Upload model_predict_df.py

model_predict_df.py

@@ -0,0 +1,95 @@
+import os
+import numpy as np
+import pandas as pd
+from copy import copy
+import joblib
+import tensorflow as tf
+from mastml.models import EnsembleModel
+import keras
+from keras.models import Sequential
+from keras.layers import Dense, Dropout, BatchNormalization
+from keras.wrappers.scikit_learn import KerasRegressor
+import matplotlib.pyplot as plt
+from scipy.spatial.distance import cdist
+
+def rebuild_model(model_folder):
+    PATH = os.getcwd()
+
+    # We need to define the function that builds the network architecture
+    def keras_model():
+        model = Sequential()
+        model.add(Dense(1024, input_dim=9, kernel_initializer='normal', activation='relu'))
+        model.add(Dropout(0.3))
+        model.add(Dense(1024, kernel_initializer='normal', activation='relu'))
+        model.add(Dropout(0.3))
+        model.add(Dense(1, kernel_initializer='normal'))
+        model.compile(loss='mean_squared_error', optimizer='adam')
+
+        return model
+
+    model_keras = KerasRegressor(build_fn=keras_model, epochs=250, batch_size=100, verbose=0)
+    model_bagged_keras_rebuild = EnsembleModel(model=model_keras, n_estimators=10)
+
+    num_models = 10
+    models = list()
+    for i in range(num_models):
+        models.append(tf.keras.models.load_model(os.path.join(PATH, 'RPV_model'+'/'+model_folder+'/keras_model_' + str(i))))
+
+    model_bagged_keras_rebuild.model.estimators_ = models
+    model_bagged_keras_rebuild.model.estimators_features_ = [np.arange(0, 9) for i in models]
+
+    return model_bagged_keras_rebuild
+
+def get_preds_ebars(model, df_featurized, preprocessor, return_ebars=True):
+    preds_each = list()
+    ebars_each = list()
+
+    df_featurized_scaled = preprocessor.transform(pd.DataFrame(df_featurized))
+
+    if return_ebars == True:
+        for i, x in df_featurized_scaled.iterrows():
+            preds_per_data = list()
+            for m in model.model.estimators_:
+                preds_per_data.append(m.predict(pd.DataFrame(x).T, verbose=0)) #pd.DataFrame(x).T
+            preds_each.append(np.mean(preds_per_data))
+            ebars_each.append(np.std(preds_per_data))
+
+    else:
+        preds_each = model.predict(df_featurized_scaled)
+        ebars_each = [np.nan for i in range(preds_each.shape[0])]
+
+    if return_ebars == True:
+        a = -0.041
+        b = 2.041
+        c = 3.124
+        ebars_each_recal = a*np.array(ebars_each)**2 + b*np.array(ebars_each) + c
+    else:
+        ebars_each_recal = ebars_each
+
+
+    return np.array(preds_each).ravel(), np.array(ebars_each_recal).ravel()
+
+def make_predictions_DNN(df_featurized, model_folder):
+    PATH = os.getcwd()
+
+    # Rebuild the saved model
+    model = rebuild_model(model_folder)
+
+    # Normalize the input features
+    preprocessor = joblib.load(os.path.join(PATH, os.path.join('RPV_model', model_folder+'/StandardScaler.pkl')))
+    
+    # Get predictions and error bars from model
+    preds, ebars = get_preds_ebars(model, df_featurized, preprocessor, return_ebars=True)
+
+    pred_dict = {'preds':preds,
+                 'ebars':ebars}
+
+    return pd.DataFrame(pred_dict)
+
+def test(df):
+    #pred_dict = {'preds': ['This is a test'], 'ebars': ['This is a test']}
+    pred_arr = np.array([['here is some data'], ['here are some ebars']])
+    model = keras.models.load_model('keras_model_0')
+    return pred_arr
+    #return pd.DataFrame(pred_dict)
+    #return np.sqrt(x)