1626177120

# Improving OS Configuration with Comtrya [DevOps Office Hours Ep. 05 Featuring David McKay]

In this episode of DevOps Directive Office Hours, I will be joined by David McKay, aka RawKode to work on his project comtrya (https://github.com/comtrya/comtrya).

Comtrya is a tool (written in Rust) to help provision a fresh operating system with the packages and configuration needed to be productive as fast as possible.

He will demo the current functionality and then we will work together to add some new feature!

#comtrya #devops

1626177120

## Improving OS Configuration with Comtrya [DevOps Office Hours Ep. 05 Featuring David McKay]

In this episode of DevOps Directive Office Hours, I will be joined by David McKay, aka RawKode to work on his project comtrya (https://github.com/comtrya/comtrya).

Comtrya is a tool (written in Rust) to help provision a fresh operating system with the packages and configuration needed to be productive as fast as possible.

He will demo the current functionality and then we will work together to add some new feature!

#comtrya #devops

1602401329

## How to Extend your DevOps Strategy For Success in the Cloud?

DevOps and Cloud computing are joined at the hip, now that fact is well appreciated by the organizations that engaged in SaaS cloud and developed applications in the Cloud. During the COVID crisis period, most of the organizations have started using cloud computing services and implementing a cloud-first strategy to establish their remote operations. Similarly, the extended DevOps strategy will make the development process more agile with automated test cases.

According to the survey in EMEA, IT decision-makers have observed a 129%* improvement in the overall software development process when performing DevOps on the Cloud. This success result was just 81% when practicing only DevOps and 67%* when leveraging Cloud without DevOps. Not only that, but the practice has also made the software predictability better, improve the customer experience as well as speed up software delivery 2.6* times faster.

3 Core Principle to fit DevOps Strategy

If you consider implementing DevOps in concert with the Cloud, then the

below core principle will guide you to utilize the strategy.

• It is indispensable to follow a continuous process, including all stages from Dev to deploy with the help of auto-provisioning resources of the target platform.
• The team always keeps an eye on major and minor application changes that can typically appear within a few hours of development to operation. However, the support of unlimited resource provisioning is needed at the stage of deployment.
• Cloud or hybrid configuration can associate this process, but you must confirm that configuration should support multiple cloud brands like Microsoft, AWS, Google, any public and private cloud models.

Guide to Remold Business with DevOps and Cloud

Companies are now re-inventing themselves to become better at sensing the next big thing their customers need and finding ways with the Cloud based DevOps to get ahead of the competition.

#devops #devops-principles #azure-devops #devops-transformation #good-company #devops-tools #devops-top-story #devops-infrastructure

1652543820

## react-native-background-fetch

Background Fetch is a very simple plugin which attempts to awaken an app in the background about every 15 minutes, providing a short period of background running-time. This plugin will execute your provided callbackFn whenever a background-fetch event occurs.

There is no way to increase the rate which a fetch-event occurs and this plugin sets the rate to the most frequent possible — you will never receive an event faster than 15 minutes. The operating-system will automatically throttle the rate the background-fetch events occur based upon usage patterns. Eg: if user hasn't turned on their phone for a long period of time, fetch events will occur less frequently or if an iOS user disables background refresh they may not happen at all.

:new: Background Fetch now provides a scheduleTask method for scheduling arbitrary "one-shot" or periodic tasks.

### iOS

• There is no way to increase the rate which a fetch-event occurs and this plugin sets the rate to the most frequent possible — you will never receive an event faster than 15 minutes. The operating-system will automatically throttle the rate the background-fetch events occur based upon usage patterns. Eg: if user hasn't turned on their phone for a long period of time, fetch events will occur less frequently.
• scheduleTask seems only to fire when the device is plugged into power.
• ⚠️ When your app is terminated, iOS no longer fires events — There is no such thing as stopOnTerminate: false for iOS.
• iOS can take days before Apple's machine-learning algorithm settles in and begins regularly firing events. Do not sit staring at your logs waiting for an event to fire. If your simulated events work, that's all you need to know that everything is correctly configured.
• If the user doesn't open your iOS app for long periods of time, iOS will stop firing events.

## Installing the plugin

⚠️ If you have a previous version of react-native-background-fetch < 2.7.0 installed into react-native >= 0.60, you should first unlink your previous version as react-native link is no longer required.

### With npm

$npm install --save react-native-background-fetch ## Setup Guides ### iOS Setup #### react-native >= 0.60 ### Android Setup #### react-native >= 0.60 ## Example ℹ️ This repo contains its own Example App. See /example import React from 'react'; import { SafeAreaView, StyleSheet, ScrollView, View, Text, FlatList, StatusBar, } from 'react-native'; import { Header, Colors } from 'react-native/Libraries/NewAppScreen'; import BackgroundFetch from "react-native-background-fetch"; class App extends React.Component { constructor(props) { super(props); this.state = { events: [] }; } componentDidMount() { // Initialize BackgroundFetch ONLY ONCE when component mounts. this.initBackgroundFetch(); } async initBackgroundFetch() { // BackgroundFetch event handler. const onEvent = async (taskId) => { console.log('[BackgroundFetch] task: ', taskId); // Do your background work... await this.addEvent(taskId); // IMPORTANT: You must signal to the OS that your task is complete. BackgroundFetch.finish(taskId); } // Timeout callback is executed when your Task has exceeded its allowed running-time. // You must stop what you're doing immediately BackgroundFetch.finish(taskId) const onTimeout = async (taskId) => { console.warn('[BackgroundFetch] TIMEOUT task: ', taskId); BackgroundFetch.finish(taskId); } // Initialize BackgroundFetch only once when component mounts. let status = await BackgroundFetch.configure({minimumFetchInterval: 15}, onEvent, onTimeout); console.log('[BackgroundFetch] configure status: ', status); } // Add a BackgroundFetch event to <FlatList> addEvent(taskId) { // Simulate a possibly long-running asynchronous task with a Promise. return new Promise((resolve, reject) => { this.setState(state => ({ events: [...state.events, { taskId: taskId, timestamp: (new Date()).toString() }] })); resolve(); }); } render() { return ( <> <StatusBar barStyle="dark-content" /> <SafeAreaView> <ScrollView contentInsetAdjustmentBehavior="automatic" style={styles.scrollView}> <Header /> <View style={styles.body}> <View style={styles.sectionContainer}> <Text style={styles.sectionTitle}>BackgroundFetch Demo</Text> </View> </View> </ScrollView> <View style={styles.sectionContainer}> <FlatList data={this.state.events} renderItem={({item}) => (<Text>[{item.taskId}]: {item.timestamp}</Text>)} keyExtractor={item => item.timestamp} /> </View> </SafeAreaView> </> ); } } const styles = StyleSheet.create({ scrollView: { backgroundColor: Colors.lighter, }, body: { backgroundColor: Colors.white, }, sectionContainer: { marginTop: 32, paddingHorizontal: 24, }, sectionTitle: { fontSize: 24, fontWeight: '600', color: Colors.black, }, sectionDescription: { marginTop: 8, fontSize: 18, fontWeight: '400', color: Colors.dark, }, }); export default App; ### Executing Custom Tasks In addition to the default background-fetch task defined by BackgroundFetch.configure, you may also execute your own arbitrary "oneshot" or periodic tasks (iOS requires additional Setup Instructions). However, all events will be fired into the Callback provided to BackgroundFetch#configure: ### ⚠️ iOS: • scheduleTask on iOS seems only to run when the device is plugged into power. • scheduleTask on iOS are designed for low-priority tasks, such as purging cache files — they tend to be unreliable for mission-critical tasks. scheduleTask will never run as frequently as you want. • The default fetch event is much more reliable and fires far more often. • scheduleTask on iOS stop when the user terminates the app. There is no such thing as stopOnTerminate: false for iOS. // Step 1: Configure BackgroundFetch as usual. let status = await BackgroundFetch.configure({ minimumFetchInterval: 15 }, async (taskId) => { // <-- Event callback // This is the fetch-event callback. console.log("[BackgroundFetch] taskId: ", taskId); // Use a switch statement to route task-handling. switch (taskId) { case 'com.foo.customtask': print("Received custom task"); break; default: print("Default fetch task"); } // Finish, providing received taskId. BackgroundFetch.finish(taskId); }, async (taskId) => { // <-- Task timeout callback // This task has exceeded its allowed running-time. // You must stop what you're doing and immediately .finish(taskId) BackgroundFetch.finish(taskId); }); // Step 2: Schedule a custom "oneshot" task "com.foo.customtask" to execute 5000ms from now. BackgroundFetch.scheduleTask({ taskId: "com.foo.customtask", forceAlarmManager: true, delay: 5000 // <-- milliseconds }); API Documentation ## Config ### Common Options #### @param {Integer} minimumFetchInterval [15] The minimum interval in minutes to execute background fetch events. Defaults to 15 minutes. Note: Background-fetch events will never occur at a frequency higher than every 15 minutes. Apple uses a secret algorithm to adjust the frequency of fetch events, presumably based upon usage patterns of the app. Fetch events can occur less often than your configured minimumFetchInterval. #### @param {Integer} delay (milliseconds) ℹ️ Valid only for BackgroundFetch.scheduleTask. The minimum number of milliseconds in future that task should execute. #### @param {Boolean} periodic [false] ℹ️ Valid only for BackgroundFetch.scheduleTask. Defaults to false. Set true to execute the task repeatedly. When false, the task will execute just once. ### Android Options #### @config {Boolean} stopOnTerminate [true] Set false to continue background-fetch events after user terminates the app. Default to true. #### @config {Boolean} startOnBoot [false] Set true to initiate background-fetch events when the device is rebooted. Defaults to false. NOTE: startOnBoot requires stopOnTerminate: false. #### @config {Boolean} forceAlarmManager [false] By default, the plugin will use Android's JobScheduler when possible. The JobScheduler API prioritizes for battery-life, throttling task-execution based upon device usage and battery level. Configuring forceAlarmManager: true will bypass JobScheduler to use Android's older AlarmManager API, resulting in more accurate task-execution at the cost of higher battery usage. let status = await BackgroundFetch.configure({ minimumFetchInterval: 15, forceAlarmManager: true }, async (taskId) => { // <-- Event callback console.log("[BackgroundFetch] taskId: ", taskId); BackgroundFetch.finish(taskId); }, async (taskId) => { // <-- Task timeout callback // This task has exceeded its allowed running-time. // You must stop what you're doing and immediately .finish(taskId) BackgroundFetch.finish(taskId); }); . . . // And with with #scheduleTask BackgroundFetch.scheduleTask({ taskId: 'com.foo.customtask', delay: 5000, // milliseconds forceAlarmManager: true, periodic: false }); #### @config {Boolean} enableHeadless [false] Set true to enable React Native's Headless JS mechanism, for handling fetch events after app termination. • 📂 index.js (MUST BE IN index.js): import BackgroundFetch from "react-native-background-fetch"; let MyHeadlessTask = async (event) => { // Get task id from event {}: let taskId = event.taskId; let isTimeout = event.timeout; // <-- true when your background-time has expired. if (isTimeout) { // This task has exceeded its allowed running-time. // You must stop what you're doing immediately finish(taskId) console.log('[BackgroundFetch] Headless TIMEOUT:', taskId); BackgroundFetch.finish(taskId); return; } console.log('[BackgroundFetch HeadlessTask] start: ', taskId); // Perform an example HTTP request. // Important: await asychronous tasks when using HeadlessJS. let response = await fetch('https://reactnative.dev/movies.json'); let responseJson = await response.json(); console.log('[BackgroundFetch HeadlessTask] response: ', responseJson); // Required: Signal to native code that your task is complete. // If you don't do this, your app could be terminated and/or assigned // battery-blame for consuming too much time in background. BackgroundFetch.finish(taskId); } // Register your BackgroundFetch HeadlessTask BackgroundFetch.registerHeadlessTask(MyHeadlessTask); #### @config {integer} requiredNetworkType [BackgroundFetch.NETWORK_TYPE_NONE] Set basic description of the kind of network your job requires. If your job doesn't need a network connection, you don't need to use this option as the default value is BackgroundFetch.NETWORK_TYPE_NONE. #### @config {Boolean} requiresBatteryNotLow [false] Specify that to run this job, the device's battery level must not be low. This defaults to false. If true, the job will only run when the battery level is not low, which is generally the point where the user is given a "low battery" warning. #### @config {Boolean} requiresStorageNotLow [false] Specify that to run this job, the device's available storage must not be low. This defaults to false. If true, the job will only run when the device is not in a low storage state, which is generally the point where the user is given a "low storage" warning. #### @config {Boolean} requiresCharging [false] Specify that to run this job, the device must be charging (or be a non-battery-powered device connected to permanent power, such as Android TV devices). This defaults to false. #### @config {Boolean} requiresDeviceIdle [false] When set true, ensure that this job will not run if the device is in active use. The default state is false: that is, the for the job to be runnable even when someone is interacting with the device. This state is a loose definition provided by the system. In general, it means that the device is not currently being used interactively, and has not been in use for some time. As such, it is a good time to perform resource heavy jobs. Bear in mind that battery usage will still be attributed to your application, and shown to the user in battery stats. ## Methods ## Debugging ### iOS #### 🆕 BGTaskScheduler API for iOS 13+ • ⚠️ At the time of writing, the new task simulator does not yet work in Simulator; Only real devices. • See Apple docs Starting and Terminating Tasks During Development • After running your app in XCode, Click the [||] button to initiate a Breakpoint. • In the console (lldb), paste the following command (Note: use cursor up/down keys to cycle through previously run commands): e -l objc -- (void)[[BGTaskScheduler sharedScheduler] _simulateLaunchForTaskWithIdentifier:@"com.transistorsoft.fetch"] • Click the [ > ] button to continue. The task will execute and the Callback function provided to BackgroundFetch.configure will receive the event. #### Simulating task-timeout events • Only the new BGTaskScheduler api supports simulated task-timeout events. To simulate a task-timeout, your fetchCallback must not call BackgroundFetch.finish(taskId): let status = await BackgroundFetch.configure({ minimumFetchInterval: 15 }, async (taskId) => { // <-- Event callback. // This is the task callback. console.log("[BackgroundFetch] taskId", taskId); //BackgroundFetch.finish(taskId); // <-- Disable .finish(taskId) when simulating an iOS task timeout }, async (taskId) => { // <-- Event timeout callback // This task has exceeded its allowed running-time. // You must stop what you're doing and immediately .finish(taskId) print("[BackgroundFetch] TIMEOUT taskId:", taskId); BackgroundFetch.finish(taskId); }); • Now simulate an iOS task timeout as follows, in the same manner as simulating an event above: e -l objc -- (void)[[BGTaskScheduler sharedScheduler] _simulateExpirationForTaskWithIdentifier:@"com.transistorsoft.fetch"] #### Old BackgroundFetch API • Simulate background fetch events in XCode using Debug->Simulate Background Fetch • iOS can take some hours or even days to start a consistently scheduling background-fetch events since iOS schedules fetch events based upon the user's patterns of activity. If Simulate Background Fetch works, your can be sure that everything is working fine. You just need to wait. ### Android • Observe plugin logs in $ adb logcat:
$adb logcat *:S ReactNative:V ReactNativeJS:V TSBackgroundFetch:V • Simulate a background-fetch event on a device (insert <your.application.id>) (only works for sdk 21+: $ adb shell cmd jobscheduler run -f <your.application.id> 999
• For devices with sdk <21, simulate a "Headless JS" event with (insert <your.application.id>)
$adb shell am broadcast -a <your.application.id>.event.BACKGROUND_FETCH Download Details: Author: transistorsoft Source Code: https://github.com/transistorsoft/react-native-background-fetch License: MIT license 1641276000 ## DeltaPy⁠⁠ — Tabular Data Augmentation & Feature Engineering ## DeltaPy⁠⁠ — Tabular Data Augmentation & Feature Engineering ## Finance Quant Machine Learning ### Introduction Tabular augmentation is a new experimental space that makes use of novel and traditional data generation and synthesisation techniques to improve model prediction success. It is in essence a process of modular feature engineering and observation engineering while emphasising the order of augmentation to achieve the best predicted outcome from a given information set. DeltaPy was created with finance applications in mind, but it can be broadly applied to any data-rich environment. To take full advantage of tabular augmentation for time-series you would perform the techniques in the following order: (1) transforming, (2) interacting, (3) mapping, (4) extracting, and (5) synthesising. What follows is a practical example of how the above methodology can be used. The purpose here is to establish a framework for table augmentation and to point and guide the user to existing packages. For most the Colab Notebook format might be preferred. I have enabled comments if you want to ask question or address any issues you uncover. For anything pressing use the issues tab. Also have a look at the SSRN report for a more succinct insights. Data augmentation can be defined as any method that could increase the size or improve the quality of a dataset by generating new features or instances without the collection of additional data-points. Data augmentation is of particular importance in image classification tasks where additional data can be created by cropping, padding, or flipping existing images. Tabular cross-sectional and time-series prediction tasks can also benefit from augmentation. Here we divide tabular augmentation into columnular and row-wise methods. Row-wise methods are further divided into extraction and data synthesisation techniques, whereas columnular methods are divided into transformation, interaction, and mapping methods. See the Skeleton Example, for a combination of multiple methods that lead to a halfing of the mean squared error. #### Installation & Citation pip install deltapy  @software{deltapy, title = {{DeltaPy}: Tabular Data Augmentation}, author = {Snow, Derek}, url = {https://github.com/firmai/deltapy/}, version = {0.1.0}, date = {2020-04-11}, }   Snow, Derek, DeltaPy: A Framework for Tabular Data Augmentation in Python (April 22, 2020). Available at SSRN: https://ssrn.com/abstract=3582219  ### Function Glossary Transformation df_out = transform.robust_scaler(df.copy(), drop=["Close_1"]); df_out.head() df_out = transform.standard_scaler(df.copy(), drop=["Close"]); df_out.head() df_out = transform.fast_fracdiff(df.copy(), ["Close","Open"],0.5); df_out.head() df_out = transform.windsorization(df.copy(),"Close",para,strategy='both'); df_out.head() df_out = transform.operations(df.copy(),["Close"]); df_out.head() df_out = transform.triple_exponential_smoothing(df.copy(),["Close"], 12, .2,.2,.2,0); df_out = transform.naive_dec(df.copy(), ["Close","Open"]); df_out.head() df_out = transform.bkb(df.copy(), ["Close"]); df_out.head() df_out = transform.butter_lowpass_filter(df.copy(),["Close"],4); df_out.head() df_out = transform.instantaneous_phases(df.copy(), ["Close"]); df_out.head() df_out = transform.kalman_feat(df.copy(), ["Close"]); df_out.head() df_out = transform.perd_feat(df.copy(),["Close"]); df_out.head() df_out = transform.fft_feat(df.copy(), ["Close"]); df_out.head() df_out = transform.harmonicradar_cw(df.copy(), ["Close"],0.3,0.2); df_out.head() df_out = transform.saw(df.copy(),["Close","Open"]); df_out.head() df_out = transform.modify(df.copy(),["Close"]); df_out.head() df_out = transform.multiple_rolling(df, columns=["Close"]); df_out.head() df_out = transform.multiple_lags(df, start=1, end=3, columns=["Close"]); df_out.head() df_out = transform.prophet_feat(df.copy().reset_index(),["Close","Open"],"Date", "D"); df_out.head()  Interaction df_out = interact.lowess(df.copy(), ["Open","Volume"], df["Close"], f=0.25, iter=3); df_out.head() df_out = interact.autoregression(df.copy()); df_out.head() df_out = interact.muldiv(df.copy(), ["Close","Open"]); df_out.head() df_out = interact.decision_tree_disc(df.copy(), ["Close"]); df_out.head() df_out = interact.quantile_normalize(df.copy(), drop=["Close"]); df_out.head() df_out = interact.tech(df.copy()); df_out.head() df_out = interact.genetic_feat(df.copy()); df_out.head()  Mapping df_out = mapper.pca_feature(df.copy(),variance_or_components=0.80,drop_cols=["Close_1"]); df_out.head() df_out = mapper.cross_lag(df.copy()); df_out.head() df_out = mapper.a_chi(df.copy()); df_out.head() df_out = mapper.encoder_dataset(df.copy(), ["Close_1"], 15); df_out.head() df_out = mapper.lle_feat(df.copy(),["Close_1"],4); df_out.head() df_out = mapper.feature_agg(df.copy(),["Close_1"],4 ); df_out.head() df_out = mapper.neigh_feat(df.copy(),["Close_1"],4 ); df_out.head()  Extraction extract.abs_energy(df["Close"]) extract.cid_ce(df["Close"], True) extract.mean_abs_change(df["Close"]) extract.mean_second_derivative_central(df["Close"]) extract.variance_larger_than_standard_deviation(df["Close"]) extract.var_index(df["Close"].values,var_index_param) extract.symmetry_looking(df["Close"]) extract.has_duplicate_max(df["Close"]) extract.partial_autocorrelation(df["Close"]) extract.augmented_dickey_fuller(df["Close"]) extract.gskew(df["Close"]) extract.stetson_mean(df["Close"]) extract.length(df["Close"]) extract.count_above_mean(df["Close"]) extract.longest_strike_below_mean(df["Close"]) extract.wozniak(df["Close"]) extract.last_location_of_maximum(df["Close"]) extract.fft_coefficient(df["Close"]) extract.ar_coefficient(df["Close"]) extract.index_mass_quantile(df["Close"]) extract.number_cwt_peaks(df["Close"]) extract.spkt_welch_density(df["Close"]) extract.linear_trend_timewise(df["Close"]) extract.c3(df["Close"]) extract.binned_entropy(df["Close"]) extract.svd_entropy(df["Close"].values) extract.hjorth_complexity(df["Close"]) extract.max_langevin_fixed_point(df["Close"]) extract.percent_amplitude(df["Close"]) extract.cad_prob(df["Close"]) extract.zero_crossing_derivative(df["Close"]) extract.detrended_fluctuation_analysis(df["Close"]) extract.fisher_information(df["Close"]) extract.higuchi_fractal_dimension(df["Close"]) extract.petrosian_fractal_dimension(df["Close"]) extract.hurst_exponent(df["Close"]) extract.largest_lyauponov_exponent(df["Close"]) extract.whelch_method(df["Close"]) extract.find_freq(df["Close"]) extract.flux_perc(df["Close"]) extract.range_cum_s(df["Close"]) extract.structure_func(df["Close"]) extract.kurtosis(df["Close"]) extract.stetson_k(df["Close"])  Test sets should ideally not be preprocessed with the training data, as in such a way one could be peaking ahead in the training data. The preprocessing parameters should be identified on the test set and then applied on the test set, i.e., the test set should not have an impact on the transformation applied. As an example, you would learn the parameters of PCA decomposition on the training set and then apply the parameters to both the train and the test set. The benefit of pipelines become clear when one wants to apply multiple augmentation methods. It makes it easy to learn the parameters and then apply them widely. For the most part, this notebook does not concern itself with 'peaking ahead' or pipelines, for some functions, one might have to restructure to code and make use of open source packages to create your preferred solution. ## Documentation by Example Notebook Dependencies pip install deltapy  pip install pykalman pip install tsaug pip install ta pip install tsaug pip install pandasvault pip install gplearn pip install ta pip install seasonal pip install pandasvault  ### Data and Package Load import pandas as pd import numpy as np from deltapy import transform, interact, mapper, extract import warnings warnings.filterwarnings('ignore') def data_copy(): df = pd.read_csv("https://github.com/firmai/random-assets-two/raw/master/numpy/tsla.csv") df["Close_1"] = df["Close"].shift(-1) df = df.dropna() df["Date"] = pd.to_datetime(df["Date"]) df = df.set_index("Date") return df df = data_copy(); df.head()  Some of these categories are fluid and some techniques could fit into multiple buckets. This is an attempt to find an exhaustive number of techniques, but not an exhaustive list of implementations of the techniques. For example, there are thousands of ways to smooth a time-series, but we have only includes 1-2 techniques of interest under each category. ### (1) Transformation: 1. Scaling/Normalisation 2. Standardisation 3. Differencing 4. Capping 5. Operations 6. Smoothing 7. Decomposing 8. Filtering 9. Spectral Analysis 10. Waveforms 11. Modifications 12. Rolling 13. Lagging 14. Forecast Model ### (2) Interaction: 1. Regressions 2. Operators 3. Discretising 4. Normalising 5. Distance 6. Speciality 7. Genetic ### (3) Mapping: 1. Eigen Decomposition 2. Cross Decomposition 3. Kernel Approximation 4. Autoencoder 5. Manifold Learning 6. Clustering 7. Neighbouring ### (4) Extraction: 1. Energy 2. Distance 3. Differencing 4. Derivative 5. Volatility 6. Shape 7. Occurrence 8. Autocorrelation 9. Stochasticity 10. Averages 11. Size 12. Count 13. Streaks 14. Location 15. Model Coefficients 16. Quantile 17. Peaks 18. Density 19. Linearity 20. Non-linearity 21. Entropy 22. Fixed Points 23. Amplitude 24. Probability 25. Crossings 26. Fluctuation 27. Information 28. Fractals 29. Exponent 30. Spectral Analysis 31. Percentile 32. Range 33. Structural 34. Distribution ## (1) Transformation Here transformation is any method that includes only one feature as an input to produce a new feature/s. Transformations can be applied to cross-section and time-series data. Some transformations are exclusive to time-series data (smoothing, filtering), but a handful of functions apply to both. Where the time series methods has a centred mean, or are forward-looking, there is a need to recalculate the outputed time series on a running basis to ensure that information of the future does not leak into the model. The last value of this recalculated series or an extracted feature from this series can then be used as a running value that is only backward looking, satisfying the no 'peaking' ahead rule. There are some packaged in Python that dynamically create time series and extracts their features, but none that incoropates the dynamic creation of a time series in combination with a wide application of prespecified list of extractions. Because this technique is expensive, we have a preference for models that only take historical data into account. In this section we will include a list of all types of transformations, those that only use present information (operations), those that incorporate all values (interpolation methods), those that only include past values (smoothing functions), and those that incorporate a subset window of lagging and leading values (select filters). Only those that use historical values or are turned into prediction methods can be used out of the box. The entire time series can be used in the model development process for historical value methods, and only the forecasted values can be used for prediction models. Curve fitting can involve either interpolation, where an exact fit to the data is required, or smoothing, in which a "smooth" function is constructed that approximately fits the data. When using an interpolation method, you are taking future information into account e.g, cubic spline. You can use interpolation methods to forecast into the future (extrapolation), and then use those forecasts in a training set. Or you could recalculate the interpolation for each time step and then extract features out of that series (extraction method). Interpolation and other forward-looking methods can be used if they are turned into prediction problems, then the forecasted values can be trained and tested on, and the fitted data can be diregarded. In the list presented below the first five methods can be used for cross-section and time series data, after that the time-series only methods follow. #### (1) Scaling/Normalisation There are a multitude of scaling methods available. Scaling generally gets applied to the entire dataset and is especially necessary for certain algorithms. K-means make use of euclidean distance hence the need for scaling. For PCA because we are trying to identify the feature with maximus variance we also need scaling. Similarly, we need scaled features for gradient descent. Any algorithm that is not based on a distance measure is not affected by feature scaling. Some of the methods include range scalers like minimum-maximum scaler, maximum absolute scaler or even standardisation methods like the standard scaler can be used for scaling. The example used here is robust scaler. Normalisation is a good technique when you don't know the distribution of the data. Scaling looks into the future, so parameters have to be training on a training set and applied to a test set. (i) Robust Scaler Scaling according to the interquartile range, making it robust to outliers. def robust_scaler(df, drop=None,quantile_range=(25, 75) ): if drop: keep = df[drop] df = df.drop(drop, axis=1) center = np.median(df, axis=0) quantiles = np.percentile(df, quantile_range, axis=0) scale = quantiles[1] - quantiles[0] df = (df - center) / scale if drop: df = pd.concat((keep,df),axis=1) return df df_out = transform.robust_scaler(df.copy(), drop=["Close_1"]); df_out.head()  #### (2) Standardisation When using a standardisation method, it is often more effective when the attribute itself if Gaussian. It is also useful to apply the technique when the model you want to use makes assumptions of Gaussian distributions like linear regression, logistic regression, and linear discriminant analysis. For most applications, standardisation is recommended. (i) Standard Scaler Standardize features by removing the mean and scaling to unit variance def standard_scaler(df,drop ): if drop: keep = df[drop] df = df.drop(drop, axis=1) mean = np.mean(df, axis=0) scale = np.std(df, axis=0) df = (df - mean) / scale if drop: df = pd.concat((keep,df),axis=1) return df df_out = transform.standard_scaler(df.copy(), drop=["Close"]); df_out.head()  #### (3) Differencing Computing the differences between consecutive observation, normally used to obtain a stationary time series. (i) Fractional Differencing Fractional differencing, allows us to achieve stationarity while maintaining the maximum amount of memory compared to integer differencing. import pylab as pl def fast_fracdiff(x, cols, d): for col in cols: T = len(x[col]) np2 = int(2 ** np.ceil(np.log2(2 * T - 1))) k = np.arange(1, T) b = (1,) + tuple(np.cumprod((k - d - 1) / k)) z = (0,) * (np2 - T) z1 = b + z z2 = tuple(x[col]) + z dx = pl.ifft(pl.fft(z1) * pl.fft(z2)) x[col+"_frac"] = np.real(dx[0:T]) return x df_out = transform.fast_fracdiff(df.copy(), ["Close","Open"],0.5); df_out.head()  #### (4) Capping Any method that provides sets a floor and a cap to a feature's value. Capping can affect the distribution of data, so it should not be exagerated. One can cap values by using the average, by using the max and min values, or by an arbitrary extreme value. (i) Winzorisation The transformation of features by limiting extreme values in the statistical data to reduce the effect of possibly spurious outliers by replacing it with a certain percentile value. def outlier_detect(data,col,threshold=1,method="IQR"): if method == "IQR": IQR = data[col].quantile(0.75) - data[col].quantile(0.25) Lower_fence = data[col].quantile(0.25) - (IQR * threshold) Upper_fence = data[col].quantile(0.75) + (IQR * threshold) if method == "STD": Upper_fence = data[col].mean() + threshold * data[col].std() Lower_fence = data[col].mean() - threshold * data[col].std() if method == "OWN": Upper_fence = data[col].mean() + threshold * data[col].std() Lower_fence = data[col].mean() - threshold * data[col].std() if method =="MAD": median = data[col].median() median_absolute_deviation = np.median([np.abs(y - median) for y in data[col]]) modified_z_scores = pd.Series([0.6745 * (y - median) / median_absolute_deviation for y in data[col]]) outlier_index = np.abs(modified_z_scores) > threshold print('Num of outlier detected:',outlier_index.value_counts()[1]) print('Proportion of outlier detected',outlier_index.value_counts()[1]/len(outlier_index)) return outlier_index, (median_absolute_deviation, median_absolute_deviation) para = (Upper_fence, Lower_fence) tmp = pd.concat([data[col]>Upper_fence,data[col]<Lower_fence],axis=1) outlier_index = tmp.any(axis=1) print('Num of outlier detected:',outlier_index.value_counts()[1]) print('Proportion of outlier detected',outlier_index.value_counts()[1]/len(outlier_index)) return outlier_index, para def windsorization(data,col,para,strategy='both'): """ top-coding & bottom coding (capping the maximum of a distribution at an arbitrarily set value,vice versa) """ data_copy = data.copy(deep=True) if strategy == 'both': data_copy.loc[data_copy[col]>para[0],col] = para[0] data_copy.loc[data_copy[col]<para[1],col] = para[1] elif strategy == 'top': data_copy.loc[data_copy[col]>para[0],col] = para[0] elif strategy == 'bottom': data_copy.loc[data_copy[col]<para[1],col] = para[1] return data_copy _, para = transform.outlier_detect(df, "Close") df_out = transform.windsorization(df.copy(),"Close",para,strategy='both'); df_out.head()  #### (5) Operations Operations here are treated like traditional transformations. It is the replacement of a variable by a function of that variable. In a stronger sense, a transformation is a replacement that changes the shape of a distribution or relationship. (i) Power, Log, Recipricol, Square Root def operations(df,features): df_new = df[features] df_new = df_new - df_new.min() sqr_name = [str(fa)+"_POWER_2" for fa in df_new.columns] log_p_name = [str(fa)+"_LOG_p_one_abs" for fa in df_new.columns] rec_p_name = [str(fa)+"_RECIP_p_one" for fa in df_new.columns] sqrt_name = [str(fa)+"_SQRT_p_one" for fa in df_new.columns] df_sqr = pd.DataFrame(np.power(df_new.values, 2),columns=sqr_name, index=df.index) df_log = pd.DataFrame(np.log(df_new.add(1).abs().values),columns=log_p_name, index=df.index) df_rec = pd.DataFrame(np.reciprocal(df_new.add(1).values),columns=rec_p_name, index=df.index) df_sqrt = pd.DataFrame(np.sqrt(df_new.abs().add(1).values),columns=sqrt_name, index=df.index) dfs = [df, df_sqr, df_log, df_rec, df_sqrt] df= pd.concat(dfs, axis=1) return df df_out = transform.operations(df.copy(),["Close"]); df_out.head()  #### (6) Smoothing Here we maintain that any method that has a component of historical averaging is a smoothing method such as a simple moving average and single, double and tripple exponential smoothing methods. These forms of non-causal filters are also popular in signal processing and are called filters, where exponential smoothing is called an IIR filter and a moving average a FIR filter with equal weighting factors. (i) Tripple Exponential Smoothing (Holt-Winters Exponential Smoothing) The Holt-Winters seasonal method comprises the forecast equation and three smoothing equations — one for the level$ℓt$, one for the trend &bt&, and one for the seasonal component$st$. This particular version is performed by looking at the last 12 periods. For that reason, the first 12 records should be disregarded because they can't make use of the required window size for a fair calculation. The calculation is such that values are still provided for those periods based on whatever data might be available. def initial_trend(series, slen): sum = 0.0 for i in range(slen): sum += float(series[i+slen] - series[i]) / slen return sum / slen def initial_seasonal_components(series, slen): seasonals = {} season_averages = [] n_seasons = int(len(series)/slen) # compute season averages for j in range(n_seasons): season_averages.append(sum(series[slen*j:slen*j+slen])/float(slen)) # compute initial values for i in range(slen): sum_of_vals_over_avg = 0.0 for j in range(n_seasons): sum_of_vals_over_avg += series[slen*j+i]-season_averages[j] seasonals[i] = sum_of_vals_over_avg/n_seasons return seasonals def triple_exponential_smoothing(df,cols, slen, alpha, beta, gamma, n_preds): for col in cols: result = [] seasonals = initial_seasonal_components(df[col], slen) for i in range(len(df[col])+n_preds): if i == 0: # initial values smooth = df[col][0] trend = initial_trend(df[col], slen) result.append(df[col][0]) continue if i >= len(df[col]): # we are forecasting m = i - len(df[col]) + 1 result.append((smooth + m*trend) + seasonals[i%slen]) else: val = df[col][i] last_smooth, smooth = smooth, alpha*(val-seasonals[i%slen]) + (1-alpha)*(smooth+trend) trend = beta * (smooth-last_smooth) + (1-beta)*trend seasonals[i%slen] = gamma*(val-smooth) + (1-gamma)*seasonals[i%slen] result.append(smooth+trend+seasonals[i%slen]) df[col+"_TES"] = result #print(seasonals) return df df_out= transform.triple_exponential_smoothing(df.copy(),["Close"], 12, .2,.2,.2,0); df_out.head()  #### (7) Decomposing Decomposition procedures are used in time series to describe the trend and seasonal factors in a time series. More extensive decompositions might also include long-run cycles, holiday effects, day of week effects and so on. Here, we’ll only consider trend and seasonal decompositions. A naive decomposition makes use of moving averages, other decomposition methods are available that make use of LOESS. (i) Naive Decomposition The base trend takes historical information into account and established moving averages; it does not have to be linear. To estimate the seasonal component for each season, simply average the detrended values for that season. If the seasonal variation looks constant, we should use the additive model. If the magnitude is increasing as a function of time, we will use multiplicative. Here because it is predictive in nature we are using a one sided moving average, as opposed to a two-sided centred average. import statsmodels.api as sm def naive_dec(df, columns, freq=2): for col in columns: decomposition = sm.tsa.seasonal_decompose(df[col], model='additive', freq = freq, two_sided=False) df[col+"_NDDT" ] = decomposition.trend df[col+"_NDDT"] = decomposition.seasonal df[col+"_NDDT"] = decomposition.resid return df df_out = transform.naive_dec(df.copy(), ["Close","Open"]); df_out.head()  #### (8) Filtering It is often useful to either low-pass filter (smooth) time series in order to reveal low-frequency features and trends, or to high-pass filter (detrend) time series in order to isolate high frequency transients (e.g. storms). Low pass filters use historical values, high-pass filters detrends with low-pass filters, so also indirectly uses historical values. There are a few filters available, closely associated with decompositions and smoothing functions. The Hodrick-Prescott filter separates a time-series$yt$into a trend$τt$and a cyclical component$ζt$. The Christiano-Fitzgerald filter is a generalization of Baxter-King filter and can be seen as weighted moving average. (i) Baxter-King Bandpass The Baxter-King filter is intended to explicitly deal with the periodicity of the business cycle. By applying their band-pass filter to a series, they produce a new series that does not contain fluctuations at higher or lower than those of the business cycle. The parameters are arbitrarily chosen. This method uses a centred moving average that has to be changed to a lagged moving average before it can be used as an input feature. The maximum period of oscillation should be used as the point to truncate the dataset, as that part of the time series does not incorporate all the required datapoints. import statsmodels.api as sm def bkb(df, cols): for col in cols: df[col+"_BPF"] = sm.tsa.filters.bkfilter(df[[col]].values, 2, 10, len(df)-1) return df df_out = transform.bkb(df.copy(), ["Close"]); df_out.head()  (ii) Butter Lowpass (IIR Filter Design) The Butterworth filter is a type of signal processing filter designed to have a frequency response as flat as possible in the passban. Like other filtersm the first few values have to be disregarded for accurate downstream prediction. Instead of disregarding these values on a per case basis, they can be diregarded in one chunk once the database of transformed features have been developed. from scipy import signal, integrate def butter_lowpass(cutoff, fs=20, order=5): nyq = 0.5 * fs normal_cutoff = cutoff / nyq b, a = signal.butter(order, normal_cutoff, btype='low', analog=False) return b, a def butter_lowpass_filter(df,cols, cutoff, fs=20, order=5): b, a = butter_lowpass(cutoff, fs, order=order) for col in cols: df[col+"_BUTTER"] = signal.lfilter(b, a, df[col]) return df df_out = transform.butter_lowpass_filter(df.copy(),["Close"],4); df_out.head()  (iii) Hilbert Transform Angle The Hilbert transform is a time-domain to time-domain transformation which shifts the phase of a signal by 90 degrees. It is also a centred measure and would be difficult to use in a time series prediction setting, unless it is recalculated on a per step basis or transformed to be based on historical values only. from scipy import signal import numpy as np def instantaneous_phases(df,cols): for col in cols: df[col+"_HILLB"] = np.unwrap(np.angle(signal.hilbert(df[col], axis=0)), axis=0) return df df_out = transform.instantaneous_phases(df.copy(), ["Close"]); df_out.head()  (iiiv) Unscented Kalman Filter The Kalman filter is better suited for estimating things that change over time. The most tangible example is tracking moving objects. A Kalman filter will be very close to the actual trajectory because it says the most recent measurement is more important than the older ones. The Unscented Kalman Filter (UKF) is a model based-techniques that recursively estimates the states (and with some modifications also parameters) of a nonlinear, dynamic, discrete-time system. The UKF is based on the typical prediction-correction style methods. The Kalman Smoother incorporates future values, the Filter doesn't and can be used for online prediction. The normal Kalman filter is a forward filter in the sense that it makes forecast of the current state using only current and past observations, whereas the smoother is based on computing a suitable linear combination of two filters, which are ran in forward and backward directions. from pykalman import UnscentedKalmanFilter def kalman_feat(df, cols): for col in cols: ukf = UnscentedKalmanFilter(lambda x, w: x + np.sin(w), lambda x, v: x + v, observation_covariance=0.1) (filtered_state_means, filtered_state_covariances) = ukf.filter(df[col]) (smoothed_state_means, smoothed_state_covariances) = ukf.smooth(df[col]) df[col+"_UKFSMOOTH"] = smoothed_state_means.flatten() df[col+"_UKFFILTER"] = filtered_state_means.flatten() return df df_out = transform.kalman_feat(df.copy(), ["Close"]); df_out.head()  #### (9) Spectral Analysis There are a range of functions for spectral analysis. You can use periodograms and the welch method to estimate the power spectral density. You can also use the welch method to estimate the cross power spectral density. Other techniques include spectograms, Lomb-Scargle periodograms and, short time fourier transform. (i) Periodogram This returns an array of sample frequencies and the power spectrum of x, or the power spectral density of x. from scipy import signal def perd_feat(df, cols): for col in cols: sig = signal.periodogram(df[col],fs=1, return_onesided=False) df[col+"_FREQ"] = sig[0] df[col+"_POWER"] = sig[1] return df df_out = transform.perd_feat(df.copy(),["Close"]); df_out.head()  (ii) Fast Fourier Transform The FFT, or fast fourier transform is an algorithm that essentially uses convolution techniques to efficiently find the magnitude and location of the tones that make up the signal of interest. We can often play with the FFT spectrum, by adding and removing successive tones (which is akin to selectively filtering particular tones that make up the signal), in order to obtain a smoothed version of the underlying signal. This takes the entire signal into account, and as a result has to be recalculated on a running basis to avoid peaking into the future. def fft_feat(df, cols): for col in cols: fft_df = np.fft.fft(np.asarray(df[col].tolist())) fft_df = pd.DataFrame({'fft':fft_df}) df[col+'_FFTABS'] = fft_df['fft'].apply(lambda x: np.abs(x)).values df[col+'_FFTANGLE'] = fft_df['fft'].apply(lambda x: np.angle(x)).values return df df_out = transform.fft_feat(df.copy(), ["Close"]); df_out.head()  #### (10) Waveforms The waveform of a signal is the shape of its graph as a function of time. (i) Continuous Wave Radar from scipy import signal def harmonicradar_cw(df, cols, fs,fc): for col in cols: ttxt = f'CW: {fc} Hz' #%% input t = df[col] tx = np.sin(2*np.pi*fc*t) _,Pxx = signal.welch(tx,fs) #%% diode d = (signal.square(2*np.pi*fc*t)) d[d<0] = 0. #%% output of diode rx = tx * d df[col+"_HARRAD"] = rx.values return df df_out = transform.harmonicradar_cw(df.copy(), ["Close"],0.3,0.2); df_out.head()  (ii) Saw Tooth Return a periodic sawtooth or triangle waveform. def saw(df, cols): for col in cols: df[col+" SAW"] = signal.sawtooth(df[col]) return df df_out = transform.saw(df.copy(),["Close","Open"]); df_out.head()  (9) Modifications A range of modification usually applied ot images, these values would have to be recalculate for each time-series. (i) Various Techniques from tsaug import * def modify(df, cols): for col in cols: series = df[col].values df[col+"_magnify"], _ = magnify(series, series) df[col+"_affine"], _ = affine(series, series) df[col+"_crop"], _ = crop(series, series) df[col+"_cross_sum"], _ = cross_sum(series, series) df[col+"_resample"], _ = resample(series, series) df[col+"_trend"], _ = trend(series, series) df[col+"_random_affine"], _ = random_time_warp(series, series) df[col+"_random_crop"], _ = random_crop(series, series) df[col+"_random_cross_sum"], _ = random_cross_sum(series, series) df[col+"_random_sidetrack"], _ = random_sidetrack(series, series) df[col+"_random_time_warp"], _ = random_time_warp(series, series) df[col+"_random_magnify"], _ = random_magnify(series, series) df[col+"_random_jitter"], _ = random_jitter(series, series) df[col+"_random_trend"], _ = random_trend(series, series) return df df_out = transform.modify(df.copy(),["Close"]); df_out.head()  #### (11) Rolling Features that are calculated on a rolling basis over fixed window size. (i) Mean, Standard Deviation def multiple_rolling(df, windows = [1,2], functions=["mean","std"], columns=None): windows = [1+a for a in windows] if not columns: columns = df.columns.to_list() rolling_dfs = (df[columns].rolling(i) # 1. Create window .agg(functions) # 1. Aggregate .rename({col: '{0}_{1:d}'.format(col, i) for col in columns}, axis=1) # 2. Rename columns for i in windows) # For each window df_out = pd.concat((df, *rolling_dfs), axis=1) da = df_out.iloc[:,len(df.columns):] da = [col[0] + "_" + col[1] for col in da.columns.to_list()] df_out.columns = df.columns.to_list() + da return df_out # 3. Concatenate dataframes df_out = transform.multiple_rolling(df, columns=["Close"]); df_out.head()  #### (12) Lagging Lagged values from existing features. (i) Single Steps def multiple_lags(df, start=1, end=3,columns=None): if not columns: columns = df.columns.to_list() lags = range(start, end+1) # Just two lags for demonstration. df = df.assign(**{ '{}_t_{}'.format(col, t): df[col].shift(t) for t in lags for col in columns }) return df df_out = transform.multiple_lags(df, start=1, end=3, columns=["Close"]); df_out.head()  #### (13) Forecast Model There are a range of time series model that can be implemented like AR, MA, ARMA, ARIMA, SARIMA, SARIMAX, VAR, VARMA, VARMAX, SES, and HWES. The models can be divided into autoregressive models and smoothing models. In an autoregression model, we forecast the variable of interest using a linear combination of past values of the variable. Each method might requre specific tuning and parameters to suit your prediction task. You need to drop a certain amount of historical data that you use during the fitting stage. Models that take seasonality into account need more training data. (i) Prophet Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality. You can apply additive models to your training data but also interactive models like deep learning models. The problem is that because these models have learned from future observations, there would this be a need to recalculate the time series on a running basis, or to only include the predicted as opposed to fitted values in future training and test sets. In this example, I train on 150 data points to illustrate how the remaining or so 100 datapoints can be used in a new prediction problem. You can plot with df["PROPHET"].plot() to see the effect. You can apply additive models to your training data but also interactive models like deep learning models. The problem is that these models have learned from future observations, there would this be a need to recalculate the time series on a running basis, or to only include the predicted as opposed to fitted values in future training and test sets. from fbprophet import Prophet def prophet_feat(df, cols,date, freq,train_size=150): def prophet_dataframe(df): df.columns = ['ds','y'] return df def original_dataframe(df, freq, name): prophet_pred = pd.DataFrame({"Date" : df['ds'], name : df["yhat"]}) prophet_pred = prophet_pred.set_index("Date") #prophet_pred.index.freq = pd.tseries.frequencies.to_offset(freq) return prophet_pred[name].values for col in cols: model = Prophet(daily_seasonality=True) fb = model.fit(prophet_dataframe(df[[date, col]].head(train_size))) forecast_len = len(df) - train_size future = model.make_future_dataframe(periods=forecast_len,freq=freq) future_pred = model.predict(future) df[col+"_PROPHET"] = list(original_dataframe(future_pred,freq,col)) return df df_out = transform.prophet_feat(df.copy().reset_index(),["Close","Open"],"Date", "D"); df_out.head()  ## (2) Interaction Interactions are defined as methods that require more than one feature to create an additional feature. Here we include normalising and discretising techniques that are non-feature specific. Almost all of these method can be applied to cross-section method. The only methods that are time specific is the technical features in the speciality section and the autoregression model. #### (1) Regression Regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the 'outcome variable') and one or more independent variables. (i) Lowess Smoother The lowess smoother is a robust locally weighted regression. The function fits a nonparametric regression curve to a scatterplot. from math import ceil import numpy as np from scipy import linalg import math def lowess(df, cols, y, f=2. / 3., iter=3): for col in cols: n = len(df[col]) r = int(ceil(f * n)) h = [np.sort(np.abs(df[col] - df[col][i]))[r] for i in range(n)] w = np.clip(np.abs((df[col][:, None] - df[col][None, :]) / h), 0.0, 1.0) w = (1 - w ** 3) ** 3 yest = np.zeros(n) delta = np.ones(n) for iteration in range(iter): for i in range(n): weights = delta * w[:, i] b = np.array([np.sum(weights * y), np.sum(weights * y * df[col])]) A = np.array([[np.sum(weights), np.sum(weights * df[col])], [np.sum(weights * df[col]), np.sum(weights * df[col] * df[col])]]) beta = linalg.solve(A, b) yest[i] = beta[0] + beta[1] * df[col][i] residuals = y - yest s = np.median(np.abs(residuals)) delta = np.clip(residuals / (6.0 * s), -1, 1) delta = (1 - delta ** 2) ** 2 df[col+"_LOWESS"] = yest return df df_out = interact.lowess(df.copy(), ["Open","Volume"], df["Close"], f=0.25, iter=3); df_out.head()  Autoregression Autoregression is a time series model that uses observations from previous time steps as input to a regression equation to predict the value at the next time step from statsmodels.tsa.ar_model import AR from timeit import default_timer as timer def autoregression(df, drop=None, settings={"autoreg_lag":4}): autoreg_lag = settings["autoreg_lag"] if drop: keep = df[drop] df = df.drop([drop],axis=1).values n_channels = df.shape[0] t = timer() channels_regg = np.zeros((n_channels, autoreg_lag + 1)) for i in range(0, n_channels): fitted_model = AR(df.values[i, :]).fit(autoreg_lag) # TODO: This is not the same as Matlab's for some reasons! # kk = ARMAResults(fitted_model) # autore_vals, dummy1, dummy2 = arburg(x[i, :], autoreg_lag) # This looks like Matlab's but slow channels_regg[i, 0: len(fitted_model.params)] = np.real(fitted_model.params) for i in range(channels_regg.shape[1]): df["LAG_"+str(i+1)] = channels_regg[:,i] if drop: df = pd.concat((keep,df),axis=1) t = timer() - t return df df_out = interact.autoregression(df.copy()); df_out.head()  #### (2) Operator Looking at interaction between different features. Here the methods employed are multiplication and division. (i) Multiplication and Division def muldiv(df, feature_list): for feat in feature_list: for feat_two in feature_list: if feat==feat_two: continue else: df[feat+"/"+feat_two] = df[feat]/(df[feat_two]-df[feat_two].min()) #zero division guard df[feat+"_X_"+feat_two] = df[feat]*(df[feat_two]) return df df_out = interact.muldiv(df.copy(), ["Close","Open"]); df_out.head()  #### (3) Discretising In statistics and machine learning, discretization refers to the process of converting or partitioning continuous attributes, features or variables to discretized or nominal attributes (i) Decision Tree Discretiser The first method that will be applies here is a supersived discretiser. Discretisation with Decision Trees consists of using a decision tree to identify the optimal splitting points that would determine the bins or contiguous intervals. from sklearn.tree import DecisionTreeRegressor def decision_tree_disc(df, cols, depth=4 ): for col in cols: df[col +"_m1"] = df[col].shift(1) df = df.iloc[1:,:] tree_model = DecisionTreeRegressor(max_depth=depth,random_state=0) tree_model.fit(df[col +"_m1"].to_frame(), df[col]) df[col+"_Disc"] = tree_model.predict(df[col +"_m1"].to_frame()) return df df_out = interact.decision_tree_disc(df.copy(), ["Close"]); df_out.head()  #### (4) Normalising Normalising normally pertains to the scaling of data. There are many method available, interacting normalising methods makes use of all the feature's attributes to do the scaling. (i) Quantile Normalisation In statistics, quantile normalization is a technique for making two distributions identical in statistical properties. import numpy as np import pandas as pd def quantile_normalize(df, drop): if drop: keep = df[drop] df = df.drop(drop,axis=1) #compute rank dic = {} for col in df: dic.update({col : sorted(df[col])}) sorted_df = pd.DataFrame(dic) rank = sorted_df.mean(axis = 1).tolist() #sort for col in df: t = np.searchsorted(np.sort(df[col]), df[col]) df[col] = [rank[i] for i in t] if drop: df = pd.concat((keep,df),axis=1) return df df_out = interact.quantile_normalize(df.copy(), drop=["Close"]); df_out.head()  #### (5) Distance There are multiple types of distance functions like Euclidean, Mahalanobis, and Minkowski distance. Here we are using a contrived example in a location based haversine distance. (i) Haversine Distance The Haversine (or great circle) distance is the angular distance between two points on the surface of a sphere. from math import sin, cos, sqrt, atan2, radians def haversine_distance(row, lon="Open", lat="Close"): c_lat,c_long = radians(52.5200), radians(13.4050) R = 6373.0 long = radians(row['Open']) lat = radians(row['Close']) dlon = long - c_long dlat = lat - c_lat a = sin(dlat / 2)**2 + cos(lat) * cos(c_lat) * sin(dlon / 2)**2 c = 2 * atan2(sqrt(a), sqrt(1 - a)) return R * c df_out['distance_central'] = df.apply(interact.haversine_distance,axis=1); df_out.head()  #### (6) Speciality (i) Technical Features Technical indicators are heuristic or mathematical calculations based on the price, volume, or open interest of a security or contract used by traders who follow technical analysis. By analyzing historical data, technical analysts use indicators to predict future price movements. import ta def tech(df): return ta.add_all_ta_features(df, open="Open", high="High", low="Low", close="Close", volume="Volume") df_out = interact.tech(df.copy()); df_out.head()  #### (7) Genetic Genetic programming has shown promise in constructing feature by osing original features to form high-level ones that can help algorithms achieve better performance. (i) Symbolic Transformer A symbolic transformer is a supervised transformer that begins by building a population of naive random formulas to represent a relationship. df.head()  from gplearn.genetic import SymbolicTransformer def genetic_feat(df, num_gen=20, num_comp=10): function_set = ['add', 'sub', 'mul', 'div', 'sqrt', 'log', 'abs', 'neg', 'inv','tan'] gp = SymbolicTransformer(generations=num_gen, population_size=200, hall_of_fame=100, n_components=num_comp, function_set=function_set, parsimony_coefficient=0.0005, max_samples=0.9, verbose=1, random_state=0, n_jobs=6) gen_feats = gp.fit_transform(df.drop("Close_1", axis=1), df["Close_1"]); df.iloc[:,:8] gen_feats = pd.DataFrame(gen_feats, columns=["gen_"+str(a) for a in range(gen_feats.shape[1])]) gen_feats.index = df.index return pd.concat((df,gen_feats),axis=1) df_out = interact.genetic_feat(df.copy()); df_out.head()  ## (3) Mapping Methods that help with the summarisation of features by remapping them to achieve some aim like the maximisation of variability or class separability. These methods tend to be unsupervised, but can also take an supervised form. #### (1) Eigen Decomposition Eigendecomposition or sometimes spectral decomposition is the factorization of a matrix into a canonical form, whereby the matrix is represented in terms of its eigenvalues and eigenvectors. Some examples are LDA and PCA. (i) Principal Component Analysis Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. def pca_feature(df, memory_issues=False,mem_iss_component=False,variance_or_components=0.80,n_components=5 ,drop_cols=None, non_linear=True): if non_linear: pca = KernelPCA(n_components = n_components, kernel='rbf', fit_inverse_transform=True, random_state = 33, remove_zero_eig= True) else: if memory_issues: if not mem_iss_component: raise ValueError("If you have memory issues, you have to preselect mem_iss_component") pca = IncrementalPCA(mem_iss_component) else: if variance_or_components>1: pca = PCA(n_components=variance_or_components) else: # automated selection based on variance pca = PCA(n_components=variance_or_components,svd_solver="full") if drop_cols: X_pca = pca.fit_transform(df.drop(drop_cols,axis=1)) return pd.concat((df[drop_cols],pd.DataFrame(X_pca, columns=["PCA_"+str(i+1) for i in range(X_pca.shape[1])],index=df.index)),axis=1) else: X_pca = pca.fit_transform(df) return pd.DataFrame(X_pca, columns=["PCA_"+str(i+1) for i in range(X_pca.shape[1])],index=df.index) return df df_out = mapper.pca_feature(df.copy(), variance_or_components=0.9, n_components=8,non_linear=False)  #### (2) Cross Decomposition These families of algorithms are useful to find linear relations between two multivariate datasets. (1) Canonical Correlation Analysis Canonical-correlation analysis (CCA) is a way of inferring information from cross-covariance matrices. from sklearn.cross_decomposition import CCA def cross_lag(df, drop=None, lags=1, components=4 ): if drop: keep = df[drop] df = df.drop([drop],axis=1) df_2 = df.shift(lags) df = df.iloc[lags:,:] df_2 = df_2.dropna().reset_index(drop=True) cca = CCA(n_components=components) cca.fit(df_2, df) X_c, df_2 = cca.transform(df_2, df) df_2 = pd.DataFrame(df_2, index=df.index) df_2 = df.add_prefix('crd_') if drop: df = pd.concat([keep,df,df_2],axis=1) else: df = pd.concat([df,df_2],axis=1) return df df_out = mapper.cross_lag(df.copy()); df_out.head()  #### (3) Kernel Approximation Functions that approximate the feature mappings that correspond to certain kernels, as they are used for example in support vector machines. (i) Additive Chi2 Kernel Computes the additive chi-squared kernel between observations in X and Y The chi-squared kernel is computed between each pair of rows in X and Y. X and Y have to be non-negative. from sklearn.kernel_approximation import AdditiveChi2Sampler def a_chi(df, drop=None, lags=1, sample_steps=2 ): if drop: keep = df[drop] df = df.drop([drop],axis=1) df_2 = df.shift(lags) df = df.iloc[lags:,:] df_2 = df_2.dropna().reset_index(drop=True) chi2sampler = AdditiveChi2Sampler(sample_steps=sample_steps) df_2 = chi2sampler.fit_transform(df_2, df["Close"]) df_2 = pd.DataFrame(df_2, index=df.index) df_2 = df.add_prefix('achi_') if drop: df = pd.concat([keep,df,df_2],axis=1) else: df = pd.concat([df,df_2],axis=1) return df df_out = mapper.a_chi(df.copy()); df_out.head()  #### (4) Autoencoder An autoencoder is a type of artificial neural network used to learn efficient data codings in an unsupervised manner. The aim of an autoencoder is to learn a representation (encoding) for a set of data, typically for dimensionality reduction, by training the network to ignore noise. (i) Feed Forward The simplest form of an autoencoder is a feedforward, non-recurrent neural network similar to single layer perceptrons that participate in multilayer perceptrons from sklearn.preprocessing import minmax_scale import tensorflow as tf import numpy as np def encoder_dataset(df, drop=None, dimesions=20): if drop: train_scaled = minmax_scale(df.drop(drop,axis=1).values, axis = 0) else: train_scaled = minmax_scale(df.values, axis = 0) # define the number of encoding dimensions encoding_dim = dimesions # define the number of features ncol = train_scaled.shape[1] input_dim = tf.keras.Input(shape = (ncol, )) # Encoder Layers encoded1 = tf.keras.layers.Dense(3000, activation = 'relu')(input_dim) encoded2 = tf.keras.layers.Dense(2750, activation = 'relu')(encoded1) encoded3 = tf.keras.layers.Dense(2500, activation = 'relu')(encoded2) encoded4 = tf.keras.layers.Dense(750, activation = 'relu')(encoded3) encoded5 = tf.keras.layers.Dense(500, activation = 'relu')(encoded4) encoded6 = tf.keras.layers.Dense(250, activation = 'relu')(encoded5) encoded7 = tf.keras.layers.Dense(encoding_dim, activation = 'relu')(encoded6) encoder = tf.keras.Model(inputs = input_dim, outputs = encoded7) encoded_input = tf.keras.Input(shape = (encoding_dim, )) encoded_train = pd.DataFrame(encoder.predict(train_scaled),index=df.index) encoded_train = encoded_train.add_prefix('encoded_') if drop: encoded_train = pd.concat((df[drop],encoded_train),axis=1) return encoded_train df_out = mapper.encoder_dataset(df.copy(), ["Close_1"], 15); df_out.head()  df_out.head()  #### (5) Manifold Learning Manifold Learning can be thought of as an attempt to generalize linear frameworks like PCA to be sensitive to non-linear structure in data. (i) Local Linear Embedding Locally Linear Embedding is a method of non-linear dimensionality reduction. It tries to reduce these n-Dimensions while trying to preserve the geometric features of the original non-linear feature structure. from sklearn.manifold import LocallyLinearEmbedding def lle_feat(df, drop=None, components=4): if drop: keep = df[drop] df = df.drop(drop, axis=1) embedding = LocallyLinearEmbedding(n_components=components) em = embedding.fit_transform(df) df = pd.DataFrame(em,index=df.index) df = df.add_prefix('lle_') if drop: df = pd.concat((keep,df),axis=1) return df df_out = mapper.lle_feat(df.copy(),["Close_1"],4); df_out.head()  #### (6) Clustering Most clustering techniques start with a bottom up approach: each observation starts in its own cluster, and clusters are successively merged together with some measure. Although these clustering techniques are typically used for observations, it can also be used for feature dimensionality reduction; especially hierarchical clustering techniques. (i) Feature Agglomeration Feature agglomerative uses clustering to group together features that look very similar, thus decreasing the number of features. import numpy as np from sklearn import datasets, cluster def feature_agg(df, drop=None, components=4): if drop: keep = df[drop] df = df.drop(drop, axis=1) components = min(df.shape[1]-1,components) agglo = cluster.FeatureAgglomeration(n_clusters=components) agglo.fit(df) df = pd.DataFrame(agglo.transform(df),index=df.index) df = df.add_prefix('feagg_') if drop: return pd.concat((keep,df),axis=1) else: return df df_out = mapper.feature_agg(df.copy(),["Close_1"],4 ); df_out.head()  #### (7) Neigbouring Neighbouring points can be calculated using distance metrics like Hamming, Manhattan, Minkowski distance. The principle behind nearest neighbor methods is to find a predefined number of training samples closest in distance to the new point, and predict the label from these. (i) Nearest Neighbours Unsupervised learner for implementing neighbor searches. from sklearn.neighbors import NearestNeighbors def neigh_feat(df, drop, neighbors=6): if drop: keep = df[drop] df = df.drop(drop, axis=1) components = min(df.shape[0]-1,neighbors) neigh = NearestNeighbors(n_neighbors=neighbors) neigh.fit(df) neigh = neigh.kneighbors()[0] df = pd.DataFrame(neigh, index=df.index) df = df.add_prefix('neigh_') if drop: return pd.concat((keep,df),axis=1) else: return df return df df_out = mapper.neigh_feat(df.copy(),["Close_1"],4 ); df_out.head()  ## (4) Extraction When working with extraction, you have decide the size of the time series history to take into account when calculating a collection of walk-forward feature values. To facilitate our extraction, we use an excellent package called TSfresh, and also some of their default features. For completeness, we also include 12 or so custom features to be added to the extraction pipeline. The time series methods in the transformation section and the interaction section are similar to the methods we will uncover in the extraction section, however, for transformation and interaction methods the output is an entire new time series, whereas extraction methods takes as input multiple constructed time series and extracts a singular value from each time series to reconstruct an entirely new time series. Some methods naturally fit better in one format over another, e.g., lags are too expensive for extraction; time series decomposition only has to be performed once, because it has a low level of 'leakage' so is better suited to transformation; and forecast methods attempt to predict multiple future training samples, so won't work with extraction that only delivers one value per time series. Furthermore all non time-series (cross-sectional) transformation and extraction techniques can not make use of extraction as it is solely a time-series method. Lastly, when we want to double apply specific functions we can apply it as a transformation/interaction then all the extraction methods can be applied to this feature as well. For example, if we calculate a smoothing function (transformation) then all other extraction functions (median, entropy, linearity etc.) can now be applied to that smoothing function, including the application of the smoothing function itself, e.g., a double smooth, double lag, double filter etc. So separating these methods out give us great flexibility. Decorator def set_property(key, value): """ This method returns a decorator that sets the property key of the function to value """ def decorate_func(func): setattr(func, key, value) if func.__doc__ and key == "fctype": func.__doc__ = func.__doc__ + "\n\n *This function is of type: " + value + "*\n" return func return decorate_func  #### (1) Energy You can calculate the linear, non-linear and absolute energy of a time series. In signal processing, the energy$E_S$of a continuous-time signal$x(t)$is defined as the area under the squared magnitude of the considered signal. Mathematically,$E_{s}=\langle x(t), x(t)\rangle=\int_{-\infty}^{\infty}|x(t)|^{2} d t$(i) Absolute Energy Returns the absolute energy of the time series which is the sum over the squared values #-> In Package def abs_energy(x): if not isinstance(x, (np.ndarray, pd.Series)): x = np.asarray(x) return np.dot(x, x) extract.abs_energy(df["Close"])  #### (2) Distance Here we widely define distance measures as those that take a difference between attributes or series of datapoints. (i) Complexity-Invariant Distance This function calculator is an estimate for a time series complexity. #-> In Package def cid_ce(x, normalize): if not isinstance(x, (np.ndarray, pd.Series)): x = np.asarray(x) if normalize: s = np.std(x) if s!=0: x = (x - np.mean(x))/s else: return 0.0 x = np.diff(x) return np.sqrt(np.dot(x, x)) extract.cid_ce(df["Close"], True)  #### (3) Differencing Many alternatives to differencing exists, one can for example take the difference of every other value, take the squared difference, take the fractional difference, or like our example, take the mean absolute difference. (i) Mean Absolute Change Returns the mean over the absolute differences between subsequent time series values. #-> In Package def mean_abs_change(x): return np.mean(np.abs(np.diff(x))) extract.mean_abs_change(df["Close"])  #### (4) Derivative Features where the emphasis is on the rate of change. (i) Mean Central Second Derivative Returns the mean value of a central approximation of the second derivative #-> In Package def _roll(a, shift): if not isinstance(a, np.ndarray): a = np.asarray(a) idx = shift % len(a) return np.concatenate([a[-idx:], a[:-idx]]) def mean_second_derivative_central(x): diff = (_roll(x, 1) - 2 * np.array(x) + _roll(x, -1)) / 2.0 return np.mean(diff[1:-1]) extract.mean_second_derivative_central(df["Close"])  #### (5) Volatility Volatility is a statistical measure of the dispersion of a time-series. (i) Variance Larger than Standard Deviation #-> In Package def variance_larger_than_standard_deviation(x): y = np.var(x) return y > np.sqrt(y) extract.variance_larger_than_standard_deviation(df["Close"])  (ii) Variability Index Variability Index is a way to measure how smooth or 'variable' a time series is. var_index_param = {"Volume":df["Volume"].values, "Open": df["Open"].values} @set_property("fctype", "combiner") @set_property("custom", True) def var_index(time,param=var_index_param): final = [] keys = [] for key, magnitude in param.items(): w = 1.0 / np.power(np.subtract(time[1:], time[:-1]), 2) w_mean = np.mean(w) N = len(time) sigma2 = np.var(magnitude) S1 = sum(w * (magnitude[1:] - magnitude[:-1]) ** 2) S2 = sum(w) eta_e = (w_mean * np.power(time[N - 1] - time[0], 2) * S1 / (sigma2 * S2 * N ** 2)) final.append(eta_e) keys.append(key) return {"Interact__{}".format(k): eta_e for eta_e, k in zip(final,keys) } extract.var_index(df["Close"].values,var_index_param)  #### (6) Shape Features that emphasises a particular shape not ordinarily considered as a distribution statistic. Extends to derivations of the original time series too For example a feature looking at the sinusoidal shape of an autocorrelation plot. (i) Symmetrical Boolean variable denoting if the distribution of x looks symmetric. #-> In Package def symmetry_looking(x, param=[{"r": 0.2}]): if not isinstance(x, (np.ndarray, pd.Series)): x = np.asarray(x) mean_median_difference = np.abs(np.mean(x) - np.median(x)) max_min_difference = np.max(x) - np.min(x) return [("r_{}".format(r["r"]), mean_median_difference < (r["r"] * max_min_difference)) for r in param] extract.symmetry_looking(df["Close"])  #### (7) Occurrence Looking at the occurrence, and reoccurence of defined values. (i) Has Duplicate Max #-> In Package def has_duplicate_max(x): """ Checks if the maximum value of x is observed more than once :param x: the time series to calculate the feature of :type x: numpy.ndarray :return: the value of this feature :return type: bool """ if not isinstance(x, (np.ndarray, pd.Series)): x = np.asarray(x) return np.sum(x == np.max(x)) >= 2 extract.has_duplicate_max(df["Close"])  #### (8) Autocorrelation Autocorrelation, also known as serial correlation, is the correlation of a signal with a delayed copy of itself as a function of delay. (i) Partial Autocorrelation Partial autocorrelation is a summary of the relationship between an observation in a time series with observations at prior time steps with the relationships of intervening observations removed. #-> In Package from statsmodels.tsa.stattools import acf, adfuller, pacf def partial_autocorrelation(x, param=[{"lag": 1}]): # Check the difference between demanded lags by param and possible lags to calculate (depends on len(x)) max_demanded_lag = max([lag["lag"] for lag in param]) n = len(x) # Check if list is too short to make calculations if n <= 1: pacf_coeffs = [np.nan] * (max_demanded_lag + 1) else: if (n <= max_demanded_lag): max_lag = n - 1 else: max_lag = max_demanded_lag pacf_coeffs = list(pacf(x, method="ld", nlags=max_lag)) pacf_coeffs = pacf_coeffs + [np.nan] * max(0, (max_demanded_lag - max_lag)) return [("lag_{}".format(lag["lag"]), pacf_coeffs[lag["lag"]]) for lag in param] extract.partial_autocorrelation(df["Close"])  #### (9) Stochasticity Stochastic refers to a randomly determined process. Any features trying to capture stochasticity by degree or type are included under this branch. (i) Augmented Dickey Fuller The Augmented Dickey-Fuller test is a hypothesis test which checks whether a unit root is present in a time series sample. #-> In Package def augmented_dickey_fuller(x, param=[{"attr": "teststat"}]): res = None try: res = adfuller(x) except LinAlgError: res = np.NaN, np.NaN, np.NaN except ValueError: # occurs if sample size is too small res = np.NaN, np.NaN, np.NaN except MissingDataError: # is thrown for e.g. inf or nan in the data res = np.NaN, np.NaN, np.NaN return [('attr_"{}"'.format(config["attr"]), res[0] if config["attr"] == "teststat" else res[1] if config["attr"] == "pvalue" else res[2] if config["attr"] == "usedlag" else np.NaN) for config in param] extract.augmented_dickey_fuller(df["Close"])  #### (10) Averages (i) Median of Magnitudes Skew @set_property("fctype", "simple") @set_property("custom", True) def gskew(x): interpolation="nearest" median_mag = np.median(x) F_3_value = np.percentile(x, 3, interpolation=interpolation) F_97_value = np.percentile(x, 97, interpolation=interpolation) skew = (np.median(x[x <= F_3_value]) + np.median(x[x >= F_97_value]) - 2 * median_mag) return skew extract.gskew(df["Close"])  (ii) Stetson Mean An iteratively weighted mean used in the Stetson variability index stestson_param = {"weight":100., "alpha":2., "beta":2., "tol":1.e-6, "nmax":20} @set_property("fctype", "combiner") @set_property("custom", True) def stetson_mean(x, param=stestson_param): weight= stestson_param["weight"] alpha= stestson_param["alpha"] beta = stestson_param["beta"] tol= stestson_param["tol"] nmax= stestson_param["nmax"] mu = np.median(x) for i in range(nmax): resid = x - mu resid_err = np.abs(resid) * np.sqrt(weight) weight1 = weight / (1. + (resid_err / alpha)**beta) weight1 /= weight1.mean() diff = np.mean(x * weight1) - mu mu += diff if (np.abs(diff) < tol*np.abs(mu) or np.abs(diff) < tol): break return mu extract.stetson_mean(df["Close"])  #### (11) Size (i) Lenght #-> In Package def length(x): return len(x) extract.length(df["Close"])  #### (12) Count (i) Count Above Mean Returns the number of values in x that are higher than the mean of x #-> In Package def count_above_mean(x): m = np.mean(x) return np.where(x > m)[0].size extract.count_above_mean(df["Close"])  #### (13) Streaks (i) Longest Strike Below Mean Returns the length of the longest consecutive subsequence in x that is smaller than the mean of x #-> In Package import itertools def get_length_sequences_where(x): if len(x) == 0: return [0] else: res = [len(list(group)) for value, group in itertools.groupby(x) if value == 1] return res if len(res) > 0 else [0] def longest_strike_below_mean(x): if not isinstance(x, (np.ndarray, pd.Series)): x = np.asarray(x) return np.max(get_length_sequences_where(x <= np.mean(x))) if x.size > 0 else 0 extract.longest_strike_below_mean(df["Close"])  (ii) Wozniak This is an astronomical feature, we count the number of three consecutive data points that are brighter or fainter than$2σ$and normalize the number by$N−2$woz_param = [{"consecutiveStar": n} for n in [2, 4]] @set_property("fctype", "combiner") @set_property("custom", True) def wozniak(magnitude, param=woz_param): iters = [] for consecutiveStar in [stars["consecutiveStar"] for stars in param]: N = len(magnitude) if N < consecutiveStar: return 0 sigma = np.std(magnitude) m = np.mean(magnitude) count = 0 for i in range(N - consecutiveStar + 1): flag = 0 for j in range(consecutiveStar): if(magnitude[i + j] > m + 2 * sigma or magnitude[i + j] < m - 2 * sigma): flag = 1 else: flag = 0 break if flag: count = count + 1 iters.append(count * 1.0 / (N - consecutiveStar + 1)) return [("consecutiveStar_{}".format(config["consecutiveStar"]), iters[en] ) for en, config in enumerate(param)] extract.wozniak(df["Close"])  #### (14) Location (i) Last location of Maximum Returns the relative last location of the maximum value of x. last_location_of_minimum(x), #-> In Package def last_location_of_maximum(x): x = np.asarray(x) return 1.0 - np.argmax(x[::-1]) / len(x) if len(x) > 0 else np.NaN extract.last_location_of_maximum(df["Close"])  #### (15) Model Coefficients Any coefficient that are obtained from a model that might help in the prediction problem. For example here we might include coefficients of polynomial$h(x)$, which has been fitted to the deterministic dynamics of Langevin model. (i) FFT Coefficient Calculates the fourier coefficients of the one-dimensional discrete Fourier Transform for real input. #-> In Package def fft_coefficient(x, param = [{"coeff": 10, "attr": "real"}]): assert min([config["coeff"] for config in param]) >= 0, "Coefficients must be positive or zero." assert set([config["attr"] for config in param]) <= set(["imag", "real", "abs", "angle"]), \ 'Attribute must be "real", "imag", "angle" or "abs"' fft = np.fft.rfft(x) def complex_agg(x, agg): if agg == "real": return x.real elif agg == "imag": return x.imag elif agg == "abs": return np.abs(x) elif agg == "angle": return np.angle(x, deg=True) res = [complex_agg(fft[config["coeff"]], config["attr"]) if config["coeff"] < len(fft) else np.NaN for config in param] index = [('coeff_{}__attr_"{}"'.format(config["coeff"], config["attr"]),res[0]) for config in param] return index extract.fft_coefficient(df["Close"])  (ii) AR Coefficient This feature calculator fits the unconditional maximum likelihood of an autoregressive AR(k) process. #-> In Package from statsmodels.tsa.ar_model import AR def ar_coefficient(x, param=[{"coeff": 5, "k": 5}]): calculated_ar_params = {} x_as_list = list(x) calculated_AR = AR(x_as_list) res = {} for parameter_combination in param: k = parameter_combination["k"] p = parameter_combination["coeff"] column_name = "k_{}__coeff_{}".format(k, p) if k not in calculated_ar_params: try: calculated_ar_params[k] = calculated_AR.fit(maxlag=k, solver="mle").params except (LinAlgError, ValueError): calculated_ar_params[k] = [np.NaN]*k mod = calculated_ar_params[k] if p <= k: try: res[column_name] = mod[p] except IndexError: res[column_name] = 0 else: res[column_name] = np.NaN return [(key, value) for key, value in res.items()] extract.ar_coefficient(df["Close"])  #### (16) Quantiles This includes finding normal quantile values in the series, but also quantile derived measures like change quantiles and index max quantiles. (i) Index Mass Quantile The relative index$i$where$q%$of the mass of the time series$x$lie left of$i$. #-> In Package def index_mass_quantile(x, param=[{"q": 0.3}]): x = np.asarray(x) abs_x = np.abs(x) s = sum(abs_x) if s == 0: # all values in x are zero or it has length 0 return [("q_{}".format(config["q"]), np.NaN) for config in param] else: # at least one value is not zero mass_centralized = np.cumsum(abs_x) / s return [("q_{}".format(config["q"]), (np.argmax(mass_centralized >= config["q"])+1)/len(x)) for config in param] extract.index_mass_quantile(df["Close"])  #### (17) Peaks (i) Number of CWT Peaks This feature calculator searches for different peaks in x. from scipy.signal import cwt, find_peaks_cwt, ricker, welch cwt_param = [ka for ka in [2,6,9]] @set_property("fctype", "combiner") @set_property("custom", True) def number_cwt_peaks(x, param=cwt_param): return [("CWTPeak_{}".format(n), len(find_peaks_cwt(vector=x, widths=np.array(list(range(1, n + 1))), wavelet=ricker))) for n in param] extract.number_cwt_peaks(df["Close"])  #### (18) Density The density, and more specifically the power spectral density of the signal describes the power present in the signal as a function of frequency, per unit frequency. (i) Cross Power Spectral Density This feature calculator estimates the cross power spectral density of the time series$x$at different frequencies. #-> In Package def spkt_welch_density(x, param=[{"coeff": 5}]): freq, pxx = welch(x, nperseg=min(len(x), 256)) coeff = [config["coeff"] for config in param] indices = ["coeff_{}".format(i) for i in coeff] if len(pxx) <= np.max(coeff): # There are fewer data points in the time series than requested coefficients # filter coefficients that are not contained in pxx reduced_coeff = [coefficient for coefficient in coeff if len(pxx) > coefficient] not_calculated_coefficients = [coefficient for coefficient in coeff if coefficient not in reduced_coeff] # Fill up the rest of the requested coefficients with np.NaNs return zip(indices, list(pxx[reduced_coeff]) + [np.NaN] * len(not_calculated_coefficients)) else: return pxx[coeff].ravel()[0] extract.spkt_welch_density(df["Close"])  #### (19) Linearity Any measure of linearity that might make use of something like the linear least-squares regression for the values of the time series. This can be against the time series minus one and many other alternatives. (i) Linear Trend Time Wise Calculate a linear least-squares regression for the values of the time series versus the sequence from 0 to length of the time series minus one. from scipy.stats import linregress #-> In Package def linear_trend_timewise(x, param= [{"attr": "pvalue"}]): ix = x.index # Get differences between each timestamp and the first timestamp in seconds. # Then convert to hours and reshape for linear regression times_seconds = (ix - ix[0]).total_seconds() times_hours = np.asarray(times_seconds / float(3600)) linReg = linregress(times_hours, x.values) return [("attr_\"{}\"".format(config["attr"]), getattr(linReg, config["attr"])) for config in param] extract.linear_trend_timewise(df["Close"])  #### (20) Non-Linearity (i) Schreiber Non-Linearity #-> In Package def c3(x, lag=3): if not isinstance(x, (np.ndarray, pd.Series)): x = np.asarray(x) n = x.size if 2 * lag >= n: return 0 else: return np.mean((_roll(x, 2 * -lag) * _roll(x, -lag) * x)[0:(n - 2 * lag)]) extract.c3(df["Close"])  #### (21) Entropy Any feature looking at the complexity of a time series. This is typically used in medical signal disciplines (EEG, EMG). There are multiple types of measures like spectral entropy, permutation entropy, sample entropy, approximate entropy, Lempel-Ziv complexity and other. This includes entropy measures and there derivations. (i) Binned Entropy Bins the values of x into max_bins equidistant bins. #-> In Package def binned_entropy(x, max_bins=10): if not isinstance(x, (np.ndarray, pd.Series)): x = np.asarray(x) hist, bin_edges = np.histogram(x, bins=max_bins) probs = hist / x.size return - np.sum(p * np.math.log(p) for p in probs if p != 0) extract.binned_entropy(df["Close"])  (ii) SVD Entropy SVD entropy is an indicator of the number of eigenvectors that are needed for an adequate explanation of the data set. svd_param = [{"Tau": ta, "DE": de} for ta in [4] for de in [3,6]] def _embed_seq(X,Tau,D): N =len(X) if D * Tau > N: print("Cannot build such a matrix, because D * Tau > N") exit() if Tau<1: print("Tau has to be at least 1") exit() Y= np.zeros((N - (D - 1) * Tau, D)) for i in range(0, N - (D - 1) * Tau): for j in range(0, D): Y[i][j] = X[i + j * Tau] return Y @set_property("fctype", "combiner") @set_property("custom", True) def svd_entropy(epochs, param=svd_param): axis=0 final = [] for par in param: def svd_entropy_1d(X, Tau, DE): Y = _embed_seq(X, Tau, DE) W = np.linalg.svd(Y, compute_uv=0) W /= sum(W) # normalize singular values return -1 * np.sum(W * np.log(W)) Tau = par["Tau"] DE = par["DE"] final.append(np.apply_along_axis(svd_entropy_1d, axis, epochs, Tau, DE).ravel()[0]) return [("Tau_\"{}\"__De_{}\"".format(par["Tau"], par["DE"]), final[en]) for en, par in enumerate(param)] extract.svd_entropy(df["Close"].values)  (iii) Hjort The Complexity parameter represents the change in frequency. The parameter compares the signal's similarity to a pure sine wave, where the value converges to 1 if the signal is more similar. def _hjorth_mobility(epochs): diff = np.diff(epochs, axis=0) sigma0 = np.std(epochs, axis=0) sigma1 = np.std(diff, axis=0) return np.divide(sigma1, sigma0) @set_property("fctype", "simple") @set_property("custom", True) def hjorth_complexity(epochs): diff1 = np.diff(epochs, axis=0) diff2 = np.diff(diff1, axis=0) sigma1 = np.std(diff1, axis=0) sigma2 = np.std(diff2, axis=0) return np.divide(np.divide(sigma2, sigma1), _hjorth_mobility(epochs)) extract.hjorth_complexity(df["Close"])  #### (22) Fixed Points Fixed points and equilibria as identified from fitted models. (i) Langevin Fixed Points Largest fixed point of dynamics$max\ {h(x)=0}$estimated from polynomial$h(x)\$ which has been fitted to the deterministic dynamics of Langevin model

#-> In Package
def _estimate_friedrich_coefficients(x, m, r):
assert m > 0, "Order of polynomial need to be positive integer, found {}".format(m)
df = pd.DataFrame({'signal': x[:-1], 'delta': np.diff(x)})
try:
df['quantiles'] = pd.qcut(df.signal, r)
except ValueError:
return [np.NaN] * (m + 1)

quantiles = df.groupby('quantiles')

result = pd.DataFrame({'x_mean': quantiles.signal.mean(), 'y_mean': quantiles.delta.mean()})
result.dropna(inplace=True)

try:
return np.polyfit(result.x_mean, result.y_mean, deg=m)
except (np.linalg.LinAlgError, ValueError):
return [np.NaN] * (m + 1)

def max_langevin_fixed_point(x, r=3, m=30):
coeff = _estimate_friedrich_coefficients(x, m, r)

try:
max_fixed_point = np.max(np.real(np.roots(coeff)))
except (np.linalg.LinAlgError, ValueError):
return np.nan

return max_fixed_point

extract.max_langevin_fixed_point(df["Close"])


#### (23) Amplitude

Features derived from peaked values in either the positive or negative direction.

(i) Willison Amplitude

This feature is defined as the amount of times that the change in the signal amplitude exceeds a threshold.

will_param = [ka for ka in [0.2,3]]

@set_property("fctype", "combiner")
@set_property("custom", True)
def willison_amplitude(X, param=will_param):
return [("Thresh_{}".format(n),np.sum(np.abs(np.diff(X)) >= n)) for n in param]

extract.willison_amplitude(df["Close"])


(ii) Percent Amplitude

Returns the largest distance from the median value, measured as a percentage of the median

perc_param = [{"base":ba, "exponent":exp} for ba in [3,5] for exp in [-0.1,-0.2]]

@set_property("fctype", "combiner")
@set_property("custom", True)
def percent_amplitude(x, param =perc_param):
final = []
for par in param:
linear_scale_data = par["base"] ** (par["exponent"] * x)
y_max = np.max(linear_scale_data)
y_min = np.min(linear_scale_data)
y_med = np.median(linear_scale_data)
final.append(max(abs((y_max - y_med) / y_med), abs((y_med - y_min) / y_med)))

return [("Base_{}__Exp{}".format(pa["base"],pa["exponent"]),fin) for fin, pa in zip(final,param)]

extract.percent_amplitude(df["Close"])


#### (24) Probability

Given the observed distribution of time lags cads, compute the probability that the next observation occurs within time minutes of an arbitrary epoch.

#-> fixes required
import scipy.stats as stats

@set_property("fctype", "combiner")
@set_property("custom", True)
return [("time_{}".format(time), stats.percentileofscore(cads, float(time) / (24.0 * 60.0)) / 100.0) for time in param]



#### (25) Crossings

Calculates the crossing of the series with other defined values or series.

(i) Zero Crossing Derivative

The positioning of the edge point is located at the zero crossing of the first derivative of the filter.

zero_param = [0.01, 8]

@set_property("fctype", "combiner")
@set_property("custom", True)
def zero_crossing_derivative(epochs, param=zero_param):
diff = np.diff(epochs)
norm = diff-diff.mean()
return [("e_{}".format(e), np.apply_along_axis(lambda epoch: np.sum(((epoch[:-5] <= e) & (epoch[5:] > e))), 0, norm).ravel()[0]) for e in param]

extract.zero_crossing_derivative(df["Close"])


#### (26) Fluctuations

These features are again from medical signal sciences, but under this category we would include values such as fluctuation based entropy measures, fluctuation of correlation dynamics, and co-fluctuations.

(i) Detrended Fluctuation Analysis (DFA)

DFA Calculate the Hurst exponent using DFA analysis.

from scipy.stats import kurtosis as _kurt
from scipy.stats import skew as _skew
import numpy as np

@set_property("fctype", "simple")
@set_property("custom", True)
def detrended_fluctuation_analysis(epochs):
def dfa_1d(X, Ave=None, L=None):
X = np.array(X)

if Ave is None:
Ave = np.mean(X)

Y = np.cumsum(X)
Y -= Ave

if L is None:
L = np.floor(len(X) * 1 / (
2 ** np.array(list(range(1, int(np.log2(len(X))) - 4))))
)

F = np.zeros(len(L))  # F(n) of different given box length n

for i in range(0, len(L)):
n = int(L[i])  # for each box length L[i]
if n == 0:
print("time series is too short while the box length is too big")
print("abort")
exit()
for j in range(0, len(X), n):  # for each box
if j + n < len(X):
c = list(range(j, j + n))
# coordinates of time in the box
c = np.vstack([c, np.ones(n)]).T
# the value of data in the box
y = Y[j:j + n]
# add residue in this box
F[i] += np.linalg.lstsq(c, y, rcond=None)[1]
F[i] /= ((len(X) / n) * n)
F = np.sqrt(F)

stacked = np.vstack([np.log(L), np.ones(len(L))])
stacked_t = stacked.T
Alpha = np.linalg.lstsq(stacked_t, np.log(F), rcond=None)

return Alpha[0][0]

return np.apply_along_axis(dfa_1d, 0, epochs).ravel()[0]

extract.detrended_fluctuation_analysis(df["Close"])


#### (27) Information

Closely related to entropy and complexity measures. Any measure that attempts to measure the amount of information from an observable variable is included here.

(i) Fisher Information

Fisher information is a statistical information concept distinct from, and earlier than, Shannon information in communication theory.

def _embed_seq(X, Tau, D):

shape = (X.size - Tau * (D - 1), D)
strides = (X.itemsize, Tau * X.itemsize)
return np.lib.stride_tricks.as_strided(X, shape=shape, strides=strides)

fisher_param = [{"Tau":ta, "DE":de} for ta in [3,15] for de in [10,5]]

@set_property("fctype", "combiner")
@set_property("custom", True)
def fisher_information(epochs, param=fisher_param):
def fisher_info_1d(a, tau, de):
# taken from pyeeg improvements

mat = _embed_seq(a, tau, de)
W = np.linalg.svd(mat, compute_uv=False)
W /= sum(W)  # normalize singular values
FI_v = (W[1:] - W[:-1]) ** 2 / W[:-1]
return np.sum(FI_v)

return [("Tau_{}__DE_{}".format(par["Tau"], par["DE"]),np.apply_along_axis(fisher_info_1d, 0, epochs, par["Tau"], par["DE"]).ravel()[0]) for par in param]

extract.fisher_information(df["Close"])


#### (28) Fractals

In mathematics, more specifically in fractal geometry, a fractal dimension is a ratio providing a statistical index of complexity comparing how detail in a pattern (strictly speaking, a fractal pattern) changes with the scale at which it is measured.

(i) Highuchi Fractal

Compute a Higuchi Fractal Dimension of a time series

hig_para = [{"Kmax": 3},{"Kmax": 5}]

@set_property("fctype", "combiner")
@set_property("custom", True)
def higuchi_fractal_dimension(epochs, param=hig_para):
def hfd_1d(X, Kmax):

L = []
x = []
N = len(X)
for k in range(1, Kmax):
Lk = []
for m in range(0, k):
Lmk = 0
for i in range(1, int(np.floor((N - m) / k))):
Lmk += abs(X[m + i * k] - X[m + i * k - k])
Lmk = Lmk * (N - 1) / np.floor((N - m) / float(k)) / k
Lk.append(Lmk)
L.append(np.log(np.mean(Lk)))
x.append([np.log(float(1) / k), 1])

(p, r1, r2, s) = np.linalg.lstsq(x, L, rcond=None)
return p[0]

return [("Kmax_{}".format(config["Kmax"]), np.apply_along_axis(hfd_1d, 0, epochs, config["Kmax"]).ravel()[0] ) for  config in param]

extract.higuchi_fractal_dimension(df["Close"])


(ii) Petrosian Fractal

Compute a Petrosian Fractal Dimension of a time series.

@set_property("fctype", "simple")
@set_property("custom", True)
def petrosian_fractal_dimension(epochs):
def pfd_1d(X, D=None):
# taken from pyeeg
"""Compute Petrosian Fractal Dimension of a time series from either two
cases below:
1. X, the time series of type list (default)
2. D, the first order differential sequence of X (if D is provided,
recommended to speed up)
In case 1, D is computed using Numpy's difference function.
To speed up, it is recommended to compute D before calling this function
because D may also be used by other functions whereas computing it here
again will slow down.
"""
if D is None:
D = np.diff(X)
D = D.tolist()
N_delta = 0  # number of sign changes in derivative of the signal
for i in range(1, len(D)):
if D[i] * D[i - 1] < 0:
N_delta += 1
n = len(X)
return np.log10(n) / (np.log10(n) + np.log10(n / n + 0.4 * N_delta))
return np.apply_along_axis(pfd_1d, 0, epochs).ravel()[0]

extract.petrosian_fractal_dimension(df["Close"])


#### (29) Exponent

(i) Hurst Exponent

The Hurst exponent is used as a measure of long-term memory of time series. It relates to the autocorrelations of the time series, and the rate at which these decrease as the lag between pairs of values increases.

@set_property("fctype", "simple")
@set_property("custom", True)
def hurst_exponent(epochs):
def hurst_1d(X):

X = np.array(X)
N = X.size
T = np.arange(1, N + 1)
Y = np.cumsum(X)
Ave_T = Y / T

S_T = np.zeros(N)
R_T = np.zeros(N)
for i in range(N):
S_T[i] = np.std(X[:i + 1])
X_T = Y - T * Ave_T[i]
R_T[i] = np.ptp(X_T[:i + 1])

for i in range(1, len(S_T)):
if np.diff(S_T)[i - 1] != 0:
break
for j in range(1, len(R_T)):
if np.diff(R_T)[j - 1] != 0:
break
k = max(i, j)
assert k < 10, "rethink it!"

R_S = R_T[k:] / S_T[k:]
R_S = np.log(R_S)

n = np.log(T)[k:]
A = np.column_stack((n, np.ones(n.size)))
[m, c] = np.linalg.lstsq(A, R_S, rcond=None)[0]
H = m
return H
return np.apply_along_axis(hurst_1d, 0, epochs).ravel()[0]

extract.hurst_exponent(df["Close"])


(ii) Largest Lyauponov Exponent

In mathematics the Lyapunov exponent or Lyapunov characteristic exponent of a dynamical system is a quantity that characterizes the rate of separation of infinitesimally close trajectories.

def _embed_seq(X, Tau, D):
shape = (X.size - Tau * (D - 1), D)
strides = (X.itemsize, Tau * X.itemsize)
return np.lib.stride_tricks.as_strided(X, shape=shape, strides=strides)

lyaup_param = [{"Tau":4, "n":3, "T":10, "fs":9},{"Tau":8, "n":7, "T":15, "fs":6}]

@set_property("fctype", "combiner")
@set_property("custom", True)
def largest_lyauponov_exponent(epochs, param=lyaup_param):
def LLE_1d(x, tau, n, T, fs):

Em = _embed_seq(x, tau, n)
M = len(Em)
A = np.tile(Em, (len(Em), 1, 1))
B = np.transpose(A, [1, 0, 2])
square_dists = (A - B) ** 2  # square_dists[i,j,k] = (Em[i][k]-Em[j][k])^2
D = np.sqrt(square_dists[:, :, :].sum(axis=2))  # D[i,j] = ||Em[i]-Em[j]||_2

# Exclude elements within T of the diagonal
band = np.tri(D.shape[0], k=T) - np.tri(D.shape[0], k=-T - 1)
band[band == 1] = np.inf
neighbors = (D + band).argmin(axis=0)  # nearest neighbors more than T steps away

# in_bounds[i,j] = (i+j <= M-1 and i+neighbors[j] <= M-1)
inc = np.tile(np.arange(M), (M, 1))
row_inds = (np.tile(np.arange(M), (M, 1)).T + inc)
col_inds = (np.tile(neighbors, (M, 1)) + inc.T)
in_bounds = np.logical_and(row_inds <= M - 1, col_inds <= M - 1)
# Uncomment for old (miscounted) version
# in_bounds = numpy.logical_and(row_inds < M - 1, col_inds < M - 1)
row_inds[~in_bounds] = 0
col_inds[~in_bounds] = 0

# neighbor_dists[i,j] = ||Em[i+j]-Em[i+neighbors[j]]||_2
J = (~neighbor_dists.mask).sum(axis=1)  # number of in-bounds indices by row
# Set invalid (zero) values to 1; log(1) = 0 so sum is unchanged

neighbor_dists[neighbor_dists == 0] = 1

# !!! this fixes the divide by zero in log error !!!
neighbor_dists.data[neighbor_dists.data == 0] = 1

d_ij = np.sum(np.log(neighbor_dists.data), axis=1)
mean_d = d_ij[J > 0] / J[J > 0]

x = np.arange(len(mean_d))
X = np.vstack((x, np.ones(len(mean_d)))).T
[m, c] = np.linalg.lstsq(X, mean_d, rcond=None)[0]
Lexp = fs * m
return Lexp

return [("Tau_{}__n_{}__T_{}__fs_{}".format(par["Tau"], par["n"], par["T"], par["fs"]), np.apply_along_axis(LLE_1d, 0, epochs, par["Tau"], par["n"], par["T"], par["fs"]).ravel()[0]) for par in param]

extract.largest_lyauponov_exponent(df["Close"])


#### (30) Spectral Analysis

Spectral analysis is analysis in terms of a spectrum of frequencies or related quantities such as energies, eigenvalues, etc.

(i) Whelch Method

The Whelch Method is an approach for spectral density estimation. It is used in physics, engineering, and applied mathematics for estimating the power of a signal at different frequencies.

from scipy import signal, integrate

whelch_param = [100,200]

@set_property("fctype", "combiner")
@set_property("custom", True)
def whelch_method(data, param=whelch_param):

final = []
for Fs in param:
f, pxx = signal.welch(data, fs=Fs, nperseg=1024)
d = {'psd': pxx, 'freqs': f}
df = pd.DataFrame(data=d)
dfs = df.sort_values(['psd'], ascending=False)
rows = dfs.iloc[:10]
final.append(rows['freqs'].mean())

return [("Fs_{}".format(pa),fin) for pa, fin in zip(param,final)]

extract.whelch_method(df["Close"])

#-> Basically same as above
freq_param = [{"fs":50, "sel":15},{"fs":200, "sel":20}]

@set_property("fctype", "combiner")
@set_property("custom", True)
def find_freq(serie, param=freq_param):

final = []
for par in param:
fft0 = np.fft.rfft(serie*np.hanning(len(serie)))
freqs = np.fft.rfftfreq(len(serie), d=1.0/par["fs"])
fftmod = np.array([np.sqrt(fft0[i].real**2 + fft0[i].imag**2) for i in range(0, len(fft0))])
d = {'fft': fftmod, 'freq': freqs}
df = pd.DataFrame(d)
hop = df.sort_values(['fft'], ascending=False)
rows = hop.iloc[:par["sel"]]
final.append(rows['freq'].mean())

return [("Fs_{}__sel{}".format(pa["fs"],pa["sel"]),fin) for pa, fin in zip(param,final)]

extract.find_freq(df["Close"])


#### (31) Percentile

(i) Flux Percentile

Flux (or radiant flux) is the total amount of energy that crosses a unit area per unit time. Flux is an astronomical value, measured in joules per square metre per second (joules/m2/s), or watts per square metre. Here we provide the ratio of flux percentiles.

#-> In Package

import math
def flux_perc(magnitude):
sorted_data = np.sort(magnitude)
lc_length = len(sorted_data)

F_60_index = int(math.ceil(0.60 * lc_length))
F_40_index = int(math.ceil(0.40 * lc_length))
F_5_index = int(math.ceil(0.05 * lc_length))
F_95_index = int(math.ceil(0.95 * lc_length))

F_40_60 = sorted_data[F_60_index] - sorted_data[F_40_index]
F_5_95 = sorted_data[F_95_index] - sorted_data[F_5_index]
F_mid20 = F_40_60 / F_5_95

return {"FluxPercentileRatioMid20": F_mid20}

extract.flux_perc(df["Close"])


#### (32) Range

(i) Range of Cummulative Sum

@set_property("fctype", "simple")
@set_property("custom", True)
def range_cum_s(magnitude):
sigma = np.std(magnitude)
N = len(magnitude)
m = np.mean(magnitude)
s = np.cumsum(magnitude - m) * 1.0 / (N * sigma)
R = np.max(s) - np.min(s)
return {"Rcs": R}

extract.range_cum_s(df["Close"])


#### (33) Structural

Structural features, potential placeholders for future research.

(i) Structure Function

The structure function of rotation measures (RMs) contains information on electron density and magnetic field fluctuations when used i astronomy. It becomes a custom feature when used with your own unique time series data.

from scipy.interpolate import interp1d

struct_param = {"Volume":df["Volume"].values, "Open": df["Open"].values}

@set_property("fctype", "combiner")
@set_property("custom", True)
def structure_func(time, param=struct_param):

dict_final = {}
for key, magnitude in param.items():
dict_final[key] = []
Nsf, Np = 100, 100
sf1, sf2, sf3 = np.zeros(Nsf), np.zeros(Nsf), np.zeros(Nsf)
f = interp1d(time, magnitude)

time_int = np.linspace(np.min(time), np.max(time), Np)
mag_int = f(time_int)

for tau in np.arange(1, Nsf):
sf1[tau - 1] = np.mean(
np.power(np.abs(mag_int[0:Np - tau] - mag_int[tau:Np]), 1.0))
sf2[tau - 1] = np.mean(
np.abs(np.power(
np.abs(mag_int[0:Np - tau] - mag_int[tau:Np]), 2.0)))
sf3[tau - 1] = np.mean(
np.abs(np.power(
np.abs(mag_int[0:Np - tau] - mag_int[tau:Np]), 3.0)))
sf1_log = np.log10(np.trim_zeros(sf1))
sf2_log = np.log10(np.trim_zeros(sf2))
sf3_log = np.log10(np.trim_zeros(sf3))

if len(sf1_log) and len(sf2_log):
m_21, b_21 = np.polyfit(sf1_log, sf2_log, 1)
else:

m_21 = np.nan

if len(sf1_log) and len(sf3_log):
m_31, b_31 = np.polyfit(sf1_log, sf3_log, 1)
else:

m_31 = np.nan

if len(sf2_log) and len(sf3_log):
m_32, b_32 = np.polyfit(sf2_log, sf3_log, 1)
else:

m_32 = np.nan
dict_final[key].append(m_21)
dict_final[key].append(m_31)
dict_final[key].append(m_32)

return [("StructureFunction_{}__m_{}".format(key, name), li)  for key, lis in dict_final.items() for name, li in zip([21,31,32], lis)]

struct_param = {"Volume":df["Volume"].values, "Open": df["Open"].values}

extract.structure_func(df["Close"],struct_param)


#### (34) Distribution

(i) Kurtosis

#-> In Package
def kurtosis(x):

if not isinstance(x, pd.Series):
x = pd.Series(x)
return pd.Series.kurtosis(x)

extract.kurtosis(df["Close"])


(ii) Stetson Kurtosis

@set_property("fctype", "simple")
@set_property("custom", True)
def stetson_k(x):
"""A robust kurtosis statistic."""
n = len(x)
x0 = stetson_mean(x, 1./20**2)
delta_x = np.sqrt(n / (n - 1.)) * (x - x0) / 20
ta = 1. / 0.798 * np.mean(np.abs(delta_x)) / np.sqrt(np.mean(delta_x**2))
return ta

extract.stetson_k(df["Close"])


## (5) Synthesise

Time-Series synthesisation (TSS) happens before the feature extraction step and Cross Sectional Synthesisation (CSS) happens after the feature extraction step. Currently I will only include a CSS package, in the future, I would further work on developing out this section. This area still has a lot of performance and stability issues. In the future it might be a more viable candidate to improve prediction.

from lightgbm import LGBMRegressor
from sklearn.metrics import mean_squared_error

def model(df_final):
model = LGBMRegressor()
train = df_final[~df_final.isin(test)].dropna()
model = model.fit(train.drop(["Close_1"],axis=1),train["Close_1"])
preds = model.predict(test.drop(["Close_1"],axis=1))
train = df_final[~df_final.isin(test)].dropna()
model = model.fit(train.drop(["Close_1"],axis=1),train["Close_1"])
val = mean_squared_error(test["Close_1"],preds);
return val

pip install ctgan

from ctgan import CTGANSynthesizer

#discrete_columns = [""]
ctgan = CTGANSynthesizer()
ctgan.fit(df,epochs=10) #15


Random Benchmark

np.random.seed(1)
df_in = df.copy()
df_in["Close_1"] = np.random.permutation(df_in["Close_1"].values)
model(df_in)


Generated Performance

df_gen = ctgan.sample(len(df_in)*100)
model(df_gen)


As expected a cross-sectional technique, does not work well on time-series data, in the future, other methods will be investigated.

## (6) Skeleton Example

Here I will perform tabular agumenting methods on a small dataset single digit features and around 250 instances. This is not necessarily the best sized dataset to highlight the performance of tabular augmentation as some method like extraction would be overkill as it would lead to dimensionality problems. It is also good to know that there are close to infinite number of ways to perform these augmentation methods. In the future, automated augmentation methods can guide the experiment process.

The approach taken in this skeleton is to develop running models that are tested after each augmentation to highlight what methods might work well on this particular dataset. The metric we will use is mean squared error. In this implementation we do not have special hold-out sets.

The above framework of implementation will be consulted, but one still have to be strategic as to when you apply what function, and you have to make sure that you are processing your data with appropriate techniques (drop null values, fill null values) at the appropriate time.

#### Validation

Develop Model and Define Metric

from lightgbm import LGBMRegressor
from sklearn.metrics import mean_squared_error

def model(df_final):
model = LGBMRegressor()
train = df_final[~df_final.isin(test)].dropna()
model = model.fit(train.drop(["Close_1"],axis=1),train["Close_1"])
preds = model.predict(test.drop(["Close_1"],axis=1))
train = df_final[~df_final.isin(test)].dropna()
model = model.fit(train.drop(["Close_1"],axis=1),train["Close_1"])
val = mean_squared_error(test["Close_1"],preds);
return val


df = data_copy()

model(df)

302.61676570345287


(1) (7) (i) Transformation - Decomposition - Naive

## If Inferred Seasonality is Too Large Default to Five
seasons = transform.infer_seasonality(df["Close"],index=0)
df_out = transform.naive_dec(df.copy(), ["Close","Open"], freq=5)
model(df_out) #improvement

274.34477082783525


(1) (8) (i) Transformation - Filter - Baxter-King-Bandpass

df_out = transform.bkb(df_out, ["Close","Low"])
df_best = df_out.copy()
model(df_out) #improvement

267.1826850968307


(1) (3) (i) Transformation - Differentiation - Fractional

df_out = transform.fast_fracdiff(df_out, ["Close_BPF"],0.5)
model(df_out) #null

267.7083192402742


(1) (1) (i) Transformation - Scaling - Robust Scaler

df_out = df_out.dropna()
df_out = transform.robust_scaler(df_out, drop=["Close_1"])
model(df_out) #noisy

270.96980399571214


(2) (2) (i) Interactions - Operator - Multiplication/Division

df_out.head()

df_out = interact.muldiv(df_out, ["Close","Open_NDDS","Low_BPF"])
model(df_out) #noisy

285.6420643864313

df_r = df_out.copy()


(2) (6) (i) Interactions - Speciality - Technical

import ta
df = interact.tech(df)
df_out = pd.merge(df_out,  df.iloc[:,7:], left_index=True, right_index=True, how="left")


Clean Dataframe and Metric

"""Droping column where missing values are above a threshold"""
df_out = df_out.dropna(thresh = len(df_out)*0.95, axis = "columns")
df_out = df_out.dropna()
df_out = df_out.replace([np.inf, -np.inf], np.nan).ffill().fillna(0)
close = df_out["Close"].copy()
df_d = df_out.copy()
model(df_out) #improve

592.52971755184


(3) (1) (i) Mapping - Eigen Decomposition - PCA

from sklearn.decomposition import PCA, IncrementalPCA, KernelPCA

df_out = transform.robust_scaler(df_out, drop=["Close_1"])

df_out = df_out.replace([np.inf, -np.inf], np.nan).ffill().fillna(0)
df_out = mapper.pca_feature(df_out, drop_cols=["Close_1"], variance_or_components=0.9, n_components=8,non_linear=False)

model(df_out) #noisy but not too bad given the 10 fold dimensionality reduction

687.158330455884


(4) Extracting

Here at first, I show the functions that have been added to the DeltaPy fork of tsfresh. You have to add your own personal adjustments based on the features you would like to construct. I am using self-developed features, but you can also use TSFresh's community functions.

The following files have been appropriately ammended (Get in contact for advice)

(4) (10) (i) Extracting - Averages - GSkew

extract.gskew(df_out["PCA_1"])

-0.7903067336449059


(4) (21) (ii) Extracting - Entropy - SVD Entropy

svd_param = [{"Tau": ta, "DE": de}
for ta in [4]
for de in [3,6]]

extract.svd_entropy(df_out["PCA_1"],svd_param)

[('Tau_"4"__De_3"', 0.7234823323374294),
('Tau_"4"__De_6"', 1.3014347840145244)]


(4) (13) (ii) Extracting - Streaks - Wozniak

woz_param = [{"consecutiveStar": n} for n in [2, 4]]

extract.wozniak(df_out["PCA_1"],woz_param)

[('consecutiveStar_2', 0.012658227848101266), ('consecutiveStar_4', 0.0)]


(4) (28) (i) Extracting - Fractal - Higuchi

hig_param = [{"Kmax": 3},{"Kmax": 5}]

extract.higuchi_fractal_dimension(df_out["PCA_1"],hig_param)

[('Kmax_3', 0.577913816027104), ('Kmax_5', 0.8176960510304725)]


(4) (5) (ii) Extracting - Volatility - Variability Index

var_index_param = {"Volume":df["Volume"].values, "Open": df["Open"].values}

extract.var_index(df["Close"].values,var_index_param)

{'Interact__Open': 0.00396022538846289,
'Interact__Volume': 0.20550155114176533}


Time Series Extraction

pip install git+git://github.com/firmai/tsfresh.git

#Construct the preferred input dataframe.
from tsfresh.utilities.dataframe_functions import roll_time_series
df_out["ID"] = 0
periods = 30
df_out = df_out.reset_index()
df_ts = roll_time_series(df_out,"ID","Date",None,1,periods)
counts = df_ts['ID'].value_counts()
df_ts = df_ts[df_ts['ID'].isin(counts[counts > periods].index)]

#Perform extraction
from tsfresh.feature_extraction import extract_features, CustomFCParameters
settings_dict = CustomFCParameters()
settings_dict["var_index"] = {"PCA_1":None, "PCA_2": None}
df_feat = extract_features(df_ts.drop(["Close_1"],axis=1),default_fc_parameters=settings_dict,column_id="ID",column_sort="Date")

Feature Extraction: 100%|██████████| 5/5 [00:10<00:00,  2.14s/it]

# Cleaning operations
import pandasvault as pv
df_feat2 = df_feat.copy()
df_feat = df_feat.dropna(thresh = len(df_feat)*0.50, axis = "columns")
df_feat_cons = pv.constant_feature_detect(data=df_feat,threshold=0.9)
df_feat = df_feat.drop(df_feat_cons, axis=1)
df_feat = df_feat.ffill()
df_feat = pd.merge(df_feat,df[["Close_1"]],left_index=True,right_index=True,how="left")
print(df_feat.shape)
model(df_feat) #noisy

7  variables are found to be almost constant
(208, 48)
2064.7813982935995

from tsfresh import select_features
from tsfresh.utilities.dataframe_functions import impute

impute(df_feat)
df_feat_2 = select_features(df_feat.drop(["Close_1"],axis=1),df_feat["Close_1"],fdr_level=0.05)
df_feat_2["Close_1"] = df_feat["Close_1"]
model(df_feat_2) #improvement (b/ not an augmentation method)

1577.5273071299482


(3) (6) (i) Feature Agglomoration;   (1)(2)(i) Standard Scaler.

Like in this step, after (1), (2), (3), (4) and (5), you can often circle back to the initial steps to normalise the data and dimensionally reduce the data for the final model.

import numpy as np
from sklearn import datasets, cluster

def feature_agg(df, drop, components):
components = min(df.shape[1]-1,components)
agglo = cluster.FeatureAgglomeration(n_clusters=components,)
df = df.drop(drop,axis=1)
agglo.fit(df)
df = pd.DataFrame(agglo.transform(df))

return df

df_final = transform.standard_scaler(df_feat_2, drop=["Close_1"])
df_final = mapper.feature_agg(df_final,["Close_1"],4)
df_final.index = df_feat.index
df_final["Close_1"] = df_feat["Close_1"]
model(df_final) #noisy

1949.89085894338


Final Model After Applying 13 Arbitrary Augmentation Techniques

model(df_final) #improvement

1949.89085894338


Original Model Before Augmentation

df_org = df.iloc[:,:7][df.index.isin(df_final.index)]
model(df_org)

389.783990984133


Best Model After Developing 8 Augmenting Features

df_best = df_best.replace([np.inf, -np.inf], np.nan).ffill().fillna(0)
model(df_best)

267.1826850968307


Commentary

There are countless ways in which the current model can be improved, this can take on an automated process where all techniques are tested against a hold out set, for example, we can perform the operation below, and even though it improves the score here, there is a need for more robust tests. The skeleton example above is not meant to highlight the performance of the package. It simply serves as an example of how one can go about applying augmentation methods.

Quite naturally this example suffers from dimensionality issues with array shapes reaching (208, 48), furthermore you would need a sample that is at least 50-100 times larger before machine learning methods start to make sense.

Nonetheless, in this example, Transformation, Interactions and Mappings (applied to extraction output) performed fairly well. Extraction augmentation was overkill, but created a reasonable model when dimensionally reduced. A better selection of one of the 50+ augmentation methods and the order of augmentation could further help improve the outcome if robustly tested against development sets.

[1] DeltaPy Development

Author: firmai
Source Code: https://github.com/firmai/deltapy

#engineering

1600351200

## DevOps Basics: What You Should Know

Once an industry term becomes popular, particularly in technology, it can be difficult to get an accurate definition. Everyone assumes that the basics are common knowledge and moves on. However, if your company has been discussing DevOps, or if you are interested in learning more about it, here are some basics you should know.

### What Is DevOps?

DevOps refers to the restructuring of the traditional software application cycle to support Agile development and continuous improvement/continuous delivery. Traditionally, the software was created in large-scale, monolithic bundles. New features and new releases were created in large packages and released in full-scale, infrequent, major deployments.

This structure is no longer effective in the modern business environment. Companies are under increasing pressure to be agile. They must respond rapidly to changes in the business environment to remain competitive. Software development needs to be completely changed as a process so that incremental improvements can be made frequently – ideally, several times per day.

However, changing a development lifecycle completely requires major changes – in people and culture, process, and enabling tooling – to be effective. DevOps was created by the breaking down of cycles between development and operations, combining two separate functions in application development. These changes intend to support agile, secure, continuous improvements, and frequent releases.

#devops #devops adoption #devops benefits #q& #a #devops goals #devops migration #devops questions