semvec/pypi-packages huggingface dataset:
predictnow-client 0.0.9
A restful client library, designed to access predictnow restful api.
License: BSD
Requires Python: >=3.7

Usage:
=================================

from predictnow.pdapi import PredictNowClient
import pandas as pd
api_key = "%KeyProvidedToEachOfOurSubscriber"  

api_host = "http://%VMIP%"  # current makeshift api host
username = "helloWorld"
email = "helloWorld@yourmail.com"

client = PredictNowClient(api_host,api_key)

 # You will need to edit this input dataset file path and labelname!

file_path = 'my_amazing_features.xlsx'
labelname = 'Next_day_strategy_return'
import os


  # For classification problems

params = {'timeseries': 'yes', 'type': 'classification',  'feature_selection': 'shap', 'analysis': 'none', 'boost': 'gbdt',  'testsize': '0.2', 'weights': 'no', 'eda': 'yes', 'prob_calib': 'no', 'mode': 'train'}

  # For regression problems, suitable for CPO

params = {'timeseries': 'yes', 'type': 'regression',   'feature_selection': 'none', 'analysis': 'none', 'boost': 'gbdt', 'testsize': '0.2', 'weights': 'no', 'eda': 'yes', 'prob_calib': 'no', 'mode': 'train'}

response = client.create_model( username=username,
    model_name="test1",
    params=params
  )
  
  



from pandas import read_excel
df = read_excel(file_path, engine="openpyxl")  # Same here
df.name = "testdataframe"  # Optional, but recommended

########## TRAIN MODE ###################################
response = client.train(
	model_name="test1",
	input_df=df,
	label=labelname,
	username=username,
	email=email)


status = client.getstatus(
        username=username,
    train_id=response["train_id"]
)


if status["state"] == "COMPLETED":

	response = client.getresult(
	model_name="test1",
	username=username,
	)
	predicted_targets_cv = pd.read_json(response.predicted_targets_cv)
	print("predicted_targets_cv")
	print(predicted_targets_cv)

	predicted_targets_test = pd.read_json(response.predicted_targets_test)
	print("predicted_targets_test")
	print(predicted_targets_test)

	if response.feature_importance:
		feature_importance = pd.read_json(response.feature_importance)
		print("feature_importance")
		print(feature_importance)


	performance_metrics = pd.read_json(response.performance_metrics)
	print("performance_metrics")
	print(performance_metrics)

########## LIVE MODE ###################################
if status["state"] == "COMPLETED":
	df = read_csv("example_input_live.csv") # live input data
	df.name = "myfirstpredictname"  # optional, but recommended
      # Making live predictions
	response = client.predict(
	model_name="test1",
	input_df=df,username=username,
	eda="yes",
	prob_calib=params["prob_calib"],
	)
	y_pred = pd.read_json(response.labels)
	print("THE LABELS")
	print(y_pred)