ml/model update

ml/model.py

@@ -1,3 +1,10 @@
+# Databricks notebook source
+import pandas as pd
+
+import multiprocessing
+import logging
+from sklearn.ensemble import RandomForestClassifier
+import numpy as np
 import pickle
 from sklearn.metrics import fbeta_score, precision_score, recall_score
 from ml.data import process_data
@@ -20,7 +27,9 @@ def train_model(X_train, y_train):
         Trained machine learning model.
     """
    # TODO: implement the function
-    pass
+    hgc = RandomForestClassifier()
+    model = hgc.fit(X_train, y_train)
+    return model
 
 
 def compute_model_metrics(y, preds):
@@ -60,7 +69,8 @@ def inference(model, X):
         Predictions from the model.
     """
     # TODO: implement the function
-    pass
+    preds = model.predict(X)
+    return preds
 
 def save_model(model, path):
     """ Serializes model to a file.
@@ -73,12 +83,16 @@ def save_model(model, path):
         Path to save pickle file.
     """
     # TODO: implement the function
-    pass
+    with open(path, 'wb') as f:
+        pickle.dump(model, f)
 
 def load_model(path):
     """ Loads pickle file from `path` and returns it."""
     # TODO: implement the function
-    pass
+    with open(path, 'rb') as f:
+        model = pickle.load(f)
+    return model
+
 
 
 def performance_on_categorical_slice(
@@ -122,7 +136,14 @@ def performance_on_categorical_slice(
         # your code here
         # for input data, use data in column given as "column_name", with the slice_value 
         # use training = False
+        X = slice_data,
+        categorical_features=categorical_features,
+        label = label,
+        training = False,
+        encoder = encoder,
+        lb = lb
+
     )
-    preds = # your code here to get prediction on X_slice using the inference function
+    preds = inference(model, X_slice)
     precision, recall, fbeta = compute_model_metrics(y_slice, preds)
     return precision, recall, fbeta