Python AttributeError: 'DataFrame' object 沒有屬性 'predict'

Question

我是Python的新手。目前，我正在做一個機器學習項目。 我想在另一段代碼中重新使用 model，所以我成功生成了 pickle 文件，代碼如下。

import pickle
from sklearn.externals import joblib
import pandas as pd
from sklearn.preprocessing import MinMaxScaler

sc = MinMaxScaler()

model = joblib.load('toyota_corolla_model.pkl')
x = pd.read_csv('Dataset.csv')

train_dates = pd.to_datetime(x['Date'])
cols = list(x)[1:2]
df_for_training = x[cols].astype(float)

x_test = sc.fit_transform(df_for_training)

y_pred = model.predict(x_test)

當我運行代碼時，它顯示以下錯誤。

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-16-0b1337d9f786> in <module>()
----> 1 y_pred = model.predict[x_test]

/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py in __getattr__(self, name)
   5139             if self._info_axis._can_hold_identifiers_and_holds_name(name):
   5140                 return self[name]
-> 5141             return object.__getattribute__(self, name)
   5142 
   5143     def __setattr__(self, name: str, value) -> None:

AttributeError: 'DataFrame' object has no attribute 'predict'

model訓練如下。

import numpy as np
from keras.models import Sequential
from keras.layers import LSTM
from keras.layers import Dense, Dropout
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.preprocessing import StandardScaler
import seaborn as sns

df = pd.read_csv('Dataset.csv')

# Separate dates for future plotting
train_dates = pd.to_datetime(df['Date'])

# Variables for taining
# Select the vehicle model which need to predict price
cols = list(df)[1:2]

df_for_training = df[cols].astype(float)

df_for_plot = df_for_training.tail(500)
df_for_plot.plot.line()

# Normalize the dataset
scaler = StandardScaler()
scaler = scaler.fit(df_for_training)
df_for_training_scaled = scaler.transform(df_for_training)

# Training series
trainX = []
# Prediction series
trainY = []

# Number of months we want to predict for future
n_future = 1
# Number of past months we want to use for predict the future
n_past = 14

for i in range(n_past, len(df_for_training_scaled) - n_future +1):
    trainX.append(df_for_training_scaled[i - n_past:i, 
                                         0:df_for_training.shape[1]])
    trainY.append(df_for_training_scaled[i + n_future - 1:i + n_future, 0])

# Convert trainX and trainY into arrays
trainX, trainY = np.array(trainX), np.array(trainY)

print('trainX shape == {}.'.format(trainX.shape))
print('trainY shape == {}.'.format(trainY.shape))

model = Sequential()
model.add(LSTM(64, activation='relu', 
               input_shape=(trainX.shape[1], trainX.shape[2]), 
               return_sequences=True))
model.add(LSTM(32, activation='relu', return_sequences=False))
model.add(Dropout(0.2))
model.add(Dense(trainY.shape[1]))

model.compile(optimizer='adam', loss='mse', metrics=['accuracy'])
model.summary()

import tensorflow as tf

model.compile(optimizer='sgd', 
              loss=tf.keras.losses.categorical_crossentropy, 
              metrics=['accuracy'])

history = model.fit(trainX, trainY, 
                    epochs=10, 
                    batch_size=10, 
                    validation_split=0.1, 
                    verbose=1)

plt.plot(history.history['accuracy'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train'], loc='upper left')
plt.show()

# Forecast
# Start with the last day of training date and predict the future

# Redefining n_future to extend prediction months 
# beyond the original n_future months
n_future = 50
forecast_period_dates = pd.date_range(list(train_dates)[-1],
                                      periods = n_future, 
                                      freq = '1m').tolist()

# Forecast
forecast = model.predict(trainX[-n_future:])

# Inverse transforamtion
forecast_copies = np.repeat(forecast, df_for_training.shape[1], axis=-1)
y_pred_future = scaler.inverse_transform(forecast_copies)[:,0]

# Convert timestamp to date
forecast_dates = []
for time_i in forecast_period_dates:
  forecast_dates.append(time_i.date())

df_forecast = pd.DataFrame({'Date':np.array(forecast_dates),
                            'Toyota_corolla':y_pred_future})
df_forecast['Date'] = pd.to_datetime(df_forecast['Date'])

original = df[['Date', 'Toyota_corolla']]
original['Date'] = pd.to_datetime(original['Date'])
original = original.loc[original['Date'] >= '2015-1-1']

sns.lineplot(original['Date'], 
             original['Toyota_corolla'], 
             label = 'Original data')
sns.lineplot(df_forecast['Date'], 
             df_forecast['Toyota_corolla'], 
             label = 'Predicted data')

這就是我生成 pickle 文件的方式。

import pickle
from sklearn.externals import joblib
filename = 'toyota_corolla_model.pkl'
joblib.dump(df_forecast, filename)

請幫忙。

Answer 1

您正在保存 dataframe：

joblib.dump(df_forecast, filename)

相反，您需要保存您的 model：

joblib.dump(<your model name here>, filename)

像這樣：

joblib.dump(model, filename)