https://github.com/mlrun/metrics-gen
dummy metrics generator
https://github.com/mlrun/metrics-gen
Last synced: 5 months ago
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dummy metrics generator
- Host: GitHub
- URL: https://github.com/mlrun/metrics-gen
- Owner: mlrun
- License: apache-2.0
- Created: 2022-01-06T14:50:14.000Z (over 4 years ago)
- Default Branch: development
- Last Pushed: 2023-03-20T14:26:52.000Z (over 3 years ago)
- Last Synced: 2025-06-07T07:06:06.297Z (about 1 year ago)
- Language: Python
- Size: 31.3 KB
- Stars: 0
- Watchers: 1
- Forks: 3
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# metrics-gen
dummy metrics generator
## Getting Started
Metrics generator is built upon three main components:
- **Deployment**: The indexes of the table, for example:
- symbol in stock market.
- (data_center, device_id) for devices in data centers
- **Static Data**: Static data regarding the deployment, for example:
- model_number for a device
- score for a model
- **Metrics**: Continuous metrics to generate about the deployment, for example:
- cpu_utilization of a device
- price of a stock
The first step in setting up the generator is creating a deployment. Then using the deployment, you can generate static data or continuous stream of metrics.
### Create a deployment from configuration
To create a deployment from configuration you need to provide a **yaml** file containing the following:
```yaml
deployment:
:
faker:
num_items:
```
Where `level_name` will be the name of the index, `faker_type` is the name of the [faker generator](https://github.com/joke2k/faker) and `num_items` is how many keys to create for this index.
Each provided level will create another `num_items` instances for each entry in it's previous levels.
**Example**: Given the following configuration yaml file:
```yaml
deployment:
device:
faker: msisdn
num_items: 2
core:
faker: msisdn
num_items: 2
```
and running the following command:
```python
from metrics_gen.deployment_generator import deployment_generator
dep_gen = deployment_generator()
deployment = dep_gen.generate_deployment(configuration=configuration)
```
Will generate the following example deployment:
| | device | core |
|- |- |-|
| 0 | 4120271911677 | 6950611701382 |
| 1 | 4120271911677 | 2255426557707 |
| 2 | 4120271911677 | 7717168891372 |
| 3 | 2260158002886 | 3213635322383 |
| 4 | 2260158002886 | 4007792940086 |
| 5 | 2260158002886 | 3720953132595 |
**Notice** that each extra level, multiplies the number of items created by `num_item`, thus we got 2 * 3 = 6 items created.
### Create Static Data
To create a static data generator you need to supply a deployment dataframe and a configuration yaml.
The static data generator knows how to generator from two kinds of feature configurations: **range** and **choice** which should be specified in the yaml.
```yaml
static:
:
kind: range
min_range: , defaults to 0
max_range:
as_integer: , defaults to False
:
kind: choice
choices:
```
Each provided feature will generate a new feature column in the generated dataframe.
Example: Given the following yaml:
```yaml
static:
models:
kind: range
min_range: 10
max_range: 15
as_integer: True
country:
kind: choice
choices: [A, B, C, D, E, F, G]
```
And the previous deployment:
```python
from metrics_gen.static_data_generator import Static_data_generator
static_data_generator = Static_data_generator(
deployment, static_configuration
)
generated_df = static_data_generator.generate_static_data()
```
Will generate the following dataframe:
| | device | core | models | country |
|-- |------- |----- |------- |-----
| 0 | 4120271911677 | 6950611701382 | 13 | A |
| 1 | 4120271911677 | 2255426557707 | 14 | C |
| 2 | 4120271911677 | 7717168891372 | 14 | G |
| 3 | 2260158002886 | 3213635322383 | 14 | G |
| 4 | 2260158002886 | 4007792940086 | 11 | G |
| 5 | 2260158002886 | 3720953132595 | 14 | D |
### Create Continuous Metrics
To create a continuous metrics stream you need to provide a deployment dataframe and metrics creation configuration yaml.
```yaml
errors:
rate_in_ticks: < ~ticks between errors>
length_in_ticks: < ~length of error mode>
timestamps:
interval:
Each configured feature will generate additional metric for your deployment.
Example: Given the following yaml
```yaml
errors: {length_in_ticks: 10, rate_in_ticks: 5}
timestamps: {interval: 5s, stochastic_interval: true}
metrics:
cpu_utilization:
accuracy: 2
distribution: normal
distribution_params: {mu: 70, noise: 0, sigma: 10}
is_threshold_below: true
past_based_value: false
produce_max: false
produce_min: false
validation:
distribution: {max: 1, min: -1, validate: false}
metric: {max: 100, min: 0, validate: true}
throughput:
accuracy: 2
distribution: normal
distribution_params: {mu: 250, noise: 0, sigma: 20}
is_threshold_below: false
past_based_value: false
produce_max: false
produce_min: false
validation:
distribution: {max: 1, min: -1, validate: false}
metric: {max: 300, min: 0, validate: true}
```
And the previous deployment:
```python
from metrics_gen.metrics_generator import Generator_df
metrics_generator = Generator_df(metrics_configuration, user_hierarchy=deployment)
generator = metrics_generator.generate(as_df=True)
df = next(generator)
```
Will generate the following dataframe:
| timestamp | core | device | cpu_utilization | cpu_utilization_is_error | throughput | throughput_is_error | is_error |
|---------------------------- |--------------- |--------------- |-------------------- |-------------------------- |-------------------- |--------------------- |---------- |
| 2022-01-31 19:20:21.007087 | 2113309831673 | 4469221325973 | 100.0 | True | 0.0 | True | True |
| 2022-01-31 19:20:21.007087 | 2115933686087 | 4469221325973 | 100.0 | True | 235.0679405785135 | False | False |
| 2022-01-31 19:20:21.007087 | 0175482390171 | 4469221325973 | 70.26657388732976 | False | 208.34378630077305 | False | False |
| 2022-01-31 19:20:21.007087 | 1626403145660 | 4038890878426 | 59.932750968399404 | False | 217.4335871243806 | False | False |
| 2022-01-31 19:20:21.007087 | 7247058922310 | 4038890878426 | 83.98361382584898 | False | 265.3476318369042 | False | False |
| 2022-01-31 19:20:21.007087 | 7030239128061 | 4038890878426 | 100.0 | False | 225.16604191632058 | False | False |
To generate new samples all we need to do is call `next(generator)` and a new sample will be generated.