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Configuration error
import pandas as pd | |
import skops.io as sio | |
from sklearn.compose import ColumnTransformer | |
from sklearn.ensemble import RandomForestClassifier | |
from sklearn.impute import SimpleImputer | |
from sklearn.metrics import accuracy_score, f1_score | |
from sklearn.pipeline import Pipeline | |
from sklearn.preprocessing import OrdinalEncoder, StandardScaler | |
# Loading the Data | |
drug_df = pd.read_csv("Data/drug.csv") | |
drug_df = drug_df.sample(frac=1) | |
# Train Test Split | |
from sklearn.model_selection import train_test_split | |
X = drug_df.drop("Drug", axis=1).values | |
y = drug_df.Drug.values | |
X_train, X_test, y_train, y_test = train_test_split( | |
X, y, test_size=0.3, random_state=125 | |
) | |
# Pipeline | |
cat_col = [1, 2, 3] | |
num_col = [0, 4] | |
transform = ColumnTransformer( | |
[ | |
("encoder", OrdinalEncoder(), cat_col), | |
("num_imputer", SimpleImputer(strategy="median"), num_col), | |
("num_scaler", StandardScaler(), num_col), | |
] | |
) | |
pipe = Pipeline( | |
steps=[ | |
("preprocessing", transform), | |
("model", RandomForestClassifier(n_estimators=10, random_state=125)), | |
] | |
) | |
# Training | |
pipe.fit(X_train, y_train) | |
# Model Evaluation | |
predictions = pipe.predict(X_test) | |
accuracy = accuracy_score(y_test, predictions) | |
f1 = f1_score(y_test, predictions, average="macro") | |
print("Accuracy:", str(round(accuracy, 2) * 100) + "%", "F1:", round(f1, 2)) | |
# Confusion Matrix Plot | |
import matplotlib.pyplot as plt | |
from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix | |
predictions = pipe.predict(X_test) | |
cm = confusion_matrix(y_test, predictions, labels=pipe.classes_) | |
disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=pipe.classes_) | |
disp.plot() | |
plt.savefig("./Results/model_results.png", dpi=120) | |
# Write metrics to file | |
with open("./Results/metrics.txt", "w") as outfile: | |
outfile.write(f"\nAccuracy = {round(accuracy, 2)}, F1 Score = {round(f1, 2)}") | |
# Saving the model file | |
sio.dump(pipe, "./Model/drug_pipeline.skops") | |