add model metrics in model_card.md

starter/model/model_metrics.txt

@@ -0,0 +1,2 @@
+precision,recall,fbeta_score
+0.8159117305458768,0.5614011720831114,0.665141234022408

starter/model_card.md

@@ -28,7 +28,14 @@ For additional information see the Model Card paper: https://arxiv.org/pdf/1810.
 
 ## Metrics
 
-* The model is evaluated by F1 score.
+* The model is evaluated by precision, recall, and F1 scores.
+* The scores are following:
+
+    | Metrics | Scores |
+    | ------- | ------ |
+    | Precision | 0.8159 |
+    | Recall | 0.5614 |
+    | F1 | 0.6651 |
 
 ## Ethical Considerations
 

starter/starter/model_metrics.py

@@ -0,0 +1,28 @@
+import pandas as pd
+from joblib import load
+from ml import model, data
+from train_model import CAT_FEATURES
+
+
+def compute_score(datapath='../data/census_cleaned.csv'):
+    data_df = pd.read_csv(datapath)
+
+    model_trained = load("../model/model_trained.joblib")
+    encoder = load("../model/encoder.joblib")
+    label = load("../model/lb.joblib")
+
+    X_test, y_test, _, _ = data.process_data(
+        data_df,
+        categorical_features=CAT_FEATURES,
+        label="salary", encoder=encoder, lb=label, training=False)
+    y_preds = model_trained.predict(X_test)
+    prc, rcl, fb1 = model.compute_model_metrics(y_test, y_preds)
+
+    metrics_df = pd.DataFrame(
+        {"precision": prc, "recall": rcl, "fbeta_score": fb1}, index=[0])
+
+    metrics_df.to_csv('../model/model_metrics.txt', index=False)
+
+
+if __name__ == '__main__':
+    compute_score()