forest_monitoring / train.py
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import joblib
from sklearn.datasets import fetch_openml
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.compose import make_column_transformer
from sklearn.pipeline import make_pipeline
from sklearn.model_selection import train_test_split, RandomizedSearchCV
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, classification_report
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from warnings import filterwarnings
filterwarnings('ignore')
df = pd.read_csv("/content/forest_health_data_with_target.csv")
target = 'Health_Status'
numeric_features = [
'Latitude',
'Longitude',
'DBH',
'Tree_Height',
'Crown_Width_North_South',
'Crown_Width_East_West',
'Slope',
'Elevation',
'Temperature',
'Humidity',
'Soil_TN',
'Soil_TP',
'Soil_AP',
'Soil_AN',
'Menhinick_Index',
'Gleason_Index',
'Fire_Risk_Index'
]
print("Creating data subsets")
X = df[numeric_features]
y = df[target]
Xtrain, Xtest, ytrain, ytest = train_test_split(
X, y,
test_size=0.2,
random_state=42
)
preprocessor = make_column_transformer(
(StandardScaler(), numeric_features),
)
model_logistic_regression = LogisticRegression(n_jobs=-1)
print("Estimating Best Model Pipeline")
model_pipeline = make_pipeline(
preprocessor,
model_logistic_regression
)
param_distribution = {
"logisticregression__C": [0.001, 0.01, 0.1, 0.5, 1]
}
rand_search_cv = RandomizedSearchCV(
model_pipeline,
param_distribution,
n_iter=3,
cv=3,
random_state=42
)
rand_search_cv.fit(Xtrain, ytrain)
print("Logging Metrics")
print(f"Accuracy: {rand_search_cv.best_score_}")
print("Serializing Model")
saved_model_path = "model.joblib"
joblib.dump(rand_search_cv.best_estimator_, saved_model_path)