View churn.py
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import plotly.express as px | |
fig = px.scatter(x=data['tenure'], y=data['TotalCharges'], | |
color = data['Churn'], template = 'presentation', | |
opacity = 0.5, facet_col = data['Contract'], | |
title = 'Customer Churn by Tenure, Charges, and Contract Type', | |
labels = {'x' : 'Customer Tenure', 'y' : 'Total Charges $'}) | |
fig.show() |
View fastapi_tutorial2.py
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import requests | |
def get_predictions(carat_weight, cut, color, clarity, polish, symmetry, report): | |
url = 'http://localhost:8000/predict?carat_weight={carat_weight}&cut={cut}&color={color}&clarity={clarity}&polish={polish}&symmetry={symmetry}&report={report}'\ | |
.format(carat_weight = carat_weight, cut = cut,\ | |
color = color, clarity = clarity, polish = polish, symmetry = symmetry, report = report) | |
x = requests.post(url) | |
print(x.text) |
View fastapi_tutorial.py
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# 1. Library imports | |
import pandas as pd | |
from pycaret.regression import load_model, predict_model | |
from fastapi import FastAPI | |
import uvicorn | |
# 2. Create the app object | |
app = FastAPI() | |
#. Load trained Pipeline |
View gradio_step2.py
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import gradio as gr | |
model = gr.inputs.Dropdown(list(compare_model_results['Model']), label="Model") | |
sepal_length = gr.inputs.Slider(minimum=1, maximum=10, default=data['sepal_length'].mean(), label = 'sepal_length') | |
sepal_width = gr.inputs.Slider(minimum=1, maximum=10, default=data['sepal_width'].mean(), label = 'sepal_width') | |
petal_length = gr.inputs.Slider(minimum=1, maximum=10, default=data['petal_length'].mean(), label = 'petal_length') | |
petal_width = gr.inputs.Slider(minimum=1, maximum=10, default=data['petal_width'].mean(), label = 'petal_width') | |
gr.Interface(predict, [model,sepal_length,sepal_width,petal_length,petal_width], "label", live=True).launch() |
View gradio_step1.py
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# creating a predict function to be passed into gradio UI | |
def predict(model, sepal_length, sepal_width, petal_length, petal_width): | |
df = pd.DataFrame.from_dict({'sepal_length': [sepal_length], 'sepal_width': [sepal_width], | |
'petal_length': [petal_length], 'petal_width': [petal_width]}) | |
model_index = list(compare_model_results['Model']).index(model) | |
model = best[model_index] | |
pred = predict_model(model, df, raw_score=True) | |
return {'Iris-setosa': pred['Score_Iris-setosa'][0].astype('float64'), |
View timeseries_training_loop.py
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from tqdm import tqdm | |
from pycaret.regression import * | |
all_ts = data['time_series'].unique() | |
all_results = [] | |
final_model = {} | |
for i in tqdm(all_ts): | |