| # Licensed to the Apache Software Foundation (ASF) under one |
| # or more contributor license agreements. See the NOTICE file |
| # distributed with this work for additional information |
| # regarding copyright ownership. The ASF licenses this file |
| # to you under the Apache License, Version 2.0 (the |
| # "License"); you may not use this file except in compliance |
| # with the License. You may obtain a copy of the License at |
| |
| # http://www.apache.org/licenses/LICENSE-2.0.html |
| |
| # Unless required by applicable law or agreed to in writing, software |
| # distributed under the License is distributed on an "AS IS" BASIS, |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| # See the License for the specific language governing permissions and |
| # limitations under the License. |
| |
| """ |
| A client that talks to tensorflow_serving loaded with kaggle model. |
| |
| The client read kaggle feature data set, queries the service with |
| such feature data to get predictions, and calculates the inference error rate. |
| |
| """ |
| |
| import pickle |
| import json |
| import requests |
| import numpy as np |
| from typing import List |
| import pandas as pd |
| import datetime |
| import math |
| import sys |
| |
| # from predictor_dl_model.pipeline.util import get_dow |
| |
| |
| def get_start_end(records_len, train_window, forward_offset): |
| start = records_len - train_window - forward_offset |
| end = records_len - forward_offset |
| return start, end |
| |
| |
| def get_dow(day_list): |
| dow_list = [] |
| for day in day_list: |
| dow = datetime.datetime.strptime(day, '%Y-%m-%d').weekday() |
| dow_list.append(dow) |
| |
| week_period = 7.0 / (2 * math.pi) |
| sin_list = [math.sin(x / week_period) for x in dow_list] |
| cos_list = [math.cos(x / week_period) for x in dow_list] |
| return (sin_list, cos_list) |
| |
| |
| def lag_indexes(day_list): |
| """ |
| Calculates indexes for 3, 6, 9, 12 months backward lag for the given date range |
| :param begin: start of date range |
| :param end: end of date range |
| :return: List of 4 Series, one for each lag. For each Series, index is date in range(begin, end), value is an index |
| of target (lagged) date in a same Series. If target date is out of (begin,end) range, index is -1 |
| """ |
| date_range = pd.date_range(day_list[0], day_list[-1]) |
| # key is date, value is day index |
| base_index = pd.Series(np.arange(0, len(date_range)), index=date_range) |
| |
| def lag(offset): |
| dates = date_range - offset |
| return pd.Series(data=base_index[dates].fillna(-1).astype(np.int64).values, index=date_range) |
| |
| return [lag(pd.DateOffset(days=m)) for m in (30, 60)] |
| |
| |
| def make_pred_input(duration, train_window, predict_window, full_record_exp, x_hits, dow, lagged_ix, pf_age, pf_si, |
| pf_network, |
| pf_gender, page_ix, pf_price_cat, |
| page_popularity, quarter_autocorr, forward_offset): |
| """ |
| Main method. Assembles input data into final tensors |
| """ |
| # x_dow, y_dow = tf.split(dow, [train_window, predict_window], axis=0) |
| x_dow = dow[:train_window] |
| y_dow = dow[train_window:] |
| # Normalize hits |
| mean = np.mean(x_hits) |
| std = np.std(x_hits) |
| if std == 0: |
| std = 1 |
| norm_x_hits = [(_ - mean) / std for _ in x_hits] |
| |
| # lagged_ix = np.where(lagged_ix==-1, np.NaN, lagged_ix) |
| cropped_lags = lagged_ix |
| # Mask for -1 (no data) lag indexes |
| lag_mask = cropped_lags < 0 |
| # Convert -1 to 0 for gather(), it don't accept anything exotic |
| cropped_lags = np.maximum(cropped_lags, 0) |
| cropped_lags = np.where(cropped_lags > len(full_record_exp) - 1, len(full_record_exp) - 1, cropped_lags) |
| # Translate lag indexes to hit values |
| lagged_hit = np.take(full_record_exp, cropped_lags) |
| # Convert masked (see above) or NaN lagged hits to zeros |
| lag_zeros = np.zeros_like(lagged_hit) |
| lagged_hit = np.where(lag_mask | np.isnan( |
| lagged_hit), lag_zeros, lagged_hit) |
| start, end = get_start_end(duration, train_window, forward_offset) |
| lagged_hit = lagged_hit[start:end + predict_window] |
| norm_lagged_hits = np.divide(np.subtract(lagged_hit, mean), std) |
| |
| # stat = {'mean':mean, 'std':std} |
| # Split lagged hits to train and predict |
| x_lagged, y_lagged = norm_lagged_hits[: |
| train_window], norm_lagged_hits[train_window:] |
| |
| # Combine all page features into single tensor |
| stacked_features = np.stack([page_popularity, quarter_autocorr]) |
| |
| flat_ucdoc_features = np.concatenate([pf_age, pf_si, pf_network, pf_gender, pf_price_cat, stacked_features], |
| axis=0) # pf_region |
| ucdoc_features = np.expand_dims(flat_ucdoc_features, 0) |
| |
| # Train features |
| x_features = np.concatenate([ |
| # [n_days] -> [n_days, 1] |
| np.expand_dims(norm_x_hits, -1), |
| x_dow, |
| x_lagged, |
| # Stretch ucdoc_features to all training days |
| # [1, features] -> [n_days, features] |
| np.tile(ucdoc_features, [train_window, 1])], axis=1 |
| ) |
| y_features = np.concatenate([ |
| # [n_days] -> [n_days, 1] |
| y_dow, |
| y_lagged, |
| # Stretch ucdoc_features to all testing days |
| # [1, features] -> [n_days, features] |
| np.tile(ucdoc_features, [predict_window, 1]) |
| ], axis=1) |
| |
| return x_hits, x_features, norm_x_hits, x_lagged, y_features, mean, std, flat_ucdoc_features, page_ix # , stat |
| |
| |
| def get_predict_post_body(model_stats, day_list, day_list_cut, page_ix, pf_age, pf_si, pf_network, pf_gender, |
| full_record, hits, pf_price_cat, predict_day_list, forward_offset): |
| |
| |
| train_window = model_stats['model']['train_window'] # comes from cfg |
| predict_window = model_stats['model']['predict_window'] # comes from cfg |
| x_hits = np.log(np.add(hits, 1)).tolist() # ln + 1 |
| full_record_exp = np.log(np.add(full_record, 1)).tolist() |
| |
| # TODO: Added |
| duration = len(full_record_exp) |
| |
| if len(day_list_cut) != train_window + predict_window: |
| raise Exception('day_list_cut and train window + predicti_window do not match. {} {} {}'.format( |
| len(day_list_cut), train_window, predict_window)) |
| |
| dow = get_dow(day_list_cut) |
| dow = [[dow[0][i], dow[1][i]] for i in range(train_window + predict_window)] |
| for x in predict_day_list: |
| if x not in day_list: |
| day_list.extend(predict_day_list) |
| lagged_indx = np.stack(lag_indexes(day_list), axis=-1) |
| |
| # not used in the model (but we should keep it) |
| page_popularity = np.mean(full_record) |
| page_popularity = ( |
| page_popularity - model_stats['stats']['page_popularity'][0]) / \ |
| model_stats['stats']['page_popularity'][1] |
| quarter_autocorr = 1 |
| |
| # x_hits, x_features, norm_x_hits, x_lagged, y_features, mean, std, flat_ucdoc_features, page_ix |
| truex, timex, normx, laggedx, timey, normmean, normstd, pgfeatures, pageix = make_pred_input(duration, |
| train_window, |
| predict_window, |
| full_record_exp, |
| x_hits, dow, |
| lagged_indx, pf_age, |
| pf_si, pf_network, |
| pf_gender, page_ix, |
| pf_price_cat, |
| page_popularity, |
| quarter_autocorr, forward_offset) |
| |
| # ys are not important] |
| truey = [1 for _ in range(predict_window)] |
| normy = [1 for _ in range(predict_window)] |
| |
| instance = {"truex": truex, "timex": timex.tolist(), "normx": normx, |
| "laggedx": laggedx.tolist(), |
| "truey": truey, "timey": timey.tolist(), "normy": normy, |
| "normmean": normmean, |
| "normstd": normstd, "page_features": pgfeatures.tolist(), |
| "pageix": pageix} |
| # print(instance) |
| return instance # , stat |
| |
| |
| def predict(serving_url, model_stats, day_list, ucdoc_attribute_map, forward_offset): |
| page_ix = ucdoc_attribute_map['page_ix'] |
| pf_age = [ucdoc_attribute_map['a__n'], ucdoc_attribute_map['a_1_n'], ucdoc_attribute_map['a_2_n'], ucdoc_attribute_map['a_3_n'], |
| ucdoc_attribute_map['a_4_n'], ucdoc_attribute_map['a_5_n'], ucdoc_attribute_map['a_6_n']] |
| pf_si = ucdoc_attribute_map['si_vec_n'] |
| pf_network = [ucdoc_attribute_map['t_2G_n'], ucdoc_attribute_map['t_3G_n'], ucdoc_attribute_map['t_4G_n'], |
| ucdoc_attribute_map['t_UNKNOWN_n'], ucdoc_attribute_map['t_WIFI_n'], ] |
| pf_gender = [ucdoc_attribute_map['g__n'], ucdoc_attribute_map['g_g_f_n'], ucdoc_attribute_map['g_g_m_n'], ucdoc_attribute_map['g_g_x_n']] |
| pf_price_cat = [ucdoc_attribute_map['price_cat_1_n'], ucdoc_attribute_map['price_cat_2_n'], ucdoc_attribute_map['price_cat_3_n']] |
| full_record = np.expm1(ucdoc_attribute_map['ts_n']) |
| |
| # days_list is sorted from past to present [2018-01-01, 2018-01-02, ...] |
| prediction_results = [] |
| # for full_record, price_cat in records_hour_price_list: |
| train_window = model_stats['model']['train_window'] |
| prediction_window = model_stats['model']['predict_window'] |
| start = len(full_record) - train_window - forward_offset |
| end = len(full_record) - forward_offset |
| hits = full_record[start:end] |
| |
| day_list_cut = day_list[start:end] |
| predict_day = [datetime.datetime.strptime(day_list_cut[-1], "%Y-%m-%d").date() + datetime.timedelta(days=x + 1) for |
| x in range(prediction_window)] |
| predict_day_list = [x.strftime("%Y-%m-%d") for x in predict_day] |
| day_list_cut.extend(predict_day_list) |
| |
| body = {"instances": []} |
| instance = get_predict_post_body( |
| model_stats, day_list, day_list_cut, page_ix, pf_age, pf_si, pf_network, pf_gender, full_record, hits, pf_price_cat, predict_day_list, forward_offset) |
| body['instances'].append(instance) |
| |
| body_json = json.dumps(body) |
| result = requests.post(serving_url, data=body_json).json() |
| predictions = result['predictions'][0] |
| predictions = np.round(np.expm1(predictions)) |
| prediction_results.append(predictions.tolist()) |
| |
| return prediction_results, predict_day_list |
| |
| |
| if __name__ == '__main__': # record is equal to window size |
| URL = "http://10.193.217.105:8508/v1/models/dl_20210706:predict" |
| |
| model_stats = { |
| "model": {"days": ["2020-03-01", "2020-03-02", "2020-03-03", "2020-03-04", "2020-03-05", "2020-03-06", "2020-03-07", |
| "2020-03-08", "2020-03-09", "2020-03-10", "2020-03-11", "2020-03-12", "2020-03-13", "2020-03-14", |
| "2020-03-15", "2020-03-16", "2020-03-17", "2020-03-18", "2020-03-19", "2020-03-20", "2020-03-21", |
| "2020-03-22", "2020-03-23", "2020-03-24", "2020-03-25", "2020-03-26", "2020-03-27", "2020-03-28", |
| "2020-03-29", "2020-03-30", "2020-03-31", "2020-04-01", "2020-04-02", "2020-04-03", "2020-04-04", |
| "2020-04-05", "2020-04-06", "2020-04-07", "2020-04-08", "2020-04-09", "2020-04-10", "2020-04-11", |
| "2020-04-12", "2020-04-13", "2020-04-14", "2020-04-15", "2020-04-16", "2020-04-17", "2020-04-18", |
| "2020-04-19", "2020-04-20", "2020-04-21", "2020-04-22", "2020-04-23", "2020-04-24", "2020-04-25", |
| "2020-04-26", "2020-04-27", "2020-04-28", "2020-04-29", "2020-04-30", "2020-05-01", "2020-05-02", |
| "2020-05-03", "2020-05-04", "2020-05-05", "2020-05-06", "2020-05-07", "2020-05-08", "2020-05-09", |
| "2020-05-10", "2020-05-11", "2020-05-12", "2020-05-13", "2020-05-14", "2020-05-15", "2020-05-16", |
| "2020-05-17", "2020-05-18", "2020-05-19", "2020-05-20", "2020-05-21", "2020-05-22", "2020-05-23", |
| "2020-05-24", "2020-05-25", "2020-05-26", "2020-05-27", "2020-05-28", "2020-05-29", "2020-05-30", |
| "2020-05-31", "2020-06-01", "2020-06-02", "2020-06-03", "2020-06-04", "2020-06-05", "2020-06-06", |
| "2020-06-07", "2020-06-08", "2020-06-09", "2020-06-10", "2020-06-11", "2020-06-12", "2020-06-13", |
| "2020-06-14", "2020-06-15", "2020-06-16", "2020-06-17", "2020-06-18", "2020-06-19", "2020-06-20", |
| "2020-06-21", "2020-06-22", "2020-06-23", "2020-06-24", "2020-06-25", "2020-06-26", "2020-06-27", |
| "2020-06-28", "2020-06-29", "2020-06-30"], "duration": 112, |
| "holidays_norm": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], |
| "name": "model_dlpm_06242021_1635", "predict_window": 10, "train_window": 60, "version": "version_06242021_1635"}, |
| "stats": {"a_": [0.04429662466026579, 0.20533941439740103], "a_1": [0.0594024062498474, 0.23536391091868636], |
| "a_2": [0.13875638124033587, 0.3443551863453624], "a_3": [0.18247821941727763, 0.3847745538180887], |
| "a_4": [0.23540840480301564, 0.4227143144991038], "a_5": [0.20678420677160178, 0.40351830940626193], |
| "a_6": [0.1328737568768233, 0.338151429839641], "g_": [0.03802539182225981, 0.1910382838856348], |
| "g_g_f": [0.31290878405353795, 0.46186909160791834], "g_g_m": [0.6370381966764169, 0.4790592824120438], |
| "g_g_x": [0.012027627461027505, 0.10846109268711399], |
| "holiday_stats": [0.0, 0.0], "ipl_": [1.0, 1.0], "ipl_1": [0.0, 1.0], "ipl_10": [0.0, 1.0], "ipl_11": [0.0, 1.0], "ipl_12": [0.0, 1.0], "ipl_13": [0.0, 1.0], "ipl_14": [0.0, 1.0], "ipl_15": [0.0, 1.0], "ipl_16": [0.0, 1.0], "ipl_17": [0.0, 1.0], "ipl_18": [0.0, 1.0], "ipl_19": [0.0, 1.0], "ipl_2": [0.0, 1.0], "ipl_20": [0.0, 1.0], "ipl_21": [0.0, 1.0], "ipl_22": [0.0, 1.0], "ipl_23": [0.0, 1.0], "ipl_24": [0.0, 1.0], "ipl_25": [0.0, 1.0], "ipl_26": [0.0, 1.0], "ipl_27": [0.0, 1.0], "ipl_28": [0.0, 1.0], "ipl_29": [0.0, 1.0], "ipl_3": [0.0, 1.0], "ipl_30": [0.0, 1.0], "ipl_31": [0.0, 1.0], "ipl_32": [0.0, 1.0], "ipl_33": [0.0, 1.0], "ipl_34": [0.0, 1.0], "ipl_35": [0.0, 1.0], "ipl_36": [0.0, 1.0], "ipl_37": [0.0, 1.0], "ipl_38": [0.0, 1.0], "ipl_39": [0.0, 1.0], "ipl_4": [0.0, 1.0], "ipl_40": [0.0, 1.0], "ipl_41": [0.0, 1.0], "ipl_42": [0.0, 1.0], "ipl_43": [0.0, 1.0], "ipl_44": [0.0, 1.0], "ipl_45": [0.0, 1.0], "ipl_46": [0.0, 1.0], "ipl_47": [0.0, 1.0], "ipl_48": [0.0, 1.0], "ipl_49": [0.0, 1.0], "ipl_5": [0.0, 1.0], "ipl_50": [0.0, 1.0], "ipl_51": [0.0, 1.0], "ipl_52": [0.0, 1.0], "ipl_53": [0.0, 1.0], "ipl_54": [0.0, 1.0], "ipl_55": [0.0, 1.0], "ipl_56": [0.0, 1.0], "ipl_57": [0.0, 1.0], "ipl_58": [0.0, 1.0], "ipl_59": [0.0, 1.0], "ipl_6": [0.0, 1.0], "ipl_60": [0.0, 1.0], "ipl_61": [0.0, 1.0], "ipl_62": [0.0, 1.0], "ipl_63": [0.0, 1.0], "ipl_64": [0.0, 1.0], "ipl_65": [0.0, 1.0], "ipl_66": [0.0, 1.0], "ipl_67": [0.0, 1.0], "ipl_68": [0.0, 1.0], "ipl_69": [0.0, 1.0], "ipl_7": [0.0, 1.0], "ipl_70": [0.0, 1.0], "ipl_71": [0.0, 1.0], "ipl_72": [0.0, 1.0], "ipl_73": [0.0, 1.0], "ipl_74": [0.0, 1.0], "ipl_75": [0.0, 1.0], "ipl_76": [0.0, 1.0], "ipl_77": [0.0, 1.0], "ipl_78": [0.0, 1.0], "ipl_79": [0.0, 1.0], "ipl_8": [0.0, 1.0], "ipl_80": [0.0, 1.0], "ipl_81": [0.0, 1.0], "ipl_82": [0.0, 1.0], "ipl_9": [0.0, 1.0], |
| "page_popularity": [577.1606531140158, 5127.251109603019], |
| "price_cat_1": [0.6813400284793525, 0.4659575570248394], "price_cat_2": [0.1969572060256314, 0.3977003132674967], |
| "price_cat_3": [0.12170276549501612, 0.3269426904032709], "r_": [0.06428884716597416, 0.2445368641073571], "r_1": [0.017636910330801243, 0.13112898948090662], "r_10": [0.007311564935032408, 0.08479574334903354], "r_11": [0.0123057065964087, 0.10979549743303678], "r_12": [0.010682300938137547, 0.10238183510308607], "r_13": [0.012839921663653402, 0.11211387396512836], "r_14": [0.0043072282145927634, 0.0652208481532438], "r_15": [0.005510994725533859, 0.07372435219261837], "r_16": [0.009430895699953106, 0.09624349588546878], "r_17": [0.003106284001102974, 0.05540235515012807], "r_18": [0.013727307649070212, 0.11587668554591014], "r_19": [0.003658714691227056, 0.060094360148970564], "r_2": [0.006930426840847276, 0.08259001749726591], "r_20": [0.009621269428012426, 0.0972004049920124], "r_21": [0.010746271555183338, 0.10268192290785572], "r_22": [0.024583268037287404, 0.15426934195117514], "r_23": [0.005721017802866344, 0.07509957327880841], "r_24": [0.01830177624998661, 0.13353918416581523], "r_25": [0.008662228002827519, 0.0922767229097513], "r_26": [0.007283668595702679, 0.08465678118091494], "r_27": [0.008913427277260215, 0.0935936727765952], "r_28": [0.019489996624824472, 0.1377225070294911], "r_29": [0.01184853904442632, 0.10780878967593792], "r_3": [0.010360907737132814, 0.10090031978343518], "r_30": [0.005268162614988744, 0.0721404364566014], "r_31": [0.006795651437444575, 0.08178152977272346], "r_32": [0.005624180821619731, 0.07448664866729614], "r_33": [0.006411867702310304, 0.07948751561735418], "r_34": [0.006509134818396776, 0.0801375874910104], "r_35": [0.005580401140435336, 0.07418513099650123], "r_36": [1.5449449320791486E-4, 0.012364118289365515], "r_37": [0.0028466672077495808, 0.05301062239853139], "r_38": [0.010713946790356105, 0.10256955449886415], "r_39": [0.0034429989328785483, 0.0583888083763328], "r_4": [0.013300424262355068, 0.1141386154412099], "r_40": [0.021875249575959654, 0.14573846520038694], "r_41": [4.0226892652173624E-4, 0.019964317590957556], "r_42": [0.0022602156752338396, 0.04726992138193806], "r_43": [0.01415631424810564, 0.11771037667309092], "r_44": [0.004017545432034864, 0.06299519948122188], "r_45": [0.012573840249382363, 0.11103012675446208], "r_46": [0.012097359124970612, 0.10888519798168067], "r_47": [0.008092052377949565, 0.08924037498833616], "r_48": [0.0035848019436715956, 0.05947222917426611], "r_49": [0.008673773828736435, 0.0923572938291937], "r_5": [0.012552607682583329, 0.11088179550318002], "r_50": [0.006951285377653645, 0.08273353620921826], "r_51": [0.012508691815441772, 0.11067069528262007], "r_52": [0.0025252799345467265, 0.050031446895071115], "r_53": [0.006971198096591135, 0.08284058885334963], "r_54": [0.013010404494975019, 0.11285232518803595], "r_55": [0.011205366011483526, 0.10480265985095695], "r_56": [0.004091688939927208, 0.06358320428852193], "r_57": [0.009483973684679932, 0.09654761327441443], "r_58": [0.0033479429826094058, 0.05754055560727182], "r_59": [0.004110291414764129, 0.0637456977480596], "r_6": [0.017170719926898895, 0.1293825607377681], "r_60": [0.012502322863654236, 0.11068315020688722], "r_61": [0.0032739755477089547, 0.05691437647921438], "r_62": [0.004530736628742831, 0.06688532080007667], "r_63": [0.008843517281097744, 0.09324513541404153], "r_64": [0.00608604133973962, 0.07746758538416228], "r_65": [0.007703450057748544, 0.08708185205112809], "r_66": [0.0038174901754371078, 0.061442386487589006], "r_67": [0.007226517945388782, 0.08432460198736601], "r_68": [0.008491919780194587, 0.09137931662767898], "r_69": [0.010766670130327805, 0.1027959300414319], "r_7": [0.008717083083441964, 0.09258227788620865], "r_70": [0.019928163154919562, 0.13925707765065126], "r_71": [0.042367721440680677, 0.20075663714068437], "r_72": [0.03365624054137039, 0.17972947649606252], "r_73": [0.016191367690641226, 0.12569029295661116], "r_74": [0.029418237267252897, 0.1683753748704679], "r_75": [0.016422084256369943, 0.12665559500401802], "r_76": [0.036994754532839284, 0.18815474260948475], "r_77": [0.019072681048734642, 0.1362788171295267], "r_78": [0.03046848772777889, 0.17129000529130292], "r_79": [0.006810037790196545, 0.08190570131006866], "r_8": [0.01711773510902206, 0.129225322349193], "r_80": [0.04086274840775549, 0.19733000594126685], "r_81": [0.034348686325118755, 0.18150326085652782], "r_82": [0.0, 1.0], "r_9": [0.008801054123012632, 0.09300816483722156], |
| "si_15e9ddce941b11e5bdec00163e291137": [0.0021734242674061304, 0.046569378305571674], |
| "si_17dd6d8098bf11e5bdec00163e291137": [8.064153488720677E-4, 0.028386043378890852], |
| "si_5cd1c663263511e6af7500163e291137": [0.024480251817432363, 0.15453491734752325], "si_66bcd2720e5011e79bc8fa163e05184e": [0.0747118339204077, 0.2629261931283321], "si_68bcd2720e5011e79bc8fa163e05184e": [0.00832196657423368, 0.09084457158709529], "si_71bcd2720e5011e79bc8fa163e05184e": [0.0031866896500037474, 0.056360839080046306], "si_7b0d7b55ab0c11e68b7900163e3e481d": [0.07978115865997153, 0.270954508123256], "si_a290af82884e11e5bdec00163e291137": [0.08873866446826051, 0.28436658790013714], "si_a47eavw7ex": [0.21830173124484747, 0.41309393235589104], "si_a8syykhszz": [0.004017087611481676, 0.06325316286791836], "si_b6le0s4qo8": [0.0949651502660571, 0.2931672358147947], "si_d4d7362e879511e5bdec00163e291137": [0.004023083264633141, 0.06330015859143233], "si_d971z9825e": [0.0178340702990332, 0.13234828576803975], "si_d9jucwkpr3": [0.024657123585400585, 0.1550781155975643], "si_e351de37263311e6af7500163e291137": [0.04452971595593195, 0.2062691152382904], "si_f1iprgyl13": [0.05222513677583752, 0.22248105593614842], "si_j1430itab9wj3b": [0.003750281046241475, 0.0611246892742874], "si_k4werqx13k": [0.004313872442479202, 0.06553835383591226], "si_l03493p0r3": [0.022855429813385297, 0.1494427184317444], "si_l2d4ec6csv": [0.01941692273102001, 0.13798537212757175], "si_p7gsrebd4m": [0.00981188638237278, 0.09856795825116378], "si_s4z85pd1h8": [0.003141722251367758, 0.055963034419515], "si_w3wx3nv9ow5i97": [0.038240275800044965, 0.1917760865203199], "si_w9fmyd5r0i": [0.023203177696170276, 0.15054852435771146], "si_x0ej5xhk60kjwq": [0.10203102750505884, 0.3026895630759413], "si_x2fpfbm8rt": [0.012063254140747957, 0.10916852919996962], "si_z041bf6g4s": [0.01841864648130106, 0.13445986071004987], "t_2g": [7.974871208755584E-4, 0.028016668863524278], "t_3g": [0.009603467454533807, 0.09696414375152526], "t_4g": [0.41960214977751586, 0.49168826001768395], "t_unknown": [2.9121931074971E-5, 0.00519491843658891], "t_wifi": [0.5699677615611796, 0.49327218394896505]} |
| } |
| days = model_stats['model']['days'] |
| |
| x = [ |
| 4.919981, 4.912655, 4.912655, 5.1119876, 4.5217886, 5.638355, 5.673323, 5.874931, 5.6801724, 5.4026775, |
| 6.2422233, 6.150603, 5.918894, 6.251904, 6.0497336, 6.075346, 5.9939613, 6.222576, 6.1944056, 6.1025586, |
| 6.3261495, 5.811141, 5.3752785, 5.6903596, 5.4722705, 5.497168, 5.1357985, 4.919981, 5.117994, 4.9904327, |
| 5.0998664, 5.159055, 5.2983174, 4.7004805, 0.0, 4.1431346, 4.4998097, 4.6151204, 4.6728287, 4.356709, |
| 5.0238805, 5.749393, 5.4467373, 5.433722, 5.4764633, 4.9767337, 5.2832036, 5.996452, 6.011267, 6.089045, |
| 5.8318825, 5.556828, 5.590987, 5.755742, 5.8406415, 5.7525725, 5.733341, 4.4188404, 0.0, 4.454347, 3.988984, |
| 4.26268, 4.2904596, 4.804021, 4.4308167, 0.0, 6.0684257, 6.423247, 6.6795993, 6.6463904, 6.418365, 6.25575, |
| 6.1984787, 6.2841344, 6.350886, 6.4361506, 6.3733196, 6.668228, 6.2186003, 5.8230457, 6.2461066, 5.9839363, |
| 6.424869, 6.7730803, 6.510258, 6.591674, 6.767343, 6.666957, 5.529429, 5.075174, 4.248495, 0.0, 4.0943446, |
| 6.685861, 6.8330317, 6.7214255, 6.5971456, 6.629363, 6.72263, 6.5117455, 6.6795993, 6.8211074, 7.004882, |
| 6.9920964, 6.9641356, 6.991177, 6.6554403, 6.364751, 6.50129, 6.6346335, 6.43294, 6.9353704, 7.0030656, |
| 6.7968235, 7.2174435, 7.183871, 7.3225102, 7.010312, 7.4506607, 6.8855095, 6.163315, 0.0] |
| |
| days = days[:-10] |
| x = x[:-10] |
| si = [-0.04667067527770996, -0.2841551601886749, -0.29444485902786255, -0.06350809335708618, -0.1994006484746933, |
| -0.11050120741128922, -0.028408868238329887, -0.15841242671012878, -0.09160664677619934, |
| -0.056540846824645996, -0.3120572865009308, -0.5284554362297058, -0.32392826676368713, -0.06355565786361694, |
| -0.13475105166435242, 6.289364814758301, -0.21588164567947388, -0.23473970592021942, -0.06135460361838341, |
| -0.06582210212945938, -0.15293772518634796, -0.1407172530889511, -0.09954438358545303, -0.05613924190402031, |
| -0.15412424504756927, -0.3370814323425293, -0.1369824856519699] |
| |
| forward_offset = 0 |
| |
| response = predict(serving_url=URL, model_stats=model_stats, day_list=days, ucdoc_attribute_map={'uckey': 'magazinelock,1,3G,g_f,2,pt,1004,icc', |
| 'ts': [], 'price_cat': '1', |
| 'p': 462.7049255371094, 'a__n': 4.654261589050293, 'a_1_n': -0.25238537788391113, 'a_2_n': -0.40294551849365234, |
| 'a_3_n': -0.4742470979690552, 'a_4_n': -0.5568971633911133, 'a_5_n': -0.5124530792236328, 'a_6_n': -0.39294159412384033, |
| 't_UNKNOWN_n': -0.0056058494374156, 't_3G_n': -0.09904143214225769, 't_4G_n': -0.853390634059906, 't_WIFI_n': 0.8717950582504272, |
| 't_2G_n': -0.02846473827958107, 'g__n': 5.035506725311279, |
| 'g_g_f_n': -0.6774836778640747, 'g_g_m_n': -1.3297690153121948, 'g_g_x_n': -0.11089347302913666, |
| 'price_cat_1_n': 0.6838819980621338, 'price_cat_2_n': -0.49524027, 'price_cat_3_n': -0.37224495, |
| 'si_vec_n': si, 'r_vec_n': [], 'p_n': -0.02232302, 'ts_n': x, 'page_ix': 'native,d9jucwkpr3,WIFI,,,CPM,,60-1'}, forward_offset=forward_offset) |
| |
| print(response) |
