I am predicting Y based on X from past values. Our formatted CSV dataset has three columns (time_stamp, X and Y - where Y is the actual value) whose sample format is
time,X,Y
0.000561,0,10
0.000584,0,10
0.040411,5,10
0.040437,10,10
0.041638,12,10
0.041668,14,10
0.041895,15,10
0.041906,19,10
... ... ...
Before training the prediction model, here is how the plots of X and Y respectively look like the following.
Here is how I approached the problem with LSTM Recurrent Neural Networks in Python with Keras.
import numpy as np
from keras.models import Sequential
from keras.layers import LSTM, Dense
from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.metrics import mean_squared_error
np.random.seed(7)
# Load data
df = pd.read_csv('test32_C_data.csv')
n_features = 100
def create_sequences(data, window=15, step=1, prediction_distance=15):
x = []
y = []
for i in range(0, len(data) - window - prediction_distance, step):
x.append(data[i:i + window])
y.append(data[i + window + prediction_distance][1])
x, y = np.asarray(x), np.asarray(y)
return x, y
# Scaling prior to splitting
scaler = MinMaxScaler(feature_range=(0.01, 0.99))
scaled_data = scaler.fit_transform(df.loc[:, ["X", "Y"]].values)
# Build sequences
x_sequence, y_sequence = create_sequences(scaled_data)
# Create test/train split
test_len = int(len(x_sequence) * 0.90)
valid_len = int(len(x_sequence) * 0.90)
train_end = len(x_sequence) - (test_len + valid_len)
x_train, y_train = x_sequence[:train_end], y_sequence[:train_end]
x_valid, y_valid = x_sequence[train_end:train_end + valid_len], y_sequence[train_end:train_end + valid_len]
x_test, y_test = x_sequence[train_end + valid_len:], y_sequence[train_end + valid_len:]
# Initialising the RNN
model = Sequential()
# Adding the input layerand the LSTM layer
model.add(LSTM(15, input_shape=(15, 2)))
# Adding the output layer
model.add(Dense(1))
# Compiling the RNN
model.compile(loss='mse', optimizer='rmsprop')
# Fitting the RNN to the Training set
model.fit(x_train, y_train, epochs=5)
# Getting the predicted values
y_pred = model.predict(x_test)
#y_pred = scaler.inverse_transform(y_pred)
plot_colors = ['#332288', '#3cb44b']
# Plot the results
pd.DataFrame({"Actual": y_test, "Predicted": np.squeeze(y_pred)}).plot(color=plot_colors)
plt.xlabel('Time [Index]')
plt.ylabel('Values')
Finally, when I run the code - the neural model seems to capture the pattern of the signal well as it is shown below.
However, one problem that I encountered in this output is the ranges of Y. As it is shown in the first two plots, the ranges should be 0-400 as shown above and to solve that I tried to use the scaler to inverse_transform as y_pred = scaler.inverse_transform(y_pred) but this throws an error: ValueError: non-broadcastable output operand with shape (7625,1) doesn't match the broadcast shape (7625,2). How can we solve this broadcast shape error?
Basically, the scaler has remembered that it was fed 2 features(/columns). So it is expecting 2 features to invert the transformation.
Two options here.
1) You make two different scalers: scaler_x and scaler_y like this :
# Scaling prior to splitting
scaler_x = MinMaxScaler(feature_range=(0.01, 0.99))
scaler_y = MinMaxScaler(feature_range=(0.01, 0.99))
scaled_x = scaler_x.fit_transform(df.loc[:, "X"].reshape([-1, 1]))
scaled_y = scaler_y.fit_transform(df.loc[:, "Y"].reshape([-1, 1]))
scaled_data = np.column_stack((scaled_x, scaled_y))
Then you will be able to do :
y_pred = scaler_y.inverse_transform(y_pred)
2) You fake the X column in your output like this :
y_pred_reshaped = np.zeros((len(y_pred), 2))
y_pred_reshaped[:,1] = y_pred
y_pred = scaler.inverse_transform(y_pred_reshaped)[:,1]
Does that help?
EDIT
here is the full code as required
import numpy as np
from keras.models import Sequential
from keras.layers import LSTM, Dense
from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.metrics import mean_squared_error
np.random.seed(7)
# Load data
#df = pd.read_csv('test32_C_data.csv')
df = pd.DataFrame(np.random.randint(0,100, size=(100,3)), columns = ['time', 'X', 'Y'])
n_features = 100
def create_sequences(data, window=15, step=1, prediction_distance=15):
x = []
y = []
for i in range(0, len(data) - window - prediction_distance, step):
x.append(data[i:i + window])
y.append(data[i + window + prediction_distance][1])
x, y = np.asarray(x), np.asarray(y)
return x, y
# Scaling prior to splitting
scaler_x = MinMaxScaler(feature_range=(0.01, 0.99))
scaler_y = MinMaxScaler(feature_range=(0.01, 0.99))
scaled_x = scaler_x.fit_transform(df.loc[:, "X"].reshape([-1,1]))
scaled_y = scaler_y.fit_transform(df.loc[:, "Y"].reshape([-1,1]))
scaled_data = np.column_stack((scaled_x, scaled_y))
# Build sequences
x_sequence, y_sequence = create_sequences(scaled_data)
test_len = int(len(x_sequence) * 0.90)
valid_len = int(len(x_sequence) * 0.90)
train_end = len(x_sequence) - (test_len + valid_len)
x_train, y_train = x_sequence[:train_end], y_sequence[:train_end]
x_valid, y_valid = x_sequence[train_end:train_end + valid_len], y_sequence[train_end:train_end + valid_len]
x_test, y_test = x_sequence[train_end + valid_len:], y_sequence[train_end + valid_len:]
# Initialising the RNN
model = Sequential()
# Adding the input layerand the LSTM layer
model.add(LSTM(15, input_shape=(15, 2)))
# Adding the output layer
model.add(Dense(1))
# Compiling the RNN
model.compile(loss='mse', optimizer='rmsprop')
# Fitting the RNN to the Training set
model.fit(x_train, y_train, epochs=5)
# Getting the predicted values
y_pred = model.predict(x_test)
y_pred = scaler_y.inverse_transform(y_pred)
Related
I have built a model to detect depression using activity data from depresjon dataset where I have labelled depressed as 1 and non depressed as 0 and I am now trying to find out the F1 score and ROC curve of the same model and having problems while doing that
import os
import numpy
import pandas
from tensorflow import keras
from tensorflow.keras.backend import expand_dims
from tensorflow.keras.utils import to_categorical`enter code here`
from tensorflow.keras.preprocessing.sequence import pad_sequences
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn import metrics
from sklearn.metrics import f1_score, make_scorer, confusion_matrix, accuracy_score, precision_score, recall_score, precision_recall_curve
condition_folder = list(os.walk("data/condition"))[0]
control_folder = list(os.walk("data/control"))[0]
condidtion_files = ["%s/%s"%(condition_folder[0],f) for f in condition_folder[2]]
control_files = ["%s/%s"%(control_folder[0],f) for f in control_folder[2]]
x = []
y = []
for f in condidtion_files:
df = pandas.read_csv(f)
x1 = numpy.array(df['activity'].tolist())
x.append(x1)
y.append(1)
for f in control_files:
df = pandas.read_csv(f)
x1 = numpy.array(df['activity'].tolist())
x.append(x1)
y.append(0)
x = numpy.array(x)
y = numpy.array(y)
seq_len_max = max([len(x1) for x1 in x])
x = pad_sequences(
x, maxlen=seq_len_max,
dtype='int32', padding='pre', truncating='pre',
value=0.0
)
x = expand_dims(x, axis=-1)
y = to_categorical(y)
num_classes = y.shape[1]
numpy.save('x.npy', x)
numpy.save('y.npy', y)
x = numpy.load('x.npy')
y = numpy.load('y.npy')
x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2,random_state=0)
numpy.save('x_train.npy', x_train)
numpy.save('y_train.npy', y_train)
numpy.save('x_test.npy', x_test)
numpy.save('y_test.npy', y_test)
x_train = numpy.load('x_train.npy')
y_train = numpy.load('y_train.npy')
x_test = numpy.load('x_test.npy')
y_test = numpy.load('y_test.npy')
'''
(65407, 1)
import matplotlib.pyplot as plt
plt.figure()
plt.plot(x[0])
plt.show()
plt.close()
'''
def make_model(input_shape):
input_layer = keras.layers.Input(input_shape)
conv1 = keras.layers.Conv1D(filters=300, kernel_size=30, strides = 10, padding="valid")(input_layer)
conv2 = keras.layers.Conv1D(filters=300, kernel_size=30, strides = 10, padding="valid")(conv1)
conv2 = keras.layers.MaxPooling1D()(conv2)
conv3 = keras.layers.Conv1D(filters=300, kernel_size=30, strides = 10, padding="valid")(conv2)
gap = keras.layers.GlobalMaxPooling1D()(conv3)
output_layer = keras.layers.Dense(num_classes, activation="softmax")(gap)
return keras.models.Model(inputs=input_layer, outputs=output_layer)
model = make_model(input_shape=x.shape[1:])
model.compile(
optimizer="adam",
loss="categorical_crossentropy",
metrics=["acc"],
)
epochs = 10
batch_size = 60
acc = 0
while(acc <=0.95):
history = model.fit(
x_train,
y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
)
acc = history.history['acc'][-1]
y_pred = model.predict(x)
numpy.save('y_pred.npy', y_pred)
numpy.argmax(y_pred, 1)
model.save("depression.h5")
model = keras.models.load_model("depression.h5")
test_loss, test_acc = model.evaluate(x, y)
import os
import numpy
import pandas
from tensorflow import keras
from tensorflow.keras.backend import expand_dims
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.preprocessing.sequence import pad_sequences
model = keras.models.load_model("depression.h5")
def deep_depression_detector(activity_data_csv_file):
df = pandas.read_csv(activity_data_csv_file)
x = numpy.array([df['activity'].tolist()])
x = pad_sequences(
x, maxlen=65407,
dtype='int32', padding='pre', truncating='pre',
value=0.0
)
x = expand_dims(x, axis=-1)
y_pred = model.predict(x)[0]
y_score = numpy.max(y_pred)
y_label = numpy.argmax(y_pred)
if y_label == 1:
return {'prediction':'depressed', 'confidence': y_score}
else:
return {'prediction':'nondepressed', 'confidence': y_score}
import numpy as np
from sklearn.metrics import precision_recall_fscore_support
y_true = np.array(y_test)
y_pred = np.array(y_pred)
precision_recall_fscore_support(y_true, y_pred, average='weighted')
TypeError: Singleton array array('y_pred', dtype='<U6') cannot be cannot be considered a valid collection.
AND I GET THE FOLLOWING ERROR when I try to find the F1 SCORE despite following everything correctly also how should I find the ROC CURVE since that also seems to be giving some kind of error.
This code predicts the values of a specified stock up to the current date but not a date beyond the training dataset. This code is from an earlier question I had asked and so my understanding of it is rather low. I assume the solution would be a simple variable change to add the extra time but I am unaware as to which value needs to be manipulated.
import pandas as pd
import numpy as np
import yfinance as yf
import os
import matplotlib.pyplot as plt
from IPython.display import display
from keras.models import Sequential
from keras.layers import LSTM, Dense
from sklearn.preprocessing import MinMaxScaler
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
pd.options.mode.chained_assignment = None
# download the data
df = yf.download(tickers=['AAPL'], period='2y')
# split the data
train_data = df[['Close']].iloc[: - 200, :]
valid_data = df[['Close']].iloc[- 200:, :]
# scale the data
scaler = MinMaxScaler(feature_range=(0, 1))
scaler.fit(train_data)
train_data = scaler.transform(train_data)
valid_data = scaler.transform(valid_data)
# extract the training sequences
x_train, y_train = [], []
for i in range(60, train_data.shape[0]):
x_train.append(train_data[i - 60: i, 0])
y_train.append(train_data[i, 0])
x_train = np.array(x_train)
y_train = np.array(y_train)
# extract the validation sequences
x_valid = []
for i in range(60, valid_data.shape[0]):
x_valid.append(valid_data[i - 60: i, 0])
x_valid = np.array(x_valid)
# reshape the sequences
x_train = x_train.reshape(x_train.shape[0],
x_train.shape[1], 1)
x_valid = x_valid.reshape(x_valid.shape[0],
x_valid.shape[1], 1)
# train the model
model = Sequential()
model.add(LSTM(units=50, return_sequences=True,
input_shape=x_train.shape[1:]))
model.add(LSTM(units=50))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(x_train, y_train, epochs=50, batch_size=128, verbose=1)
# generate the model predictions
y_pred = model.predict(x_valid)
y_pred = scaler.inverse_transform(y_pred)
y_pred = y_pred.flatten()
# plot the model predictions
df.rename(columns={'Close': 'Actual'}, inplace=True)
df['Predicted'] = np.nan
df['Predicted'].iloc[- y_pred.shape[0]:] = y_pred
df[['Actual', 'Predicted']].plot(title='AAPL')
display(df)
plt.show()
You could train your model to predict a future sequence (e.g. the next 30 days) instead of predicting the next value (the next day) as it is currently the case.
In order to do that, you need to define the outputs as y[t: t + H] (instead of y[t] as in the current code) where y is the time series and H is the length of the forecast period (i.e. the number of days ahead that you want to forecast). You also need to set the number of outputs of the last layer equal to H (instead of equal to 1 as in the current code).
You can still define the inputs as y[t - T: t] where T is the length of the lookback period (or number of timesteps), and therefore the model's input shape is still (T, 1). The lookback period T is usually longer than the forecast period H (i.e. T > H) and it's often set equal to a multiple of H (i.e. T = m * H where m > 1 is an integer.).
import numpy as np
import pandas as pd
import yfinance as yf
import tensorflow as tf
from tensorflow.keras.layers import Dense, LSTM
from tensorflow.keras.models import Sequential
from sklearn.preprocessing import MinMaxScaler
pd.options.mode.chained_assignment = None
tf.random.set_seed(0)
# download the data
df = yf.download(tickers=['AAPL'], period='1y')
y = df['Close'].fillna(method='ffill')
y = y.values.reshape(-1, 1)
# scale the data
scaler = MinMaxScaler(feature_range=(0, 1))
scaler = scaler.fit(y)
y = scaler.transform(y)
# generate the input and output sequences
n_lookback = 60 # length of input sequences (lookback period)
n_forecast = 30 # length of output sequences (forecast period)
X = []
Y = []
for i in range(n_lookback, len(y) - n_forecast + 1):
X.append(y[i - n_lookback: i])
Y.append(y[i: i + n_forecast])
X = np.array(X)
Y = np.array(Y)
# fit the model
model = Sequential()
model.add(LSTM(units=50, return_sequences=True, input_shape=(n_lookback, 1)))
model.add(LSTM(units=50))
model.add(Dense(n_forecast))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(X, Y, epochs=100, batch_size=32, verbose=0)
# generate the forecasts
X_ = y[- n_lookback:] # last available input sequence
X_ = X_.reshape(1, n_lookback, 1)
Y_ = model.predict(X_).reshape(-1, 1)
Y_ = scaler.inverse_transform(Y_)
# organize the results in a data frame
df_past = df[['Close']].reset_index()
df_past.rename(columns={'index': 'Date', 'Close': 'Actual'}, inplace=True)
df_past['Date'] = pd.to_datetime(df_past['Date'])
df_past['Forecast'] = np.nan
df_past['Forecast'].iloc[-1] = df_past['Actual'].iloc[-1]
df_future = pd.DataFrame(columns=['Date', 'Actual', 'Forecast'])
df_future['Date'] = pd.date_range(start=df_past['Date'].iloc[-1] + pd.Timedelta(days=1), periods=n_forecast)
df_future['Forecast'] = Y_.flatten()
df_future['Actual'] = np.nan
results = df_past.append(df_future).set_index('Date')
# plot the results
results.plot(title='AAPL')
I am trying to predict 100 values of the function f(x) in the future. I found a script online where it was predicting the price of a stock into the future and decided to modify it for the purpose of my research. Unfortunately, the prediction is quite a bit off. Here is the script:
#import packages
import pandas as pd
import numpy as np
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import Dense, Dropout, LSTM
import tensorflow as tf
#for normalizing data
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler(feature_range=(0, 1))
#read the file
df = pd.read_csv('MyDataSet')
#print the head
df.head()
look_back = 60
num_periods = 20
NUM_NEURONS_FirstLayer = 128
NUM_NEURONS_SecondLayer = 64
EPOCHS = 1
num_prediction = 100
training_size = 8000 #initial
#creating dataframe
data = df.sort_index(ascending=True, axis=0)
new_data = pd.DataFrame(index=range(0,len(df)),columns=['XValue', 'YValue'])
for i in range(0,len(data)):
new_data['XValue'][i] = data['XValue'][i]
new_data['YValue'][i] = data['YValue'][i]
#setting index
new_data.index = new_data.XValue
new_data.drop('XValue', axis=1, inplace=True)
#creating train and test sets
dataset = new_data.values
train = dataset[0:training_size,:]
valid = dataset[training_size:,:]
scaler = MinMaxScaler(feature_range=(0, 1))
scaled_data = scaler.fit_transform(dataset)
x_train, y_train = [], []
for i in range(look_back,len(train)):
x_train.append(scaled_data[i-look_back:i,0])
y_train.append(scaled_data[i,0])
x_train, y_train = np.array(x_train), np.array(y_train)
x_train = np.reshape(x_train, (x_train.shape[0],x_train.shape[1],1))
#Custom Loss Function
def my_loss_fn(y_true, y_pred):
squared_difference = tf.square(y_true - y_pred)
return tf.reduce_mean(squared_difference, axis=-1)
#Training Phase
model = Sequential()
model.add(LSTM(NUM_NEURONS_FirstLayer,input_shape=(look_back,1),
return_sequences=True))
model.add(LSTM(NUM_NEURONS_SecondLayer,input_shape=(NUM_NEURONS_FirstLayer,1)))
model.add(Dense(1))
model.compile(loss=my_loss_fn, optimizer='adam')
model.fit(x_train,y_train,epochs=EPOCHS,batch_size=2, verbose=2)
inputs = dataset[(len(dataset) - len(valid) - look_back):]
inputs = inputs.reshape(-1,1)
inputs = scaler.transform(inputs)
X_test = []
for i in range(look_back,inputs.shape[0]):
X_test.append(inputs[i-look_back:i,0])
X_test = np.array(X_test)
#Validation Phase
X_test = np.reshape(X_test, (X_test.shape[0],X_test.shape[1],1))
PredictValue = model.predict(X_test)
PredictValue = scaler.inverse_transform(PredictValue)
#Measures the RMSD between the validation data and the predicted data
rms=np.sqrt(np.mean(np.power((valid-PredictValue ),2)))
train = new_data[:int(new_data.index[training_size])]
valid = new_data[int(new_data.index[training_size]):]
valid['Predictions'] = PredictValue
valid = np.asarray(valid).astype('float32')
valid = valid.reshape(-1,1)
#Prediction Phase
def predict(num_prediction, model):
prediction_list = valid[-look_back:]
for _ in range(num_prediction):
x = prediction_list[-look_back:]
x = x.reshape((1, look_back, 1))
out = model.predict(x)[0][0]
prediction_list = np.append(prediction_list, out)
prediction_list = prediction_list[look_back-1:]
return prediction_list
#Get the predicted x-value
def predict_x(num_prediction):
last_x = df['XValue'].values[-1]
XValue = range(int(last_x),int(last_x)+num_prediction+1)
prediction_x = list(XValue)
return prediction_x
#Predict the y-value and the x-value
forecast = predict(num_prediction, model)
forecast_x = predict_x(num_prediction)
The file I was using was just for the function f(x) = sin(2πx/10), where x is all natural numbers from 0 to 10,000. Following the above, I get the following plot:
Prediction-1
Granted I am using only epochs of 1 and I am busy running a new job with a value of 50, but I was hoping for something a lot better. Any advice to improve the prediction here?
I also decided to modify the script and only predict one value at a time, add it to the dataset and then re-run the script. 100 times. It takes long and it obviously has its own issues (such as using the predicted value of the previous x (step)), but it's the best solution I can think of now. This is the plot I got for it. I can attach this file if you want, but it's not the one I want to continue focusing on with this project:
Prediction-2
Any sort of help would be appreciated in getting a better prediction.
Much appreciated
I'm probably jumped into Python at the deep end here but I have followed a tutorial from start to finish which works fine, and I generally (I think) understand.
I'm trying to recreate what I learnt using my own data.
I've got this working ok as well however the tutorial showed the prediction line on the plot graph generated over the actual data.
What do I need to change for it to predict say 28 days ahead rather than on top of the data I already have?
here is my code (i say my...mostly from tutorial!)
from connectionstring import conn
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import plotly.offline as pyoff
import plotly.graph_objs as go
#import Keras
import keras
from keras.layers import Dense
from keras.models import Sequential
from keras.optimizers import Adam
from keras.callbacks import EarlyStopping
from keras.utils import np_utils
from keras.layers import LSTM
from sklearn.model_selection import KFold, cross_val_score, train_test_split
params = ("20180801","20191002")
sqlString = "SELECT ODCDAT AS DATE, SUM(ODORDQ) AS DROPIN FROM mytable.mydb WHERE ODCDAT BETWEEN %s AND %s GROUP BY ODCDAT ORDER BY ODCDAT"
command = (sqlString % params)
SQL_Query = pd.read_sql_query(command, conn)
df = pd.DataFrame(SQL_Query, columns=['DATE','DROPIN'])
df['DATE'] = pd.to_datetime(df['DATE'], format='%Y%m%d')
print(df.head(10))
#new dataframe
df_diff = df.copy()
df_diff['prev_day'] = df_diff['DROPIN'].shift(1)
df_diff = df_diff.dropna()
df_diff['diff'] = (df_diff['DROPIN'] - df_diff['prev_day'])
df_diff.head(10)
print(df_diff)
#plot monthly sales diff
plot_data = [
go.Scatter(
x=df_diff['DATE'],
y=df_diff['diff'],
)
]
plot_layout = go.Layout(
title='Daily Drop In Diff'
)
fig = go.Figure(data=plot_data, layout=plot_layout)
pyoff.plot(fig)
#create dataframe for transformation from time series to supervised
df_supervised = df_diff.drop(['prev_day'],axis=1)
#adding lags
for inc in range(1,31):
field_name = 'lag_' + str(inc)
df_supervised[field_name] = df_supervised['diff'].shift(inc)
#drop null values
df_supervised = df_supervised.dropna().reset_index(drop=True)
print(df_supervised)
# Import statsmodels.formula.api
import statsmodels.formula.api as smf
# Define the regression formula
model = smf.ols(formula='diff ~ lag_1 + lag_2 + lag_3 + lag_4 + lag_5 + lag_6 + lag_7 + lag_8 + lag_9 + lag_10 + lag_11 + lag_12 + lag_13 + lag_14 + lag_15 + lag_16 + lag_17 + lag_18 + lag_19 + lag_20 + lag_21 + lag_22 + lag_23 + lag_24 + lag_24 + lag_25 + lag_26 + lag_27 + lag_28 + lag_29 + lag_30', data=df_supervised)
# Fit the regression
model_fit = model.fit()
# Extract the adjusted r-squared
regression_adj_rsq = model_fit.rsquared_adj
print(regression_adj_rsq)
#import MinMaxScaler and create a new dataframe for LSTM model
from sklearn.preprocessing import MinMaxScaler
df_model = df_supervised.drop(['DROPIN','DATE'],axis=1)
#split train and test set
train_set, test_set = df_model[0:-28].values, df_model[-28:].values
#apply Min Max Scaler
scaler = MinMaxScaler(feature_range=(-1, 1))
scaler = scaler.fit(train_set)
# reshape training set
train_set = train_set.reshape(train_set.shape[0], train_set.shape[1])
train_set_scaled = scaler.transform(train_set)
# reshape test set
test_set = test_set.reshape(test_set.shape[0], test_set.shape[1])
test_set_scaled = scaler.transform(test_set)
X_train, y_train = train_set_scaled[:, 1:], train_set_scaled[:, 0:1]
X_train = X_train.reshape(X_train.shape[0], 1, X_train.shape[1])
X_test, y_test = test_set_scaled[:, 1:], test_set_scaled[:, 0:1]
X_test = X_test.reshape(X_test.shape[0], 1, X_test.shape[1])
model = Sequential()
model.add(LSTM(4, batch_input_shape=(1, X_train.shape[1], X_train.shape[2]), stateful=True))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(X_train, y_train, nb_epoch=100, batch_size=1, verbose=1, shuffle=False)
y_pred = model.predict(X_test,batch_size=1)
#for multistep prediction, you need to replace X_test values with the predictions coming from t-1
#reshape y_pred
y_pred = y_pred.reshape(y_pred.shape[0], 1, y_pred.shape[1])
#rebuild test set for inverse transform
pred_test_set = []
for index in range(0,len(y_pred)):
#print np.concatenate([y_pred[index],X_test[index]],axis=1)
pred_test_set.append(np.concatenate([y_pred[index],X_test[index]],axis=1))
#reshape pred_test_set
pred_test_set = np.array(pred_test_set)
pred_test_set = pred_test_set.reshape(pred_test_set.shape[0], pred_test_set.shape[2])
#inverse transform
pred_test_set_inverted = scaler.inverse_transform(pred_test_set)
#create dataframe that shows the predicted sales
result_list = []
sales_dates = list(df[-29:].DATE)
act_sales = list(df[-29:].DROPIN)
for index in range(0,len(pred_test_set_inverted)):
result_dict = {}
result_dict['pred_value'] = int(pred_test_set_inverted[index][0] + act_sales[index])
result_dict['DATE'] = sales_dates[index+1]
result_list.append(result_dict)
df_result = pd.DataFrame(result_list)
#for multistep prediction, replace act_sales with the predicted sales
print(df_result)
#merge with actual sales dataframe
df_sales_pred = pd.merge(df,df_result,on='DATE',how='left')
#plot actual and predicted
plot_data = [
go.Scatter(
x=df_sales_pred['DATE'],
y=df_sales_pred['DROPIN'],
name='actual'
),
go.Scatter(
x=df_sales_pred['DATE'],
y=df_sales_pred['pred_value'],
name='predicted'
)
]
plot_layout = go.Layout(
title='Sales Prediction'
)
fig = go.Figure(data=plot_data, layout=plot_layout)
pyoff.plot(fig)
and here is the second graph i plot which I want to predict ahead...
I'm trying to create a regression model for a dataset from .csv file but I'm getting the error
hape must be rank 2 but is rank 0 for 'MatMul_4' (op: 'MatMul') with
input shapes: [], [3].
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import tensorflow as tf
#importing data
dataset = pd.read_csv('Salary_Data.csv')
x_data = dataset.iloc[:,0].values
y_data = dataset.iloc[:,1].values
#split data into train and test
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test =train_test_split(x_data, y_data, test_size = 0.3, random_state = 0, shuffle = True)
#regression using TF
m = tf.Variable(0.45, dtype= tf.float64)
b = tf.Variable(0.15, dtype= tf.float64)
batchsize = 3
xph = tf.placeholder(tf.float64,[batchsize])
yph = tf.placeholder(tf.float64,[batchsize])
y_model = tf.add(tf.matmul(m, xph), b)
error = tf.reduce_mean(tf.square(yph - y_model))
optimizer = tf.train.GradientDescentOptimizer(learning_rate= 0.001)
train = optimizer.minimize(error)
init = tf.global_variables_initializer()
#session
with tf.Session() as sess:
sess.run(init)
batches = 7
for i in range(batches):
ranid = np.random.randint(len(x_train),size = batchsize)
feed = {xph:x_train[ranid],yph:y_train[ranid]}
sess.run(train,feed_dict = feed)
teta1, teta0 = sess.run([m,b])
plt.scatter(x_train, y_train, color = 'red')
I tried to multiply directly using operators also but I am getting the same error
m is just a scalar variable, so you can't do a matrix multiplication with it. You said multiplying directly doesn't work, but it seems to work fine for me:
y_model = m*xph + b