Function process_classification_task

pytorch-model/process_dataset.py:267–336

(-6, -12, -18, -24) hour ETH price dataset which asks network to predict, for each bucket of time period (hour, day, week), whether/how much future ETH prices will go up or down.

(idx_to_field_data, 
                                idx_to_field_labels, 
                                data_features, 
                                labels)

def process_classification_task(idx_to_field_data, 
                                idx_to_field_labels, 
                                data_features, 
                                labels):
    """
    (-6, -12, -18, -24) hour ETH price dataset which asks network to predict, for each bucket of
    time period (hour, day, week), whether/how much future ETH prices will
    go up or down.
    """
    
    # --- Remove BTC prices from future and add in 6-hours-ahead data ---
    idx_to_field_data, idx_to_field_labels, data_features, labels = preprocess_data(idx_to_field_data, 
                                                                                    idx_to_field_labels, 
                                                                                    data_features, 
                                                                                    labels)

    # --- Creates new feature sets (Eth price from 1-24 hours ago) ---
    all_eth_hours_ago = list()
    for ago in range(1, 25):
        eth_hours_ago = np.transpose(data_features)[0][:-ago][24 - ago:]
        eth_hours_ago = eth_hours_ago.reshape(1, len(eth_hours_ago))
        all_eth_hours_ago.append(eth_hours_ago)

    # eth_six_hours_ago = np.transpose(data_features)[0][:-6][18:]
    # eth_twelve_hours_ago = np.transpose(data_features)[0][:-12][12:]
    # eth_eighteen_hours_ago = np.transpose(data_features)[0][:-18][6:]
    # eth_twentyfour_hours_ago = np.transpose(data_features)[0][:-24]

    # eth_six_hours_ago = eth_six_hours_ago.reshape(1, len(eth_six_hours_ago))
    # eth_twelve_hours_ago = eth_twelve_hours_ago.reshape(1, len(eth_twelve_hours_ago))
    # eth_eighteen_hours_ago = eth_eighteen_hours_ago.reshape(1, len(eth_eighteen_hours_ago))
    # eth_twentyfour_hours_ago = eth_twentyfour_hours_ago.reshape(1, len(eth_twentyfour_hours_ago))

    data_features = np.transpose(np.concatenate(
        [np.transpose(data_features[24:])] + all_eth_hours_ago
        
    ))
    labels = labels[24:]
    
    # --- Adding to the idx to field data ---
    for ago in range(1, 25):
        idx_to_field_data.append(f"Eth price {ago} hours ago")
    # idx_to_field_data.append("eth_price_six_hours_ago")
    # idx_to_field_data.append("eth_price_twelve_hours_ago")
    # idx_to_field_data.append("eth_price_eighteen_hours_ago")
    # idx_to_field_data.append("eth_price_twentyfour_hours_ago")

    # --- Picks ONLY the 6-hours-ahead price data delta as labels ---
    labels = np.transpose(labels)[3] - np.transpose(labels)[0]
    six_hour_hist_bins = [-1800, -100, -50, -30, -15, -5, 0, 5, 15, 30, 50, 100, 1800]
    new_labels = list()
    for label_idx, label in enumerate(labels):
        for bin_idx in range(len(six_hour_hist_bins) - 1):
            if label >= six_hour_hist_bins[bin_idx] and label < six_hour_hist_bins[bin_idx + 1]:
                new_labels.append(bin_idx)
                break

    new_labels = np.asarray(new_labels, dtype=np.int64)