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fero-2.1.7
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مشاهده بیشترتوضیحات
Python client for accessing Fero API
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | fero-2.1.7 |
| نام | fero |
| نسخه کتابخانه | 2.1.7 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | - |
| ایمیل نویسنده | - |
| آدرس صفحه اصلی | https://github.com/FeroLabs/fero_client |
| آدرس اینترنتی | https://pypi.org/project/fero/ |
| مجوز | - |
# fero_client
`fero` is a client-side Python library intended to help users interact with [Fero](https://app.ferolabs.com).
## Quickstart
```python
from fero import Fero
# Create a Fero client object
fero_client = Fero(username="<your username>", password="<your password>")
# Get a specific analysis by its unique identifier
analysis = fero_client.get_analysis("5dfbbb63-8ad4-4638-9fdb-61e39952d3cf")
# Create a pandas DataFrame with factor values for this analysis
df = pd.DataFrame([{"value": 5, "value2": 2}])
# Make a prediction
prediction = analysis.make_prediction(df)
print(prediction)
'''
value value2 target_low90 target_low50 target_mid target_high50 target_high90
0 5 2 70 75 80 88 92
'''
```
## Providing Credentials
The simplest way to provide your Fero login credentials is as arguments to the `Fero` object on initialization.
```python
fero_client = Fero(username="<your username>", password="<your password>")
```
While this is fine for interactive shells, it is not ideal for a publicly viewable script. To account for this, `Fero` also supports setting the `FERO_USERNAME` and `FERO_PASSWORD` environment variables or storing your username and password in a `.fero` file in the home directory. This file needs to be in the the following format.
```
FERO_USERNAME=fero_user
FERO_PASSWORD=shouldBeAGoodPassword
```
If you are using the `Fero` client to access an on-premises installation, both the hostname for the local Fero server can be provided with `hostname="https://local.fero-site"` and an internal SSL certification via `verify="path/to/ca-bundle`. (See [here](https://docs.python-requests.org/en/master/user/advanced/#ssl-cert-verification) for additional details.) Verify is passed directly to the underlying Python `requests` package; thus, if you desire, verification can be disabled by passing `verify=False`.
```python
local_client = Fero(hostname="https://fero.self.signed", verify=False)
```
## Finding a Fero Analysis
The Fero client provides two different methods to find an `Analysis`. The first is `Fero.get_analysis` which takes a single unique identifier string (UUID) and attempts to look up the analysis matching this ID. The second method is `Fero.search_analyses` which will return an iterator of available `Analysis` objects. If no keyword arguments are provided, it will return all analyses you have available on Fero. Optionally, `name` can be provided to filter to only analyses matching that name.
#### Examples
```python
from fero import Fero
fero_client = Fero(username="<your username>", password="<your password>")
# Get a specific analysis
analysis = fero_client.get_analysis("5dfbbb63-8ad4-4638-9fdb-61e39952d3cf")
# Get all available analyses
all_analyses = fero_client.search_analyses()
# Only get "plant_A" analyses
plant_A_only = fero_client.search_analyses(name="plant_A")
```
## Using an Analysis
Along with associated properties such as `name` and `uuid`, an `Analysis` provides a variety of methods for interacting with Fero.
The first thing to call when working with an Analysis is `Analysis.has_trained_model`, which checks whether the Analysis is ready to use. This will be false if the Analysis is still being configured or if there was an error during configuration.
### Making a simple prediction
The `Analysis.make_prediction` method makes a prediction using the latest revision of the Analysis. This function can take either a pandas `DataFrame` with columns matching the expected factors or a list of dictionaries with each dictionary containing a key/value pairs for each factor. A prediction will be made for each row in the `DataFrame` or each dictionary in the list.
The return value will either be a `DataFrame` or a dictionary, depending on the initial input type. These values will have the suffixes `_lowX`, `_mid`, `_highX` added to each target name to indicate the prediction intervals. Specifically:
- `target_low90` corresponds to the 5% prediction level,
- `target_low50` corresponds to the 25% prediction level,
- `target_mid` corresponds to the mean prediction,
- `target_high50` corresponds to the 75% prediction level, and
- `target_high90` corresponds to the 95% prediction level.
The naming convention indicates that:
- 50% of the time, the corresponding measurement should fall between `(target_low50, target_high50)`, and
- 90% of the time, the corresponding measurement should fall between `(target_low90, target_high90)`.
#### Example
```python
raw_data = [{"value": 5, "value2": 2}]
# Using a DataFrame
df = pd.DataFrame([raw_data])
prediction = analysis.make_prediction(df)
print(prediction)
'''
value value2 target_low90 target_low50 target_mid target_high50 target_high90
0 5 2 10 20 30 40 50
'''
# Using a list of dicts
prediction = analysis.make_prediction(raw_data)
print(prediction)
'''
[{"value": 5, "value2": 2, "target_low90": 10, "target_low": 20, "target_mid": 30, "target_high50": 40, "target_high90": 50}]
'''
```
### Optimize
A more advanced usage of an `Analysis` is to create an optimization which will make a prediction that satistifies a specified `goal` within the context of `constraints` on other factors or targets. Currently, the Fero optimizer can be used to conduct three different types of optimizations based on an `Analysis`.
#### Example 1: Minimize a factor given constraints
Fero can be used to minimize `value` while keeping `target` within a set range. The following goal and constraint configurations would need to be provided.
```python
goal = {
"goal": "minimize",
"factor": {"name": "value", "min": 50.0, "max": 100.0}
}
constraints = [{"name": "target", "min": 100.0, "max": 200}]
opt = analysis.make_optimization("example_optimization", goal, constraints)
```
By default, Fero will use the median values of fixed factors while computing the optimization. These can be overridden with custom values by passing a dictionary of `factor`:`value` pairs as the `fixed_factors` argument to the optimization function.
```python
fixed_factors = {
"value": 10,
"value2": 20
}
opt = analysis.make_optimization("example_optimization", goal, constraints, fixed_factors)
```
#### Example 2: Maximize a target KPI given constraints
Alternatively, a `target` KPI can be maximized while constraining a `value`. Note that the same key, `factor`, is used when defining the target KPI in `goal`.
```python
goal = {
"goal": "maximize",
"factor": {"name": "target", "min": 100.0, "max": 200.0}
}
constraints = [{"name": "value", "min": 50.0, "max": 100.0}]
opt = analysis.make_optimization("example_optimization", goal, constraints)
```
By default, Fero will not incorporate confidence intervals while optimizing a target. The lower (5%) and upper (95%) bounds of the confidence intervals can be included during optimization by setting argument `include_confidence_intervals` to `True`. This will ensure that the upper or lower prediction level of the optimization result do not exceed the set `min`/`max` values for `target`. (This could have also been set to `True` in the previous example.)
```python
opt = analysis.make_optimization("example_optimization", goal, constraints, fixed_factors)
```
#### Example 3: Optimize a cost function over multiple factors
Fero also supports the idea of a cost optimization, which will weight different factors by specified cost multipliers to find the best combination of inputs. For example, to find the minimum combined cost of `value` and `value2` while meeting the expected values of `target`, you could do the following:
```python
goal = {
"goal": "minimize",
"type": "cost",
"cost_function": [{"name": "value", "min": 50.0, "max": 100.0, "cost": 5.0}, {"name": "value2", "min": 70.0, "max": 80.0.0, "cost": 9.0}]
}
constraints = [{"name": "target", "min": 100.0, "max": 200}]
opt = analysis.make_optimization("example_cost_optimization", goal, constraints)
```
In both cases, a `Prediction` object is returned, which will provide access to the results of the optimization. By default, the result will be a `DataFrame` but it can also be configured to be a list of dictionaries by specifying `format="record"` in `get_results`.
#### Example
```python
goal = {
"goal": "minimize",
"factor": {"name": "value", "min": 50.0, "max": 100.0}
}
constraints = [{"name": "target", "min": 100.0, "max": 200}]
opt = analysis.make_optimization("example_optimization", goal, constraints)
print(opt.get_results())
'''
value value2 target (5%) target (Mean) target (95%)
0 60 40 100 150 175
'''
```
## Finding a Fero Asset
The Fero client provides two different methods to find an `Asset`. The first is `Fero.get_asset`, which takes a single unique identifier string (UUID) and attempts to look up the asset matching this ID. The second method is `Fero.search_assets`, which will return an iterator of available `Asset` objects. If no keyword arguments are provided, it will return all assets you have available on the Fero website. Optionally, `name` can be provided to filter to only assets matching that name.
#### Examples
```python
from fero import Fero
fero_client = Fero(username="<your username>", password="<your password>")
# Get a specific asset
asset = fero_client.get_asset("fd57ba36-3c5d-40f5-ae0c-d7b76ab39ee5")
# Get all available assets
all_assets = fero_client.search_assets()
# Get only "plant_B" assets
plant_B_only = fero_client.search_assets(name="plant_B")
```
## Using an Asset
Along with associated properties such as `name` and `uuid`, an `Asset` provides a few methods for interacting with Fero.
The first thing to call when working with an asset is `Asset.has_trained_model`, which checks whether the Asset is ready to use. This will be false if the Asset is still being configured or if there was an error during configuration.
### Making a prediction
The `Asset.predict` method makes a prediction using the latest revision of the Asset. Fero computes predictions for all controllable factors and with those results, predictions for all target variables. Predictions are provided for the 5 time intervals following the end of the training dataset. (Interval size is determined during model configuration and training.) Optionally, you may call `Asset.predict` with an argument specifying values for one or more of the controllable factors; Fero will predict all targets using your specified values in place of its controllable factor predictions where applicable.
#### Examples
```python
# With no inputs
prediction = asset.predict()
print(prediction.columns)
['mean:Factor1', 'p5:Factor1', 'p25:Factor1', 'p75:Factor1', 'p95:Factor1',
'mean:Factor2', 'p5:Factor2', 'p25:Factor2', 'p75:Factor2', 'p95:Factor2',
'mean:Target1', 'p5:Target1', 'p25:Target1', 'p75:Target1', 'p95:Target1']
print(prediction)
'''
mean:Factor1 p5:Factor1 p25:Factor1 ... p75:Target1 p95:Target1
2020-12-25T00:00:00Z 7.937 7.253 7.688 ... 1.921 2.197
2020-12-25T01:00:00Z 8.059 6.962 7.721 ... 1.924 2.202
2020-12-25T02:00:00Z 8.193 6.754 7.692 ... 1.871 2.318
2020-12-25T03:00:00Z 8.349 6.552 7.619 ... 1.830 2.375
2020-12-25T04:00:00Z 8.492 6.199 7.498 ... 1.762 2.425
'''
# Provide specified values as a DataFrame
new_factor_values = pd.DataFrame({
"Factor1": [8.0, 8.1, 8.2, 8.3, 8.4]
})
prediction = asset.predict(new_factor_values)
print(prediction.columns)
['specified:Factor1',
'mean:Factor2', 'p5:Factor2', 'p25:Factor2', 'p75:Factor2', 'p95:Factor2',
'mean:Target1', 'p5:Target1', 'p25:Target1', 'p75:Target1', 'p95:Target1']
print(prediction)
'''
specified:Factor1 mean:Factor2 p5:Factor2 ... p75:Target1 p95:Target1
2020-12-25T00:00:00Z 8.0 13.452 11.953 ... 1.921 2.197
2020-12-25T01:00:00Z 8.1 13.119 11.762 ... 1.924 2.202
2020-12-25T02:00:00Z 8.2 13.084 11.454 ... 1.871 2.318
2020-12-25T03:00:00Z 8.3 13.003 11.352 ... 1.830 2.375
2020-12-25T04:00:00Z 8.4 12.976 11.109 ... 1.762 2.425
'''
# Provide specified values as a dictionary
new_factor_values = {
"Factor1": [8.0, 8.1, 8.2, 8.3, 8.4]
}
prediction = asset.predict(new_factor_values)
print(list(prediction.keys())
'''
[
'specified:Factor1', 'mean:Factor2', 'p5:Factor2', 'p25:Factor2', 'p75:Factor2',
'p95:Factor2', 'mean:Target1', 'p5:Target1', 'p25:Target1', 'p75:Target1', 'p95:Target1',
'index'
]
'''
print(prediction["mean:Factor2"])
'''
[
13.452, 13.119, 13.084, 13.003, 12.976
]
'''
print(prediction["index"])
'''
[
2020-12-25T00:00:00Z, 2020-12-25T01:00:00Z, 2020-12-25T02:00:00Z, 2020-12-25T03:00:00Z, 2020-12-25T04:00:00Z
]
'''
```
## Fero Processes
The Fero client provides two different methods to find a `Process`. The first is `Fero.get_process` which takes a single unique identifier string (UUID) and attempts to look up the analysis matching this ID. The second method is `Fero.search_processes` which will return an iterator of available `Process` objects. If no keyword arguments are provided, it will return all processes you have available on Fero. Optionally, `name` can be provided to filter to only processes matching that name.
Processes represent data via two main underlying entities, the `Tag` and the `Stage`. A `Tag` is a column of a specific measurement in the underlying data. A `Stage` is a logical part of a process consisting of various tags and an order relative to the other stages. For example, a steel process might have first stage for melting the steel and a later stage for casting the steel, each with corresponding measurements in the form of tags.
### Example
```python
from fero import Fero
fero_client = Fero(username="<your username>", password="<your password>")
# Get a single process
process = fero_client.get_process("c6f69e96-db4d-43ed-8837-d5827cc81112")
# Search processes by name
processes = [p for p in fero_client.search_processes(name="process X")]
# Get the tags of the process
tags = process.tags
# Get stages of the process
stages = process.stages
```
## Downloading Process Data
A `Process` object can be used to download the pandas `DataFrame` that the process would produce for analysis. Because not all tags are generally used in a analysis, a list of desired tags is required before data can be downloaded. Additionally, a target or key performance indicator (kpi) tag can be set while requesting data. Functionally, this will limit the data returned to the stage of the kpi tag and any preceding stages. For advanced and batch processes, kpis are optional; however, they are required for continuous processes because the data is computed using the observed times of the kpi.
### Example
```python
# Get all data for single process
process = fero_client.get_process("9777bae7-95af-4bea-98b9-c703ab940a05")
df = process.get_data(process.tags)
print(df)
'''
s1_factor1 s1_factor2 s2_factor1 s3_factor1 s3_factor2 s3_kpi
0 0 14 7 28.5 0 49.5
1 1 8 5 36.0 3 53.0
2 2 2 3 39.5 6 52.5
3 3 10 8 26.5 9 56.5
4 4 4 6 41.5 12 67.5
... ... ... ... ... ... ...
10395 10395 4 10397 38.0 31185 52019.0
10396 10396 0 10396 32.5 31188 52012.5
10397 10397 14 10404 40.5 31191 52046.5
10398 10398 0 10398 25.5 31194 52015.5
10399 10399 14 10406 42.0 31197 52058.0
[10400 rows x 6 columns]
'''
# Limit the process to an earlier kpi
df = process.get_data(["s1_factor1", "s3_kpi"], kpis=["s2_factor1"])
'''
dt s2_factor1 s1_factor1
0 2020-03-01 00:00:00+00:00 10 <NA>
1 2020-03-01 00:01:00+00:00 162 <NA>
2 2020-03-01 00:02:00+00:00 12 16
3 2020-03-01 00:03:00+00:00 12 15
4 2020-03-01 00:04:00+00:00 56 162
... ... ... ...
1994 2020-03-02 09:14:00+00:00 2006 65
1995 2020-03-02 09:15:00+00:00 2007 20
1996 2020-03-02 09:16:00+00:00 415 174
1997 2020-03-02 09:17:00+00:00 2001 166
1998 2020-03-02 09:18:00+00:00 0 2
[1999 rows x 3 columns]
'''
```
نیازمندی
| مقدار | نام |
|---|---|
| - | requests |
| <1.5.0,>=1.2.0 | pandas |
| <3.16.0,>=3.8.0 | marshmallow |
| - | azure-storage-blob |
| <7.0,>=6.0.0 | pyarrow |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.7, <4 | Python |
نحوه نصب
نصب پکیج whl fero-2.1.7:
pip install fero-2.1.7.whl
نصب پکیج tar.gz fero-2.1.7:
pip install fero-2.1.7.tar.gz