-
Notifications
You must be signed in to change notification settings - Fork 0
/
naivepredictor.py
184 lines (170 loc) · 8.12 KB
/
naivepredictor.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
# coding: UTF-8
import numpy as np
import pandas as pd
from dataformatter import DataFormatter
# Se usa Keras como la librería para manejar
# las redes neuronales.
from keras.models import Model
from keras.layers import Input, Dense
class NaivePredictor(object):
"""docstring for NaivePredictor"""
def __init__(self, dataset_path=None, input_window_size=4, rolling_window_size=5,
columns_to_standardize=None, columns_to_windowize=None, data=None):
self.dataset_path = dataset_path
self.input_window_size = input_window_size
self.rolling_window_size = rolling_window_size
self.columns_to_standardize = columns_to_standardize
self.columns_to_windowize = columns_to_windowize
self.data = data
self.__preprocess_data()
self.__create_model()
def predict(self, point=None):
"""Point debe ser un Data Frame de Pandas con las información
necesaria para realizar la predicción."""
# 1. Standardize point with training mean and standard deviation.
# 2. Add it to the data.
if point is None:
df = self.data
else:
test_data = self.__standardize_features_for_test(point, self.columns_to_standardize, self.column_means, self.column_stds)
df = pd.concat([self.data, test_data])
# 3. Windowize.
fmt = DataFormatter()
X, Y = fmt.windowize_series(df.as_matrix(), size=self.input_window_size, column_indexes=self.columns_to_windowize)
# 4. Extract the last window.
last_window = fmt.get_last_window(df.as_matrix(), size=self.input_window_size, column_indexes=self.columns_to_windowize)
last_window = last_window[None, :]
# 5. Compute the error.
train_score = self.model.evaluate(X, Y, verbose=0)
train_score = np.array([train_score[0], np.sqrt(train_score[0]), train_score[1], train_score[2]*100])
# 6. Make the prediction.
prediction = np.squeeze(self.model.predict(last_window))
# 7. Computing prediction intervals
pred_upper = prediction + 1.96 * train_score[1]
pred_lower = prediction - 1.96 * train_score[1]
# Revert standardization
prediction = prediction * self.column_stds[u'Close'] + self.column_means[u'Close']
pred_upper = pred_upper * self.column_stds[u'Close'] + self.column_means[u'Close']
pred_lower = pred_lower * self.column_stds[u'Close'] + self.column_means[u'Close']
return prediction, pred_lower, pred_upper
def fit_model(self, epochs=200, verbose=0):
"""Entrenar el modelo para producción."""
# Standardize inputs
self.data, self.column_means, self.column_stds = self.__standardize_features(self.data, self.columns_to_standardize)
# Windowize dataset
fmt = DataFormatter()
self.X, self.Y = fmt.windowize_series(self.data.as_matrix(), size=self.input_window_size, column_indexes=self.columns_to_windowize)
self.model.fit(self.X, self.Y, epochs=epochs, batch_size=32, verbose=verbose)
def test_model(self, n_splits=9, cv_runs=10, epochs=100, verbose=2):
"""Evaluación del modelo usando validación cruzada
hacia adelante."""
from sklearn.model_selection import TimeSeriesSplit
self.metrics = ['MSE', 'RMSE', 'MAE', 'MAPE']
train_scores = np.zeros((cv_runs, n_splits, len(self.metrics)))
test_scores = np.zeros((cv_runs, n_splits, len(self.metrics)))
fmt = DataFormatter()
tscv = TimeSeriesSplit(n_splits=n_splits)
for j in xrange(cv_runs):
#print('\nCross-validation run %i' % (j+1))
i = 1
for train_index, test_index in tscv.split(self.data['Close'].values):
# División del conjunto de datos en entrenamiento y prueba
train_df = self.data.loc[train_index]
test_df = self.data.loc[test_index]
# Estandarización del conjunto de datos
train_data, training_means, training_stds = self.__standardize_features(train_df, self.columns_to_standardize)
test_data = self.__standardize_features_for_test(test_df, self.columns_to_standardize, training_means, training_stds)
# Extracción de ventanas de datos
trainX, trainY = fmt.windowize_series(train_data.as_matrix(), size=self.input_window_size, column_indexes=self.columns_to_windowize)
testX, testY = fmt.windowize_series(test_data.as_matrix(), size=self.input_window_size, column_indexes=self.columns_to_windowize)
# Ajustando el modelo
# print('Fold %i' % (i))
self.model.fit(trainX, trainY, epochs=epochs, batch_size=32, validation_data=(testX, testY), verbose=verbose)
# Evaluando cada partición de la validación cruzada hacia adelante
train_score = self.model.evaluate(trainX, trainY, verbose=verbose)
train_score = np.array([train_score[0], np.sqrt(train_score[0]), train_score[1], train_score[2]*100])
test_score = self.model.evaluate(testX, testY, verbose=verbose)
test_score = np.array([test_score[0], np.sqrt(test_score[0]), test_score[1], test_score[2]*100])
# print('Train Score: %.5f MSE, %.5f RMSE, %.5f MAE, %.5f%% MAPE' % (train_score[0], train_score[1], train_score[2], train_score[3]))
# print('Test Score: %.5f MSE, %.5f RMSE, %.5f MAE, %.5f%% MAPE\n' % (test_score[0], test_score[1], test_score[2], test_score[3]))
# [0: MSE, 1: RMSE, 2: MAE, 3: MAPE]
train_scores[j, i-1, :] = train_score
test_scores[j, i-1, :] = test_score
i += 1
self.train_results = train_scores.mean(axis=0).mean(axis=0)
self.test_results = test_scores.mean(axis=0).mean(axis=0)
# print(train_results)
# print(test_results)
def compile_model(self):
metrics = ['mean_squared_error', 'mean_absolute_error', 'mean_absolute_percentage_error']
self.model.compile(loss='mean_squared_error', optimizer='adam', metrics=metrics)
def __create_model(self):
n = self.input_window_size*len(self.columns_to_windowize) + self.data.shape[1] - len(self.columns_to_windowize)
main_input = Input(shape=(n,), name='main_input', dtype='float32')
x = Dense(n, activation='relu')(main_input)
x = Dense(int(n*0.8), activation='relu')(x)
x = Dense(int(n*0.6), activation='relu')(x)
x = Dense(int(n*0.4), activation='relu')(x)
x = Dense(int(n*0.2), activation='relu')(x)
main_output = Dense(1, activation='linear', name='main_output')(x)
self.model = Model(inputs=main_input, outputs=main_output)
def __standardize_features(self, data, columns):
"""Estandarización de los datos de entrenamiento.
"""
# Revisar si las columnas existen.
if columns is None:
raise('Passed column names is None.')
if len(columns) == 0:
raise('Passed column names is empty.')
column_means = {}
column_stds = {}
for column in columns:
mean = data.loc[:, column].mean()
std = data.loc[:, column].std()
data.loc[:, column] = (data.loc[:, column] - mean) / std
column_means[column] = mean
column_stds[column] = std
return data, column_means, column_stds
def __standardize_features_for_test(self, data, columns, training_means, training_stds):
"""Estandarización de los datos de prueba usando la media
y desviación estándar de los datos de entrenamiento.
"""
# Revisar si las columnas existen.
if columns is None:
raise('Passed column names is None.')
if len(columns) == 0:
raise('Passed column names is empty.')
for column in columns:
data.loc[:, (column)] = (data.loc[:, (column)] - training_means[column]) / training_stds[column]
return data
def __preprocess_data(self):
"""Preprocesamiento del conjunto de datos."""
if (self.data is None):
df = pd.read_csv(self.dataset_path)
#df['Close'] = df['Adj Close'] # DELETE
date = pd.to_datetime(df['Date'])
df.insert(0, 'Month', date.dt.month)
df.insert(1, 'Day', date.dt.day)
#df = df.drop('Adj Close', axis=1)
#df = df.drop('Date', axis=1)
new_column_order = ['Month', 'Day', 'Volume', 'Open', 'High', 'Low', 'Close']
self.data = df.reindex(columns=new_column_order)
else:
pass
def tester():
"""Método exclusivo para pruebas locales de funcionamiento."""
columns_to_standardize = ['Volume', 'Open', 'High', 'Low', 'Close']
columns_to_windowize = [2, 3, 4, 5, 6]
input_window_size = 5
dataset_path = "datasets/AAPL.csv"
predictor = NaivePredictor(dataset_path,
columns_to_standardize=columns_to_standardize,
columns_to_windowize=columns_to_windowize,
input_window_size=input_window_size)
predictor.compile_model()
predictor.test_model(n_splits=9, epochs=90, verbose=0)
def test():
naivePredictor=NaivePredictor()
naivePredictor.tester()
if __name__ == '__main__':
test()