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#! /usr/bin/env python3
# Copyright Yahoo. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
# coding: utf-8
import json
import random
import lightgbm as lgb
import numpy as np
import pandas as pd
def category_value(arr):
values = { np.NaN: 0, "a":1, "b":2, "c":3, "d":4, "e":5, "i":1, "j":2, "k":3, "l":4, "m":5 }
return np.array([ 0.21 * values[i] for i in arr ])
# Create training set
num_examples = 100000
missing_prob = 0.01
features = pd.DataFrame({
"numerical_1": np.random.random(num_examples),
"numerical_2": np.random.random(num_examples),
"categorical_1": pd.Series(np.random.permutation(["a", "b", "c", "d", "e"] * int(num_examples/5)), dtype="category"),
"categorical_2": pd.Series(np.random.permutation(["i", "j", "k", "l", "m"] * int(num_examples/5)), dtype="category"),
})
# randomly insert missing values
for i in range(int(num_examples * len(features.columns) * missing_prob)):
features.loc[random.randint(0, num_examples-1), features.columns[random.randint(0, len(features.columns)-1)]] = None
# create targets (with 0.0 as default for missing values)
target = features["numerical_1"] + features["numerical_2"] + category_value(features["categorical_1"]) + category_value(features["categorical_2"])
lgb_train = lgb.Dataset(features, target)
# Train model
params = {
'objective': 'mse',
'metric': {'l2', 'l1'},
'num_leaves': 3,
}
model = lgb.train(params, lgb_train, num_boost_round=2)
# Save model
with open("regression.json", "w") as f:
json.dump(model.dump_model(), f, indent=2)
# Predict (for comparison with Vespa evaluation)
predict_features = pd.DataFrame({
"numerical_1": pd.Series([ None, 0.1, None, 0.7]),
"numerical_2": pd.Series([np.NaN, 0.2, 0.5, 0.8]),
"categorical_1": pd.Series([ None, "a", "b", None], dtype="category"),
"categorical_2": pd.Series([ None, "i", "j", "m"], dtype="category"),
})
print(model.predict(predict_features))
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