update gplearn

src/tabular/feature_engineering/new_featureEngeneer/__init__.py

@@ -0,0 +1,6 @@
+# encoding:utf-8
+"""
+@author: sugang
+@time: 2020/1/21 4:26 下午
+@desc: 
+"""

src/tabular/feature_engineering/new_featureEngeneer/gplearn.py

@@ -0,0 +1,44 @@
+# encoding:utf-8
+"""
+@author: sugang
+@time: 2020/1/21 3:15 下午
+@desc:
+"""
+import pandas as pd
+from sklearn import datasets
+from gplearn.genetic import SymbolicTransformer
+from sklearn.model_selection import train_test_split
+
+
+
+class GPConfig:
+    def __init__(self, generation=1000, population_size=5000, hall_of_fame=100, n_components=10,
+                 parsimony_coefficient=0.0005, max_samples=0.9):
+        self.generation = generation
+        self.population_size = population_size
+        self.hall_of_fame = hall_of_fame
+        self.n_components = n_components
+        self.parsimony_coefficient = parsimony_coefficient
+        self.max_samples = max_samples
+        self.function_set = ['add', 'sub', 'mul', 'div', 'log', 'sqrt', 'abs', 'neg', 'max', 'min']
+
+
+def gp_feature_generator(df_train, df_test, y_name, var_list, gp_config):
+    gp = SymbolicTransformer(generations=gp_config.generation, population_size=gp_config.population_size,
+                             hall_of_fame=gp_config.hall_of_fame, n_components=gp_config.n_components,
+                             function_set=gp_config.function_set,
+                             parsimony_coefficient=gp_config.parsimony_coefficient,
+                             max_samples=gp_config.max_samples, verbose=1,
+                             random_state=0, n_jobs=3)
+
+    y_train = df_train[[y_name]]
+    if var_list is None:
+        X_train = df_train.drop(y_name, axis=1)
+        X_test = df_test.drop(y_name, axis=1)
+    else:
+        X_train = df_train[var_list]
+        X_test = df_train[var_list]
+
+    gp.fit(X_train, y_train)
+
+    return gp.transform(X_test)