Merge ef81eda into c0cf8a0

binary_classifier_Titanic.py

@@ -0,0 +1,47 @@
+import pandas as pd
+import numpy as np
+from supervised.automl import AutoML
+import os
+
+from sklearn.metrics import accuracy_score
+
+"""
+df = pd.read_csv("tests/data/Titanic/train.csv")
+
+X = df[df.columns[2:]]
+y = df["Survived"]
+
+automl = AutoML(mode="Explain")
+automl.fit(X, y)
+pred = automl.predict(X)
+
+print("Train accuracy", accuracy_score(y, pred))
+test = pd.read_csv("tests/data/Titanic/test_with_Survived.csv")
+pred = automl.predict(test)
+print("Test accuracy", accuracy_score(test["Survived"], pred))
+"""
+
+import pandas as pd
+import numpy as np
+from sklearn.metrics import accuracy_score
+from supervised import AutoML
+
+train = pd.read_csv(
+    "https://raw.githubusercontent.com/pplonski/datasets-for-start/master/Titanic/train.csv"
+)
+print(train.head())
+
+X = train[train.columns[2:]]
+y = train["Survived"]
+
+# automl = AutoML(mode="Compete") # default mode is Explain
+automl = AutoML(total_time_limit=120)  # default mode is Explain
+
+automl.fit(X, y)
+
+test = pd.read_csv(
+    "https://raw.githubusercontent.com/pplonski/datasets-for-start/master/Titanic/test_with_Survived.csv"
+)
+predictions = automl.predict(test)
+print(predictions)
+print(f"Accuracy: {accuracy_score(test['Survived'], predictions)*100.0:.2f}%")

loader.py

@@ -0,0 +1,11 @@
+from supervised import AutoML
+import pandas as pd
+from sklearn.metrics import accuracy_score
+
+test = pd.read_csv(
+    "https://raw.githubusercontent.com/pplonski/datasets-for-start/master/Titanic/test_with_Survived.csv"
+)
+
+automl = AutoML(results_path="AutoML_1")
+p=automl.predict(test,"Ensemble")
+print(f"Accuracy: {accuracy_score(test['Survived'], p)*100.0:.2f}%")

supervised/automl.py

@@ -336,7 +336,7 @@ def fit(self, X, y, sample_weight=None, cv=None):
         """
         return self._fit(X, y, sample_weight, cv)
 
-    def predict(self, X):
+    def predict(self, X, model=None): 
         """
         Computes predictions from AutoML best model.
 
@@ -353,7 +353,7 @@ def predict(self, X):
         Raises:
             AutoMLException: Model has not yet been fitted.
         """
-        return self._predict(X)
+        return self._predict(X, model)
 
     def predict_proba(self, X):
         """

supervised/base_automl.py

@@ -1301,9 +1301,21 @@ def _base_predict(self, X, model=None):
         else:
             return predictions
 
-    def _predict(self, X):
+    def _predict(self, X, modelname=None):
 
-        predictions = self._base_predict(X)
+        model = None
+        if not self._models:
+            self.load(self.results_path)
+        if modelname != None:
+            for i in self._models:
+                if modelname == i.get_name():
+                    model = i
+
+
+        if modelname != None and model == None:
+            raise AutoMLException("invaild model")
+
+        predictions = self._base_predict(X, model)
         # Return predictions
         # If classification task the result is in column 'label'
         # If regression task the result is in column 'prediction'