Retrain model with new version of MLConsole

README.md

@@ -22,14 +22,14 @@ model-index:
         metrics:
           - type: mae
             name: Mean absolute error
-            value: 0.21668575704097748
+            value: 0.22897861897945404
           - type: loss
             name: Model loss
-            value: 0.08084399998188019
+            value: 0.08849651366472244
 
 ---
 
-# avocado.csv (#0)
-Trained on [ML Console](https://mlconsole.com).
+# regression model trained on "nateraw/avocado-prices"
+🤖 [Load and use this model](https://mlconsole.com/model/hf/julien-c/avocado-prices-v2) in one click.
+🧑‍💻 [Train your own model](https://mlconsole.com) on ML Console.
 
-[Load the model on ML Console](https://mlconsole.com/model/hf/julien-c/avocado-prices).

model.json

@@ -10,7 +10,7 @@
 					"config": {
 						"batch_input_shape": [
 							null,
-							68
+							55
 						],
 						"dtype": "float32",
 						"sparse": false,
@@ -201,7 +201,7 @@
 				{
 					"name": "dense_Dense1/kernel",
 					"shape": [
-						68,
+						55,
 						64
 					],
 					"dtype": "float32"

modelRun.json

@@ -2,7 +2,7 @@
 	"id": 0,
 	"name": "model #1",
 	"modelConfig": {
-		"_mlconsole_type": "e",
+		"_mlconsole_type": "ModelConfig",
 		"value": {
 			"task": "regression",
 			"optimizer": "adagrad",
@@ -19,8 +19,30 @@
 		}
 	},
 	"status": 2,
-	"datasetName": "avocado.csv",
+	"datasetName": "nateraw/avocado-prices/avocado.csv",
+	"huggingFaceDataset": "nateraw/avocado-prices",
 	"features": [
+		{
+			"name": "",
+			"used": true,
+			"type": 0,
+			"statistics": {
+				"type_percentage": {
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+					"value": {
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+				"example_value_per_type": {
+					"_mlconsole_type": "Map",
+					"value": {
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+				"unique_values": 53,
+				"presence_percentage": 1
+			}
+		},
 		{
 			"name": "4046",
 			"used": true,
@@ -35,7 +57,7 @@
 				"example_value_per_type": {
 					"_mlconsole_type": "Map",
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@@ -56,7 +78,7 @@
 				"example_value_per_type": {
 					"_mlconsole_type": "Map",
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@@ -77,13 +99,34 @@
 				"example_value_per_type": {
 					"_mlconsole_type": "Map",
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 				"unique_values": 12071,
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 		},
+		{
+			"name": "Date",
+			"used": true,
+			"type": 1,
+			"statistics": {
+				"type_percentage": {
+					"_mlconsole_type": "Map",
+					"value": {
+						"string": 1
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+				"example_value_per_type": {
+					"_mlconsole_type": "Map",
+					"value": {
+						"string": "2017-10-22"
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+				"unique_values": 169,
+				"presence_percentage": 1
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+		},
 		{
 			"name": "Large Bags",
 			"used": true,
@@ -98,7 +141,7 @@
 				"example_value_per_type": {
 					"_mlconsole_type": "Map",
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-						"number": 156.72
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@@ -119,7 +162,7 @@
 				"example_value_per_type": {
 					"_mlconsole_type": "Map",
 					"value": {
-						"string": "DallasFtWorth"
+						"string": "Seattle"
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 				"unique_values": 54,
@@ -140,7 +183,7 @@
 				"example_value_per_type": {
 					"_mlconsole_type": "Map",
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@@ -161,7 +204,7 @@
 				"example_value_per_type": {
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@@ -182,7 +225,7 @@
 				"example_value_per_type": {
 					"_mlconsole_type": "Map",
 					"value": {
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@@ -203,7 +246,7 @@
 				"example_value_per_type": {
 					"_mlconsole_type": "Map",
 					"value": {
-						"string": "organic"
+						"string": "conventional"
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@@ -224,7 +267,7 @@
 				"example_value_per_type": {
 					"_mlconsole_type": "Map",
 					"value": {
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+						"number": 279.78
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@@ -267,7 +310,7 @@
 			"example_value_per_type": {
 				"_mlconsole_type": "Map",
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-					"number": 1.45
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@@ -278,24 +321,24 @@
 		"_mlconsole_type": "Map",
 		"value": {
 			"loss": {
-				"_mlconsole_type": "e",
+				"_mlconsole_type": "MetricValues",
 				"value": {
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 					"values": [
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+						0.08849651366472244
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 			"mae": {
-				"_mlconsole_type": "e",
+				"_mlconsole_type": "MetricValues",
 				"value": {
 					"epochs": [
 						0
 					],
 					"values": [
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@@ -306,315 +349,75 @@
 		"_mlconsole_type": "Map",
 		"value": {
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-				"_mlconsole_type": "e",
+				"_mlconsole_type": "StandardScaler",
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+					"config": {},
+					"mean": 291967.65625,
+					"stdev": 1259982.125
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 			"4225": {
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+					"stdev": 1212555.5
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+			"": {
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 			"Small Bags": {
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@@ -622,87 +425,308 @@
 	"encoders": {
 		"_mlconsole_type": "Map",
 		"value": {
+			"Date": {
+				"_mlconsole_type": "OneHotEncoder",
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+					"labels": [
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+						"2015-09-06",
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+						"2015-10-04",
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 			"region": {
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 				"value": {
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-						"Chicago",
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+						"Houston",
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-						"conventional"
-					]
+					"config": {
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+						"organic"
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 						2015,
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+						2016,
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