Arjeus Guevarra arjay55
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| _,optvalues,resobj=load('hyperparam_best.joblib') | |
| outparams=linkopt(['input_layer','hidden_1','navpu_infl','uni_reg','dropout_rate','hidden_reg'],optvalues) | |
| acc_sigma=np.min(resobj.func_vals) #target validation error |
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| ct = 0 | |
| for _ in test_data: | |
| ct += 1 | |
| powcoeffs = np.arange(ct) | |
| loss_base = np.full(ct,0.9) | |
| loss_array = np.power(loss_base, powcoeffs) |
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| for _, test in test_data: | |
| predictions = model.predict(tf.boolean_mask( | |
| reshape_sample, boolmask, axis=1)) | |
| y_pred.append(predictions[0][0]) | |
| y_true.append(test[0][0]) | |
| reshape_sample = np.roll(reshape_sample, -1, axis=1) | |
| reshape_sample[0][-1] = predictions |
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| res = gp_minimize(objective, # the function to minimize | |
| space, # the bounds on each dimension of x | |
| acq_func="LCB", # the acquisition function | |
| n_calls=78, # the number of evaluations of f #78 | |
| n_random_starts=5, # the number of random initialization points | |
| random_state=0) # the random seed |
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| space = [Integer(2, 21, name='input_layer'), #number of neurons in input layer | |
| Integer(1,3, name='hidden_1'), #number of neurons in the 1st hidden layer | |
| Real(0.0, 0.3, name='navpu_infl'), #adjustment of class weighting in the mutual_unit(navpu) and the inflation. | |
| Real(0.0, 0.8, name='uni_reg'), # regularization factor in the 1st hidden layer | |
| Real(0.0, 0.5, name='dropout_rate'), #dropout fraction on the weights outside the 1st hidden layer | |
| Real(0.0, 0.8, name='hidden_reg'), #regularization at the final layer | |
| ] |
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| masked_indices=kwargs.pop('masked_indices',[4,6,7,9,10,11,12,14,15]) | |
| boolmask=np.full(est_period,True) | |
| boolmask[masked_indices]=False | |
| model = tf.keras.Sequential() | |
| model.add(tf.keras.layers.Dense(kwargs.pop('input_layer',7), | |
| input_shape=(tf.boolean_mask(train_data[0][0],boolmask,axis=1).shape[1], | |
| train_data[0][0].shape[2]), | |
| kernel_initializer='he_normal', | |
| kernel_regularizer=tf.keras.regularizers.l1( |
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| def form_dataset(normalized_data, est_period, **kwargs): | |
| percent_validation = kwargs.pop('percent_validation', 0.8) | |
| rel_norm=normalized_data | |
| # end_index=(int(((rel_norm.shape[0]-est_period)*0.9)//batch_size))*batch_size-2 | |
| # batch size to be changed to 1 | |
| # end_index_test=( |
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| dfcorr = dfrel.corr() | |
| heatmap(dfrel.corr()) |
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| model = auto.arima(ts(data$inflation,frequency = 1),seasonal = FALSE,stepwise=FALSE, | |
| approximation = TRUE, max.p = myp, max.q = myq, max.P = myp, | |
| max.Q = myq, max.d = 4, max.D=4, | |
| max.order=120, num.cores = 3,parallel = TRUE) | |
| flength=15 | |
| fcast_no_holdout <- forecast(model,flength) |
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| dftemp = pd.read_csv('pb_forecast_f.csv', index_col=None) | |
| get_outernan(dfrel.loc[:, 'inflation']) #gets the x and y values | |
| dfrel.iloc[-15:, 2] = dftemp.loc[:, 'Point Forecast'].values | |
| dftemp = pd.read_csv('pb_forecast_b.csv', index_col=None) | |
| dfrel.iloc[:5, 2][::-1] = dftemp.loc[:, 'Point Forecast'].values | |
| dfrel.iloc[:5, 2][::-1] = dftemp.loc[:, 'Point Forecast'].values | |
| fill_data(dfrel) #fills the missing data through forward, backwards fill, and linear interpolation |
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