Train model

import os
import sys

sys.path.append("../../../")

import json
import pickle

import fasttreeshap
import numpy as np
import pandas as pd
import shap
from sklearn.model_selection import RepeatedKFold, cross_val_score

%reload_ext autoreload
%autoreload 2

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Load Training Data

COUNTRY_CODE = "ph"
ROLLOUT_DATE = "-".join(os.getcwd().split("/")[-2].split("-")[:3])

# CSV file contains all data
# Metadata JSON file lists the feature columns and label column
data = pd.read_csv(f"{ROLLOUT_DATE}-training-data.csv")
with open(f"{ROLLOUT_DATE}-training-data-columns.json", "r") as file:
    column_metadata = json.load(file)

features = data[column_metadata["features"]]
labels = data[column_metadata["label"]]

Cross-Validation

# Set parameters
CV_K_FOLDS = 5
CV_NUM_REPEATS = 5
RANDOM_SEED = 42

print(f"Performing {CV_K_FOLDS}-fold CV...")
cv = RepeatedKFold(
    n_splits=CV_K_FOLDS,
    n_repeats=CV_NUM_REPEATS,
    random_state=RANDOM_SEED,
)

print(cv.split(features))

Performing 5-fold CV...
<generator object _RepeatedSplits.split at 0x7ff647383970>

from sklearn.ensemble import RandomForestRegressor

model = RandomForestRegressor(n_estimators=100, random_state=RANDOM_SEED, verbose=0)

R_cv = cross_val_score(model, features.values, labels.values.ravel(), cv=cv)
cv_mean = round(np.array(R_cv).mean(), 2)
cv_std = round(np.array(R_cv).std(), 2)

print("Cross validation scores are: ", R_cv)
print(f"Cross validation R^2 mean: {cv_mean}")
print(f"Cross validation R^2 std: {cv_std}")

Cross validation scores are:  [0.55682521 0.63091503 0.59480566 0.61487913 0.58103858 0.58468717
 0.6176015  0.55499181 0.56994707 0.63015257 0.64765313 0.61932703
 0.59837317 0.54697645 0.54584293 0.53601267 0.59513112 0.6302872
 0.55665255 0.59870962 0.58082884 0.5416791  0.62810948 0.63953924
 0.5935612 ]
Cross validation R^2 mean: 0.59
Cross validation R^2 std: 0.03

Train the final model

For training the final model, we train on all the available data.

model = RandomForestRegressor(n_estimators=100, random_state=RANDOM_SEED, verbose=0)
model.fit(features.values, labels.values.ravel())

RandomForestRegressor(random_state=42)

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model_save_path = f"./{ROLLOUT_DATE}-{COUNTRY_CODE}-single-country-model.pkl"
with open(model_save_path, "wb") as file:
    pickle.dump(model, file)

SHAP

explainer = fasttreeshap.TreeExplainer(model, algorithm="auto", n_jobs=-1)
shap_values = explainer(features).values

shap.summary_plot(
    shap_values, features, feature_names=features.columns, plot_type="bar"
)

shap.summary_plot(shap_values, features.values, feature_names=features.columns)

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