add input_example

lab8/trainScript.py

@@ -10,7 +10,7 @@ import mlflow.keras
 from urllib.parse import urlparse
 
 
-mlflow.set_tracking_uri("http://172.17.0.1:5000")
+# mlflow.set_tracking_uri("http://172.17.0.1:5000")
 mlflow.set_experiment('s444417')
 
 # train params
@@ -112,10 +112,12 @@ def train():
   
   tracking_url_type_store = urlparse(mlflow.get_tracking_uri()).scheme
   
+  sampleInp = [0.0, 0.0, 2.0, 904.129525, 1.000000, 1.000000, 20.098413, 79.107860]
+  # expected value is 49.7
   if tracking_url_type_store != "file":
-      mlflow.keras.log_model(linear_model, "linear-model", registered_model_name="HousePriceLinear", signature=signature)
+      mlflow.keras.log_model(linear_model, "linear-model", registered_model_name="HousePriceLinear", signature=signature, input_example=np.array(sampleInp))
   else:
-      mlflow.keras.log_model(linear_model, "model", signature=signature)
+      mlflow.keras.log_model(linear_model, "model", signature=signature, input_example=np.array(sampleInp))
 
 if __name__ == '__main__':
     train()

result.txt

@@ -1,2 +1,2 @@
-predictions: [188.40611, 79.26458, 79.07924, 79.702156, 249.17542, -1.7695923, 54.21414, 78.15069, 114.4659, 248.62257]
-expected: [ 74. 140.  35.  42. 260.  15.  25. 190.  40. 200.]
+predictions: [70.94439, 65.28393, -20.83638, 653.3567, 156.35808, 120.63458, 80.068924, 165.0669, 42.61284, -10.327789]
+expected: [ 37.5  48.   15.  300.   55.3  56.   43.   58.   25.   70. ]

sampleinp.txt

@@ -0,0 +1,9 @@
+UNDER_CONSTRUCTION      0.000000
+RERA                    0.000000
+BHK_NO.                 2.000000
+SQUARE_FT             904.129525
+READY_TO_MOVE           1.000000
+RESALE                  1.000000
+LONGITUDE              20.098413
+LATITUDE               79.107860
+Name: 0, dtype: float64