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# Define tracking function | |
def log_lineareg(experimentID, run_name, params, abt_train, abt_test, debug=False): | |
""" | |
Function to start a run within a existing experiment | |
:param experimentID: unique ID associated to original experiment | |
:param run_name: label for the name of the run | |
:param params: ters used for the run, such as arguments | |
:param abt_train: analytical base table for training | |
:param abt_test: analytical base table for testing |
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''' | |
This module runs the entire Dash Application | |
''' | |
import dash_table | |
import dash_core_components as component | |
import dash_html_components as html | |
from dash.dependencies import Input, Output | |
from app import * |
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''' | |
This module contains the layout of the application | |
''' | |
import dash_table | |
import dash_core_components as component | |
import dash_html_components as html | |
#Customerid Input | |
c_id_component = component.Input(id="uid", |
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''' | |
This module contains the predict callback | |
''' | |
from dash.dependencies import Input, Output | |
from app import * | |
@app.callback([Output('table', component_property='columns'), Output('table', component_property='data')],[Input(component_id='uid', component_property='value')]) | |
def predict(uid): | |
columns = [] |
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└── src | |
└── score_interactive_endpoint | |
├── Dockerfile | |
├── app.py | |
├── callbacks.py | |
├── layouts.py | |
├── model | |
│ └── model_aa0lcc9j.pkl | |
├── requirements.txt | |
└── run.py |
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def reader(df): | |
""" | |
return a data parsed with Reader class | |
args: | |
df: pandas dataframe | |
returns: | |
data parsed | |
""" | |
mindata = df.rating.min() | |
maxdata = df.rating.max() |
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model_name = 'Champion' | |
artifact_path = "model" | |
model_uri = "runs:/{run_id}/{artifact_path}".format(run_id=run_id, artifact_path=artifact_path) | |
model_details = mlflow.register_model(model_uri=model_uri, name=model_name) | |
client = MlflowClient() | |
client.update_registered_model( | |
name=model_details.name, | |
description="This model provides recommendation for specific users and items based on purchase data. The data consists of user transactions" |
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