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predictor.py

+import pandas as pd
+import numpy as np
+from statsmodels.tsa.seasonal import seasonal_decompose
+from sklearn.preprocessing import MinMaxScaler
+from keras.preprocessing.sequence import TimeseriesGenerator
+from keras.models import Sequential
+from keras.layers import Dense
+from keras.layers import LSTM
+import tensorflow as tf
+
+model = tf.keras.models.load_model('model.h5')
+
+
+def getData(month_count):
+
+    #df = pd.read_csv('new_data.csv', index_col='Date', parse_dates=True)
+    df = pd.read_csv('new_data.csv', sep=",", index_col='Date', parse_dates=['Date'])
+    df.index.freq = 'MS'
+    df.head()
+
+    results = seasonal_decompose(df['Bill'])
+
+    train = df.iloc[:174]
+    # test = df.iloc[160:]
+    scaler = MinMaxScaler()
+
+    scaler.fit(train)
+    scaled_train = scaler.transform(train)
+    # scaled_test = scaler.transform(test)
+
+    n_features = 1
+    n_input = 12
+    generator = TimeseriesGenerator(scaled_train, scaled_train, length=n_input, batch_size=1)
+
+    # define model
+    model = Sequential()
+    model.add(LSTM(100, activation='relu', input_shape=(n_input, n_features)))
+    model.add(Dense(1))
+    model.compile(optimizer='adam', loss='mse')
+
+    model.summary()
+    # model.fit(generator, epochs=25)
+
+    # loss_per_epoch = model.history.history['loss']
+    # plt.plot(range(len(loss_per_epoch)),loss_per_epoch)
+    last_train_batch = scaled_train[-12:]
+    last_train_batch = last_train_batch.reshape((1, n_input, n_features))
+    #model.save('model.h5')
+
+    model.predict(last_train_batch)
+
+    test_predictions = []
+
+    first_eval_batch = scaled_train[-n_input:]
+    current_batch = first_eval_batch.reshape((1, n_input, n_features))
+
+    # print("length is " + str(len(test)))
+
+    value = int(month_count)
+
+    for i in range(value):
+        # get the prediction value for the first batch
+        current_pred = model.predict(current_batch)[0]
+
+        # append the prediction into the array
+        test_predictions.append(current_pred)
+
+        # use the prediction to update the batch and remove the first value
+        current_batch = np.append(current_batch[:, 1:, :], [[current_pred]], axis=1)
+
+    # test.head()
+    true_predictions = scaler.inverse_transform(test_predictions)
+
+    tt = true_predictions.reshape(-1)
+
+    data = []
+    i = 0
+    for x in tt:
+        obj = {'index': i, 'value': x}
+        i = i + 1
+        data.append(obj)
+
+    return data
+
+
+# print(getData(6))
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