add sklearn base

ltsm/common/sklearn.py

@@ -0,0 +1,78 @@
+import numpy as np
+import pandas as pd
+
+def get_default_hyperparameter(primitive, hyperparameter):
+
+        # check if input legal hyperparameter
+        hyperparam_buf = list(primitive.metadata.get_hyperparams().defaults().keys())
+        hyperparam_input = list(hyperparameter.keys())
+        if not set(hyperparam_buf) > set(hyperparam_input):
+            invalid_hyperparam = list(set(hyperparam_input) - set(hyperparam_buf))
+            raise TypeError(primitive.__name__ + ' got unexpected keyword argument ' + str(invalid_hyperparam))
+
+        hyperparams_class = primitive.metadata.get_hyperparams()
+        hyperparams = hyperparams_class.defaults()
+
+        if len(hyperparameter.items()) != 0:
+            hyperparams = hyperparams.replace(hyperparameter)
+
+        return hyperparams
+
+class BaseSKI:
+
+    def __init__(self, primitive, **hyperparameter):
+
+        self.fit_available = True if 'fit' in primitive.__dict__ else False
+        self.predict_available = True if 'produce' in primitive.__dict__ else False
+        self.predict_score_available = True if 'produce_score' in dir(primitive) else False
+        self.produce_available = True if 'produce' in primitive.__dict__ else False
+
+        hyperparams = get_default_hyperparameter(primitive, hyperparameter)
+        self.primitives = primitive(hyperparams=hyperparams)
+
+    def _sys_data_check(self, data):
+        if self.system_num == 1:
+            if type(data) is np.ndarray and data.ndim == 2:
+                data = [data] # np.expand_dims(data, axis=0)
+            else:
+                raise AttributeError('For system_num = 1, input data should be 2D numpy array.')
+        elif self.system_num > 1:
+            if type(data) is list and len(data) == self.system_num:
+                for ts_data in data:
+                    if type(ts_data) is np.ndarray and ts_data.ndim == 2:
+                        continue
+                    else:
+                        raise AttributeError('For system_num > 1, each element of input list should be 2D numpy arrays.')
+            else:
+                raise AttributeError('For system_num > 1, input data should be the list of `system_num` 2D numpy arrays.')
+
+        return data
+
+    def fit(self, data):
+
+        if not self.fit_available:
+            raise AttributeError('type object ' + self.__class__.__name__ + ' has no attribute \'fit\'')
+
+        data = self._sys_data_check(data)
+
+        for sys_idx, primitive in enumerate(self.primitives):
+            sys_data = data[sys_idx]
+            sys_data = self._transform(sys_data)
+            primitive.set_training_data(inputs=sys_data)
+            primitive.fit()
+
+        return
+
+    def predict(self, data):
+
+        if not self.predict_available:
+            raise AttributeError('type object ' + self.__class__.__name__ + ' has no attribute \'predict\'')
+
+        data = self._sys_data_check(data)
+        output_data = self._forward(data, '_produce')
+
+        return output_data
+
+    def _transform(self, X):     #transform the ndarray to d3m dataframe, select columns to use
+        column_name = [str(col_index) for col_index in range(X.shape[1])]
+        return pd.DataFrame(X, columns=column_name, generate_metadata=True)