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digraph G {

        rankdir=LR
	splines=line
        nodesep=.05;
        
        node [label=""];
        
        subgraph cluster_0 {
		color=white;

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require(ggplot2)

theta <- seq(0,2*pi,length.out = 100)
circle <- data.frame(x = cos(theta), y = sin(theta))
p <- ggplot(circle,aes(x,y)) + geom_path()

loadings <- data.frame(ir.pca$rotation, 
                       .names = row.names(ir.pca$rotation))
p + geom_text(data=loadings, 
              mapping=aes(x = PC1, y = PC2, label = .names, colour = .names)) +

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!pip install numpy pandas pyvespa lightgbm

import json
import lightgbm as lgb
import numpy as np
import pandas as pd

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!rm -fr lightgbm
vespa_docker.container.stop(timeout=600)
vespa_docker.container.remove()

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assert features["model_prediction"].tolist() == features["vespa_relevance"].tolist()

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vespa_relevance = []
for idx, row in features.iterrows():
    vespa_relevance.append(
        compute_vespa_relevance(
            id_value=idx, 
            query_value = row["query(value)"]
        )
    )
features["vespa_relevance"] = vespa_relevance
features

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def compute_vespa_relevance(id_value, query_value):
    hits = app.query(
        body={
            "yql": "select * from sources * where id = {}".format(str(id_value)),
            "ranking": "classify",
            "ranking.features.query(value)": features.loc[id_value, "query(value)"],
            "hits": 1
        }
    ).hits
    return hits[0]["relevance"]

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features["model_prediction"] = model.predict(features)
features

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feed_batch = [
    {
        "id": idx, 
        "fields": {
            "id": idx,
            "numeric": row["attribute(numeric)"], 
            "categorical": row["attribute(categorical)"]
        }
    } for idx, row in features.iterrows()
]

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from vespa.deployment import VespaDocker

vespa_docker = VespaDocker()
app = vespa_docker.deploy_from_disk(
    application_name="lightgbm", 
    application_root="lightgbm"
)