Merge pull request #6 from Aignosi/feature/SIENTIAPDE-1252
SIENTIAPDE-1252: Implement Model Saving to MLflow and Artifact Logging
This commit is contained in:
Bruno Domingues
6
.github/workflows/quality-gate.yml
vendored
6
.github/workflows/quality-gate.yml
vendored
@@ -193,12 +193,12 @@ jobs:
|
||||
df -h
|
||||
|
||||
- name: 🔧 Setup Python
|
||||
uses: actions/setup-python@v4
|
||||
uses: actions/setup-python@v5
|
||||
with:
|
||||
python-version: "3.11"
|
||||
|
||||
- name: 🗄️ Cache Python dependencies
|
||||
uses: actions/cache@v3
|
||||
- name: 💾 Cache pip packages
|
||||
uses: actions/cache@v4
|
||||
with:
|
||||
path: ~/.cache/pip
|
||||
key: ${{ runner.os }}-pip-${{ hashFiles('requirements.txt', 'requirements-dev.txt') }}
|
||||
|
||||
@@ -344,3 +344,107 @@ class MLFlow(BaseActivity):
|
||||
)
|
||||
self.error(trace, metadata=metadata)
|
||||
raise e
|
||||
|
||||
@activity.defn(name='save_model')
|
||||
async def save_model(self, input_data: dict[str, Any]) -> dict[str, Any]:
|
||||
"""
|
||||
Save a trained ML model and its artifacts to MLflow with comprehensive error handling.
|
||||
|
||||
This activity orchestrates the complete model saving pipeline:
|
||||
1. Generates the next run name for the experiment
|
||||
2. Creates and organizes artifacts (reports, data files)
|
||||
3. Logs model, parameters, metrics, and artifacts to MLflow
|
||||
4. Returns success/failure status with results or error message
|
||||
|
||||
The activity does NOT raise exceptions on failure - it catches all errors,
|
||||
sends notifications, and returns a failure status. This allows the workflow
|
||||
to handle the error gracefully and update the database accordingly.
|
||||
|
||||
Args:
|
||||
input_data: Configuration for model saving operation
|
||||
Required keys:
|
||||
- metadata (dict): Workflow execution metadata
|
||||
- train_result (TrainModelResult): Training result with model and metrics
|
||||
|
||||
Returns:
|
||||
dict: Save result with the following structure:
|
||||
{
|
||||
'success': bool, # True if saving succeeded, False otherwise
|
||||
'result': TrainModelResult | None, # Updated result if success=True
|
||||
'error_message': str | None # Error message if success=False
|
||||
}
|
||||
|
||||
Example:
|
||||
# Successful save
|
||||
result = await save_model({
|
||||
'metadata': {'workflow_id': 'save-123', 'experiment_run_id': 456},
|
||||
'train_result': TrainModelResult(...)
|
||||
})
|
||||
# Returns: {'success': True, 'result': TrainModelResult(...), 'error_message': None}
|
||||
|
||||
# Failed save
|
||||
# Returns: {'success': False, 'result': None, 'error_message': 'Error details...'}
|
||||
"""
|
||||
metadata = input_data.get('metadata', {})
|
||||
train_result = input_data['train_result']
|
||||
|
||||
try:
|
||||
experiment_name = train_result.params.experiment_name
|
||||
|
||||
self.info(
|
||||
f'Starting model save for experiment: {experiment_name}',
|
||||
metadata,
|
||||
)
|
||||
|
||||
# Step 1: Generate next run name
|
||||
self.info('Generating run name', metadata)
|
||||
train_result.run_name = self.model_monitoring_repository.get_next_run_name(
|
||||
experiment_name
|
||||
)
|
||||
self.info(f'Generated run name: {train_result.run_name}', metadata)
|
||||
|
||||
# Step 2: Generate artifacts (reports, CSV files)
|
||||
self.info('Generating artifacts', metadata)
|
||||
train_result = self.model_monitoring_repository.generate_artifacts(train_result)
|
||||
self.info('Artifacts generated successfully', metadata)
|
||||
|
||||
# Step 3: Save run to MLflow
|
||||
self.info('Saving run to MLflow', metadata)
|
||||
self.model_monitoring_repository.save_run(train_result)
|
||||
|
||||
self.info(
|
||||
f'Model saved successfully - Run: {train_result.run_name}, '
|
||||
f'Experiment: {experiment_name}',
|
||||
metadata,
|
||||
)
|
||||
|
||||
return {
|
||||
'success': True,
|
||||
'result': train_result,
|
||||
'error_message': None,
|
||||
}
|
||||
|
||||
except Exception as e: # noqa: BLE001
|
||||
error_msg = f'Error saving model - Experiment: {train_result.params.experiment_name if train_result and train_result.params else "unknown"}, Error: {str(e)}'
|
||||
trace = traceback.format_exc()
|
||||
|
||||
# Send notification (MongoDB)
|
||||
self.send_notification(
|
||||
metadata=metadata,
|
||||
notification_id='SAVE_MODEL_ERROR',
|
||||
message=error_msg,
|
||||
block='save_model',
|
||||
level=NotificationLevel.ERROR,
|
||||
attachment_content=trace,
|
||||
)
|
||||
|
||||
# Log error with metadata
|
||||
self.error(trace, metadata=metadata)
|
||||
|
||||
# Return failure result (do NOT raise exception)
|
||||
# This allows workflow to update database with error status
|
||||
return {
|
||||
'success': False,
|
||||
'result': None,
|
||||
'error_message': str(e),
|
||||
}
|
||||
|
||||
167
model_manager/reports/header.html
Normal file
167
model_manager/reports/header.html
Normal file
@@ -0,0 +1,167 @@
|
||||
<!DOCTYPE html>
|
||||
<html lang="en">
|
||||
<head>
|
||||
<meta charset="UTF-8" />
|
||||
<meta http-equiv="X-UA-Compatible" content="IE=edge" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
|
||||
<link rel="stylesheet" href="style.css" />
|
||||
|
||||
<title>Report</title>
|
||||
|
||||
<style>
|
||||
* {
|
||||
font-family: "Franklin Gothic Medium", "Arial Narrow", Arial, sans-serif;
|
||||
margin: 0;
|
||||
padding: 0;
|
||||
box-sizing: border-box;
|
||||
}
|
||||
|
||||
.logo {
|
||||
padding-top: 70;
|
||||
padding-bottom: 70;
|
||||
position: absolute;
|
||||
margin-left: -48px;
|
||||
}
|
||||
|
||||
h1 {
|
||||
color: #fff;
|
||||
position: absolute;
|
||||
margin-left: 45%;
|
||||
}
|
||||
|
||||
html,
|
||||
body {
|
||||
scroll-behavior: smooth;
|
||||
}
|
||||
|
||||
section {
|
||||
padding-top: 90px;
|
||||
width: 100%;
|
||||
display: fixed;
|
||||
justify-content: center;
|
||||
align-items: center;
|
||||
background-color: rgb(217, 217, 214, 0.7);
|
||||
}
|
||||
|
||||
.material-symbols-outlined {
|
||||
font-variation-settings: "FILL" 0, "wght" 400, "GRAD" 0, "opsz" 24;
|
||||
color: #ffff;
|
||||
}
|
||||
|
||||
/* Tooltip text */
|
||||
.tooltiptext {
|
||||
visibility: hidden;
|
||||
background-color: rgb(0, 30, 96, 0.9);
|
||||
padding: 10px;
|
||||
margin-left: -90px;
|
||||
font-size: 16px;
|
||||
position: absolute;
|
||||
top: 85px;
|
||||
border-bottom-left-radius: 12px;
|
||||
border-bottom-right-radius: 12px;
|
||||
}
|
||||
|
||||
/* Show the tooltip text when you mouse over the tooltip container */
|
||||
.material-symbols-outlined:hover .tooltiptext {
|
||||
visibility: visible;
|
||||
}
|
||||
|
||||
header {
|
||||
position: fixed;
|
||||
top: 0;
|
||||
width: 100%;
|
||||
height: 85px;
|
||||
background: rgb(0, 30, 96, 0.95);
|
||||
display: flex;
|
||||
justify-content: space-between;
|
||||
align-items: center;
|
||||
padding: 10px 50px 0;
|
||||
}
|
||||
|
||||
header nav {
|
||||
display: absolute;
|
||||
margin-left: 80%;
|
||||
gap: 10px;
|
||||
}
|
||||
|
||||
header nav a {
|
||||
position: relative;
|
||||
text-decoration: none;
|
||||
padding: 12px 18px;
|
||||
color: #fff;
|
||||
font-weight: 500;
|
||||
}
|
||||
|
||||
header nav a.active {
|
||||
background-color: #001540;
|
||||
position: relative;
|
||||
border-radius: 12px;
|
||||
}
|
||||
</style>
|
||||
|
||||
<link
|
||||
rel="stylesheet"
|
||||
href="https://fonts.googleapis.com/css2?family=Material+Symbols+Outlined:opsz,wght,FILL,GRAD@20..48,100..700,0..1,-50..200"
|
||||
/>
|
||||
</head>
|
||||
<body>
|
||||
<main>
|
||||
<header>
|
||||
<a href="#" class="logo">
|
||||
<img
|
||||
src="https://aignosi.blob.core.windows.net/sientia/20231016-Aignosi_Logo_WHITE.png"
|
||||
alt="Aignosi Logo"
|
||||
width="247"
|
||||
height="70"
|
||||
/>
|
||||
</a>
|
||||
<h1>Report</h1>
|
||||
<nav>
|
||||
<a href="#data_quality" class="active"> Summary </a>
|
||||
<a href="#data_drift"> Drift </a>
|
||||
<a href="#regression"> Regression </a>
|
||||
</nav>
|
||||
<div class="material-symbols-outlined">
|
||||
info
|
||||
<p class="tooltiptext">
|
||||
Note that "current" <br />
|
||||
is related to the test <br />
|
||||
set while "reference" <br />
|
||||
refers to the training <br />
|
||||
set
|
||||
</p>
|
||||
</div>
|
||||
</header>
|
||||
<div class="quality_div">
|
||||
<section id="data_quality"></section>
|
||||
</div>
|
||||
<div class="data_drift_div">
|
||||
<section id="data_drift"></section>
|
||||
</div>
|
||||
<div class="regression_div">
|
||||
<section id="regression"></section>
|
||||
</div>
|
||||
</main>
|
||||
|
||||
<script>
|
||||
let sec = document.querySelectorAll("section");
|
||||
let links = document.querySelectorAll("nav a");
|
||||
|
||||
window.onscroll = () => {
|
||||
sec.forEach((section) => {
|
||||
let top = window.scrollY;
|
||||
let offset = section.offsetTop;
|
||||
let height = section.offsetHeight;
|
||||
let id = section.getAttribute("id");
|
||||
|
||||
if (top >= offset && top < offset + height) {
|
||||
links.forEach((link) => {
|
||||
link.classList.remove("active");
|
||||
document.querySelector("nav a[href*=" + id + "]").classList.add("active");
|
||||
});
|
||||
}
|
||||
});
|
||||
};
|
||||
</script>
|
||||
</body>
|
||||
</html>
|
||||
@@ -34,7 +34,6 @@ class TrainModelParams:
|
||||
shuffle (bool): Whether to shuffle the data during train/test split.
|
||||
experiment_run_id (int): Unique identifier for the experiment run.
|
||||
experiment_name (str): Name of the experiment for tracking.
|
||||
experiment_description (str): Description of the experiment.
|
||||
removed_intervals (list): List of time intervals to remove from the data.
|
||||
"""
|
||||
|
||||
@@ -56,7 +55,6 @@ class TrainModelParams:
|
||||
shuffle: bool
|
||||
experiment_run_id: int
|
||||
experiment_name: str
|
||||
experiment_description: str
|
||||
removed_intervals: list
|
||||
|
||||
@classmethod
|
||||
@@ -114,9 +112,6 @@ class TrainModelParams:
|
||||
data.get('experiment_run_id'), int, 'experiment_run_id'
|
||||
),
|
||||
experiment_name=cls._check_none(data.get('experiment_name'), str, 'experiment_name'),
|
||||
experiment_description=cls._check_none(
|
||||
data.get('experiment_description'), str, 'experiment_description'
|
||||
),
|
||||
removed_intervals=cls._check_type(
|
||||
data.get('removed_intervals'), list, 'removed_intervals'
|
||||
),
|
||||
|
||||
@@ -11,16 +11,21 @@ By Monitoring we mean the evaluation of the performance of models, the generatio
|
||||
|
||||
"""
|
||||
|
||||
import shutil
|
||||
import traceback
|
||||
from datetime import datetime
|
||||
from os import makedirs, path, remove
|
||||
|
||||
import mlflow
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from sientia.ModelServing import ModelServing # type: ignore[import-untyped]
|
||||
from sientia.reports import Reports # type: ignore[import-untyped]
|
||||
from sientia_do.observability.logger import Logger
|
||||
from sientia_do.temporal.constants import DATETIME_FORMAT_WITH_TZ
|
||||
|
||||
from model_manager.utils.models.train_model_result import TrainModelResult
|
||||
|
||||
|
||||
class MLFlowRepository:
|
||||
def __init__(self, host, username, password, logger: Logger):
|
||||
@@ -455,3 +460,375 @@ class MLFlowRepository:
|
||||
metadata['mlflow_experiment_id'] = experiment_id
|
||||
|
||||
return metadata
|
||||
|
||||
def get_next_run_name_new(self, experiment_name: str) -> str:
|
||||
"""
|
||||
Generates the next run name for a given experiment.
|
||||
|
||||
Args:
|
||||
experiment_name (str): The name of the experiment for which the next run name is being generated.
|
||||
|
||||
Returns:
|
||||
str: A unique run name in the format "<experiment_name>-<next_run_number>".
|
||||
"""
|
||||
runs = self.model_serving.search_runs_by_name(
|
||||
experiment_names=[experiment_name], order_by=['start_time desc']
|
||||
)
|
||||
|
||||
next_run_number = len(runs) + 1
|
||||
return f'{experiment_name}-{next_run_number}'
|
||||
|
||||
def generate_artifacts(self, data: TrainModelResult) -> TrainModelResult:
|
||||
"""
|
||||
Generates and organizes artifacts related to the training process, such as reports and data files.
|
||||
|
||||
Args:
|
||||
data: The training model result containing the datasets, model, and parameters.
|
||||
|
||||
Returns:
|
||||
The updated result object with paths to the generated artifacts.
|
||||
|
||||
Raises:
|
||||
FileNotFoundError: If the reports directory or header.html file does not exist.
|
||||
ValueError: If run_name is not set.
|
||||
"""
|
||||
# Validate that run_name is set
|
||||
if not data.run_name:
|
||||
error_msg = 'run_name must be set before generating artifacts'
|
||||
self.logger.error(error_msg)
|
||||
raise ValueError(error_msg)
|
||||
|
||||
reference_data, current_data = self._init_artifacts_data(data)
|
||||
base_path = self._get_reports_directory()
|
||||
|
||||
# Validate that reports directory exists
|
||||
if not path.exists(base_path):
|
||||
error_msg = f'Reports directory does not exist: {base_path}'
|
||||
self.logger.error(error_msg)
|
||||
raise FileNotFoundError(error_msg)
|
||||
|
||||
data.run_dir = self._create_run_directory(base_path, data.run_name)
|
||||
header_file_path = path.join(base_path, 'header.html')
|
||||
|
||||
# Validate that header.html exists
|
||||
if not path.exists(header_file_path):
|
||||
error_msg = f'Header file does not exist: {header_file_path}'
|
||||
self.logger.error(error_msg)
|
||||
raise FileNotFoundError(error_msg)
|
||||
|
||||
self._setup_run_directory(data.run_dir, header_file_path)
|
||||
return self._generate_report(reference_data, current_data, data)
|
||||
|
||||
def save_run(self, data: TrainModelResult):
|
||||
"""
|
||||
Logs the details of a machine learning run, including parameters, metrics, models, and artifacts,
|
||||
to the Sientia tracking system.
|
||||
|
||||
Args:
|
||||
data: The training model result containing the datasets, model, parameters,
|
||||
and evaluation metrics.
|
||||
|
||||
Raises:
|
||||
ValueError: If required metrics or artifacts are missing.
|
||||
Exception: If MLflow logging fails for any reason.
|
||||
"""
|
||||
# Validate that required artifacts exist before attempting to log
|
||||
if not data.report_path or not path.exists(data.report_path):
|
||||
error_msg = f'Report file does not exist: {data.report_path}'
|
||||
self.logger.error(error_msg)
|
||||
raise ValueError(error_msg)
|
||||
|
||||
if not data.train_data_path or not path.exists(data.train_data_path):
|
||||
error_msg = f'Training data file does not exist: {data.train_data_path}'
|
||||
self.logger.error(error_msg)
|
||||
raise ValueError(error_msg)
|
||||
|
||||
if not data.test_data_path or not path.exists(data.test_data_path):
|
||||
error_msg = f'Test data file does not exist: {data.test_data_path}'
|
||||
self.logger.error(error_msg)
|
||||
raise ValueError(error_msg)
|
||||
|
||||
# Validate that metrics are present
|
||||
if data.mse_val is None or data.r2_val is None or data.mae_val is None:
|
||||
error_msg = 'One or more metrics (MSE, R2, MAE) are None'
|
||||
self.logger.error(error_msg)
|
||||
raise ValueError(error_msg)
|
||||
|
||||
try:
|
||||
# Prepare parameters
|
||||
train_test_split = f'{data.params.train_size}-{100 - data.params.train_size}'
|
||||
interval_strs = [
|
||||
(str(interval[0]), str(interval[1]))
|
||||
for interval in (data.params.removed_intervals or [])
|
||||
]
|
||||
|
||||
# Set experiment and create run
|
||||
self.model_serving.set_experiment(data.params.experiment_name)
|
||||
self.logger.info(
|
||||
f"Logging run '{data.run_name}' to experiment '{data.params.experiment_name}'"
|
||||
)
|
||||
|
||||
with self.model_serving.save_experiment(
|
||||
run_name=data.run_name, description=data.params.experiment_name
|
||||
):
|
||||
# Log model parameters
|
||||
self.model_serving.log_param('model_type', 'Linear Regression')
|
||||
self.model_serving.log_param('target_variable', data.params.target_variable)
|
||||
self.model_serving.log_param('input_variables', data.params.variable_columns)
|
||||
self.model_serving.log_param('lag_train', data.params.lag_train)
|
||||
self.model_serving.log_param('lag_val', data.params.lag_val)
|
||||
self.model_serving.log_param('ma', data.params.window)
|
||||
self.model_serving.log_param('low_lim', data.params.low_lim)
|
||||
self.model_serving.log_param('upp_lim', data.params.upp_lim)
|
||||
self.model_serving.log_param('normalized', data.scaler_dict)
|
||||
self.model_serving.log_param('ar', data.params.include_ar)
|
||||
self.model_serving.log_param('Train_test_split', train_test_split)
|
||||
self.model_serving.log_param('Removed_intervals', interval_strs)
|
||||
self.model_serving.log_param('Retrain', False)
|
||||
|
||||
# Log evaluation metrics
|
||||
self.model_serving.log_metric('MSE', data.mse_val)
|
||||
self.model_serving.log_metric('R2', data.r2_val)
|
||||
self.model_serving.log_metric('MAE', data.mae_val)
|
||||
|
||||
# Log models
|
||||
self.model_serving.log_model(data.process_data, 'data_model')
|
||||
self.model_serving.log_model(data.regr, 'prediction_model')
|
||||
|
||||
# Log artifacts
|
||||
self.model_serving.log_artifact(data.report_path)
|
||||
self.model_serving.log_artifact(data.train_data_path)
|
||||
self.model_serving.log_artifact(data.test_data_path)
|
||||
|
||||
self.logger.info(
|
||||
f"Successfully logged run '{data.run_name}' with metrics: MSE={data.mse_val:.4f}, R2={data.r2_val:.4f}, MAE={data.mae_val:.4f}"
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
error_msg = f"Failed to save run '{data.run_name}' to MLflow: {str(e)}"
|
||||
self.logger.error(error_msg)
|
||||
raise RuntimeError(error_msg) from e
|
||||
|
||||
def _init_artifacts_data(self, data: TrainModelResult) -> tuple[pd.DataFrame, pd.DataFrame]:
|
||||
"""
|
||||
Prepares the reference and current datasets for artifact generation.
|
||||
|
||||
Args:
|
||||
data: The training model result containing the datasets and model.
|
||||
|
||||
Returns:
|
||||
tuple: A tuple containing:
|
||||
- reference_data: The training dataset with predictions added.
|
||||
- current_data: The testing dataset with predictions added.
|
||||
|
||||
Raises:
|
||||
ValueError: If training or test datasets are empty or invalid.
|
||||
AttributeError: If required attributes are missing from the data object.
|
||||
"""
|
||||
# Validate that required DataFrames are not empty
|
||||
# Note: x_train, y_train, x_test, y_test, and regr are required fields in TrainModelResult
|
||||
# so we only check if they are empty, not None
|
||||
if data.x_train.empty:
|
||||
error_msg = 'Training features (x_train) are empty'
|
||||
self.logger.error(error_msg)
|
||||
raise ValueError(error_msg)
|
||||
|
||||
if data.y_train.empty:
|
||||
error_msg = 'Training target (y_train) is empty'
|
||||
self.logger.error(error_msg)
|
||||
raise ValueError(error_msg)
|
||||
|
||||
if data.x_test.empty:
|
||||
error_msg = 'Test features (x_test) are empty'
|
||||
self.logger.error(error_msg)
|
||||
raise ValueError(error_msg)
|
||||
|
||||
if data.y_test.empty:
|
||||
error_msg = 'Test target (y_test) is empty'
|
||||
self.logger.error(error_msg)
|
||||
raise ValueError(error_msg)
|
||||
|
||||
# Validate that predictions exist (y_pred is optional, so check for None)
|
||||
if data.y_pred is None:
|
||||
error_msg = 'Test predictions (y_pred) are None'
|
||||
self.logger.error(error_msg)
|
||||
raise ValueError(error_msg)
|
||||
|
||||
# Prepare reference data (training set)
|
||||
reference_data = pd.concat([data.x_train, data.y_train], axis=1)
|
||||
reference_data = reference_data.rename(columns={data.params.target_variable: 'target'})
|
||||
reference_data['prediction'] = data.regr.predict(data.x_train)
|
||||
|
||||
# Prepare current data (test set)
|
||||
current_data = pd.concat([data.x_test, data.y_test], axis=1)
|
||||
current_data = current_data.rename(columns={data.params.target_variable: 'target'})
|
||||
current_data['prediction'] = data.y_pred
|
||||
|
||||
return reference_data, current_data
|
||||
|
||||
def _create_run_directory(self, base_path: str, run_name: str) -> str:
|
||||
"""
|
||||
Creates a directory inside the 'reports' folder with the run name and a timestamp.
|
||||
|
||||
Uses microsecond precision in timestamp to minimize collision probability
|
||||
in high-concurrency scenarios.
|
||||
|
||||
Args:
|
||||
base_path (str): The path to the 'reports' folder.
|
||||
run_name (str): The name of the run.
|
||||
|
||||
Returns:
|
||||
str: The path to the created directory.
|
||||
|
||||
Raises:
|
||||
PermissionError: If there are insufficient permissions to create the directory.
|
||||
OSError: If directory creation fails for any other reason.
|
||||
"""
|
||||
# Use microsecond precision to reduce collision probability
|
||||
timestamp = datetime.now().strftime('%Y%m%d_%H%M%S_%f')
|
||||
run_dir = path.join(base_path, f'{run_name}_{timestamp}')
|
||||
|
||||
try:
|
||||
makedirs(run_dir, exist_ok=True)
|
||||
self.logger.info(f'Created run directory: {run_dir}')
|
||||
return run_dir
|
||||
except PermissionError as e:
|
||||
error_msg = f'Permission denied when creating directory: {run_dir}'
|
||||
self.logger.error(error_msg)
|
||||
raise PermissionError(error_msg) from e
|
||||
except OSError as e:
|
||||
error_msg = f'Failed to create directory {run_dir}: {str(e)}'
|
||||
self.logger.error(error_msg)
|
||||
raise OSError(error_msg) from e
|
||||
|
||||
def _setup_run_directory(self, run_dir: str, header_file_path: str):
|
||||
"""
|
||||
Creates empty files and copies a header file into the specified run directory.
|
||||
|
||||
Note: Lock removed as each run has its own unique directory, so no synchronization
|
||||
is needed between different runs. File operations within the same directory are
|
||||
atomic at the OS level.
|
||||
|
||||
Args:
|
||||
run_dir (str): The path to the run directory where the files will be created.
|
||||
header_file_path (str): The path to the header.html file to be copied.
|
||||
|
||||
Raises:
|
||||
FileNotFoundError: If the header file does not exist.
|
||||
PermissionError: If there are insufficient permissions to create files.
|
||||
OSError: If file creation or copying fails for any other reason.
|
||||
"""
|
||||
empty_files = ['data_drift.html', 'data_quality.html', 'regression.html']
|
||||
|
||||
try:
|
||||
# Create empty placeholder files
|
||||
for file_name in empty_files:
|
||||
file_path = path.join(run_dir, file_name)
|
||||
with open(file_path, 'w'):
|
||||
pass # Create empty file
|
||||
|
||||
# Copy header file to run directory
|
||||
header_dest = path.join(run_dir, 'header.html')
|
||||
shutil.copy(header_file_path, header_dest)
|
||||
|
||||
self.logger.info(f'Run directory setup completed successfully in: {run_dir}')
|
||||
|
||||
except FileNotFoundError as e:
|
||||
error_msg = f'Header file not found: {header_file_path}'
|
||||
self.logger.error(error_msg)
|
||||
raise FileNotFoundError(error_msg) from e
|
||||
except PermissionError as e:
|
||||
error_msg = f'Permission denied when setting up directory: {run_dir}'
|
||||
self.logger.error(error_msg)
|
||||
raise PermissionError(error_msg) from e
|
||||
except OSError as e:
|
||||
error_msg = f'Failed to setup run directory {run_dir}: {str(e)}'
|
||||
self.logger.error(error_msg)
|
||||
raise OSError(error_msg) from e
|
||||
|
||||
def _generate_report(
|
||||
self, reference_data: pd.DataFrame, current_data: pd.DataFrame, data: TrainModelResult
|
||||
) -> TrainModelResult:
|
||||
"""
|
||||
Generates a comprehensive report summarizing data quality, data drift, and regression analysis.
|
||||
|
||||
Args:
|
||||
reference_data (pd.DataFrame): The training dataset with predictions added.
|
||||
current_data (pd.DataFrame): The testing dataset with predictions added.
|
||||
data: The training model result containing the datasets, model, and parameters.
|
||||
|
||||
Returns:
|
||||
The updated result object with paths to the generated report and data files.
|
||||
|
||||
Raises:
|
||||
ValueError: If data conversion to float64 fails or DataFrames are invalid.
|
||||
PermissionError: If there are insufficient permissions to write files.
|
||||
OSError: If file writing fails for any other reason.
|
||||
"""
|
||||
try:
|
||||
# Convert data to float64 for report generation
|
||||
# This may raise ValueError if data contains non-numeric values
|
||||
reference_data_float = reference_data.astype(np.float64)
|
||||
current_data_float = current_data.astype(np.float64)
|
||||
|
||||
# Initialize report generator
|
||||
report = Reports(
|
||||
reference_data=reference_data_float,
|
||||
current_data=current_data_float,
|
||||
base_path=data.run_dir,
|
||||
)
|
||||
|
||||
# Generate report sections
|
||||
report.add_data_quality_section(columns=data.params.variable_columns + ['target'])
|
||||
report.add_data_drift_section(columns=data.params.variable_columns + ['target'])
|
||||
report.add_regression_section()
|
||||
|
||||
# Validate that run_dir is set (should be set by _create_run_directory)
|
||||
if not data.run_dir:
|
||||
error_msg = 'run_dir is not set after directory creation'
|
||||
self.logger.error(error_msg)
|
||||
raise ValueError(error_msg)
|
||||
|
||||
# Save HTML report
|
||||
data.report_path = path.join(data.run_dir, 'report.html')
|
||||
report.save_all_sections_html(data.report_path)
|
||||
self.logger.info(f'Generated HTML report: {data.report_path}')
|
||||
|
||||
# Save training data CSV
|
||||
data.train_data_path = path.join(data.run_dir, 'train_data.csv')
|
||||
reference_data.to_csv(data.train_data_path, index=False)
|
||||
self.logger.info(f'Saved training data: {data.train_data_path}')
|
||||
|
||||
# Save test data CSV
|
||||
data.test_data_path = path.join(data.run_dir, 'test_data.csv')
|
||||
current_data.to_csv(data.test_data_path, index=False)
|
||||
self.logger.info(f'Saved test data: {data.test_data_path}')
|
||||
|
||||
return data
|
||||
|
||||
except ValueError as e:
|
||||
error_msg = f'Failed to convert data to float64 for report generation: {str(e)}'
|
||||
self.logger.error(error_msg)
|
||||
raise ValueError(error_msg) from e
|
||||
except PermissionError as e:
|
||||
error_msg = f'Permission denied when writing report files to: {data.run_dir}'
|
||||
self.logger.error(error_msg)
|
||||
raise PermissionError(error_msg) from e
|
||||
except OSError as e:
|
||||
error_msg = f'Failed to generate report in {data.run_dir}: {str(e)}'
|
||||
self.logger.error(error_msg)
|
||||
raise OSError(error_msg) from e
|
||||
|
||||
def _get_reports_directory(self) -> str:
|
||||
"""
|
||||
Get the absolute path to the reports directory.
|
||||
|
||||
Returns:
|
||||
str: Absolute path to model_manager/reports directory.
|
||||
"""
|
||||
# Get the directory where this file is located (model_manager/utils/repository/)
|
||||
current_file_dir = path.dirname(path.abspath(__file__))
|
||||
# Navigate up to model_manager/ and then to reports/
|
||||
model_manager_dir = path.dirname(path.dirname(current_file_dir))
|
||||
reports_dir = path.join(model_manager_dir, 'reports')
|
||||
return reports_dir
|
||||
|
||||
@@ -299,3 +299,251 @@ async def test_update_production_model_error(mlflow):
|
||||
)
|
||||
else:
|
||||
raise AssertionError('No exception raised')
|
||||
|
||||
|
||||
@mark.asyncio
|
||||
async def test_save_model_success(mlflow):
|
||||
"""Test save_model successfully saves model and artifacts to MLflow."""
|
||||
from model_manager.utils.models.train_model_params import TrainModelParams
|
||||
from model_manager.utils.models.train_model_result import TrainModelResult
|
||||
|
||||
# Mock train result
|
||||
params = MagicMock(spec=TrainModelParams)
|
||||
params.experiment_name = 'test_experiment'
|
||||
|
||||
train_result = MagicMock(spec=TrainModelResult)
|
||||
train_result.params = params
|
||||
train_result.run_name = None # Will be set by get_next_run_name
|
||||
|
||||
# Mock repository methods
|
||||
mlflow.model_monitoring_repository.get_next_run_name.return_value = 'test_experiment-1'
|
||||
mlflow.model_monitoring_repository.generate_artifacts.return_value = train_result
|
||||
mlflow.model_monitoring_repository.save_run.return_value = None
|
||||
|
||||
input_data = {
|
||||
**metadata,
|
||||
'train_result': train_result,
|
||||
}
|
||||
|
||||
# Call the method
|
||||
response = await mlflow.save_model(input_data)
|
||||
|
||||
# Verify repository methods were called
|
||||
mlflow.model_monitoring_repository.get_next_run_name.assert_called_once_with('test_experiment')
|
||||
mlflow.model_monitoring_repository.generate_artifacts.assert_called_once_with(train_result)
|
||||
mlflow.model_monitoring_repository.save_run.assert_called_once_with(train_result)
|
||||
|
||||
# Verify response
|
||||
assert response['success'] is True
|
||||
assert response['result'] == train_result
|
||||
assert response['error_message'] is None
|
||||
assert train_result.run_name == 'test_experiment-1'
|
||||
|
||||
|
||||
@mark.asyncio
|
||||
async def test_save_model_get_next_run_name_error(mlflow):
|
||||
"""Test save_model handles error during get_next_run_name."""
|
||||
from model_manager.utils.models.train_model_params import TrainModelParams
|
||||
from model_manager.utils.models.train_model_result import TrainModelResult
|
||||
|
||||
params = MagicMock(spec=TrainModelParams)
|
||||
params.experiment_name = 'test_experiment'
|
||||
|
||||
train_result = MagicMock(spec=TrainModelResult)
|
||||
train_result.params = params
|
||||
|
||||
# Mock error in get_next_run_name
|
||||
mlflow.model_monitoring_repository.get_next_run_name.side_effect = Exception(
|
||||
'MLflow connection error'
|
||||
)
|
||||
|
||||
input_data = {
|
||||
**metadata,
|
||||
'train_result': train_result,
|
||||
}
|
||||
|
||||
# Call the method
|
||||
response = await mlflow.save_model(input_data)
|
||||
|
||||
# Verify error handling
|
||||
assert response['success'] is False
|
||||
assert response['result'] is None
|
||||
assert 'MLflow connection error' in response['error_message']
|
||||
|
||||
# Verify notification was sent
|
||||
mlflow.send_notification.assert_called_once_with(
|
||||
metadata=metadata['metadata'],
|
||||
notification_id='SAVE_MODEL_ERROR',
|
||||
message=ANY,
|
||||
block='save_model',
|
||||
level=NotificationLevel.ERROR,
|
||||
attachment_content=ANY,
|
||||
)
|
||||
|
||||
|
||||
@mark.asyncio
|
||||
async def test_save_model_generate_artifacts_error(mlflow):
|
||||
"""Test save_model handles error during generate_artifacts."""
|
||||
from model_manager.utils.models.train_model_params import TrainModelParams
|
||||
from model_manager.utils.models.train_model_result import TrainModelResult
|
||||
|
||||
params = MagicMock(spec=TrainModelParams)
|
||||
params.experiment_name = 'test_experiment'
|
||||
|
||||
train_result = MagicMock(spec=TrainModelResult)
|
||||
train_result.params = params
|
||||
|
||||
# Mock successful get_next_run_name but error in generate_artifacts
|
||||
mlflow.model_monitoring_repository.get_next_run_name.return_value = 'test_experiment-1'
|
||||
mlflow.model_monitoring_repository.generate_artifacts.side_effect = FileNotFoundError(
|
||||
'Reports directory does not exist'
|
||||
)
|
||||
|
||||
input_data = {
|
||||
**metadata,
|
||||
'train_result': train_result,
|
||||
}
|
||||
|
||||
# Call the method
|
||||
response = await mlflow.save_model(input_data)
|
||||
|
||||
# Verify error handling
|
||||
assert response['success'] is False
|
||||
assert response['result'] is None
|
||||
assert 'Reports directory does not exist' in response['error_message']
|
||||
|
||||
# Verify notification was sent
|
||||
mlflow.send_notification.assert_called_once_with(
|
||||
metadata=metadata['metadata'],
|
||||
notification_id='SAVE_MODEL_ERROR',
|
||||
message=ANY,
|
||||
block='save_model',
|
||||
level=NotificationLevel.ERROR,
|
||||
attachment_content=ANY,
|
||||
)
|
||||
|
||||
|
||||
@mark.asyncio
|
||||
async def test_save_model_save_run_error(mlflow):
|
||||
"""Test save_model handles error during save_run."""
|
||||
from model_manager.utils.models.train_model_params import TrainModelParams
|
||||
from model_manager.utils.models.train_model_result import TrainModelResult
|
||||
|
||||
params = MagicMock(spec=TrainModelParams)
|
||||
params.experiment_name = 'test_experiment'
|
||||
|
||||
train_result = MagicMock(spec=TrainModelResult)
|
||||
train_result.params = params
|
||||
|
||||
# Mock successful get_next_run_name and generate_artifacts but error in save_run
|
||||
mlflow.model_monitoring_repository.get_next_run_name.return_value = 'test_experiment-1'
|
||||
mlflow.model_monitoring_repository.generate_artifacts.return_value = train_result
|
||||
mlflow.model_monitoring_repository.save_run.side_effect = ValueError(
|
||||
'One or more metrics (MSE, R2, MAE) are None'
|
||||
)
|
||||
|
||||
input_data = {
|
||||
**metadata,
|
||||
'train_result': train_result,
|
||||
}
|
||||
|
||||
# Call the method
|
||||
response = await mlflow.save_model(input_data)
|
||||
|
||||
# Verify error handling
|
||||
assert response['success'] is False
|
||||
assert response['result'] is None
|
||||
assert 'One or more metrics (MSE, R2, MAE) are None' in response['error_message']
|
||||
|
||||
# Verify notification was sent
|
||||
mlflow.send_notification.assert_called_once_with(
|
||||
metadata=metadata['metadata'],
|
||||
notification_id='SAVE_MODEL_ERROR',
|
||||
message=ANY,
|
||||
block='save_model',
|
||||
level=NotificationLevel.ERROR,
|
||||
attachment_content=ANY,
|
||||
)
|
||||
|
||||
|
||||
@mark.asyncio
|
||||
async def test_save_model_missing_metadata(mlflow):
|
||||
"""Test save_model handles missing metadata gracefully."""
|
||||
from model_manager.utils.models.train_model_params import TrainModelParams
|
||||
from model_manager.utils.models.train_model_result import TrainModelResult
|
||||
|
||||
params = MagicMock(spec=TrainModelParams)
|
||||
params.experiment_name = 'test_experiment'
|
||||
|
||||
train_result = MagicMock(spec=TrainModelResult)
|
||||
train_result.params = params
|
||||
|
||||
# Mock repository methods
|
||||
mlflow.model_monitoring_repository.get_next_run_name.return_value = 'test_experiment-1'
|
||||
mlflow.model_monitoring_repository.generate_artifacts.return_value = train_result
|
||||
mlflow.model_monitoring_repository.save_run.return_value = None
|
||||
|
||||
# Input data without metadata
|
||||
input_data = {
|
||||
'train_result': train_result,
|
||||
}
|
||||
|
||||
# Call the method
|
||||
response = await mlflow.save_model(input_data)
|
||||
|
||||
# Verify it still works (metadata defaults to {})
|
||||
assert response['success'] is True
|
||||
assert response['result'] == train_result
|
||||
assert response['error_message'] is None
|
||||
|
||||
|
||||
@mark.asyncio
|
||||
async def test_save_model_complete_flow(mlflow):
|
||||
"""Test save_model complete flow with all steps."""
|
||||
from model_manager.utils.models.train_model_params import TrainModelParams
|
||||
from model_manager.utils.models.train_model_result import TrainModelResult
|
||||
|
||||
params = MagicMock(spec=TrainModelParams)
|
||||
params.experiment_name = 'production_model'
|
||||
|
||||
train_result = MagicMock(spec=TrainModelResult)
|
||||
train_result.params = params
|
||||
train_result.run_name = None
|
||||
train_result.run_dir = None
|
||||
train_result.report_path = None
|
||||
|
||||
# Mock complete flow
|
||||
mlflow.model_monitoring_repository.get_next_run_name.return_value = 'production_model-5'
|
||||
|
||||
# After generate_artifacts, paths should be set
|
||||
updated_result = MagicMock(spec=TrainModelResult)
|
||||
updated_result.params = params
|
||||
updated_result.run_name = 'production_model-5'
|
||||
updated_result.run_dir = '/reports/production_model-5_20231010'
|
||||
updated_result.report_path = '/reports/production_model-5_20231010/report.html'
|
||||
updated_result.train_data_path = '/reports/production_model-5_20231010/train_data.csv'
|
||||
updated_result.test_data_path = '/reports/production_model-5_20231010/test_data.csv'
|
||||
|
||||
mlflow.model_monitoring_repository.generate_artifacts.return_value = updated_result
|
||||
mlflow.model_monitoring_repository.save_run.return_value = None
|
||||
|
||||
input_data = {
|
||||
**metadata,
|
||||
'train_result': train_result,
|
||||
}
|
||||
|
||||
# Call the method
|
||||
response = await mlflow.save_model(input_data)
|
||||
|
||||
# Verify complete flow
|
||||
mlflow.model_monitoring_repository.get_next_run_name.assert_called_once_with('production_model')
|
||||
mlflow.model_monitoring_repository.generate_artifacts.assert_called_once()
|
||||
mlflow.model_monitoring_repository.save_run.assert_called_once_with(updated_result)
|
||||
|
||||
# Verify response
|
||||
assert response['success'] is True
|
||||
assert response['result'] == updated_result
|
||||
assert response['error_message'] is None
|
||||
assert updated_result.run_name == 'production_model-5'
|
||||
assert updated_result.run_dir is not None
|
||||
assert updated_result.report_path is not None
|
||||
|
||||
@@ -61,7 +61,6 @@ async def test_train_model_success(mock_training_repository_class):
|
||||
'upp_lim': {'feature1': 100.0, 'feature2': 100.0},
|
||||
'window': 10,
|
||||
'experiment_name': 'test_experiment',
|
||||
'experiment_description': 'Test experiment',
|
||||
'removed_intervals': [],
|
||||
}
|
||||
|
||||
@@ -118,7 +117,6 @@ async def test_train_model_invalid_file_type(mock_training_repository_class):
|
||||
'upp_lim': {'feature1': 100.0},
|
||||
'window': 10,
|
||||
'experiment_name': 'test_experiment',
|
||||
'experiment_description': 'Test experiment',
|
||||
'removed_intervals': [],
|
||||
},
|
||||
}
|
||||
@@ -169,7 +167,6 @@ async def test_train_model_training_error(mock_training_repository_class):
|
||||
'upp_lim': {'feature1': 100.0},
|
||||
'window': 10,
|
||||
'experiment_name': 'test_experiment',
|
||||
'experiment_description': 'Test experiment',
|
||||
'removed_intervals': [],
|
||||
},
|
||||
}
|
||||
@@ -219,7 +216,6 @@ async def test_train_model_sends_notification_on_error(mock_training_repository_
|
||||
'upp_lim': {'feature1': 100.0},
|
||||
'window': 10,
|
||||
'experiment_name': 'test_experiment',
|
||||
'experiment_description': 'Test experiment',
|
||||
'removed_intervals': [],
|
||||
},
|
||||
}
|
||||
@@ -274,7 +270,6 @@ async def test_train_model_after_calculation_error(mock_training_repository_clas
|
||||
'upp_lim': {'feature1': 100.0},
|
||||
'window': 10,
|
||||
'experiment_name': 'test_experiment',
|
||||
'experiment_description': 'Test experiment',
|
||||
'removed_intervals': [],
|
||||
},
|
||||
}
|
||||
|
||||
@@ -26,8 +26,7 @@ def valid_params_dict():
|
||||
'train_size': 80,
|
||||
'shuffle': True,
|
||||
'experiment_run_id': 123,
|
||||
'experiment_name': 'test-experiment',
|
||||
'experiment_description': 'Test experiment description',
|
||||
'experiment_name': 'Test experiment name',
|
||||
'removed_intervals': [],
|
||||
}
|
||||
|
||||
@@ -53,8 +52,7 @@ def test_train_model_params_creation_with_valid_params(valid_params_dict):
|
||||
assert params.train_size == 80
|
||||
assert params.shuffle is True
|
||||
assert params.experiment_run_id == 123
|
||||
assert params.experiment_name == 'test-experiment'
|
||||
assert params.experiment_description == 'Test experiment description'
|
||||
assert params.experiment_name == 'Test experiment name'
|
||||
assert params.removed_intervals == []
|
||||
|
||||
|
||||
@@ -201,13 +199,13 @@ def test_train_model_params_removed_intervals_with_values(valid_params_dict):
|
||||
|
||||
|
||||
def test_train_model_params_all_fields_count():
|
||||
"""Test that TrainModelParams has exactly 20 required fields."""
|
||||
"""Test that TrainModelParams has exactly 19 required fields."""
|
||||
import inspect
|
||||
|
||||
sig = inspect.signature(TrainModelParams.__init__)
|
||||
# Subtract 1 for 'self'
|
||||
param_count = len(sig.parameters) - 1
|
||||
assert param_count == 20
|
||||
assert param_count == 19
|
||||
|
||||
|
||||
def test_train_model_params_with_minimal_valid_data():
|
||||
@@ -230,8 +228,7 @@ def test_train_model_params_with_minimal_valid_data():
|
||||
train_size=50,
|
||||
shuffle=False,
|
||||
experiment_run_id=1,
|
||||
experiment_name='exp',
|
||||
experiment_description='desc',
|
||||
experiment_name='name',
|
||||
removed_intervals=[],
|
||||
)
|
||||
|
||||
@@ -261,7 +258,6 @@ def test_train_model_params_check_none_method():
|
||||
'shuffle': True,
|
||||
'experiment_run_id': 123,
|
||||
'experiment_name': 'exp',
|
||||
'experiment_description': 'desc',
|
||||
'removed_intervals': [],
|
||||
}
|
||||
|
||||
@@ -293,7 +289,6 @@ def test_train_model_params_check_type_method():
|
||||
'shuffle': True,
|
||||
'experiment_run_id': 123,
|
||||
'experiment_name': 'exp',
|
||||
'experiment_description': 'desc',
|
||||
'removed_intervals': [],
|
||||
}
|
||||
|
||||
|
||||
@@ -31,7 +31,6 @@ def sample_params():
|
||||
shuffle=True,
|
||||
experiment_run_id=123,
|
||||
experiment_name='test-experiment',
|
||||
experiment_description='Test experiment description',
|
||||
removed_intervals=[],
|
||||
)
|
||||
|
||||
|
||||
@@ -497,3 +497,680 @@ def test_update_production_model(mlflow_repository):
|
||||
'mlflow_run_id': '0',
|
||||
'mlflow_experiment_id': '0',
|
||||
}
|
||||
|
||||
|
||||
# ========== Tests for Model Artifact Generation Methods ==========
|
||||
|
||||
|
||||
def test_get_next_run_name_new(mlflow_repository):
|
||||
"""Test get_next_run_name generates correct run name based on existing runs."""
|
||||
mlflow_repository.model_serving.search_runs_by_name.return_value = [
|
||||
MagicMock(),
|
||||
MagicMock(),
|
||||
MagicMock(),
|
||||
]
|
||||
|
||||
result = mlflow_repository.get_next_run_name_new('test_experiment')
|
||||
|
||||
mlflow_repository.model_serving.search_runs_by_name.assert_called_once_with(
|
||||
experiment_names=['test_experiment'], order_by=['start_time desc']
|
||||
)
|
||||
assert result == 'test_experiment-4'
|
||||
|
||||
|
||||
def test_get_next_run_name_new_first_run(mlflow_repository):
|
||||
"""Test get_next_run_name for first run (no existing runs)."""
|
||||
mlflow_repository.model_serving.search_runs_by_name.return_value = []
|
||||
|
||||
result = mlflow_repository.get_next_run_name_new('new_experiment')
|
||||
|
||||
assert result == 'new_experiment-1'
|
||||
|
||||
|
||||
@patch('model_manager.utils.repository.model_repository.path')
|
||||
def test_generate_artifacts_success(mock_path, mlflow_repository):
|
||||
"""Test generate_artifacts successfully creates all artifacts."""
|
||||
from model_manager.utils.models.train_model_params import TrainModelParams
|
||||
from model_manager.utils.models.train_model_result import TrainModelResult
|
||||
|
||||
# Mock data
|
||||
params = MagicMock(spec=TrainModelParams)
|
||||
params.target_variable = 'target'
|
||||
params.variable_columns = ['feat1', 'feat2']
|
||||
params.experiment_name = 'test_exp'
|
||||
|
||||
data = MagicMock(spec=TrainModelResult)
|
||||
data.run_name = 'test_run-1'
|
||||
data.params = params
|
||||
data.x_train = DataFrame({'feat1': [1, 2], 'feat2': [3, 4]})
|
||||
data.y_train = DataFrame({'target': [5, 6]})
|
||||
data.x_test = DataFrame({'feat1': [7, 8], 'feat2': [9, 10]})
|
||||
data.y_test = DataFrame({'target': [11, 12]})
|
||||
data.regr = MagicMock()
|
||||
data.regr.predict = MagicMock(return_value=np.array([5.1, 6.1]))
|
||||
data.y_pred = np.array([11.1, 12.1])
|
||||
|
||||
# Mock path operations
|
||||
mock_path.exists.return_value = True
|
||||
mock_path.join.side_effect = lambda *args: '/'.join(args)
|
||||
|
||||
# Mock private methods
|
||||
mlflow_repository._get_reports_directory = MagicMock(return_value='/reports')
|
||||
mlflow_repository._create_run_directory = MagicMock(return_value='/reports/test_run-1_20231010')
|
||||
mlflow_repository._setup_run_directory = MagicMock()
|
||||
mlflow_repository._generate_report = MagicMock(return_value=data)
|
||||
|
||||
result = mlflow_repository.generate_artifacts(data)
|
||||
|
||||
# Assertions
|
||||
mlflow_repository._get_reports_directory.assert_called_once()
|
||||
mlflow_repository._create_run_directory.assert_called_once_with('/reports', 'test_run-1')
|
||||
mlflow_repository._setup_run_directory.assert_called_once()
|
||||
mlflow_repository._generate_report.assert_called_once()
|
||||
assert result == data
|
||||
|
||||
|
||||
def test_generate_artifacts_missing_run_name(mlflow_repository):
|
||||
"""Test generate_artifacts raises ValueError when run_name is not set."""
|
||||
from model_manager.utils.models.train_model_result import TrainModelResult
|
||||
|
||||
data = MagicMock(spec=TrainModelResult)
|
||||
data.run_name = None
|
||||
|
||||
with pytest.raises(ValueError) as exc_info:
|
||||
mlflow_repository.generate_artifacts(data)
|
||||
|
||||
assert 'run_name must be set before generating artifacts' in str(exc_info.value)
|
||||
|
||||
|
||||
@patch('model_manager.utils.repository.model_repository.path')
|
||||
def test_generate_artifacts_reports_directory_not_exists(mock_path, mlflow_repository):
|
||||
"""Test generate_artifacts raises FileNotFoundError when reports directory doesn't exist."""
|
||||
from model_manager.utils.models.train_model_params import TrainModelParams
|
||||
from model_manager.utils.models.train_model_result import TrainModelResult
|
||||
|
||||
params = MagicMock(spec=TrainModelParams)
|
||||
params.target_variable = 'target'
|
||||
|
||||
data = MagicMock(spec=TrainModelResult)
|
||||
data.run_name = 'test_run-1'
|
||||
data.params = params
|
||||
data.x_train = DataFrame({'feat1': [1]})
|
||||
data.y_train = DataFrame({'target': [2]})
|
||||
data.x_test = DataFrame({'feat1': [3]})
|
||||
data.y_test = DataFrame({'target': [4]})
|
||||
data.regr = MagicMock()
|
||||
data.y_pred = np.array([4.1])
|
||||
|
||||
mlflow_repository._get_reports_directory = MagicMock(return_value='/reports')
|
||||
mock_path.exists.return_value = False
|
||||
|
||||
with pytest.raises(FileNotFoundError) as exc_info:
|
||||
mlflow_repository.generate_artifacts(data)
|
||||
|
||||
assert 'Reports directory does not exist' in str(exc_info.value)
|
||||
|
||||
|
||||
@patch('model_manager.utils.repository.model_repository.path')
|
||||
def test_generate_artifacts_header_file_not_exists(mock_path, mlflow_repository):
|
||||
"""Test generate_artifacts raises FileNotFoundError when header.html doesn't exist."""
|
||||
from model_manager.utils.models.train_model_params import TrainModelParams
|
||||
from model_manager.utils.models.train_model_result import TrainModelResult
|
||||
|
||||
params = MagicMock(spec=TrainModelParams)
|
||||
params.target_variable = 'target'
|
||||
|
||||
data = MagicMock(spec=TrainModelResult)
|
||||
data.run_name = 'test_run-1'
|
||||
data.params = params
|
||||
data.x_train = DataFrame({'feat1': [1]})
|
||||
data.y_train = DataFrame({'target': [2]})
|
||||
data.x_test = DataFrame({'feat1': [3]})
|
||||
data.y_test = DataFrame({'target': [4]})
|
||||
data.regr = MagicMock()
|
||||
data.regr.predict = MagicMock(return_value=np.array([2.1]))
|
||||
data.y_pred = np.array([4.1])
|
||||
|
||||
mlflow_repository._get_reports_directory = MagicMock(return_value='/reports')
|
||||
mlflow_repository._create_run_directory = MagicMock(return_value='/reports/test_run-1_20231010')
|
||||
|
||||
# First call returns True (reports dir exists), second returns False (header.html doesn't exist)
|
||||
mock_path.exists.side_effect = [True, False]
|
||||
mock_path.join.side_effect = lambda *args: '/'.join(args)
|
||||
|
||||
with pytest.raises(FileNotFoundError) as exc_info:
|
||||
mlflow_repository.generate_artifacts(data)
|
||||
|
||||
assert 'Header file does not exist' in str(exc_info.value)
|
||||
|
||||
|
||||
@patch('model_manager.utils.repository.model_repository.path')
|
||||
def test_save_run_success(mock_path, mlflow_repository):
|
||||
"""Test save_run successfully logs all parameters, metrics, models, and artifacts."""
|
||||
from model_manager.utils.models.train_model_params import TrainModelParams
|
||||
from model_manager.utils.models.train_model_result import TrainModelResult
|
||||
|
||||
params = MagicMock(spec=TrainModelParams)
|
||||
params.train_size = 80
|
||||
params.removed_intervals = [(1, 10), (20, 30)]
|
||||
params.experiment_name = 'test_exp'
|
||||
params.target_variable = 'target'
|
||||
params.variable_columns = ['feat1', 'feat2']
|
||||
params.lag_train = 5
|
||||
params.lag_val = 3
|
||||
params.window = 10
|
||||
params.low_lim = 0.0
|
||||
params.upp_lim = 1.0
|
||||
params.include_ar = True
|
||||
|
||||
data = MagicMock(spec=TrainModelResult)
|
||||
data.run_name = 'test_run-1'
|
||||
data.params = params
|
||||
data.report_path = '/reports/report.html'
|
||||
data.train_data_path = '/reports/train.csv'
|
||||
data.test_data_path = '/reports/test.csv'
|
||||
data.mse_val = 0.123
|
||||
data.r2_val = 0.987
|
||||
data.mae_val = 0.456
|
||||
data.scaler_dict = {'scaler': 'minmax'}
|
||||
data.process_data = MagicMock()
|
||||
data.regr = MagicMock()
|
||||
|
||||
mock_path.exists.return_value = True
|
||||
|
||||
mlflow_repository.save_run(data)
|
||||
|
||||
# Verify experiment was set
|
||||
mlflow_repository.model_serving.set_experiment.assert_called_once_with('test_exp')
|
||||
|
||||
# Verify parameters were logged
|
||||
assert mlflow_repository.model_serving.log_param.call_count == 13
|
||||
|
||||
# Verify metrics were logged
|
||||
mlflow_repository.model_serving.log_metric.assert_any_call('MSE', 0.123)
|
||||
mlflow_repository.model_serving.log_metric.assert_any_call('R2', 0.987)
|
||||
mlflow_repository.model_serving.log_metric.assert_any_call('MAE', 0.456)
|
||||
|
||||
# Verify models were logged
|
||||
mlflow_repository.model_serving.log_model.assert_any_call(data.process_data, 'data_model')
|
||||
mlflow_repository.model_serving.log_model.assert_any_call(data.regr, 'prediction_model')
|
||||
|
||||
# Verify artifacts were logged
|
||||
mlflow_repository.model_serving.log_artifact.assert_any_call('/reports/report.html')
|
||||
mlflow_repository.model_serving.log_artifact.assert_any_call('/reports/train.csv')
|
||||
mlflow_repository.model_serving.log_artifact.assert_any_call('/reports/test.csv')
|
||||
|
||||
|
||||
@patch('model_manager.utils.repository.model_repository.path')
|
||||
def test_save_run_missing_report_path(mock_path, mlflow_repository):
|
||||
"""Test save_run raises ValueError when report_path is missing."""
|
||||
from model_manager.utils.models.train_model_result import TrainModelResult
|
||||
|
||||
data = MagicMock(spec=TrainModelResult)
|
||||
data.report_path = None
|
||||
|
||||
with pytest.raises(ValueError) as exc_info:
|
||||
mlflow_repository.save_run(data)
|
||||
|
||||
assert 'Report file does not exist' in str(exc_info.value)
|
||||
|
||||
|
||||
@patch('model_manager.utils.repository.model_repository.path')
|
||||
def test_save_run_missing_metrics(mock_path, mlflow_repository):
|
||||
"""Test save_run raises ValueError when metrics are None."""
|
||||
from model_manager.utils.models.train_model_result import TrainModelResult
|
||||
|
||||
data = MagicMock(spec=TrainModelResult)
|
||||
data.report_path = '/reports/report.html'
|
||||
data.train_data_path = '/reports/train.csv'
|
||||
data.test_data_path = '/reports/test.csv'
|
||||
data.mse_val = None
|
||||
data.r2_val = 0.987
|
||||
data.mae_val = 0.456
|
||||
|
||||
mock_path.exists.return_value = True
|
||||
|
||||
with pytest.raises(ValueError) as exc_info:
|
||||
mlflow_repository.save_run(data)
|
||||
|
||||
assert 'One or more metrics (MSE, R2, MAE) are None' in str(exc_info.value)
|
||||
|
||||
|
||||
@patch('model_manager.utils.repository.model_repository.path')
|
||||
def test_save_run_mlflow_error(mock_path, mlflow_repository):
|
||||
"""Test save_run handles MLflow errors gracefully."""
|
||||
from model_manager.utils.models.train_model_params import TrainModelParams
|
||||
from model_manager.utils.models.train_model_result import TrainModelResult
|
||||
|
||||
params = MagicMock(spec=TrainModelParams)
|
||||
params.train_size = 80
|
||||
params.removed_intervals = []
|
||||
params.experiment_name = 'test_exp'
|
||||
|
||||
data = MagicMock(spec=TrainModelResult)
|
||||
data.run_name = 'test_run-1'
|
||||
data.params = params
|
||||
data.report_path = '/reports/report.html'
|
||||
data.train_data_path = '/reports/train.csv'
|
||||
data.test_data_path = '/reports/test.csv'
|
||||
data.mse_val = 0.123
|
||||
data.r2_val = 0.987
|
||||
data.mae_val = 0.456
|
||||
|
||||
mock_path.exists.return_value = True
|
||||
mlflow_repository.model_serving.set_experiment.side_effect = Exception(
|
||||
'MLflow connection error'
|
||||
)
|
||||
|
||||
with pytest.raises(RuntimeError) as exc_info:
|
||||
mlflow_repository.save_run(data)
|
||||
|
||||
assert 'Failed to save run' in str(exc_info.value)
|
||||
assert 'MLflow connection error' in str(exc_info.value)
|
||||
|
||||
|
||||
# ========== Additional Tests for 100% Coverage ==========
|
||||
|
||||
|
||||
def test_init_artifacts_data_empty_x_train(mlflow_repository):
|
||||
"""Test _init_artifacts_data raises ValueError when x_train is empty."""
|
||||
from model_manager.utils.models.train_model_params import TrainModelParams
|
||||
from model_manager.utils.models.train_model_result import TrainModelResult
|
||||
|
||||
params = MagicMock(spec=TrainModelParams)
|
||||
data = MagicMock(spec=TrainModelResult)
|
||||
data.params = params
|
||||
data.x_train = DataFrame() # Empty DataFrame
|
||||
data.y_train = DataFrame({'target': [1]})
|
||||
data.x_test = DataFrame({'feat1': [1]})
|
||||
data.y_test = DataFrame({'target': [1]})
|
||||
|
||||
with pytest.raises(ValueError) as exc_info:
|
||||
mlflow_repository._init_artifacts_data(data)
|
||||
|
||||
assert 'Training features (x_train) are empty' in str(exc_info.value)
|
||||
|
||||
|
||||
def test_init_artifacts_data_empty_y_train(mlflow_repository):
|
||||
"""Test _init_artifacts_data raises ValueError when y_train is empty."""
|
||||
from model_manager.utils.models.train_model_params import TrainModelParams
|
||||
from model_manager.utils.models.train_model_result import TrainModelResult
|
||||
|
||||
params = MagicMock(spec=TrainModelParams)
|
||||
data = MagicMock(spec=TrainModelResult)
|
||||
data.params = params
|
||||
data.x_train = DataFrame({'feat1': [1]})
|
||||
data.y_train = DataFrame() # Empty DataFrame
|
||||
data.x_test = DataFrame({'feat1': [1]})
|
||||
data.y_test = DataFrame({'target': [1]})
|
||||
|
||||
with pytest.raises(ValueError) as exc_info:
|
||||
mlflow_repository._init_artifacts_data(data)
|
||||
|
||||
assert 'Training target (y_train) is empty' in str(exc_info.value)
|
||||
|
||||
|
||||
def test_init_artifacts_data_empty_x_test(mlflow_repository):
|
||||
"""Test _init_artifacts_data raises ValueError when x_test is empty."""
|
||||
from model_manager.utils.models.train_model_params import TrainModelParams
|
||||
from model_manager.utils.models.train_model_result import TrainModelResult
|
||||
|
||||
params = MagicMock(spec=TrainModelParams)
|
||||
data = MagicMock(spec=TrainModelResult)
|
||||
data.params = params
|
||||
data.x_train = DataFrame({'feat1': [1]})
|
||||
data.y_train = DataFrame({'target': [1]})
|
||||
data.x_test = DataFrame() # Empty DataFrame
|
||||
data.y_test = DataFrame({'target': [1]})
|
||||
|
||||
with pytest.raises(ValueError) as exc_info:
|
||||
mlflow_repository._init_artifacts_data(data)
|
||||
|
||||
assert 'Test features (x_test) are empty' in str(exc_info.value)
|
||||
|
||||
|
||||
def test_init_artifacts_data_empty_y_test(mlflow_repository):
|
||||
"""Test _init_artifacts_data raises ValueError when y_test is empty."""
|
||||
from model_manager.utils.models.train_model_params import TrainModelParams
|
||||
from model_manager.utils.models.train_model_result import TrainModelResult
|
||||
|
||||
params = MagicMock(spec=TrainModelParams)
|
||||
data = MagicMock(spec=TrainModelResult)
|
||||
data.params = params
|
||||
data.x_train = DataFrame({'feat1': [1]})
|
||||
data.y_train = DataFrame({'target': [1]})
|
||||
data.x_test = DataFrame({'feat1': [1]})
|
||||
data.y_test = DataFrame() # Empty DataFrame
|
||||
|
||||
with pytest.raises(ValueError) as exc_info:
|
||||
mlflow_repository._init_artifacts_data(data)
|
||||
|
||||
assert 'Test target (y_test) is empty' in str(exc_info.value)
|
||||
|
||||
|
||||
def test_init_artifacts_data_none_y_pred(mlflow_repository):
|
||||
"""Test _init_artifacts_data raises ValueError when y_pred is None."""
|
||||
from model_manager.utils.models.train_model_params import TrainModelParams
|
||||
from model_manager.utils.models.train_model_result import TrainModelResult
|
||||
|
||||
params = MagicMock(spec=TrainModelParams)
|
||||
data = MagicMock(spec=TrainModelResult)
|
||||
data.params = params
|
||||
data.x_train = DataFrame({'feat1': [1]})
|
||||
data.y_train = DataFrame({'target': [1]})
|
||||
data.x_test = DataFrame({'feat1': [1]})
|
||||
data.y_test = DataFrame({'target': [1]})
|
||||
data.y_pred = None
|
||||
|
||||
with pytest.raises(ValueError) as exc_info:
|
||||
mlflow_repository._init_artifacts_data(data)
|
||||
|
||||
assert 'Test predictions (y_pred) are None' in str(exc_info.value)
|
||||
|
||||
|
||||
def test_init_artifacts_data_success(mlflow_repository):
|
||||
"""Test _init_artifacts_data successfully prepares data."""
|
||||
from model_manager.utils.models.train_model_params import TrainModelParams
|
||||
from model_manager.utils.models.train_model_result import TrainModelResult
|
||||
|
||||
params = MagicMock(spec=TrainModelParams)
|
||||
params.target_variable = 'target'
|
||||
|
||||
data = MagicMock(spec=TrainModelResult)
|
||||
data.params = params
|
||||
data.x_train = DataFrame({'feat1': [1, 2]})
|
||||
data.y_train = DataFrame({'target': [3, 4]})
|
||||
data.x_test = DataFrame({'feat1': [5, 6]})
|
||||
data.y_test = DataFrame({'target': [7, 8]})
|
||||
data.regr = MagicMock()
|
||||
data.regr.predict = MagicMock(return_value=np.array([3.1, 4.1]))
|
||||
data.y_pred = np.array([7.1, 8.1])
|
||||
|
||||
reference_data, current_data = mlflow_repository._init_artifacts_data(data)
|
||||
|
||||
assert 'target' in reference_data.columns
|
||||
assert 'prediction' in reference_data.columns
|
||||
assert 'target' in current_data.columns
|
||||
assert 'prediction' in current_data.columns
|
||||
assert len(reference_data) == 2
|
||||
assert len(current_data) == 2
|
||||
|
||||
|
||||
@patch('model_manager.utils.repository.model_repository.makedirs')
|
||||
@patch('model_manager.utils.repository.model_repository.path')
|
||||
def test_create_run_directory_success(mock_path, mock_makedirs, mlflow_repository):
|
||||
"""Test _create_run_directory successfully creates directory."""
|
||||
mock_path.join.return_value = '/reports/test_run_20231010_123456_123456'
|
||||
|
||||
result = mlflow_repository._create_run_directory('/reports', 'test_run')
|
||||
|
||||
mock_makedirs.assert_called_once_with('/reports/test_run_20231010_123456_123456', exist_ok=True)
|
||||
assert result == '/reports/test_run_20231010_123456_123456'
|
||||
|
||||
|
||||
@patch('model_manager.utils.repository.model_repository.makedirs')
|
||||
@patch('model_manager.utils.repository.model_repository.path')
|
||||
def test_create_run_directory_permission_error(mock_path, mock_makedirs, mlflow_repository):
|
||||
"""Test _create_run_directory handles PermissionError."""
|
||||
mock_path.join.return_value = '/reports/test_run_20231010'
|
||||
mock_makedirs.side_effect = PermissionError('Permission denied')
|
||||
|
||||
with pytest.raises(PermissionError) as exc_info:
|
||||
mlflow_repository._create_run_directory('/reports', 'test_run')
|
||||
|
||||
assert 'Permission denied when creating directory' in str(exc_info.value)
|
||||
|
||||
|
||||
@patch('model_manager.utils.repository.model_repository.makedirs')
|
||||
@patch('model_manager.utils.repository.model_repository.path')
|
||||
def test_create_run_directory_os_error(mock_path, mock_makedirs, mlflow_repository):
|
||||
"""Test _create_run_directory handles OSError."""
|
||||
mock_path.join.return_value = '/reports/test_run_20231010'
|
||||
mock_makedirs.side_effect = OSError('Disk full')
|
||||
|
||||
with pytest.raises(OSError) as exc_info:
|
||||
mlflow_repository._create_run_directory('/reports', 'test_run')
|
||||
|
||||
assert 'Failed to create directory' in str(exc_info.value)
|
||||
|
||||
|
||||
@patch('model_manager.utils.repository.model_repository.shutil')
|
||||
@patch('model_manager.utils.repository.model_repository.path')
|
||||
def test_setup_run_directory_success(mock_path, mock_shutil, mlflow_repository):
|
||||
"""Test _setup_run_directory successfully sets up directory."""
|
||||
mock_path.join.side_effect = lambda *args: '/'.join(args)
|
||||
mock_open = MagicMock()
|
||||
|
||||
with patch('builtins.open', mock_open):
|
||||
mlflow_repository._setup_run_directory('/run_dir', '/reports/header.html')
|
||||
|
||||
assert mock_open.call_count == 3 # 3 empty files
|
||||
mock_shutil.copy.assert_called_once_with('/reports/header.html', '/run_dir/header.html')
|
||||
|
||||
|
||||
@patch('model_manager.utils.repository.model_repository.shutil')
|
||||
@patch('model_manager.utils.repository.model_repository.path')
|
||||
def test_setup_run_directory_file_not_found(mock_path, mock_shutil, mlflow_repository):
|
||||
"""Test _setup_run_directory handles FileNotFoundError."""
|
||||
mock_path.join.side_effect = lambda *args: '/'.join(args)
|
||||
mock_shutil.copy.side_effect = FileNotFoundError('Header not found')
|
||||
|
||||
mock_open = MagicMock()
|
||||
with patch('builtins.open', mock_open):
|
||||
with pytest.raises(FileNotFoundError) as exc_info:
|
||||
mlflow_repository._setup_run_directory('/run_dir', '/reports/header.html')
|
||||
|
||||
assert 'Header file not found' in str(exc_info.value)
|
||||
|
||||
|
||||
@patch('model_manager.utils.repository.model_repository.shutil')
|
||||
@patch('model_manager.utils.repository.model_repository.path')
|
||||
def test_setup_run_directory_permission_error(mock_path, mock_shutil, mlflow_repository):
|
||||
"""Test _setup_run_directory handles PermissionError."""
|
||||
mock_path.join.side_effect = lambda *args: '/'.join(args)
|
||||
|
||||
mock_open = MagicMock()
|
||||
mock_open.side_effect = PermissionError('Permission denied')
|
||||
|
||||
with patch('builtins.open', mock_open):
|
||||
with pytest.raises(PermissionError) as exc_info:
|
||||
mlflow_repository._setup_run_directory('/run_dir', '/reports/header.html')
|
||||
|
||||
assert 'Permission denied when setting up directory' in str(exc_info.value)
|
||||
|
||||
|
||||
@patch('model_manager.utils.repository.model_repository.shutil')
|
||||
@patch('model_manager.utils.repository.model_repository.path')
|
||||
def test_setup_run_directory_os_error(mock_path, mock_shutil, mlflow_repository):
|
||||
"""Test _setup_run_directory handles OSError."""
|
||||
mock_path.join.side_effect = lambda *args: '/'.join(args)
|
||||
|
||||
mock_open = MagicMock()
|
||||
mock_open.side_effect = OSError('Disk error')
|
||||
|
||||
with patch('builtins.open', mock_open):
|
||||
with pytest.raises(OSError) as exc_info:
|
||||
mlflow_repository._setup_run_directory('/run_dir', '/reports/header.html')
|
||||
|
||||
assert 'Failed to setup run directory' in str(exc_info.value)
|
||||
|
||||
|
||||
@patch('model_manager.utils.repository.model_repository.Reports')
|
||||
@patch('model_manager.utils.repository.model_repository.path')
|
||||
def test_generate_report_success(mock_path, mock_reports, mlflow_repository):
|
||||
"""Test _generate_report successfully generates all reports."""
|
||||
from model_manager.utils.models.train_model_params import TrainModelParams
|
||||
from model_manager.utils.models.train_model_result import TrainModelResult
|
||||
|
||||
params = MagicMock(spec=TrainModelParams)
|
||||
params.variable_columns = ['feat1', 'feat2']
|
||||
|
||||
data = MagicMock(spec=TrainModelResult)
|
||||
data.params = params
|
||||
data.run_dir = '/run_dir'
|
||||
|
||||
reference_data = DataFrame(
|
||||
{'feat1': [1.0], 'feat2': [2.0], 'target': [3.0], 'prediction': [3.1]}
|
||||
)
|
||||
current_data = DataFrame({'feat1': [4.0], 'feat2': [5.0], 'target': [6.0], 'prediction': [6.1]})
|
||||
|
||||
mock_path.join.side_effect = lambda *args: '/'.join(args)
|
||||
mock_report_instance = MagicMock()
|
||||
mock_reports.return_value = mock_report_instance
|
||||
|
||||
# Mock DataFrame.to_csv to avoid actual file writing
|
||||
with patch.object(DataFrame, 'to_csv'):
|
||||
result = mlflow_repository._generate_report(reference_data, current_data, data)
|
||||
|
||||
mock_reports.assert_called_once()
|
||||
mock_report_instance.add_data_quality_section.assert_called_once()
|
||||
mock_report_instance.add_data_drift_section.assert_called_once()
|
||||
mock_report_instance.add_regression_section.assert_called_once()
|
||||
mock_report_instance.save_all_sections_html.assert_called_once()
|
||||
assert result == data
|
||||
|
||||
|
||||
@patch('model_manager.utils.repository.model_repository.path')
|
||||
def test_generate_report_value_error(mock_path, mlflow_repository):
|
||||
"""Test _generate_report handles ValueError from data conversion."""
|
||||
from model_manager.utils.models.train_model_params import TrainModelParams
|
||||
from model_manager.utils.models.train_model_result import TrainModelResult
|
||||
|
||||
params = MagicMock(spec=TrainModelParams)
|
||||
data = MagicMock(spec=TrainModelResult)
|
||||
data.params = params
|
||||
data.run_dir = '/run_dir'
|
||||
|
||||
# DataFrame with non-numeric data
|
||||
reference_data = DataFrame({'feat1': ['a', 'b']})
|
||||
current_data = DataFrame({'feat1': ['c', 'd']})
|
||||
|
||||
with pytest.raises(ValueError) as exc_info:
|
||||
mlflow_repository._generate_report(reference_data, current_data, data)
|
||||
|
||||
assert 'Failed to convert data to float64' in str(exc_info.value)
|
||||
|
||||
|
||||
@patch('model_manager.utils.repository.model_repository.Reports')
|
||||
@patch('model_manager.utils.repository.model_repository.path')
|
||||
def test_generate_report_permission_error(mock_path, mock_reports, mlflow_repository):
|
||||
"""Test _generate_report handles PermissionError."""
|
||||
from model_manager.utils.models.train_model_params import TrainModelParams
|
||||
from model_manager.utils.models.train_model_result import TrainModelResult
|
||||
|
||||
params = MagicMock(spec=TrainModelParams)
|
||||
params.variable_columns = ['feat1']
|
||||
|
||||
data = MagicMock(spec=TrainModelResult)
|
||||
data.params = params
|
||||
data.run_dir = '/run_dir'
|
||||
|
||||
reference_data = DataFrame({'feat1': [1.0]})
|
||||
current_data = DataFrame({'feat1': [2.0]})
|
||||
|
||||
mock_path.join.side_effect = lambda *args: '/'.join(args)
|
||||
mock_report_instance = MagicMock()
|
||||
mock_reports.return_value = mock_report_instance
|
||||
mock_report_instance.save_all_sections_html.side_effect = PermissionError('Permission denied')
|
||||
|
||||
with pytest.raises(PermissionError) as exc_info:
|
||||
mlflow_repository._generate_report(reference_data, current_data, data)
|
||||
|
||||
assert 'Permission denied when writing report files' in str(exc_info.value)
|
||||
|
||||
|
||||
@patch('model_manager.utils.repository.model_repository.Reports')
|
||||
@patch('model_manager.utils.repository.model_repository.path')
|
||||
def test_generate_report_os_error(mock_path, mock_reports, mlflow_repository):
|
||||
"""Test _generate_report handles OSError."""
|
||||
from model_manager.utils.models.train_model_params import TrainModelParams
|
||||
from model_manager.utils.models.train_model_result import TrainModelResult
|
||||
|
||||
params = MagicMock(spec=TrainModelParams)
|
||||
params.variable_columns = ['feat1']
|
||||
|
||||
data = MagicMock(spec=TrainModelResult)
|
||||
data.params = params
|
||||
data.run_dir = '/run_dir'
|
||||
|
||||
reference_data = DataFrame({'feat1': [1.0]})
|
||||
current_data = DataFrame({'feat1': [2.0]})
|
||||
|
||||
mock_path.join.side_effect = lambda *args: '/'.join(args)
|
||||
mock_report_instance = MagicMock()
|
||||
mock_reports.return_value = mock_report_instance
|
||||
mock_report_instance.save_all_sections_html.side_effect = OSError('Disk error')
|
||||
|
||||
with pytest.raises(OSError) as exc_info:
|
||||
mlflow_repository._generate_report(reference_data, current_data, data)
|
||||
|
||||
assert 'Failed to generate report' in str(exc_info.value)
|
||||
|
||||
|
||||
@patch('model_manager.utils.repository.model_repository.Reports')
|
||||
@patch('model_manager.utils.repository.model_repository.path')
|
||||
def test_generate_report_run_dir_none(mock_path, mock_reports, mlflow_repository):
|
||||
"""Test _generate_report raises ValueError when run_dir is None."""
|
||||
from model_manager.utils.models.train_model_params import TrainModelParams
|
||||
from model_manager.utils.models.train_model_result import TrainModelResult
|
||||
|
||||
params = MagicMock(spec=TrainModelParams)
|
||||
params.variable_columns = ['feat1']
|
||||
|
||||
data = MagicMock(spec=TrainModelResult)
|
||||
data.params = params
|
||||
data.run_dir = None # Not set
|
||||
|
||||
reference_data = DataFrame({'feat1': [1.0]})
|
||||
current_data = DataFrame({'feat1': [2.0]})
|
||||
|
||||
mock_report_instance = MagicMock()
|
||||
mock_reports.return_value = mock_report_instance
|
||||
|
||||
with pytest.raises(ValueError) as exc_info:
|
||||
mlflow_repository._generate_report(reference_data, current_data, data)
|
||||
|
||||
assert 'run_dir is not set after directory creation' in str(exc_info.value)
|
||||
|
||||
|
||||
def test_get_reports_directory(mlflow_repository):
|
||||
"""Test _get_reports_directory returns correct path."""
|
||||
result = mlflow_repository._get_reports_directory()
|
||||
|
||||
assert result.endswith('model_manager/reports')
|
||||
assert 'model_manager' in result
|
||||
|
||||
|
||||
@patch('model_manager.utils.repository.model_repository.path')
|
||||
def test_save_run_missing_train_data_path(mock_path, mlflow_repository):
|
||||
"""Test save_run raises ValueError when train_data_path is missing."""
|
||||
from model_manager.utils.models.train_model_result import TrainModelResult
|
||||
|
||||
data = MagicMock(spec=TrainModelResult)
|
||||
data.report_path = '/reports/report.html'
|
||||
data.train_data_path = None
|
||||
|
||||
mock_path.exists.return_value = True
|
||||
|
||||
with pytest.raises(ValueError) as exc_info:
|
||||
mlflow_repository.save_run(data)
|
||||
|
||||
assert 'Training data file does not exist' in str(exc_info.value)
|
||||
|
||||
|
||||
@patch('model_manager.utils.repository.model_repository.path')
|
||||
def test_save_run_missing_test_data_path(mock_path, mlflow_repository):
|
||||
"""Test save_run raises ValueError when test_data_path is missing."""
|
||||
from model_manager.utils.models.train_model_result import TrainModelResult
|
||||
|
||||
data = MagicMock(spec=TrainModelResult)
|
||||
data.report_path = '/reports/report.html'
|
||||
data.train_data_path = '/reports/train.csv'
|
||||
data.test_data_path = None
|
||||
|
||||
mock_path.exists.return_value = True
|
||||
|
||||
with pytest.raises(ValueError) as exc_info:
|
||||
mlflow_repository.save_run(data)
|
||||
|
||||
assert 'Test data file does not exist' in str(exc_info.value)
|
||||
|
||||
@@ -46,7 +46,6 @@ def train_params():
|
||||
shuffle=True,
|
||||
experiment_run_id=123,
|
||||
experiment_name='test_experiment',
|
||||
experiment_description='Test experiment',
|
||||
removed_intervals=[],
|
||||
)
|
||||
|
||||
@@ -200,7 +199,6 @@ def test_init_scaler_dict_without_scaler(training_repository):
|
||||
shuffle=True,
|
||||
experiment_run_id=123,
|
||||
experiment_name='test',
|
||||
experiment_description='test',
|
||||
removed_intervals=[],
|
||||
)
|
||||
|
||||
@@ -281,7 +279,6 @@ def test_after_train_calculation_without_scaler(mock_r2, mock_mae, mock_mse, tra
|
||||
shuffle=True,
|
||||
experiment_run_id=123,
|
||||
experiment_name='test',
|
||||
experiment_description='test',
|
||||
removed_intervals=[],
|
||||
)
|
||||
|
||||
|
||||
Reference in New Issue
Block a user