predictnow-api 2.0.0

A restful client library, designed to access predictnow restful API.

TO BEGIN ANY WORK WITH PREDICTNOW.AI CLIENT, WE START BY IMPORTING AND CREATING A CLASS INSTANCE

from predictnow.pdapi import PredictNowClient import pandas as pd

api_key = "KeyProvidedToEachOfOurSubscriber"
api_host = "http://%VMIP%"

Initial variables

username = "user1"
email = "[e-mail]" client = PredictNowClient(api_host,api_key)

YOU WILL NEED TO EDIT THIS INPUT DATASET FILE PATH, LABELNAME AND MODELNAME!

file_path = 'my_amazing_features.xlsx' # ******************** labelname = 'futreturn' #might need to change this name accordingly ******************************* modelname = 'model1' # ********************************************* import os

NOW YOUR PREDICTNOW.AI CLIENT HAS BEEN SETUP.

For classification problem

params = {"timeseries": "yes", "weights": "no", "prob_calib": "no", "eda": "no", "type": "classification", "feature_selection": "shap", "analysis": "small", "boost": "gbdt", "mode": "train", "testsize": "1"}

For regression problems

params = {"timeseries": "yes", "weights": "no", "prob_calib": "no", "eda": "no", "type": "regression", "feature_selection": "shap", "analysis": "small", "boost": "gbdt", "mode": "train", "testsize": "1"}

print("THE PARAMS", params)

LET'S CREATE THE MODEL BY SENDING THE PARAMETERS TO PREDICTNOW.AI

response = client.create_model( username=username, # only letters, numbers, or underscores model_name=modelname, params=params, )

print(response)

LET'S LOAD UP THE FILE TO PANDAS IN THE LOCAL ENVIRONMENT

from pandas import read_csv # If you have the Excel file, replace read_csv with read_excel from pandas import read_excel df = read_excel(file_path) # Same here df.name = "testdataframe" # Optional, but recommended

print(df)

START TRAINING MODEL

NOTE: THIS MAY TAKE UP TO several minutes

response = client.train( model_name=modelname, input_df=df, label=labelname,

username=username,
email=email,
return_output=False

)

print("THE CLIENT HAS SENT THE DATASET TO THE SERVER AND TRIGGERED THE TRAINING MODEL TASK") print(response)

CHECK THE STATUS OF THE MODEL

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

print("Current status:") print(status)

NOW WE WILL DOWNLOAD FILES

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

response = client.getresult(
    model_name=modelname,
    username=username,
)

import pandas as pd
predicted_prob_cv = pd.read_json(response.predicted_prob_cv)
print("predicted_prob_cv")
print(predicted_prob_cv)

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


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

START PREDICTING USING THE TRAINED MODEL

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

df = read_excel("example_input_live_latest.xlsx")
df.name = "myfirstpredictname"  # optional, but recommended

# Predict demo
response = client.predict(
    model_name=modelname,
    input_df=df,
    username=username,
    eda=params["eda"],
    prob_calib=params["prob_calib"]
)