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Using Dremio for Machine Learning

In this scenario we assume your data has already been prepared on Dremio, we then pull the data using dremio-simple-query into a notebook to train a model.

from dremio_simple_query.connect import DremioConnection
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
import pandas as pd
from os import getenv
from dotenv import load_dotenv

# Load environment variables
load_dotenv()

# Establish connection to Dremio
token = getenv("TOKEN")
uri = getenv("ARROW_ENDPOINT")
dremio = DremioConnection(token, uri)

# Query data
df = dremio.toPandas("SELECT * FROM your_data_table;")

# Preprocess data (assuming df is already preprocessed for simplicity)

# Split data
X = df.drop('target_column', axis=1)  # Features
y = df['target_column']  # Target variable
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train model
model = LinearRegression()
model.fit(X_train, y_train)

# Predict and evaluate
predictions = model.predict(X_test)
mse = mean_squared_error(y_test, predictions)
print(f"Mean Squared Error: {mse}")

Understanding the Example Code

from dremio_simple_query.connect import DremioConnection

This line imports the DremioConnection class from the dremio_simple_query.connect module, which is used to establish a connection to a Dremio data source using the Arrow Flight protocol.

from sklearn.model_selection import train_test_split

Imports the train_test_split function from the sklearn.model_selection module. This function is used to split the dataset into training and testing sets.

from sklearn.linear_model import LinearRegression

Imports the LinearRegression class from sklearn.linear_model. This class is a machine learning model that will be used to perform linear regression.

from sklearn.metrics import mean_squared_error

Imports the mean_squared_error function from sklearn.metrics. This function is used to evaluate the performance of the machine learning model by calculating the mean squared error between the predicted and actual values.

import pandas as pd

Imports the Pandas library, which is a powerful tool for data manipulation and analysis. It is used here to handle the dataset in DataFrame format.

from os import getenv

Imports the getenv function from the os module. This function is used to retrieve environment variables.

from dotenv import load_dotenv

Imports the load_dotenv function from the dotenv module. This function loads environment variables from a .env file into the script, making it easier to manage sensitive information like API tokens.

# Load environment variables
load_dotenv()

Calls the load_dotenv() function to load environment variables from the .env file.

# Establish connection to Dremio
token = getenv("TOKEN")
uri = getenv("ARROW_ENDPOINT")
dremio = DremioConnection(token, uri)

Retrieves the Dremio personal access token and the Arrow Flight endpoint URI from environment variables and uses them to establish a connection to Dremio.

# Query data
df = dremio.toPandas("SELECT * FROM your_data_table;")

Executes a SQL query against the Dremio data source and retrieves the results as a Pandas DataFrame. This is done using the toPandas method of the DremioConnection object.

# Preprocess data (assuming df is already preprocessed for simplicity)

A placeholder comment indicating where data preprocessing steps (like handling missing values, encoding categorical variables, etc.) would go. This example assumes that the clean up work has been prior from Dremio).

# Split data
X = df.drop('target_column', axis=1)  # Features
y = df['target_column']  # Target variable
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

Prepares the dataset for machine learning by separating it into features (X) and the target variable (y). It then uses train_test_split to divide the data into training and testing sets, with 20% of the data used for testing.

# Train model
model = LinearRegression()
model.fit(X_train, y_train)

Initializes a LinearRegression model and fits it to the training data. This "training" process involves finding the best coefficients for the features in X_train to predict y_train.

# Predict and evaluate
predictions = model.predict(X_test)
mse = mean_squared_error(y_test, predictions)
print(f"Mean Squared Error: {mse}")

End to End Example

Make sure to adjust SQL to reflect your catalog of namespaces.

from dremio_simple_query.connect import DremioConnection
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
import pandas as pd
import numpy as np
from os import getenv
from dotenv import load_dotenv

# Load environment variables for Dremio connection
load_dotenv()
token = getenv("TOKEN")
uri = getenv("ARROW_ENDPOINT")

# Establish connection to Dremio
dremio = DremioConnection(token, uri)

# Step 1: Create a table in Dremio
create_table_sql = """
CREATE TABLE my_dremio_catalog.sample_table (
    id INT,
    feature1 DOUBLE,
    feature2 DOUBLE,
    target DOUBLE
)
"""
dremio.execute(create_table_sql)

# Step 2: Insert data with flaws into the table
insert_data_sql = """
INSERT INTO my_dremio_catalog.sample_table (id, feature1, feature2, target) VALUES
(1, 10.0, NULL, 50.5),
(2, NULL, 20.5, NULL),
(3, 15.5, 25.0, 60.0),
(4, -5.0, 30.0, 70.0)
"""
dremio.toArrow(insert_data_sql)

# Step 3: Check for data flaws (e.g., NULLs, negative values)
# This step would involve querying the data and analyzing it, 
# but for simplicity, we proceed to cleaning assuming we know the flaws.

# Step 4: Clean data flaws
# Remove or impute NULL values, correct negative values
clean_data_sql = """
UPDATE my_dremio_catalog.sample_table SET feature1 = ABS(feature1) WHERE feature1 < 0;
UPDATE my_dremio_catalog.sample_table SET feature1 = 0 WHERE feature1 IS NULL;
UPDATE my_dremio_catalog.sample_table SET target = 0 WHERE target IS NULL;
"""
dremio.toArrow(clean_data_sql)

# Step 5: Query the cleaned data
query_cleaned_data_sql = "SELECT * FROM sample_table"
df = dremio.toArrow(query_cleaned_data_sql)

# Assuming the DataFrame `df` is ready for machine learning as is

# Step 6: Split data for model training
X = df[['feature1', 'feature2']].fillna(0)  # Impute missing values if any remain
y = df['target']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train a simple Linear Regression model
model = LinearRegression()
model.fit(X_train, y_train)

# Display coefficients
print("Model Coefficients:", model.coef_)

The trained model is used to make predictions on the test dataset (X_test). The mean squared error between the predictions and the actual values (y_test) is calculated and printed. This evaluates how well the model has learned to predict the target variable. Each line of this code contributes to a workflow for querying data from Dremio, preparing it for machine learning, training a model, and evaluating its performance.