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Merged
merged 2 commits into from
Dec 27, 2019
Merged

not checking sqlflow custom models parameters #1556

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08854ee
not checking sqlflow custom models parameters
typhoonzero Dec 27, 2019
43b2540
fix ci
typhoonzero Dec 27, 2019
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2 changes: 1 addition & 1 deletion cmd/repl/repl_test.go
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Expand Up @@ -145,7 +145,7 @@ func TestComplete(t *testing.T) {

p.InsertText(`RAIN `, false, true)
c = s.completer(*p.Document())
a.Equal(14, len(c))
a.Equal(11, len(c))

p.InsertText(`DNN`, false, true)
c = s.completer(*p.Document())
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5 changes: 5 additions & 0 deletions doc/model_parameter.md
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Expand Up @@ -197,6 +197,11 @@ INTO sqlflow_models.my_dnn_model;
<td>Type</td>
<td>Description</td>
</tr>
<tr>
<td>model.*</td>
<td>attribute.unknown</td>
<td>Any model parameters defined in custom models</td>
</tr>
<tr>
<td>train.batch_size</td>
<td>int</td>
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2 changes: 1 addition & 1 deletion pkg/sql/codegen/attribute/attribute_test.go
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Expand Up @@ -43,7 +43,7 @@ func TestDictionaryValidate(t *testing.T) {
func TestPremadeModelParamsDocs(t *testing.T) {
a := assert.New(t)

a.Equal(14, len(PremadeModelParamsDocs))
a.Equal(11, len(PremadeModelParamsDocs))
a.Equal(len(PremadeModelParamsDocs["DNNClassifier"]), 12)
a.NotContains(PremadeModelParamsDocs["DNNClassifier"], "feature_columns")
a.Contains(PremadeModelParamsDocs["DNNClassifier"], "optimizer")
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29 changes: 0 additions & 29 deletions pkg/sql/codegen/attribute/model_parameters.go
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1 change: 1 addition & 0 deletions pkg/sql/codegen/tensorflow/codegen.go
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Expand Up @@ -52,6 +52,7 @@ example: "Accuracy,AUC"`, nil},
"validation.select": {attribute.String, `[default=""]
Specify the dataset for validation.
example: "SELECT * FROM iris.train LIMIT 100"`, nil},
"model.*": {attribute.Unknown, "Any model parameters defined in custom models", nil},
}

func intArrayToJSONString(ia []int) string {
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5 changes: 0 additions & 5 deletions python/extract_docstring.py
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Expand Up @@ -84,11 +84,6 @@ def parse_ctor_args(f, prefix=''):
for cls in tf_estimators:
param_doc[cls] = parse_ctor_args(eval(cls))

# SQLFlow premade models
sf_models = filter(lambda m: inspect.isclass(m[1]), inspect.getmembers(sqlflow_models))
for name, cls in sf_models:
param_doc['sqlflow_models.' + name] = parse_ctor_args(cls, ':param')

# xgboost models: gbtree, gblinear or dart
param_doc['xgboost.gbtree'] = parse_ctor_args(xgboost.XGBModel)
del param_doc['xgboost.gbtree']['booster'] # booster specified as an estimator
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