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MlModel.py

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+from sklearn.compose import ColumnTransformer
+from sklearn.pipeline import Pipeline
+from sklearn.impute import SimpleImputer
+from sklearn.preprocessing import StandardScaler, OneHotEncoder, MinMaxScaler, LabelEncoder, OrdinalEncoder
+from sklearn.linear_model import LogisticRegression, LinearRegression
+from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.feature_selection import SelectKBest, f_regression
+from sklearn.decomposition import TruncatedSVD
+from sklearn.model_selection import GridSearchCV
+from sklearn.pipeline import make_pipeline
+from sklearn.compose import make_column_transformer
+from joblib import dump, load
+
+
+class BuildMlPipeline:
+
+    def __init__(self):
+        pass
+
+    def set_estimators(self, *args):
+        estimator_db = {
+            'randomForestRegressor': RandomForestRegressor(),
+            'linearRegressor': LinearRegression(),
+        }
+        self.estimators = list(map( lambda algo: estimator_db[algo],args))
+
+    def set_scalers(self, *args):
+        scaler_db = {
+            'standardscaler':StandardScaler(),
+            'minmaxscaler':MinMaxScaler(),
+        }
+        self.scalers = list(map( lambda scaler: scaler_db[scaler],args))
+
+    def set_samplers(self, *args):
+        sampler_db = {
+            'smote':SMOTE(),
+            'smoteenn':SMOTEENN(),
+        }
+        self.samplers = list(map( lambda sampler: sampler_db[sampler],args))
+
+    def set_encoders(self, *args):
+        encoders_db = {
+            'ohe':OneHotEncoder(handle_unknown='ignore'),
+            'oe':OrdinalEncoder(),
+        }
+        self.encoders = list(map( lambda encoder: encoders_db[encoder],args))
+
+    def set_hyperparameters(self, params):
+        self.hyperparameters = params
+
+
+    def create_pipelines(self, cat_cols, cont_cols):
+        self.model_pipelines = []
+        for scaler in self.scalers:
+            pipeline_num = Pipeline(steps=[('imputer', SimpleImputer(strategy='median')),
+                                           ('scaling',scaler)])
+            for encoder in self.encoders:
+                pipeline_cat = Pipeline(steps=[('imputer', SimpleImputer(strategy='constant', fill_value='missing')),
+                                               ('encoder',encoder)])
+                preprocessor = make_column_transformer((pipeline_num, cont_cols),(pipeline_cat, cat_cols))
+
+                for estimator in self.estimators:
+                    pipeline  = make_pipeline(preprocessor, estimator)
+                    self.model_pipelines.append(pipeline)
+
+
+    def fit(self, trainX, trainY):
+        self.gs_pipelines = []
+        for idx,pipeline in enumerate(self.model_pipelines):
+            elems = list(map(lambda x:x[0] ,pipeline.steps))
+            param_grid = {}
+
+            for elem in elems:
+                if elem.lower() in self.hyperparameters:
+                    param_grid.update(self.hyperparameters[elem])
+
+            gs = GridSearchCV(pipeline, param_grid= param_grid, n_jobs=6, cv=5)
+            gs.fit(trainX, trainY)
+            print (gs.score(testX,testY),  list(map(lambda x:x[0] , gs.best_estimator_.steps)), gs.best_params_)
+
+            dump(gs, 'model'+str(idx)+'.pipeline')
+            self.gs_pipelines.append(gs)
+
+
+    def score(self, testX, testY):
+        for idx,model in enumerate(self.gs_pipelines):
+            y_pred = model.best_estimator_.predict(testX)
+            print (model.best_estimator_)
+            print (idx,confusion_matrix(y_true=testY,y_pred=y_pred))