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inm
Helper II
Joined 5 years ago
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Re: Get data from analysis service when the server is not on premises
Yes, it is exactly as you said. When I try to do it in PBI it says Could not connect to the Analysis Services server because it timed out or the server name was incorrect. But the name is correct. I copy and paste everything. And like I said, the server is owned by a third party, it is not on premises.Re: Get data from analysis service when the server is not on premises
I am not accessing through vpn, I only change the type of login to username and password and it works, but power bi doesn't let me do this, it requires that it be on the same network so I don't know how to do it. Thank you for your answer.Get data from analysis service when the server is not on premises
I have the sever's name, username and password. I can access the data through the connection in excel, but from power bi it requires that it be on premises. Is there a way to get connected in this case? I appreciate any help in advance.Re: Change column type change value from another column (Python script)
Python script: import pandas as pd import numpy as np from keras.layers import Dense, LSTM from keras.models import Sequential from keras.preprocessing.sequence import TimeseriesGenerator def get_prediction_dates(num_prediction, df): last_date = df['date'].max() date_test = pd.date_range(last_date, periods=num_prediction+1, freq='M').to_frame(index=False, name='date') date_test = date_test['date'].apply(lambda x : x.replace(day=1)) return date_test def predict(num_prediction, model): prediction_list = arr[-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 look_back = 12 batch_size = 4 arr = dataset['quantity'].values arr = arr.reshape((-1, 1)) generator = TimeseriesGenerator(arr, arr, length=look_back, batch_size = batch_size) model = Sequential() model.add( LSTM(128, activation='linear', input_shape=(look_back, 1), name="LSTM") ) model.add(Dense(1, activation='linear', name="Dense")) model.compile(optimizer='adam', loss='mse', metrics=['mean_absolute_percentage_error']) model.fit(generator, epochs=70) num_prediction = 6 forecast = predict(num_prediction, model) forecast_dates = get_prediction_dates(num_prediction, dataset) dataset = pd.DataFrame(list(zip(forecast_dates, forecast)), columns=['date', 'prediction']) dataset['date'] = pd.to_datetime(dataset['date']) I placed a seed, but is there any other way to prevent this from happening?Change column type change value from another column (Python script)
Hello. I made a python script and it runs in power query, it returns one date and one float column. When I try to change column types according to their values, they change. Changing the column type also changes the values in the same column. Here an example. This is what returns python script. I try to change the type of date column to date type and and the values in the prediction column change. In report view the values are also different from those shown in power query. In the python script, a model is created with neural networks from the keras library and returns the predictions of the initial data. I believe is due to keras, how can I avoid this? I appreciate any help. Thank you for your ideas and time.
