , 'citric acid': , 'density': , 'fixed acidity': , 'free sulfur dioxide': , 'pH': , 'quality': , 'residual sugar': , 'sulphates': , 'total sulfur dioxide': , 'volatile acidity': }\n", + "dict_keys(['alcohol', 'chlorides', 'citric acid', 'density', 'fixed acidity', 'free sulfur dioxide', 'pH', 'quality', 'residual sugar', 'sulphates', 'total sulfur dioxide', 'volatile acidity'])" + ] + } + ], + "source": [ + "print(column_views_dict)\n", + "print((column_views_dict.keys()))" + ] + }, + { + "cell_type": "markdown", + "id": "c26852b7-3cae-4b3b-b50d-7614791a96f6", + "metadata": { + "tags": [] + }, + "source": [ + "It is a dictionary with one ColumnProfileView object per column in your dataset. And we can inspect some of the metrics on each one of them, such as the counts for a given column" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "3e2b2a9a-bd0f-4a43-9a79-5d297941b158", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1599, 1599)" + ] + } + ], + "source": [ + "column_views_dict[\"density\"].get_metric(\"counts\").n.value, spark_dataframe.count()" + ] + }, + { + "cell_type": "markdown", + "id": "df9526ea-2ed8-45ae-b4d9-90cbea9c5324", + "metadata": { + "tags": [] + }, + "source": [ + "Or their mean value:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "25399cbe-4c76-4c4a-9a8b-2b712d1d0887", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.0" + ] + } + ], + "source": [ + "column_views_dict[\"density\"].get_metric(\"distribution\").mean.value" + ] + }, + { + "cell_type": "markdown", + "id": "622c35e9-6143-434a-bf53-db5cbba123af", + "metadata": { + "tags": [] + }, + "source": [ + "And now let's check how accurate whylogs did store that mean calculation." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "d063b9fe-db4d-4107-af2c-c1c2b8503ddc", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+------------------+\n", + "| avg(density)|\n", + "+------------------+\n", + "|0.9967466791744831|\n", + "+------------------+" + ] + } + ], + "source": [ + "from pyspark.sql.functions import mean\n", + "spark_dataframe.select(mean(\"density\")).show()" + ] + }, + { + "cell_type": "markdown", + "id": "f49f248a-9e9e-43be-843d-05fa364f1660", + "metadata": { + "tags": [] + }, + "source": [ + "It is not the literal exact value, but it gets really close, right? That is because we are not extracting the exact information, but we are also not sampling the data. whylogs will look at every data point and statistically decide wether or not that data point is relevant to the final calculation." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "bb8024e3-8142-479c-a49e-3ebc564cbcb2", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from whylogs.api.pyspark.experimental import collect_dataset_profile_view\n", + "\n", + "dataset_profile_view = collect_dataset_profile_view(input_df=spark_dataframe)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "2c41f63f-4c12-491a-83ae-d308706bb6a7", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " cardinality/est cardinality/lower_1 ... types/string types/tensor\n", + "column ... \n", + "alcohol 65.000010 65.000000 ... 1599 0\n", + "chlorides 153.000058 153.000000 ... 1599 0\n", + "citric acid 80.000016 80.000000 ... 1599 0\n", + "density 438.484250 432.884348 ... 1599 0\n", + "fixed acidity 96.000023 96.000000 ... 1599 0\n", + "\n", + "[5 rows x 29 columns]" + ] + } + ], + "source": [ + "import pandas as pd \n", + "\n", + "dataset_profile_view.to_pandas().head()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "PySpark", + "language": "python", + "name": "pysparkkernel" + }, + "language_info": { + "codemirror_mode": { + "name": "python", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "pyspark", + "pygments_lexer": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From fe9fc4ea876cdc2bc56bb323ff79460bf56284c7 Mon Sep 17 00:00:00 2001 From: Bhargavjd <70695037+Bhargavjd@users.noreply.github.com> Date: Fri, 22 Sep 2023 09:57:12 +0530 Subject: [PATCH 12/15] Airflow-Whylogs example (#48) * Airflow-Whylogs example * Updated the file names --- Model-Monitoring/airflow_whylog.py | 65 + Model-Monitoring/data/reference_dataset.csv | 1600 +++++++++++++++++++ Model-Monitoring/data/target_dataset.csv | 1600 +++++++++++++++++++ 3 files changed, 3265 insertions(+) create mode 100644 Model-Monitoring/airflow_whylog.py create mode 100644 Model-Monitoring/data/reference_dataset.csv create mode 100644 Model-Monitoring/data/target_dataset.csv diff --git a/Model-Monitoring/airflow_whylog.py b/Model-Monitoring/airflow_whylog.py new file mode 100644 index 00000000..b60c1f42 --- /dev/null +++ b/Model-Monitoring/airflow_whylog.py @@ -0,0 +1,65 @@ +import logging +from datetime import datetime +import os +import pandas as pd +import whylogs as why +from whylogs.core.constraints.factories import greater_than_number + +from airflow.models.dag import DAG +from airflow.operators.python import get_current_context +from airflow.operators.python import PythonOperator +from whylogs_provider.operators.whylogs import ( + WhylogsConstraintsOperator, + WhylogsSummaryDriftOperator, +) + +def file_path(relative_path): + dir = os.path.dirname(os.path.abspath(__file__)) + split_path = relative_path.split("/") + new_path = os.path.join(dir, *split_path) + return new_path + +prof_path_target="/mnt/shared/airflow-whylog/profile_wine_target.bin" +prof_path_ref="/mnt/shared/airflow-whylog/profile_wine_ref.bin" +html_path="/mnt/shared/airflow-whylog/Profile_wine.html" + +def profile_data_target(data_path=file_path("data/target_dataset.csv")): + df = pd.read_csv(data_path) + result = why.log(df) + result.writer("local").write(dest=prof_path_target) +def profile_data_ref(data_path=file_path("data/reference_dataset.csv")): + df = pd.read_csv(data_path) + result = why.log(df) + result.writer("local").write(dest=prof_path_ref) + + +with DAG( + dag_id='whylogs-wine-quality', + schedule_interval=None, + start_date=datetime.now(), + max_active_runs=1, + tags=['responsible', 'data_transformation'], +) as dag: + + + profile_data_target = PythonOperator(task_id="profile_data_target", python_callable=profile_data_target) + profile_data_ref = PythonOperator(task_id="profile_data_ref", python_callable=profile_data_ref) + + + is_in_range = WhylogsConstraintsOperator( + task_id="is_in_range", + profile_path=prof_path_target, + reader="local", + constraint=greater_than_number(column_name="quality", number=4) + ) + summary_drift = WhylogsSummaryDriftOperator( + task_id="drift_report", + target_profile_path=prof_path_target, + reference_profile_path=prof_path_ref, + reader="local", + write_report_path=html_path + ) + + ( + profile_data_target>>profile_data_ref>>is_in_range>> summary_drift + ) diff --git a/Model-Monitoring/data/reference_dataset.csv b/Model-Monitoring/data/reference_dataset.csv new file mode 100644 index 00000000..d611beb1 --- /dev/null +++ b/Model-Monitoring/data/reference_dataset.csv @@ -0,0 +1,1600 @@ +fixed acidity,volatile acidity,citric acid,residual sugar,chlorides,free sulfur dioxide,total sulfur dioxide,density,pH,sulphates,alcohol,quality +7.4,0.7,0,1.9,0.076,11,34,0.9978,3.51,0.56,9.4,5 +7.8,0.88,0,2.6,0.098,25,67,0.9968,3.2,0.68,9.8,5 +7.8,0.76,0.04,2.3,0.092,15,54,0.997,3.26,0.65,9.8,5 +11.2,0.28,0.56,1.9,0.075,17,60,0.998,3.16,0.58,9.8,6 +7.4,0.7,0,1.9,0.076,11,34,0.9978,3.51,0.56,9.4,5 +7.4,0.66,0,1.8,0.075,13,40,0.9978,3.51,0.56,9.4,5 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(#47)" (#49) This reverts commit 15f6f80f3a735e0131644ef799539a72ba3eae90. --- Model-Monitoring/Data Validation.ipynb | 641 ----------------------- Model-Monitoring/Whylogs Profiling.ipynb | 477 ----------------- 2 files changed, 1118 deletions(-) delete mode 100644 Model-Monitoring/Data Validation.ipynb delete mode 100644 Model-Monitoring/Whylogs Profiling.ipynb diff --git a/Model-Monitoring/Data Validation.ipynb b/Model-Monitoring/Data Validation.ipynb deleted file mode 100644 index 74dc7740..00000000 --- a/Model-Monitoring/Data Validation.ipynb +++ /dev/null @@ -1,641 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "aIS7i5hkEMM-" - }, - "source": [ - "# Data Validation for Spark Dataframes with whylogs" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "imgT_wtKEMNB" - }, - "source": [ - "## About the Dataset - 🛏️ Airbnb Listings in Rio de Janeiro, Brazil\n", - "\n", - "We will read data made available from Airbnb. It's a listing dataset from the city of Rio de Janeiro, Brazil. We'll access data that was adapted from the following location: \"http://data.insideairbnb.com/brazil/rj/rio-de-janeiro/2021-01-26/data/listings.csv.gz\"\n", - "\n", - "In this example, we want to do some basic data validation. Let's define those:\n", - "\n", - "- Completeness Checks\n", - " - `id` (long): should not contain any missing values\n", - " - `listing_url` (string): should not contain any missing values\n", - " - `last_review` (string): should not contain any missing values\n", - "- Consistency Checks\n", - " - `last_review` (string): date should be in the format YYYY-MM-DD\n", - " - `listing_url` (string): should be an url from airbnb (starting with https://www.airbnb.com/rooms/)\n", - " - `latitude` and `longitude` (double): should be within the range of -24 to -22 and -44 to -43 respectively\n", - " - `room_type` (string): frequent strings should be in the set of expected values\n", - "- Statistics Checks\n", - " - `reviews_per_month` (double): standard deviation should be in expected range\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "i7X1jXAaEMNJ", - "tags": [] - }, - "source": [ - "## Initializing a SparkSession\n", - "\n", - "### Execute the cell below and:\n", - "\n", - "- Select Add Endpoint\n", - "- Select Single Sign-On\n", - "- Select Create Session, selecting language as Python and in properties use \"spark.kubernetes.container.image\": \"gcr.io/mapr-252711/spark-3.4.1:202309170405R\" the whylogs -integerated spark image in properties.\n", - "- Click Create Session\n", - "\n", - "When your session is ready the Manage Sessions pane will become active, providing you the session ID. The session state will become idle which means that you are good to go!" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "cf6b87b7803340a089c0e8bee54dfa4b", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "MagicsControllerWidget(children=(Tab(children=(ManageSessionWidget(children=(HTML(value='
'), HBox(childre…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%manage_spark" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import os\n", - "os.environ['HTTP_PROXY'] = \"http://hpeproxy.its.hpecorp.net:443\"\n", - "os.environ['HTTPS_PROXY'] = \"http://hpeproxy.its.hpecorp.net:443\"" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": { - "id": "yWhIyVdmEMNJ" - }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from pyspark.sql import SparkSession\n", - "\n", - "spark = SparkSession.builder.appName('whylogs-testing').getOrCreate()\n", - "arrow_config_key = \"spark.sql.execution.arrow.pyspark.enabled\"\n", - "spark.conf.set(arrow_config_key, \"true\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "HF1aa0iOEMNK" - }, - "source": [ - "## Creating the PySpark dataframe" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": { - "id": "dKxGJ8elEMNL", - "outputId": "3ec26cae-8035-42ed-ba96-52e8aea74508" - }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from pyspark import SparkFiles\n", - "spark_dataframe = spark.read.format('parquet').load(\"file:///mounts/shared-volume/shared/airbnb_listings.parquet\")" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": { - "id": "Nw0DbogyEMNM", - "outputId": "d8779448-4d53-4d63-b218-7f01eac1ed84" - }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-RECORD 0--------------------------------------\n", - " name | Very Nice 2Br in ... \n", - " description | Discounts for lon... \n", - " listing_url | https://www.airbn... \n", - " last_review | 2020-12-26 \n", - " number_of_reviews_ltm | 13 \n", - " number_of_reviews_l30d | 0 \n", - " id | 17878 \n", - " latitude | -22.96592 \n", - " longitude | -43.17896 \n", - " availability_365 | 286 \n", - " bedrooms | 2.0 \n", - " bathrooms | null \n", - " reviews_per_month | 2.01 \n", - " room_type | Entire home/apt \n", - "only showing top 1 row" - ] - } - ], - "source": [ - "spark_dataframe.show(n=1, vertical=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": { - "id": "_409gnFJEMNM", - "outputId": "aa2d912e-5c1e-4700-9338-a9b5f78b577f" - }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "root\n", - " |-- name: string (nullable = true)\n", - " |-- description: string (nullable = true)\n", - " |-- listing_url: string (nullable = true)\n", - " |-- last_review: string (nullable = true)\n", - " |-- number_of_reviews_ltm: long (nullable = true)\n", - " |-- number_of_reviews_l30d: long (nullable = true)\n", - " |-- id: long (nullable = true)\n", - " |-- latitude: double (nullable = true)\n", - " |-- longitude: double (nullable = true)\n", - " |-- availability_365: long (nullable = true)\n", - " |-- bedrooms: double (nullable = true)\n", - " |-- bathrooms: double (nullable = true)\n", - " |-- reviews_per_month: double (nullable = true)\n", - " |-- room_type: string (nullable = true)" - ] - } - ], - "source": [ - "spark_dataframe.printSchema()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "fb4PrnKXEMNN" - }, - "source": [ - "## Creating the Condition Count Metrics\n", - "\n", - "To create a profile with the standard metrics, we can simply call `collect_dataset_profile_view` from whylog's PySpark extra module. However, if we look at our defined set of constraints, there are two of those that need to checked agains individual values:\n", - "\n", - "- `last_review` (string): date should be in the format YYYY-MM-DD\n", - "- `listing_url` (string): should be an url from airbnb (starting with https://www.airbnb.com/rooms/)\n", - "\n", - "As opposed to the other constraints, that can be checked against aggregate metrics, these two need to be checked against individual values. For that, we will create two condition count metrics. Later on, we will create metric constraints based on these metrics." - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": { - "id": "Q4O5WpVPEMNN" - }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import datetime\n", - "from whylogs.core.relations import Predicate\n", - "from typing import Any\n", - "from whylogs.core.metrics.condition_count_metric import Condition\n", - "from whylogs.core.schema import DeclarativeSchema\n", - "from whylogs.core.resolvers import STANDARD_RESOLVER\n", - "from whylogs.core.specialized_resolvers import ConditionCountMetricSpec\n", - "\n", - "def date_format(x: Any) -> bool:\n", - " date_format = '%Y-%m-%d'\n", - " try:\n", - " datetime.datetime.strptime(x, date_format)\n", - " return True\n", - " except ValueError:\n", - " return False\n", - "\n", - "last_review_conditions = {\"is_date_format\": Condition(Predicate().is_(date_format))}\n", - "listing_url_conditions = {\"url_matches_airbnb_domain\": Condition(Predicate().matches(\"^https:\\/\\/www.airbnb.com\\/rooms\"))}" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "UfcBc7wOEMNN" - }, - "source": [ - "Now that we have the our set of conditions for both columns, we can create the condition count metrics. We can do so by creating a Standard Schema and then extending it by adding the condition count metrics with `add_condition_count_metrics`:" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": { - "id": "C9P8TBx6EMNN" - }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "schema = DeclarativeSchema(STANDARD_RESOLVER)\n", - "\n", - "schema.add_resolver_spec(column_name=\"last_review\", metrics=[ConditionCountMetricSpec(last_review_conditions)])\n", - "schema.add_resolver_spec(column_name=\"listing_url\", metrics=[ConditionCountMetricSpec(listing_url_conditions)])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ElWEdtq0EMNO" - }, - "source": [ - "## Profiling the PySpark DataFrame\n", - "\n", - "Now, we can use the schema to pass to our logger through `collect_dataset_profile_view`" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": { - "id": "qRm46DHWEMNO", - "outputId": "539700f3-73e8-44d1-a577-ad01769f5f28" - }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from whylogs.api.pyspark.experimental import collect_dataset_profile_view\n", - "\n", - "dataset_profile_view = collect_dataset_profile_view(input_df=spark_dataframe, schema=schema)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "UctByqZ2EMNP" - }, - "source": [ - "This will create a profile with the standard metrics, as well as the two condition count metrics that we created. As a sanity check, let's see the metrics for the `last_review` column:" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": { - "id": "t369rIm8EMNP", - "outputId": "3197f9b7-bf8e-48c3-fe4f-696fa514652c" - }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['types', 'cardinality', 'counts', 'distribution', 'frequent_items', 'condition_count']" - ] - } - ], - "source": [ - "dataset_profile_view.get_column(\"last_review\").get_metric_names()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "I3kjXR7zEMNQ", - "tags": [] - }, - "source": [ - "## Creating and Visualizing Metric Constraints\n", - "\n", - "We have all that we need to build our set of constraints. We will use out-of-the-box factory constraints to do that:" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": { - "id": "tHOLuyXNEMNQ", - "outputId": "796d08f1-9784-4d93-9efc-e5be4e916809" - }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ReportResult(name='last_review meets condition is_date_format', passed=1, failed=0, summary=None), ReportResult(name='last_review has no missing values', passed=0, failed=1, summary=None), ReportResult(name='listing_url meets condition url_matches_airbnb_domain', passed=1, failed=0, summary=None), ReportResult(name='listing_url has no missing values', passed=1, failed=0, summary=None), ReportResult(name='latitude is in range [-24,-22]', passed=1, failed=0, summary=None), ReportResult(name='longitude is in range [-44,-43]', passed=1, failed=0, summary=None), ReportResult(name='id has no missing values', passed=1, failed=0, summary=None), ReportResult(name='reviews_per_month standard deviation between 0.8 and 1.1 (inclusive)', passed=1, failed=0, summary=None), ReportResult(name=\"room_type values in set {'Hotel room', 'Entire home/apt', 'Shared room', 'Private room'}\", passed=1, failed=0, summary=None)]" - ] - } - ], - "source": [ - "from whylogs.core.constraints.factories import condition_meets\n", - "from whylogs.core.constraints import ConstraintsBuilder\n", - "from whylogs.core.constraints.factories import no_missing_values\n", - "from whylogs.core.constraints.factories import is_in_range\n", - "from whylogs.core.constraints.factories import stddev_between_range\n", - "from whylogs.core.constraints.factories import frequent_strings_in_reference_set\n", - "\n", - "builder = ConstraintsBuilder(dataset_profile_view=dataset_profile_view)\n", - "reference_set = {\"Entire home/apt\", \"Private room\", \"Shared room\", \"Hotel room\"}\n", - "\n", - "builder.add_constraint(condition_meets(column_name=\"last_review\", condition_name=\"is_date_format\"))\n", - "builder.add_constraint(condition_meets(column_name=\"listing_url\", condition_name=\"url_matches_airbnb_domain\"))\n", - "builder.add_constraint(no_missing_values(column_name=\"last_review\"))\n", - "builder.add_constraint(no_missing_values(column_name=\"listing_url\"))\n", - "builder.add_constraint(is_in_range(column_name=\"latitude\",lower=-24,upper=-22))\n", - "builder.add_constraint(is_in_range(column_name=\"longitude\",lower=-44,upper=-43))\n", - "builder.add_constraint(no_missing_values(column_name=\"id\"))\n", - "builder.add_constraint(stddev_between_range(column_name=\"reviews_per_month\", lower=0.8, upper=1.1))\n", - "builder.add_constraint(frequent_strings_in_reference_set(column_name=\"room_type\", reference_set=reference_set))\n", - "\n", - "constraints = builder.build()\n", - "constraints.generate_constraints_report()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "m1qxKc90EMNR" - }, - "source": [ - "Now, we can visualize the constraints report using the __Notebook Profile Visualizer__:" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": { - "id": "6rnfh4wBEMNR", - "outputId": "413add67-9400-44df-b7b2-7c5b6fddc0b5" - }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "" - ] - } - ], - "source": [ - "from whylogs.viz import NotebookProfileVisualizer\n", - "visualization = NotebookProfileVisualizer()\n", - "visualization.constraints_report(constraints, cell_height=300)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "NgqO76WeEMNR" - }, - "source": [ - "Looks like we have some missing values for `last_review`. Other than that, the data looks good!" - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "An error was encountered:\n", - "'module' object is not callable\n", - "Traceback (most recent call last):\n", - "TypeError: 'module' object is not callable\n", - "\n" - ] - } - ], - "source": [ - "import os \n", - "os.getcwd() \n", - "visualization.set_profiles(target_profile_view=dataset_profile_view)\n", - "visualization.write( rendered_html=visualization.profile_summary(), html_file_name=\"/mounts/shared-volume/shared/model_monitoring/data_validation\", )" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "You can now load the html from the EZAU platform." - ] - } - ], - "metadata": { - "colab": { - "provenance": [] - }, - "kernelspec": { - "display_name": "PySpark", - "language": "python", - "name": "pysparkkernel" - }, - "language_info": { - "codemirror_mode": { - "name": "python", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "pyspark", - "pygments_lexer": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/Model-Monitoring/Whylogs Profiling.ipynb b/Model-Monitoring/Whylogs Profiling.ipynb deleted file mode 100644 index 8e1d03af..00000000 --- a/Model-Monitoring/Whylogs Profiling.ipynb +++ /dev/null @@ -1,477 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "64dc520e-42c8-427c-848b-60bfd125ab66", - "metadata": { - "tags": [] - }, - "source": [ - "## Initializing a SparkSession\n", - "\n", - "### Execute the cell below and:\n", - "\n", - "- Select Add Endpoint\n", - "- Select Single Sign-On\n", - "- Select Create Session, selecting language as Python and in properties use \"spark.kubernetes.container.image\": \"gcr.io/mapr-252711/spark-3.4.1:202309170405R\" the whylogs -integerated spark image in properties.\n", - "- Click Create Session\n", - "\n", - "When your session is ready the Manage Sessions pane will become active, providing you the session ID. The session state will become idle which means that you are good to go!" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "a65a34aa-0dfb-4f6f-9d01-75e3f9031cf1", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "ec19c5ec53f34b8b884451b859ec6ebe", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "MagicsControllerWidget(children=(Tab(children=(ManageSessionWidget(children=(HTML(value='
'), HTML(value='…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%manage_spark" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "35276080-befc-4c05-9793-bf422c1b7447", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import os\n", - "os.environ['HTTP_PROXY'] = \"http://hpeproxy.its.hpecorp.net:443\"\n", - "os.environ['HTTPS_PROXY'] = \"http://hpeproxy.its.hpecorp.net:443\"" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "da19979d-832d-4090-92b0-9e2c5bd1dfc3", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from pyspark.sql import SparkSession\n", - "spark = SparkSession.builder.appName('whylogs-testing').getOrCreate()\n", - "arrow_config_key = \"spark.sql.execution.arrow.pyspark.enabled\"\n", - "spark.conf.set(arrow_config_key, \"true\")" - ] - }, - { - "cell_type": "markdown", - "id": "6ab0829b-ba9f-45e8-a504-c65776faaf36", - "metadata": { - "tags": [] - }, - "source": [ - "For the sake of simplicity, we will read the Wine Quality dataset, available in this URL: \"http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv\"." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "d0e4b205-bd2a-4c4a-a087-176fcec920f7", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-RECORD 0----------------------\n", - " fixed acidity | 7.4 \n", - " volatile acidity | 0.7 \n", - " citric acid | 0 \n", - " residual sugar | 1.9 \n", - " chlorides | 0.076 \n", - " free sulfur dioxide | 11 \n", - " total sulfur dioxide | 34 \n", - " density | 0.9978 \n", - " pH | 3.51 \n", - " sulphates | 0.56 \n", - " alcohol | 9.4 \n", - " quality | 5 \n", - "only showing top 1 row" - ] - } - ], - "source": [ - "from pyspark import SparkFiles\n", - "spark_dataframe= spark.read.format('csv').option(\"header\",\"true\").option(\"delimiter\", \";\").load(\"file:///mounts/shared-volume/shared/winequality-red.csv\")\n", - "\n", - "spark_dataframe.show(n=1, vertical=True)\n" - ] - }, - { - "cell_type": "markdown", - "id": "24cbc8ee-755d-418e-b9d6-952f96fceadb", - "metadata": { - "tags": [] - }, - "source": [ - "### __Profiling the data with whylogs__\n", - "Now that we have a Spark DataFrame in place, let's see how easy it is to profile our data with whylogs." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "f9f5a8c3-f158-4401-aea0-b0c70a4efa50", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import whylogs\n", - "from whylogs.api.pyspark.experimental import collect_column_profile_views\n", - "\n", - "column_views_dict = collect_column_profile_views(spark_dataframe)" - ] - }, - { - "cell_type": "markdown", - "id": "6588f1f8-0042-41b6-b150-525c5fe779d2", - "metadata": { - "tags": [] - }, - "source": [ - "Yeap. It's done. It is that easy.\n", - "\n", - "But what do we get with a column_views_dict?" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "0df41833-77e9-487a-9069-d5b905381c01", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'alcohol': , 'chlorides': , 'citric acid': , 'density': , 'fixed acidity': , 'free sulfur dioxide': , 'pH': , 'quality': , 'residual sugar': , 'sulphates': , 'total sulfur dioxide': , 'volatile acidity': }\n", - "dict_keys(['alcohol', 'chlorides', 'citric acid', 'density', 'fixed acidity', 'free sulfur dioxide', 'pH', 'quality', 'residual sugar', 'sulphates', 'total sulfur dioxide', 'volatile acidity'])" - ] - } - ], - "source": [ - "print(column_views_dict)\n", - "print((column_views_dict.keys()))" - ] - }, - { - "cell_type": "markdown", - "id": "c26852b7-3cae-4b3b-b50d-7614791a96f6", - "metadata": { - "tags": [] - }, - "source": [ - "It is a dictionary with one ColumnProfileView object per column in your dataset. And we can inspect some of the metrics on each one of them, such as the counts for a given column" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "3e2b2a9a-bd0f-4a43-9a79-5d297941b158", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(1599, 1599)" - ] - } - ], - "source": [ - "column_views_dict[\"density\"].get_metric(\"counts\").n.value, spark_dataframe.count()" - ] - }, - { - "cell_type": "markdown", - "id": "df9526ea-2ed8-45ae-b4d9-90cbea9c5324", - "metadata": { - "tags": [] - }, - "source": [ - "Or their mean value:" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "25399cbe-4c76-4c4a-9a8b-2b712d1d0887", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.0" - ] - } - ], - "source": [ - "column_views_dict[\"density\"].get_metric(\"distribution\").mean.value" - ] - }, - { - "cell_type": "markdown", - "id": "622c35e9-6143-434a-bf53-db5cbba123af", - "metadata": { - "tags": [] - }, - "source": [ - "And now let's check how accurate whylogs did store that mean calculation." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "d063b9fe-db4d-4107-af2c-c1c2b8503ddc", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "+------------------+\n", - "| avg(density)|\n", - "+------------------+\n", - "|0.9967466791744831|\n", - "+------------------+" - ] - } - ], - "source": [ - "from pyspark.sql.functions import mean\n", - "spark_dataframe.select(mean(\"density\")).show()" - ] - }, - { - "cell_type": "markdown", - "id": "f49f248a-9e9e-43be-843d-05fa364f1660", - "metadata": { - "tags": [] - }, - "source": [ - "It is not the literal exact value, but it gets really close, right? That is because we are not extracting the exact information, but we are also not sampling the data. whylogs will look at every data point and statistically decide wether or not that data point is relevant to the final calculation." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "bb8024e3-8142-479c-a49e-3ebc564cbcb2", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from whylogs.api.pyspark.experimental import collect_dataset_profile_view\n", - "\n", - "dataset_profile_view = collect_dataset_profile_view(input_df=spark_dataframe)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "2c41f63f-4c12-491a-83ae-d308706bb6a7", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " cardinality/est cardinality/lower_1 ... types/string types/tensor\n", - "column ... \n", - "alcohol 65.000010 65.000000 ... 1599 0\n", - "chlorides 153.000058 153.000000 ... 1599 0\n", - "citric acid 80.000016 80.000000 ... 1599 0\n", - "density 438.484250 432.884348 ... 1599 0\n", - "fixed acidity 96.000023 96.000000 ... 1599 0\n", - "\n", - "[5 rows x 29 columns]" - ] - } - ], - "source": [ - "import pandas as pd \n", - "\n", - "dataset_profile_view.to_pandas().head()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "PySpark", - "language": "python", - "name": "pysparkkernel" - }, - "language_info": { - "codemirror_mode": { - "name": "python", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "pyspark", - "pygments_lexer": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From 81c2babcb4437392418cd4a55627d0f8c1eaf70a Mon Sep 17 00:00:00 2001 From: Ashok Manda Date: Fri, 22 Sep 2023 10:55:52 +0530 Subject: [PATCH 14/15] Revert "Airflow-Whylogs example (#48)" (#50) This reverts commit fe9fc4ea876cdc2bc56bb323ff79460bf56284c7. --- Model-Monitoring/airflow_whylog.py | 65 - Model-Monitoring/data/reference_dataset.csv | 1600 ------------------- Model-Monitoring/data/target_dataset.csv | 1600 ------------------- 3 files changed, 3265 deletions(-) delete mode 100644 Model-Monitoring/airflow_whylog.py delete mode 100644 Model-Monitoring/data/reference_dataset.csv delete mode 100644 Model-Monitoring/data/target_dataset.csv diff --git a/Model-Monitoring/airflow_whylog.py b/Model-Monitoring/airflow_whylog.py deleted file mode 100644 index b60c1f42..00000000 --- a/Model-Monitoring/airflow_whylog.py +++ /dev/null @@ -1,65 +0,0 @@ -import logging -from datetime import datetime -import os -import pandas as pd -import whylogs as why -from whylogs.core.constraints.factories import greater_than_number - -from airflow.models.dag import DAG -from airflow.operators.python import get_current_context -from airflow.operators.python import PythonOperator -from whylogs_provider.operators.whylogs import ( - WhylogsConstraintsOperator, - WhylogsSummaryDriftOperator, -) - -def file_path(relative_path): - dir = os.path.dirname(os.path.abspath(__file__)) - split_path = relative_path.split("/") - new_path = os.path.join(dir, *split_path) - return new_path - -prof_path_target="/mnt/shared/airflow-whylog/profile_wine_target.bin" -prof_path_ref="/mnt/shared/airflow-whylog/profile_wine_ref.bin" -html_path="/mnt/shared/airflow-whylog/Profile_wine.html" - -def profile_data_target(data_path=file_path("data/target_dataset.csv")): - df = pd.read_csv(data_path) - result = why.log(df) - result.writer("local").write(dest=prof_path_target) -def profile_data_ref(data_path=file_path("data/reference_dataset.csv")): - df = pd.read_csv(data_path) - result = why.log(df) - result.writer("local").write(dest=prof_path_ref) - - -with DAG( - dag_id='whylogs-wine-quality', - schedule_interval=None, - start_date=datetime.now(), - max_active_runs=1, - tags=['responsible', 'data_transformation'], -) as dag: - - - profile_data_target = PythonOperator(task_id="profile_data_target", python_callable=profile_data_target) - profile_data_ref = PythonOperator(task_id="profile_data_ref", python_callable=profile_data_ref) - - - is_in_range = WhylogsConstraintsOperator( - task_id="is_in_range", - profile_path=prof_path_target, - reader="local", - constraint=greater_than_number(column_name="quality", number=4) - ) - summary_drift = WhylogsSummaryDriftOperator( - task_id="drift_report", - target_profile_path=prof_path_target, - reference_profile_path=prof_path_ref, - reader="local", - write_report_path=html_path - ) - - ( - profile_data_target>>profile_data_ref>>is_in_range>> summary_drift - ) diff --git a/Model-Monitoring/data/reference_dataset.csv b/Model-Monitoring/data/reference_dataset.csv deleted file mode 100644 index d611beb1..00000000 --- a/Model-Monitoring/data/reference_dataset.csv +++ /dev/null @@ -1,1600 +0,0 @@ -fixed acidity,volatile acidity,citric acid,residual sugar,chlorides,free sulfur dioxide,total sulfur dioxide,density,pH,sulphates,alcohol,quality -7.4,0.7,0,1.9,0.076,11,34,0.9978,3.51,0.56,9.4,5 -7.8,0.88,0,2.6,0.098,25,67,0.9968,3.2,0.68,9.8,5 -7.8,0.76,0.04,2.3,0.092,15,54,0.997,3.26,0.65,9.8,5 -11.2,0.28,0.56,1.9,0.075,17,60,0.998,3.16,0.58,9.8,6 -7.4,0.7,0,1.9,0.076,11,34,0.9978,3.51,0.56,9.4,5 -7.4,0.66,0,1.8,0.075,13,40,0.9978,3.51,0.56,9.4,5 -7.9,0.6,0.06,1.6,0.069,15,59,0.9964,3.3,0.46,9.4,5 -7.3,0.65,0,1.2,0.065,15,21,0.9946,3.39,0.47,10,7 -7.8,0.58,0.02,2,0.073,9,18,0.9968,3.36,0.57,9.5,7 -7.5,0.5,0.36,6.1,0.071,17,102,0.9978,3.35,0.8,10.5,5 -6.7,0.58,0.08,1.8,0.097,15,65,0.9959,3.28,0.54,9.2,5 -7.5,0.5,0.36,6.1,0.071,17,102,0.9978,3.35,0.8,10.5,5 -5.6,0.615,0,1.6,0.089,16,59,0.9943,3.58,0.52,9.9,5 -7.8,0.61,0.29,1.6,0.114,9,29,0.9974,3.26,1.56,9.1,5 -8.9,0.62,0.18,3.8,0.176,52,145,0.9986,3.16,0.88,9.2,5 -8.9,0.62,0.19,3.9,0.17,51,148,0.9986,3.17,0.93,9.2,5 -8.5,0.28,0.56,1.8,0.092,35,103,0.9969,3.3,0.75,10.5,7 -8.1,0.56,0.28,1.7,0.368,16,56,0.9968,3.11,1.28,9.3,5 -7.4,0.59,0.08,4.4,0.086,6,29,0.9974,3.38,0.5,9,4 -7.9,0.32,0.51,1.8,0.341,17,56,0.9969,3.04,1.08,9.2,6 -8.9,0.22,0.48,1.8,0.077,29,60,0.9968,3.39,0.53,9.4,6 -7.6,0.39,0.31,2.3,0.082,23,71,0.9982,3.52,0.65,9.7,5 -7.9,0.43,0.21,1.6,0.106,10,37,0.9966,3.17,0.91,9.5,5 -8.5,0.49,0.11,2.3,0.084,9,67,0.9968,3.17,0.53,9.4,5 -6.9,0.4,0.14,2.4,0.085,21,40,0.9968,3.43,0.63,9.7,6 -6.3,0.39,0.16,1.4,0.08,11,23,0.9955,3.34,0.56,9.3,5 -7.6,0.41,0.24,1.8,0.08,4,11,0.9962,3.28,0.59,9.5,5 -7.9,0.43,0.21,1.6,0.106,10,37,0.9966,3.17,0.91,9.5,5 -7.1,0.71,0,1.9,0.08,14,35,0.9972,3.47,0.55,9.4,5 -7.8,0.645,0,2,0.082,8,16,0.9964,3.38,0.59,9.8,6 -6.7,0.675,0.07,2.4,0.089,17,82,0.9958,3.35,0.54,10.1,5 -6.9,0.685,0,2.5,0.105,22,37,0.9966,3.46,0.57,10.6,6 -8.3,0.655,0.12,2.3,0.083,15,113,0.9966,3.17,0.66,9.8,5 -6.9,0.605,0.12,10.7,0.073,40,83,0.9993,3.45,0.52,9.4,6 -5.2,0.32,0.25,1.8,0.103,13,50,0.9957,3.38,0.55,9.2,5 -7.8,0.645,0,5.5,0.086,5,18,0.9986,3.4,0.55,9.6,6 -7.8,0.6,0.14,2.4,0.086,3,15,0.9975,3.42,0.6,10.8,6 -8.1,0.38,0.28,2.1,0.066,13,30,0.9968,3.23,0.73,9.7,7 -5.7,1.13,0.09,1.5,0.172,7,19,0.994,3.5,0.48,9.8,4 -7.3,0.45,0.36,5.9,0.074,12,87,0.9978,3.33,0.83,10.5,5 -7.3,0.45,0.36,5.9,0.074,12,87,0.9978,3.33,0.83,10.5,5 -8.8,0.61,0.3,2.8,0.088,17,46,0.9976,3.26,0.51,9.3,4 -7.5,0.49,0.2,2.6,0.332,8,14,0.9968,3.21,0.9,10.5,6 -8.1,0.66,0.22,2.2,0.069,9,23,0.9968,3.3,1.2,10.3,5 -6.8,0.67,0.02,1.8,0.05,5,11,0.9962,3.48,0.52,9.5,5 -4.6,0.52,0.15,2.1,0.054,8,65,0.9934,3.9,0.56,13.1,4 -7.7,0.935,0.43,2.2,0.114,22,114,0.997,3.25,0.73,9.2,5 -8.7,0.29,0.52,1.6,0.113,12,37,0.9969,3.25,0.58,9.5,5 -6.4,0.4,0.23,1.6,0.066,5,12,0.9958,3.34,0.56,9.2,5 -5.6,0.31,0.37,1.4,0.074,12,96,0.9954,3.32,0.58,9.2,5 -8.8,0.66,0.26,1.7,0.074,4,23,0.9971,3.15,0.74,9.2,5 -6.6,0.52,0.04,2.2,0.069,8,15,0.9956,3.4,0.63,9.4,6 -6.6,0.5,0.04,2.1,0.068,6,14,0.9955,3.39,0.64,9.4,6 -8.6,0.38,0.36,3,0.081,30,119,0.997,3.2,0.56,9.4,5 -7.6,0.51,0.15,2.8,0.11,33,73,0.9955,3.17,0.63,10.2,6 -7.7,0.62,0.04,3.8,0.084,25,45,0.9978,3.34,0.53,9.5,5 -10.2,0.42,0.57,3.4,0.07,4,10,0.9971,3.04,0.63,9.6,5 -7.5,0.63,0.12,5.1,0.111,50,110,0.9983,3.26,0.77,9.4,5 -7.8,0.59,0.18,2.3,0.076,17,54,0.9975,3.43,0.59,10,5 -7.3,0.39,0.31,2.4,0.074,9,46,0.9962,3.41,0.54,9.4,6 -8.8,0.4,0.4,2.2,0.079,19,52,0.998,3.44,0.64,9.2,5 -7.7,0.69,0.49,1.8,0.115,20,112,0.9968,3.21,0.71,9.3,5 -7.5,0.52,0.16,1.9,0.085,12,35,0.9968,3.38,0.62,9.5,7 -7,0.735,0.05,2,0.081,13,54,0.9966,3.39,0.57,9.8,5 -7.2,0.725,0.05,4.65,0.086,4,11,0.9962,3.41,0.39,10.9,5 -7.2,0.725,0.05,4.65,0.086,4,11,0.9962,3.41,0.39,10.9,5 -7.5,0.52,0.11,1.5,0.079,11,39,0.9968,3.42,0.58,9.6,5 -6.6,0.705,0.07,1.6,0.076,6,15,0.9962,3.44,0.58,10.7,5 -9.3,0.32,0.57,2,0.074,27,65,0.9969,3.28,0.79,10.7,5 -8,0.705,0.05,1.9,0.074,8,19,0.9962,3.34,0.95,10.5,6 -7.7,0.63,0.08,1.9,0.076,15,27,0.9967,3.32,0.54,9.5,6 -7.7,0.67,0.23,2.1,0.088,17,96,0.9962,3.32,0.48,9.5,5 -7.7,0.69,0.22,1.9,0.084,18,94,0.9961,3.31,0.48,9.5,5 -8.3,0.675,0.26,2.1,0.084,11,43,0.9976,3.31,0.53,9.2,4 -9.7,0.32,0.54,2.5,0.094,28,83,0.9984,3.28,0.82,9.6,5 -8.8,0.41,0.64,2.2,0.093,9,42,0.9986,3.54,0.66,10.5,5 -8.8,0.41,0.64,2.2,0.093,9,42,0.9986,3.54,0.66,10.5,5 -6.8,0.785,0,2.4,0.104,14,30,0.9966,3.52,0.55,10.7,6 -6.7,0.75,0.12,2,0.086,12,80,0.9958,3.38,0.52,10.1,5 -8.3,0.625,0.2,1.5,0.08,27,119,0.9972,3.16,1.12,9.1,4 -6.2,0.45,0.2,1.6,0.069,3,15,0.9958,3.41,0.56,9.2,5 -7.8,0.43,0.7,1.9,0.464,22,67,0.9974,3.13,1.28,9.4,5 -7.4,0.5,0.47,2,0.086,21,73,0.997,3.36,0.57,9.1,5 -7.3,0.67,0.26,1.8,0.401,16,51,0.9969,3.16,1.14,9.4,5 -6.3,0.3,0.48,1.8,0.069,18,61,0.9959,3.44,0.78,10.3,6 -6.9,0.55,0.15,2.2,0.076,19,40,0.9961,3.41,0.59,10.1,5 -8.6,0.49,0.28,1.9,0.11,20,136,0.9972,2.93,1.95,9.9,6 -7.7,0.49,0.26,1.9,0.062,9,31,0.9966,3.39,0.64,9.6,5 -9.3,0.39,0.44,2.1,0.107,34,125,0.9978,3.14,1.22,9.5,5 -7,0.62,0.08,1.8,0.076,8,24,0.9978,3.48,0.53,9,5 -7.9,0.52,0.26,1.9,0.079,42,140,0.9964,3.23,0.54,9.5,5 -8.6,0.49,0.28,1.9,0.11,20,136,0.9972,2.93,1.95,9.9,6 -8.6,0.49,0.29,2,0.11,19,133,0.9972,2.93,1.98,9.8,5 -7.7,0.49,0.26,1.9,0.062,9,31,0.9966,3.39,0.64,9.6,5 -5,1.02,0.04,1.4,0.045,41,85,0.9938,3.75,0.48,10.5,4 -4.7,0.6,0.17,2.3,0.058,17,106,0.9932,3.85,0.6,12.9,6 -6.8,0.775,0,3,0.102,8,23,0.9965,3.45,0.56,10.7,5 -7,0.5,0.25,2,0.07,3,22,0.9963,3.25,0.63,9.2,5 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index a286dc30..00000000 --- a/Model-Monitoring/data/target_dataset.csv +++ /dev/null @@ -1,1600 +0,0 @@ -fixed acidity,volatile acidity,citric acid,residual sugar,chlorides,free sulfur dioxide,total sulfur dioxide,density,pH,sulphates,alcohol,quality -,0.7,0,1.9,0.076,11,34,0.9978,3.51,,9.4,5 -,0.88,0,2.6,0.098,25,67,,3.2,0.68,9.8,5 -,0.76,0.04,,0.092,15,54,0.997,3.26,0.65,9.8,5 -,0.28,0.56,1.9,0.075,17,60,0.998,3.16,0.58,9.8,6 -,0.7,0,1.9,0.076,11,34,0.9978,3.51,0.56,9.4,5 -,,0,1.8,0.075,13,40,0.9978,3.51,,,5 -,0.6,0.06,1.6,0.069,15,59,0.9964,3.3,0.46,9.4,5 -,,0,1.2,0.065,15,21,0.9946,3.39,0.47,10,7 -,0.58,0.02,2,0.073,9,18,0.9968,3.36,0.57,9.5,7 -,0.5,,6.1,0.071,17,102,0.9978,3.35,,10.5,5 -,0.58,0.08,,0.097,,65,0.9959,3.28,0.54,9.2,5 -,0.5,,6.1,0.071,17,,,3.35,0.8,10.5,5 -,0.615,0,1.6,0.089,16,59,,,0.52,9.9,5 -,,0.29,1.6,0.114,9,29,0.9974,3.26,1.56,,5 -,0.62,0.18,3.8,0.176,52,,,3.16,,9.2,5 -,0.62,,3.9,0.17,51,,0.9986,3.17,0.93,9.2,5 -,,0.56,1.8,0.092,35,103,0.9969,3.3,0.75,10.5,7 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-6.3,0.55,0.15,1.8,0.077,26,35,0.99314,3.32,0.82,11.6,6 -5.4,0.74,0.09,1.7,0.089,16,26,0.99402,3.67,0.56,11.6,6 -6.3,0.51,0.13,2.3,0.076,29,40,0.99574,3.42,0.75,11,6 -6.8,0.62,0.08,1.9,0.068,28,38,0.99651,3.42,0.82,9.5,6 -6.2,0.6,0.08,2,0.09,32,44,0.9949,3.45,0.58,10.5,5 -5.9,0.55,0.1,2.2,0.062,39,51,0.99512,3.52,0.76,11.2,6 -6.3,0.51,0.13,2.3,0.076,29,40,0.99574,3.42,0.75,11,6 -5.9,0.645,0.12,2,0.075,32,44,0.99547,3.57,0.71,10.2,5 -6,0.31,0.47,3.6,0.067,18,42,0.99549,3.39,0.66,11,6 From 558d52a73354018502e7d8b8160f48c30d2caacb Mon Sep 17 00:00:00 2001 From: Erdinc Kaya Date: Fri, 22 Sep 2023 16:00:38 +0100 Subject: [PATCH 15/15] updates for documentation --- .gitignore | 2 ++ E2E-Demos/Question-Answering/README.md | 33 +++++++++++++++--- .../images/image-selection.jpg | Bin 0 -> 90392 bytes .../Question-Answering/images/nb-launcher.jpg | Bin 0 -> 94859 bytes 4 files changed, 31 insertions(+), 4 deletions(-) create mode 100644 E2E-Demos/Question-Answering/images/image-selection.jpg create mode 100644 E2E-Demos/Question-Answering/images/nb-launcher.jpg diff --git a/.gitignore b/.gitignore index 227b083e..6c4b19da 100644 --- a/.gitignore +++ b/.gitignore @@ -8,3 +8,5 @@ ipython_config.py # Idea .idea + +**/.DS_store diff --git a/E2E-Demos/Question-Answering/README.md b/E2E-Demos/Question-Answering/README.md index f23b4900..d87343fe 100644 --- a/E2E-Demos/Question-Answering/README.md +++ b/E2E-Demos/Question-Answering/README.md @@ -10,19 +10,43 @@ To complete the tutorial follow the steps below: - Login to Ezmeral Unified Analytics cluster. - Create a new notebook server using the `jupyter-data-science` image. + +![select-jupyter-image](images/image-selection.jpg) + +- Wait for notebook to be ready and then "Connect" to newly created notebook. + - Clone the repository locally. -- Create a new conda environment using the specified `environment.yaml` file: + + Open the Terminal within the launcher: + + ![select-jupyter-image](images/nb-launcher.jpg) + + ```bash + git clone https://github.com/HPEEzmeral/ezua-tutorials.git ``` + + ```bash + cd ezua-tutorials/E2E-Demos/Question-Answering + ``` + +- Create a new conda environment using the specified `environment.yaml` file: + + ```bash conda env create -f environment.yaml ``` + - Activate the new conda environment: - ``` + + ```bash conda activate question-answering ``` + - Add the new conda environment as an ipykernel: - ``` + + ```bash python -m ipykernel install --user --name=question-answering ``` + - Launch the five Notebooks in order and execute the code cells. Make sure to select the `question-answering` environment kernel for each Notebook. ## How It Works @@ -40,6 +64,7 @@ KServe allows you to create an inference service using a custom predictor. Since 1. LLM Model: Move into the `llm` directory and build the Docker image using the provided Dockerfile. > For your convenience, you can use the pre-built images we have prepared for you: +> > - Vector Store: `gcr.io/mapr-252711/ezua-demos/vectorstore:v0.1.0` > - LLM Predictor: `gcr.io/mapr-252711/ezua-demos/llm-predictor:v0.1.0` > - LLM Transformer: `gcr.io/mapr-252711/ezua-demos/llm-transformer:v0.1.0` @@ -63,4 +88,4 @@ The last Notebook outlines the user's perspective. The application flow is depic 1. LLM Transformer: Get the `4` most relevant documents from the Vector Store response. 1. LLM Transformer: Create a new request to the LLM Predictor passing the `4` most relevant documents as context and the user's question. 1. LLM Predictor: Accept the request from the LLM Transformer, extract the user's question as well as the context, and answer the user's question based on the relevant context. -1. LLM Predictor: Respond to the LLM Transformer with the completion prediction. \ No newline at end of file +1. 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