{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/mchrist/Documents/Research/tsfresh/venv/lib/python2.7/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n", " from pandas.core import datetools\n" ] } ], "source": [ "from tsfresh.feature_extraction import extract_features\n", "from tsfresh.feature_extraction.settings import ComprehensiveFCParameters, MinimalFCParameters, EfficientFCParameters\n", "from tsfresh.feature_extraction.settings import from_columns\n", "\n", "import numpy as np\n", "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "This notebooks illustrates the `\"fc_parameters\"` or `\"kind_to_fc_parameters\"` dictionaries.\n", "\n", "For a detailed explanation, see also http://tsfresh.readthedocs.io/en/latest/text/feature_extraction_settings.html" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Construct a time series container" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "We construct the time series container that includes two sensor time series, _\"temperature\"_ and _\"pressure\"_, for two devices _\"a\"_ and _\"b\"_" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " id pressure temperature\n", "0 a -1 1\n", "1 a 2 2\n", "2 b -1 3\n", "3 b 7 1" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.DataFrame({\"id\": [\"a\", \"a\", \"b\", \"b\"], \"temperature\": [1,2,3,1], \"pressure\": [-1, 2, -1, 7]})\n", "df" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## The default_fc_parameters" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Which features are calculated by tsfresh is controlled by a dictionary that contains a mapping from feature calculator names to their parameters. \n", "This dictionary is called `fc_parameters`. It maps feature calculator names (=keys) to parameters (=values). As keys, always the same names as in the tsfresh.feature_extraction.feature_calculators module are used.\n", "\n", "In the following we load an exemplary dictionary" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "{'length': None,\n", " 'maximum': None,\n", " 'mean': None,\n", " 'median': None,\n", " 'minimum': None,\n", " 'standard_deviation': None,\n", " 'sum_values': None,\n", " 'variance': None}" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "settings_minimal = MinimalFCParameters() # only a few basic features\n", "settings_minimal" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "This dictionary can passed to the extract method, resulting in a few basic time series beeing calculated:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Feature Extraction: 100%|██████████| 4/4 [00:00<00:00, 16336.14it/s]\n" ] }, { "data": { "text/html": [ "
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variablepressure__lengthpressure__maximumpressure__meanpressure__medianpressure__minimumpressure__standard_deviationpressure__sum_valuespressure__variancetemperature__lengthtemperature__maximumtemperature__meantemperature__mediantemperature__minimumtemperature__standard_deviationtemperature__sum_valuestemperature__variance
id
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" ], "text/plain": [ "variable pressure__length pressure__maximum pressure__mean \\\n", "id \n", "a 2.0 2.0 0.5 \n", "b 2.0 7.0 3.0 \n", "\n", "variable pressure__median pressure__minimum pressure__standard_deviation \\\n", "id \n", "a 0.5 -1.0 1.5 \n", "b 3.0 -1.0 4.0 \n", "\n", "variable pressure__sum_values pressure__variance temperature__length \\\n", "id \n", "a 1.0 2.25 2.0 \n", "b 6.0 16.00 2.0 \n", "\n", "variable temperature__maximum temperature__mean temperature__median \\\n", "id \n", "a 2.0 1.5 1.5 \n", "b 3.0 2.0 2.0 \n", "\n", "variable temperature__minimum temperature__standard_deviation \\\n", "id \n", "a 1.0 0.5 \n", "b 1.0 1.0 \n", "\n", "variable temperature__sum_values temperature__variance \n", "id \n", "a 3.0 0.25 \n", "b 4.0 1.00 " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_tsfresh = extract_features(df, column_id=\"id\", default_fc_parameters = settings_minimal)\n", "X_tsfresh.head()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "By using the settings_minimal as value of the default_fc_parameters parameter, those settings are used for all type of time series. In this case, the `settings_minimal` dictionary is used for both _\"temperature\"_ and _\"pressure\"_ time series." ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Now, lets say we want to remove the length feature and prevent it from beeing calculated. We just delete it from the dictionary." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "{'maximum': None,\n", " 'mean': None,\n", " 'median': None,\n", " 'minimum': None,\n", " 'standard_deviation': None,\n", " 'sum_values': None,\n", " 'variance': None}" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "del settings_minimal[\"length\"]\n", "settings_minimal" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Now, if we extract features for this reduced dictionary, the length feature will not be calculated" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Feature Extraction: 100%|██████████| 4/4 [00:00<00:00, 1171.27it/s]\n" ] }, { "data": { "text/html": [ "
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variablepressure__maximumpressure__meanpressure__medianpressure__minimumpressure__standard_deviationpressure__sum_valuespressure__variancetemperature__maximumtemperature__meantemperature__mediantemperature__minimumtemperature__standard_deviationtemperature__sum_valuestemperature__variance
id
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b7.03.03.0-1.04.06.016.003.02.02.01.01.04.01.00
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" ], "text/plain": [ "variable pressure__maximum pressure__mean pressure__median \\\n", "id \n", "a 2.0 0.5 0.5 \n", "b 7.0 3.0 3.0 \n", "\n", "variable pressure__minimum pressure__standard_deviation \\\n", "id \n", "a -1.0 1.5 \n", "b -1.0 4.0 \n", "\n", "variable pressure__sum_values pressure__variance temperature__maximum \\\n", "id \n", "a 1.0 2.25 2.0 \n", "b 6.0 16.00 3.0 \n", "\n", "variable temperature__mean temperature__median temperature__minimum \\\n", "id \n", "a 1.5 1.5 1.0 \n", "b 2.0 2.0 1.0 \n", "\n", "variable temperature__standard_deviation temperature__sum_values \\\n", "id \n", "a 0.5 3.0 \n", "b 1.0 4.0 \n", "\n", "variable temperature__variance \n", "id \n", "a 0.25 \n", "b 1.00 " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_tsfresh = extract_features(df, column_id=\"id\", default_fc_parameters = settings_minimal)\n", "X_tsfresh.head()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## The kind_to_fc_parameters" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "now, lets say we do not want to calculate the same features for both type of time series. Instead there should be different sets of features for each kind.\n", "\n", "To do that, we can use the `kind_to_fc_parameters` parameter, which lets us finely specifiy which `fc_parameters` we want to use for which kind of time series:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'pressure': {'length': None, 'sum_values': None}, 'temperature': {'minimum': None, 'maximum': None}}\n" ] } ], "source": [ "fc_parameters_pressure = {\"length\": None, \n", " \"sum_values\": None}\n", "\n", "fc_parameters_temperature = {\"maximum\": None, \n", " \"minimum\": None}\n", "\n", "kind_to_fc_parameters = {\n", " \"temperature\": fc_parameters_temperature,\n", " \"pressure\": fc_parameters_pressure\n", "}\n", "\n", "print(kind_to_fc_parameters)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "So, in this case, for sensor _\"pressure\"_ both _\"max\"_ and _\"min\"_ are calculated. For the _\"temperature\"_ signal, the length and sum_values features are extracted instead." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Feature Extraction: 100%|██████████| 4/4 [00:00<00:00, 1473.37it/s]\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
variablepressure__lengthpressure__sum_valuestemperature__maximumtemperature__minimum
id
a2.01.02.01.0
b2.06.03.01.0
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" ], "text/plain": [ "variable pressure__length pressure__sum_values temperature__maximum \\\n", "id \n", "a 2.0 1.0 2.0 \n", "b 2.0 6.0 3.0 \n", "\n", "variable temperature__minimum \n", "id \n", "a 1.0 \n", "b 1.0 " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_tsfresh = extract_features(df, column_id=\"id\", kind_to_fc_parameters = kind_to_fc_parameters)\n", "X_tsfresh.head()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "So, lets say we lost the kind_to_fc_parameters dictionary. Or we apply a feature selection algorithm to drop \n", "irrelevant feature columns, so our extraction settings contain irrelevant features. \n", "\n", "In both cases, we can use the provided \"from_columns\" method to infer the creating dictionary from \n", "the dataframe containing the features" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "{'pressure': {'length': None, 'sum_values': None},\n", " 'temperature': {'maximum': None, 'minimum': None}}" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "recovered_settings = from_columns(X_tsfresh)\n", "recovered_settings" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Lets drop a column to show that the inferred settings dictionary really changes" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/html": [ "
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variablepressure__sum_valuestemperature__maximumtemperature__minimum
id
a1.02.01.0
b6.03.01.0
\n", "
" ], "text/plain": [ "variable pressure__sum_values temperature__maximum temperature__minimum\n", "id \n", "a 1.0 2.0 1.0\n", "b 6.0 3.0 1.0" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_tsfresh.iloc[:, 1:]" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "{'pressure': {'sum_values': None},\n", " 'temperature': {'maximum': None, 'minimum': None}}" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "recovered_settings = from_columns(X_tsfresh.iloc[:, 1:])\n", "recovered_settings" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## More complex dictionaries" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "We provide custom fc_parameters dictionaries with greater sets of features.\n", "\n", "The `EfficientFCParameters` contain features and parameters that should be calculated quite fastly:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false, "deletable": true, "editable": true, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "{'abs_energy': None,\n", " 'absolute_sum_of_changes': None,\n", " 'agg_autocorrelation': [{'f_agg': 'mean'},\n", " {'f_agg': 'median'},\n", " {'f_agg': 'var'}],\n", " 'agg_linear_trend': [{'attr': 'rvalue', 'chunk_len': 5, 'f_agg': 'max'},\n", " {'attr': 'rvalue', 'chunk_len': 5, 'f_agg': 'min'},\n", " {'attr': 'rvalue', 'chunk_len': 5, 'f_agg': 'mean'},\n", " {'attr': 'rvalue', 'chunk_len': 5, 'f_agg': 'var'},\n", " {'attr': 'rvalue', 'chunk_len': 10, 'f_agg': 'max'},\n", " {'attr': 'rvalue', 'chunk_len': 10, 'f_agg': 'min'},\n", " {'attr': 'rvalue', 'chunk_len': 10, 'f_agg': 'mean'},\n", " {'attr': 'rvalue', 'chunk_len': 10, 'f_agg': 'var'},\n", " {'attr': 'rvalue', 'chunk_len': 50, 'f_agg': 'max'},\n", " {'attr': 'rvalue', 'chunk_len': 50, 'f_agg': 'min'},\n", " {'attr': 'rvalue', 'chunk_len': 50, 'f_agg': 'mean'},\n", " {'attr': 'rvalue', 'chunk_len': 50, 'f_agg': 'var'},\n", " {'attr': 'intercept', 'chunk_len': 5, 'f_agg': 'max'},\n", " {'attr': 'intercept', 'chunk_len': 5, 'f_agg': 'min'},\n", " {'attr': 'intercept', 'chunk_len': 5, 'f_agg': 'mean'},\n", " {'attr': 'intercept', 'chunk_len': 5, 'f_agg': 'var'},\n", " {'attr': 'intercept', 'chunk_len': 10, 'f_agg': 'max'},\n", " {'attr': 'intercept', 'chunk_len': 10, 'f_agg': 'min'},\n", " {'attr': 'intercept', 'chunk_len': 10, 'f_agg': 'mean'},\n", " {'attr': 'intercept', 'chunk_len': 10, 'f_agg': 'var'},\n", " {'attr': 'intercept', 'chunk_len': 50, 'f_agg': 'max'},\n", " {'attr': 'intercept', 'chunk_len': 50, 'f_agg': 'min'},\n", " {'attr': 'intercept', 'chunk_len': 50, 'f_agg': 'mean'},\n", " {'attr': 'intercept', 'chunk_len': 50, 'f_agg': 'var'},\n", " {'attr': 'slope', 'chunk_len': 5, 'f_agg': 'max'},\n", " {'attr': 'slope', 'chunk_len': 5, 'f_agg': 'min'},\n", " {'attr': 'slope', 'chunk_len': 5, 'f_agg': 'mean'},\n", " {'attr': 'slope', 'chunk_len': 5, 'f_agg': 'var'},\n", " {'attr': 'slope', 'chunk_len': 10, 'f_agg': 'max'},\n", " {'attr': 'slope', 'chunk_len': 10, 'f_agg': 'min'},\n", " {'attr': 'slope', 'chunk_len': 10, 'f_agg': 'mean'},\n", " {'attr': 'slope', 'chunk_len': 10, 'f_agg': 'var'},\n", " {'attr': 'slope', 'chunk_len': 50, 'f_agg': 'max'},\n", " {'attr': 'slope', 'chunk_len': 50, 'f_agg': 'min'},\n", " {'attr': 'slope', 'chunk_len': 50, 'f_agg': 'mean'},\n", " {'attr': 'slope', 'chunk_len': 50, 'f_agg': 'var'},\n", " {'attr': 'stderr', 'chunk_len': 5, 'f_agg': 'max'},\n", " {'attr': 'stderr', 'chunk_len': 5, 'f_agg': 'min'},\n", " {'attr': 'stderr', 'chunk_len': 5, 'f_agg': 'mean'},\n", " {'attr': 'stderr', 'chunk_len': 5, 'f_agg': 'var'},\n", " {'attr': 'stderr', 'chunk_len': 10, 'f_agg': 'max'},\n", " {'attr': 'stderr', 'chunk_len': 10, 'f_agg': 'min'},\n", " {'attr': 'stderr', 'chunk_len': 10, 'f_agg': 'mean'},\n", " {'attr': 'stderr', 'chunk_len': 10, 'f_agg': 'var'},\n", " {'attr': 'stderr', 'chunk_len': 50, 'f_agg': 'max'},\n", " {'attr': 'stderr', 'chunk_len': 50, 'f_agg': 'min'},\n", " {'attr': 'stderr', 'chunk_len': 50, 'f_agg': 'mean'},\n", " {'attr': 'stderr', 'chunk_len': 50, 'f_agg': 'var'}],\n", " 'ar_coefficient': [{'coeff': 0, 'k': 10},\n", " {'coeff': 1, 'k': 10},\n", " {'coeff': 2, 'k': 10},\n", " {'coeff': 3, 'k': 10},\n", " {'coeff': 4, 'k': 10}],\n", " 'augmented_dickey_fuller': [{'attr': 'teststat'},\n", " {'attr': 'pvalue'},\n", " {'attr': 'usedlag'}],\n", " 'autocorrelation': [{'lag': 0},\n", " {'lag': 1},\n", " {'lag': 2},\n", " {'lag': 3},\n", " {'lag': 4},\n", " {'lag': 5},\n", " {'lag': 6},\n", " {'lag': 7},\n", " {'lag': 8},\n", " {'lag': 9}],\n", " 'binned_entropy': [{'max_bins': 10}],\n", " 'c3': [{'lag': 1}, {'lag': 2}, {'lag': 3}],\n", " 'change_quantiles': [{'f_agg': 'mean', 'isabs': False, 'qh': 0.2, 'ql': 0.0},\n", " 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'coeff': 46},\n", " {'attr': 'imag', 'coeff': 47},\n", " {'attr': 'imag', 'coeff': 48},\n", " {'attr': 'imag', 'coeff': 49},\n", " {'attr': 'imag', 'coeff': 50},\n", " {'attr': 'imag', 'coeff': 51},\n", " {'attr': 'imag', 'coeff': 52},\n", " {'attr': 'imag', 'coeff': 53},\n", " {'attr': 'imag', 'coeff': 54},\n", " {'attr': 'imag', 'coeff': 55},\n", " {'attr': 'imag', 'coeff': 56},\n", " {'attr': 'imag', 'coeff': 57},\n", " {'attr': 'imag', 'coeff': 58},\n", " {'attr': 'imag', 'coeff': 59},\n", " {'attr': 'imag', 'coeff': 60},\n", " {'attr': 'imag', 'coeff': 61},\n", " {'attr': 'imag', 'coeff': 62},\n", " {'attr': 'imag', 'coeff': 63},\n", " {'attr': 'imag', 'coeff': 64},\n", " {'attr': 'imag', 'coeff': 65},\n", " {'attr': 'imag', 'coeff': 66},\n", " {'attr': 'imag', 'coeff': 67},\n", " {'attr': 'imag', 'coeff': 68},\n", " {'attr': 'imag', 'coeff': 69},\n", " {'attr': 'imag', 'coeff': 70},\n", " {'attr': 'imag', 'coeff': 71},\n", " {'attr': 'imag', 'coeff': 72},\n", " {'attr': 'imag', 'coeff': 73},\n", " {'attr': 'imag', 'coeff': 74},\n", " {'attr': 'imag', 'coeff': 75},\n", " {'attr': 'imag', 'coeff': 76},\n", " {'attr': 'imag', 'coeff': 77},\n", " {'attr': 'imag', 'coeff': 78},\n", " {'attr': 'imag', 'coeff': 79},\n", " {'attr': 'imag', 'coeff': 80},\n", " {'attr': 'imag', 'coeff': 81},\n", " {'attr': 'imag', 'coeff': 82},\n", " {'attr': 'imag', 'coeff': 83},\n", " {'attr': 'imag', 'coeff': 84},\n", " {'attr': 'imag', 'coeff': 85},\n", " {'attr': 'imag', 'coeff': 86},\n", " {'attr': 'imag', 'coeff': 87},\n", " {'attr': 'imag', 'coeff': 88},\n", " {'attr': 'imag', 'coeff': 89},\n", " {'attr': 'imag', 'coeff': 90},\n", " {'attr': 'imag', 'coeff': 91},\n", " {'attr': 'imag', 'coeff': 92},\n", " {'attr': 'imag', 'coeff': 93},\n", " {'attr': 'imag', 'coeff': 94},\n", " {'attr': 'imag', 'coeff': 95},\n", " {'attr': 'imag', 'coeff': 96},\n", " {'attr': 'imag', 'coeff': 97},\n", " {'attr': 'imag', 'coeff': 98},\n", " {'attr': 'imag', 'coeff': 99}],\n", " 'first_location_of_maximum': None,\n", " 'first_location_of_minimum': None,\n", " 'friedrich_coefficients': [{'coeff': 0, 'm': 3, 'r': 30},\n", " {'coeff': 1, 'm': 3, 'r': 30},\n", " {'coeff': 2, 'm': 3, 'r': 30},\n", " {'coeff': 3, 'm': 3, 'r': 30}],\n", " 'has_duplicate': None,\n", " 'has_duplicate_max': None,\n", " 'has_duplicate_min': None,\n", " 'index_mass_quantile': [{'q': 0.1},\n", " {'q': 0.2},\n", " {'q': 0.3},\n", " {'q': 0.4},\n", " {'q': 0.6},\n", " {'q': 0.7},\n", " {'q': 0.8},\n", " {'q': 0.9}],\n", " 'kurtosis': None,\n", " 'large_standard_deviation': [{'r': 0.05},\n", " {'r': 0.1},\n", " {'r': 0.15000000000000002},\n", " {'r': 0.2},\n", " {'r': 0.25},\n", " {'r': 0.30000000000000004},\n", " {'r': 0.35000000000000003},\n", " {'r': 0.4},\n", " {'r': 0.45},\n", " {'r': 0.5},\n", " {'r': 0.55},\n", " {'r': 0.6000000000000001},\n", " {'r': 0.65},\n", " {'r': 0.7000000000000001},\n", " {'r': 0.75},\n", " {'r': 0.8},\n", " {'r': 0.8500000000000001},\n", " {'r': 0.9},\n", " {'r': 0.9500000000000001}],\n", " 'last_location_of_maximum': None,\n", " 'last_location_of_minimum': None,\n", " 'length': None,\n", " 'linear_trend': [{'attr': 'pvalue'},\n", " {'attr': 'rvalue'},\n", " {'attr': 'intercept'},\n", " {'attr': 'slope'},\n", " {'attr': 'stderr'}],\n", " 'longest_strike_above_mean': None,\n", " 'longest_strike_below_mean': None,\n", " 'max_langevin_fixed_point': [{'m': 3, 'r': 30}],\n", " 'maximum': None,\n", " 'mean': None,\n", " 'mean_abs_change': None,\n", " 'mean_change': None,\n", " 'mean_second_derivate_central': None,\n", " 'median': None,\n", " 'minimum': None,\n", " 'number_crossing_m': [{'m': 0}, {'m': -1}, {'m': 1}],\n", " 'number_cwt_peaks': [{'n': 1}, {'n': 5}],\n", " 'number_peaks': [{'n': 1}, {'n': 3}, {'n': 5}, {'n': 10}, {'n': 50}],\n", " 'percentage_of_reoccurring_datapoints_to_all_datapoints': None,\n", " 'percentage_of_reoccurring_values_to_all_values': None,\n", " 'quantile': [{'q': 0.1},\n", " {'q': 0.2},\n", " {'q': 0.3},\n", " {'q': 0.4},\n", " {'q': 0.6},\n", " {'q': 0.7},\n", " {'q': 0.8},\n", " {'q': 0.9}],\n", " 'range_count': [{'max': 1, 'min': -1}],\n", " 'ratio_value_number_to_time_series_length': None,\n", " 'skewness': None,\n", " 'spkt_welch_density': [{'coeff': 2}, {'coeff': 5}, {'coeff': 8}],\n", " 'standard_deviation': None,\n", " 'sum_of_reoccurring_data_points': None,\n", " 'sum_of_reoccurring_values': None,\n", " 'sum_values': None,\n", " 'symmetry_looking': [{'r': 0.0},\n", " {'r': 0.05},\n", " {'r': 0.1},\n", " {'r': 0.15000000000000002},\n", " {'r': 0.2},\n", " {'r': 0.25},\n", " {'r': 0.30000000000000004},\n", " {'r': 0.35000000000000003},\n", " {'r': 0.4},\n", " {'r': 0.45},\n", " {'r': 0.5},\n", " {'r': 0.55},\n", " {'r': 0.6000000000000001},\n", " {'r': 0.65},\n", " {'r': 0.7000000000000001},\n", " {'r': 0.75},\n", " {'r': 0.8},\n", " {'r': 0.8500000000000001},\n", " {'r': 0.9},\n", " {'r': 0.9500000000000001}],\n", " 'time_reversal_asymmetry_statistic': [{'lag': 1}, {'lag': 2}, {'lag': 3}],\n", " 'value_count': [{'value': 0},\n", " {'value': 1},\n", " {'value': nan},\n", " {'value': inf},\n", " {'value': -inf}],\n", " 'variance': None,\n", " 'variance_larger_than_standard_deviation': None}" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "settings_efficient = EfficientFCParameters()\n", "settings_efficient" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "The `ComprehensiveFCParameters` are the biggest set of features. It will take the longest to calculate" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "{'abs_energy': None,\n", " 'absolute_sum_of_changes': None,\n", " 'agg_autocorrelation': [{'f_agg': 'mean'},\n", " {'f_agg': 'median'},\n", " {'f_agg': 'var'}],\n", " 'agg_linear_trend': [{'attr': 'rvalue', 'chunk_len': 5, 'f_agg': 'max'},\n", " {'attr': 'rvalue', 'chunk_len': 5, 'f_agg': 'min'},\n", " {'attr': 'rvalue', 'chunk_len': 5, 'f_agg': 'mean'},\n", " {'attr': 'rvalue', 'chunk_len': 5, 'f_agg': 'var'},\n", " {'attr': 'rvalue', 'chunk_len': 10, 'f_agg': 'max'},\n", " {'attr': 'rvalue', 'chunk_len': 10, 'f_agg': 'min'},\n", " {'attr': 'rvalue', 'chunk_len': 10, 'f_agg': 'mean'},\n", " {'attr': 'rvalue', 'chunk_len': 10, 'f_agg': 'var'},\n", " {'attr': 'rvalue', 'chunk_len': 50, 'f_agg': 'max'},\n", " {'attr': 'rvalue', 'chunk_len': 50, 'f_agg': 'min'},\n", " {'attr': 'rvalue', 'chunk_len': 50, 'f_agg': 'mean'},\n", " {'attr': 'rvalue', 'chunk_len': 50, 'f_agg': 'var'},\n", " {'attr': 'intercept', 'chunk_len': 5, 'f_agg': 'max'},\n", " {'attr': 'intercept', 'chunk_len': 5, 'f_agg': 'min'},\n", " {'attr': 'intercept', 'chunk_len': 5, 'f_agg': 'mean'},\n", " {'attr': 'intercept', 'chunk_len': 5, 'f_agg': 'var'},\n", " {'attr': 'intercept', 'chunk_len': 10, 'f_agg': 'max'},\n", " {'attr': 'intercept', 'chunk_len': 10, 'f_agg': 'min'},\n", " {'attr': 'intercept', 'chunk_len': 10, 'f_agg': 'mean'},\n", " {'attr': 'intercept', 'chunk_len': 10, 'f_agg': 'var'},\n", " {'attr': 'intercept', 'chunk_len': 50, 'f_agg': 'max'},\n", " {'attr': 'intercept', 'chunk_len': 50, 'f_agg': 'min'},\n", " {'attr': 'intercept', 'chunk_len': 50, 'f_agg': 'mean'},\n", " {'attr': 'intercept', 'chunk_len': 50, 'f_agg': 'var'},\n", " {'attr': 'slope', 'chunk_len': 5, 'f_agg': 'max'},\n", " {'attr': 'slope', 'chunk_len': 5, 'f_agg': 'min'},\n", " {'attr': 'slope', 'chunk_len': 5, 'f_agg': 'mean'},\n", " {'attr': 'slope', 'chunk_len': 5, 'f_agg': 'var'},\n", " {'attr': 'slope', 'chunk_len': 10, 'f_agg': 'max'},\n", " {'attr': 'slope', 'chunk_len': 10, 'f_agg': 'min'},\n", " {'attr': 'slope', 'chunk_len': 10, 'f_agg': 'mean'},\n", " {'attr': 'slope', 'chunk_len': 10, 'f_agg': 'var'},\n", " {'attr': 'slope', 'chunk_len': 50, 'f_agg': 'max'},\n", " {'attr': 'slope', 'chunk_len': 50, 'f_agg': 'min'},\n", " {'attr': 'slope', 'chunk_len': 50, 'f_agg': 'mean'},\n", " {'attr': 'slope', 'chunk_len': 50, 'f_agg': 'var'},\n", " {'attr': 'stderr', 'chunk_len': 5, 'f_agg': 'max'},\n", " {'attr': 'stderr', 'chunk_len': 5, 'f_agg': 'min'},\n", " {'attr': 'stderr', 'chunk_len': 5, 'f_agg': 'mean'},\n", " {'attr': 'stderr', 'chunk_len': 5, 'f_agg': 'var'},\n", " {'attr': 'stderr', 'chunk_len': 10, 'f_agg': 'max'},\n", " {'attr': 'stderr', 'chunk_len': 10, 'f_agg': 'min'},\n", " {'attr': 'stderr', 'chunk_len': 10, 'f_agg': 'mean'},\n", " {'attr': 'stderr', 'chunk_len': 10, 'f_agg': 'var'},\n", " {'attr': 'stderr', 'chunk_len': 50, 'f_agg': 'max'},\n", " {'attr': 'stderr', 'chunk_len': 50, 'f_agg': 'min'},\n", " {'attr': 'stderr', 'chunk_len': 50, 'f_agg': 'mean'},\n", " {'attr': 'stderr', 'chunk_len': 50, 'f_agg': 'var'}],\n", " 'approximate_entropy': [{'m': 2, 'r': 0.1},\n", " {'m': 2, 'r': 0.3},\n", " {'m': 2, 'r': 0.5},\n", " {'m': 2, 'r': 0.7},\n", " {'m': 2, 'r': 0.9}],\n", " 'ar_coefficient': [{'coeff': 0, 'k': 10},\n", " {'coeff': 1, 'k': 10},\n", " {'coeff': 2, 'k': 10},\n", " {'coeff': 3, 'k': 10},\n", " {'coeff': 4, 'k': 10}],\n", " 'augmented_dickey_fuller': [{'attr': 'teststat'},\n", " {'attr': 'pvalue'},\n", " {'attr': 'usedlag'}],\n", " 'autocorrelation': [{'lag': 0},\n", " {'lag': 1},\n", " {'lag': 2},\n", " {'lag': 3},\n", " {'lag': 4},\n", " {'lag': 5},\n", " {'lag': 6},\n", " {'lag': 7},\n", " {'lag': 8},\n", " {'lag': 9}],\n", " 'binned_entropy': [{'max_bins': 10}],\n", " 'c3': [{'lag': 1}, {'lag': 2}, {'lag': 3}],\n", " 'change_quantiles': 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{'f_agg': 'mean', 'isabs': False, 'qh': 0.2, 'ql': 0.8},\n", " {'f_agg': 'var', 'isabs': False, 'qh': 0.2, 'ql': 0.8},\n", " {'f_agg': 'mean', 'isabs': True, 'qh': 0.2, 'ql': 0.8},\n", " {'f_agg': 'var', 'isabs': True, 'qh': 0.2, 'ql': 0.8},\n", " {'f_agg': 'mean', 'isabs': False, 'qh': 0.4, 'ql': 0.8},\n", " {'f_agg': 'var', 'isabs': False, 'qh': 0.4, 'ql': 0.8},\n", " {'f_agg': 'mean', 'isabs': True, 'qh': 0.4, 'ql': 0.8},\n", " {'f_agg': 'var', 'isabs': True, 'qh': 0.4, 'ql': 0.8},\n", " {'f_agg': 'mean', 'isabs': False, 'qh': 0.6, 'ql': 0.8},\n", " {'f_agg': 'var', 'isabs': False, 'qh': 0.6, 'ql': 0.8},\n", " {'f_agg': 'mean', 'isabs': True, 'qh': 0.6, 'ql': 0.8},\n", " {'f_agg': 'var', 'isabs': True, 'qh': 0.6, 'ql': 0.8},\n", " {'f_agg': 'mean', 'isabs': False, 'qh': 0.8, 'ql': 0.8},\n", " {'f_agg': 'var', 'isabs': False, 'qh': 0.8, 'ql': 0.8},\n", " {'f_agg': 'mean', 'isabs': True, 'qh': 0.8, 'ql': 0.8},\n", " {'f_agg': 'var', 'isabs': True, 'qh': 0.8, 'ql': 0.8},\n", " {'f_agg': 'mean', 'isabs': False, 'qh': 1.0, 'ql': 0.8},\n", " {'f_agg': 'var', 'isabs': False, 'qh': 1.0, 'ql': 0.8},\n", " {'f_agg': 'mean', 'isabs': True, 'qh': 1.0, 'ql': 0.8},\n", " {'f_agg': 'var', 'isabs': True, 'qh': 1.0, 'ql': 0.8}],\n", " 'count_above_mean': None,\n", " 'count_below_mean': None,\n", " 'cwt_coefficients': [{'coeff': 0, 'w': 2, 'widths': (2, 5, 10, 20)},\n", " {'coeff': 0, 'w': 5, 'widths': (2, 5, 10, 20)},\n", " {'coeff': 0, 'w': 10, 'widths': (2, 5, 10, 20)},\n", " {'coeff': 0, 'w': 20, 'widths': (2, 5, 10, 20)},\n", " {'coeff': 1, 'w': 2, 'widths': (2, 5, 10, 20)},\n", " {'coeff': 1, 'w': 5, 'widths': (2, 5, 10, 20)},\n", " {'coeff': 1, 'w': 10, 'widths': (2, 5, 10, 20)},\n", " {'coeff': 1, 'w': 20, 'widths': (2, 5, 10, 20)},\n", " {'coeff': 2, 'w': 2, 'widths': (2, 5, 10, 20)},\n", " {'coeff': 2, 'w': 5, 'widths': (2, 5, 10, 20)},\n", " {'coeff': 2, 'w': 10, 'widths': (2, 5, 10, 20)},\n", " {'coeff': 2, 'w': 20, 'widths': (2, 5, 10, 20)},\n", " {'coeff': 3, 'w': 2, 'widths': (2, 5, 10, 20)},\n", " {'coeff': 3, 'w': 5, 'widths': (2, 5, 10, 20)},\n", " {'coeff': 3, 'w': 10, 'widths': (2, 5, 10, 20)},\n", " {'coeff': 3, 'w': 20, 'widths': (2, 5, 10, 20)},\n", " {'coeff': 4, 'w': 2, 'widths': (2, 5, 10, 20)},\n", " {'coeff': 4, 'w': 5, 'widths': (2, 5, 10, 20)},\n", " {'coeff': 4, 'w': 10, 'widths': (2, 5, 10, 20)},\n", " {'coeff': 4, 'w': 20, 'widths': (2, 5, 10, 20)},\n", " {'coeff': 5, 'w': 2, 'widths': (2, 5, 10, 20)},\n", " {'coeff': 5, 'w': 5, 'widths': (2, 5, 10, 20)},\n", " {'coeff': 5, 'w': 10, 'widths': (2, 5, 10, 20)},\n", " {'coeff': 5, 'w': 20, 'widths': (2, 5, 10, 20)},\n", " {'coeff': 6, 'w': 2, 'widths': (2, 5, 10, 20)},\n", " {'coeff': 6, 'w': 5, 'widths': (2, 5, 10, 20)},\n", " {'coeff': 6, 'w': 10, 'widths': (2, 5, 10, 20)},\n", " {'coeff': 6, 'w': 20, 'widths': (2, 5, 10, 20)},\n", " {'coeff': 7, 'w': 2, 'widths': (2, 5, 10, 20)},\n", " {'coeff': 7, 'w': 5, 'widths': (2, 5, 10, 20)},\n", " {'coeff': 7, 'w': 10, 'widths': (2, 5, 10, 20)},\n", " {'coeff': 7, 'w': 20, 'widths': (2, 5, 10, 20)},\n", " {'coeff': 8, 'w': 2, 'widths': (2, 5, 10, 20)},\n", " {'coeff': 8, 'w': 5, 'widths': (2, 5, 10, 20)},\n", " {'coeff': 8, 'w': 10, 'widths': (2, 5, 10, 20)},\n", " {'coeff': 8, 'w': 20, 'widths': (2, 5, 10, 20)},\n", " {'coeff': 9, 'w': 2, 'widths': (2, 5, 10, 20)},\n", " {'coeff': 9, 'w': 5, 'widths': (2, 5, 10, 20)},\n", " {'coeff': 9, 'w': 10, 'widths': (2, 5, 10, 20)},\n", " {'coeff': 9, 'w': 20, 'widths': (2, 5, 10, 20)},\n", " {'coeff': 10, 'w': 2, 'widths': (2, 5, 10, 20)},\n", " {'coeff': 10, 'w': 5, 'widths': (2, 5, 10, 20)},\n", " {'coeff': 10, 'w': 10, 'widths': (2, 5, 10, 20)},\n", " {'coeff': 10, 'w': 20, 'widths': (2, 5, 10, 20)},\n", " {'coeff': 11, 'w': 2, 'widths': (2, 5, 10, 20)},\n", " {'coeff': 11, 'w': 5, 'widths': (2, 5, 10, 20)},\n", " {'coeff': 11, 'w': 10, 'widths': (2, 5, 10, 20)},\n", " {'coeff': 11, 'w': 20, 'widths': (2, 5, 10, 20)},\n", " {'coeff': 12, 'w': 2, 'widths': (2, 5, 10, 20)},\n", " {'coeff': 12, 'w': 5, 'widths': (2, 5, 10, 20)},\n", " {'coeff': 12, 'w': 10, 'widths': (2, 5, 10, 20)},\n", " {'coeff': 12, 'w': 20, 'widths': (2, 5, 10, 20)},\n", " {'coeff': 13, 'w': 2, 'widths': (2, 5, 10, 20)},\n", " {'coeff': 13, 'w': 5, 'widths': (2, 5, 10, 20)},\n", " {'coeff': 13, 'w': 10, 'widths': (2, 5, 10, 20)},\n", " {'coeff': 13, 'w': 20, 'widths': (2, 5, 10, 20)},\n", " {'coeff': 14, 'w': 2, 'widths': (2, 5, 10, 20)},\n", " {'coeff': 14, 'w': 5, 'widths': (2, 5, 10, 20)},\n", " {'coeff': 14, 'w': 10, 'widths': (2, 5, 10, 20)},\n", " {'coeff': 14, 'w': 20, 'widths': (2, 5, 10, 20)}],\n", " 'fft_coefficient': [{'attr': 'real', 'coeff': 0},\n", " {'attr': 'real', 'coeff': 1},\n", " {'attr': 'real', 'coeff': 2},\n", " {'attr': 'real', 'coeff': 3},\n", " {'attr': 'real', 'coeff': 4},\n", " {'attr': 'real', 'coeff': 5},\n", " {'attr': 'real', 'coeff': 6},\n", " {'attr': 'real', 'coeff': 7},\n", " {'attr': 'real', 'coeff': 8},\n", " {'attr': 'real', 'coeff': 9},\n", " {'attr': 'real', 'coeff': 10},\n", " {'attr': 'real', 'coeff': 11},\n", " {'attr': 'real', 'coeff': 12},\n", " {'attr': 'real', 'coeff': 13},\n", " {'attr': 'real', 'coeff': 14},\n", " {'attr': 'real', 'coeff': 15},\n", " {'attr': 'real', 'coeff': 16},\n", " {'attr': 'real', 'coeff': 17},\n", " {'attr': 'real', 'coeff': 18},\n", " {'attr': 'real', 'coeff': 19},\n", " {'attr': 'real', 'coeff': 20},\n", " {'attr': 'real', 'coeff': 21},\n", " {'attr': 'real', 'coeff': 22},\n", " {'attr': 'real', 'coeff': 23},\n", " {'attr': 'real', 'coeff': 24},\n", " {'attr': 'real', 'coeff': 25},\n", " {'attr': 'real', 'coeff': 26},\n", " {'attr': 'real', 'coeff': 27},\n", " {'attr': 'real', 'coeff': 28},\n", " {'attr': 'real', 'coeff': 29},\n", " {'attr': 'real', 'coeff': 30},\n", " {'attr': 'real', 'coeff': 31},\n", " {'attr': 'real', 'coeff': 32},\n", " {'attr': 'real', 'coeff': 33},\n", " {'attr': 'real', 'coeff': 34},\n", " {'attr': 'real', 'coeff': 35},\n", " {'attr': 'real', 'coeff': 36},\n", " {'attr': 'real', 'coeff': 37},\n", " {'attr': 'real', 'coeff': 38},\n", " {'attr': 'real', 'coeff': 39},\n", " {'attr': 'real', 'coeff': 40},\n", " {'attr': 'real', 'coeff': 41},\n", " {'attr': 'real', 'coeff': 42},\n", " {'attr': 'real', 'coeff': 43},\n", " {'attr': 'real', 'coeff': 44},\n", " {'attr': 'real', 'coeff': 45},\n", " {'attr': 'real', 'coeff': 46},\n", " {'attr': 'real', 'coeff': 47},\n", " {'attr': 'real', 'coeff': 48},\n", " {'attr': 'real', 'coeff': 49},\n", " {'attr': 'real', 'coeff': 50},\n", " {'attr': 'real', 'coeff': 51},\n", " {'attr': 'real', 'coeff': 52},\n", " {'attr': 'real', 'coeff': 53},\n", " {'attr': 'real', 'coeff': 54},\n", " {'attr': 'real', 'coeff': 55},\n", " {'attr': 'real', 'coeff': 56},\n", " {'attr': 'real', 'coeff': 57},\n", " {'attr': 'real', 'coeff': 58},\n", " {'attr': 'real', 'coeff': 59},\n", " {'attr': 'real', 'coeff': 60},\n", " {'attr': 'real', 'coeff': 61},\n", " {'attr': 'real', 'coeff': 62},\n", " {'attr': 'real', 'coeff': 63},\n", " {'attr': 'real', 'coeff': 64},\n", " {'attr': 'real', 'coeff': 65},\n", " {'attr': 'real', 'coeff': 66},\n", " {'attr': 'real', 'coeff': 67},\n", " {'attr': 'real', 'coeff': 68},\n", " {'attr': 'real', 'coeff': 69},\n", " {'attr': 'real', 'coeff': 70},\n", " {'attr': 'real', 'coeff': 71},\n", " {'attr': 'real', 'coeff': 72},\n", " {'attr': 'real', 'coeff': 73},\n", " {'attr': 'real', 'coeff': 74},\n", " {'attr': 'real', 'coeff': 75},\n", " {'attr': 'real', 'coeff': 76},\n", " {'attr': 'real', 'coeff': 77},\n", " {'attr': 'real', 'coeff': 78},\n", " {'attr': 'real', 'coeff': 79},\n", " {'attr': 'real', 'coeff': 80},\n", " {'attr': 'real', 'coeff': 81},\n", " {'attr': 'real', 'coeff': 82},\n", " {'attr': 'real', 'coeff': 83},\n", " {'attr': 'real', 'coeff': 84},\n", " {'attr': 'real', 'coeff': 85},\n", " {'attr': 'real', 'coeff': 86},\n", " {'attr': 'real', 'coeff': 87},\n", " {'attr': 'real', 'coeff': 88},\n", " {'attr': 'real', 'coeff': 89},\n", " {'attr': 'real', 'coeff': 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{'attr': 'imag', 'coeff': 45},\n", " {'attr': 'imag', 'coeff': 46},\n", " {'attr': 'imag', 'coeff': 47},\n", " {'attr': 'imag', 'coeff': 48},\n", " {'attr': 'imag', 'coeff': 49},\n", " {'attr': 'imag', 'coeff': 50},\n", " {'attr': 'imag', 'coeff': 51},\n", " {'attr': 'imag', 'coeff': 52},\n", " {'attr': 'imag', 'coeff': 53},\n", " {'attr': 'imag', 'coeff': 54},\n", " {'attr': 'imag', 'coeff': 55},\n", " {'attr': 'imag', 'coeff': 56},\n", " {'attr': 'imag', 'coeff': 57},\n", " {'attr': 'imag', 'coeff': 58},\n", " {'attr': 'imag', 'coeff': 59},\n", " {'attr': 'imag', 'coeff': 60},\n", " {'attr': 'imag', 'coeff': 61},\n", " {'attr': 'imag', 'coeff': 62},\n", " {'attr': 'imag', 'coeff': 63},\n", " {'attr': 'imag', 'coeff': 64},\n", " {'attr': 'imag', 'coeff': 65},\n", " {'attr': 'imag', 'coeff': 66},\n", " {'attr': 'imag', 'coeff': 67},\n", " {'attr': 'imag', 'coeff': 68},\n", " {'attr': 'imag', 'coeff': 69},\n", " {'attr': 'imag', 'coeff': 70},\n", " {'attr': 'imag', 'coeff': 71},\n", " {'attr': 'imag', 'coeff': 72},\n", " {'attr': 'imag', 'coeff': 73},\n", " {'attr': 'imag', 'coeff': 74},\n", " {'attr': 'imag', 'coeff': 75},\n", " {'attr': 'imag', 'coeff': 76},\n", " {'attr': 'imag', 'coeff': 77},\n", " {'attr': 'imag', 'coeff': 78},\n", " {'attr': 'imag', 'coeff': 79},\n", " {'attr': 'imag', 'coeff': 80},\n", " {'attr': 'imag', 'coeff': 81},\n", " {'attr': 'imag', 'coeff': 82},\n", " {'attr': 'imag', 'coeff': 83},\n", " {'attr': 'imag', 'coeff': 84},\n", " {'attr': 'imag', 'coeff': 85},\n", " {'attr': 'imag', 'coeff': 86},\n", " {'attr': 'imag', 'coeff': 87},\n", " {'attr': 'imag', 'coeff': 88},\n", " {'attr': 'imag', 'coeff': 89},\n", " {'attr': 'imag', 'coeff': 90},\n", " {'attr': 'imag', 'coeff': 91},\n", " {'attr': 'imag', 'coeff': 92},\n", " {'attr': 'imag', 'coeff': 93},\n", " {'attr': 'imag', 'coeff': 94},\n", " {'attr': 'imag', 'coeff': 95},\n", " {'attr': 'imag', 'coeff': 96},\n", " {'attr': 'imag', 'coeff': 97},\n", " {'attr': 'imag', 'coeff': 98},\n", " {'attr': 'imag', 'coeff': 99}],\n", " 'first_location_of_maximum': None,\n", " 'first_location_of_minimum': None,\n", " 'friedrich_coefficients': [{'coeff': 0, 'm': 3, 'r': 30},\n", " {'coeff': 1, 'm': 3, 'r': 30},\n", " {'coeff': 2, 'm': 3, 'r': 30},\n", " {'coeff': 3, 'm': 3, 'r': 30}],\n", " 'has_duplicate': None,\n", " 'has_duplicate_max': None,\n", " 'has_duplicate_min': None,\n", " 'index_mass_quantile': [{'q': 0.1},\n", " {'q': 0.2},\n", " {'q': 0.3},\n", " {'q': 0.4},\n", " {'q': 0.6},\n", " {'q': 0.7},\n", " {'q': 0.8},\n", " {'q': 0.9}],\n", " 'kurtosis': None,\n", " 'large_standard_deviation': [{'r': 0.05},\n", " {'r': 0.1},\n", " {'r': 0.15000000000000002},\n", " {'r': 0.2},\n", " {'r': 0.25},\n", " {'r': 0.30000000000000004},\n", " {'r': 0.35000000000000003},\n", " {'r': 0.4},\n", " {'r': 0.45},\n", " {'r': 0.5},\n", " {'r': 0.55},\n", " {'r': 0.6000000000000001},\n", " {'r': 0.65},\n", " {'r': 0.7000000000000001},\n", " {'r': 0.75},\n", " {'r': 0.8},\n", " {'r': 0.8500000000000001},\n", " {'r': 0.9},\n", " {'r': 0.9500000000000001}],\n", " 'last_location_of_maximum': None,\n", " 'last_location_of_minimum': None,\n", " 'length': None,\n", " 'linear_trend': [{'attr': 'pvalue'},\n", " {'attr': 'rvalue'},\n", " {'attr': 'intercept'},\n", " {'attr': 'slope'},\n", " {'attr': 'stderr'}],\n", " 'longest_strike_above_mean': None,\n", " 'longest_strike_below_mean': None,\n", " 'max_langevin_fixed_point': [{'m': 3, 'r': 30}],\n", " 'maximum': None,\n", " 'mean': None,\n", " 'mean_abs_change': None,\n", " 'mean_change': None,\n", " 'mean_second_derivate_central': None,\n", " 'median': None,\n", " 'minimum': None,\n", " 'number_crossing_m': [{'m': 0}, {'m': -1}, {'m': 1}],\n", " 'number_cwt_peaks': [{'n': 1}, {'n': 5}],\n", " 'number_peaks': [{'n': 1}, {'n': 3}, {'n': 5}, {'n': 10}, {'n': 50}],\n", " 'percentage_of_reoccurring_datapoints_to_all_datapoints': None,\n", " 'percentage_of_reoccurring_values_to_all_values': None,\n", " 'quantile': [{'q': 0.1},\n", " {'q': 0.2},\n", " {'q': 0.3},\n", " {'q': 0.4},\n", " {'q': 0.6},\n", " {'q': 0.7},\n", " {'q': 0.8},\n", " {'q': 0.9}],\n", " 'range_count': [{'max': 1, 'min': -1}],\n", " 'ratio_value_number_to_time_series_length': None,\n", " 'sample_entropy': None,\n", " 'skewness': None,\n", " 'spkt_welch_density': [{'coeff': 2}, {'coeff': 5}, {'coeff': 8}],\n", " 'standard_deviation': None,\n", " 'sum_of_reoccurring_data_points': None,\n", " 'sum_of_reoccurring_values': None,\n", " 'sum_values': None,\n", " 'symmetry_looking': [{'r': 0.0},\n", " {'r': 0.05},\n", " {'r': 0.1},\n", " {'r': 0.15000000000000002},\n", " {'r': 0.2},\n", " {'r': 0.25},\n", " {'r': 0.30000000000000004},\n", " {'r': 0.35000000000000003},\n", " {'r': 0.4},\n", " {'r': 0.45},\n", " {'r': 0.5},\n", " {'r': 0.55},\n", " {'r': 0.6000000000000001},\n", " {'r': 0.65},\n", " {'r': 0.7000000000000001},\n", " {'r': 0.75},\n", " {'r': 0.8},\n", " {'r': 0.8500000000000001},\n", " {'r': 0.9},\n", " {'r': 0.9500000000000001}],\n", " 'time_reversal_asymmetry_statistic': [{'lag': 1}, {'lag': 2}, {'lag': 3}],\n", " 'value_count': [{'value': 0},\n", " {'value': 1},\n", " {'value': nan},\n", " {'value': inf},\n", " {'value': -inf}],\n", " 'variance': None,\n", " 'variance_larger_than_standard_deviation': None}" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "settings_comprehensive = ComprehensiveFCParameters()\n", "settings_comprehensive" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "You see those parameters as values in the fc_paramter dictionary? Those are the parameters of the feature extraction methods.\n", "\n", "In detail, the value in a fc_parameters dicitonary can contain a list of dictionaries. Every dictionary in that list is one feature.\n", "\n", "So, for example" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "[{'r': 0.05},\n", " {'r': 0.1},\n", " {'r': 0.15000000000000002},\n", " {'r': 0.2},\n", " {'r': 0.25},\n", " {'r': 0.30000000000000004},\n", " {'r': 0.35000000000000003},\n", " {'r': 0.4},\n", " {'r': 0.45},\n", " {'r': 0.5},\n", " {'r': 0.55},\n", " {'r': 0.6000000000000001},\n", " {'r': 0.65},\n", " {'r': 0.7000000000000001},\n", " {'r': 0.75},\n", " {'r': 0.8},\n", " {'r': 0.8500000000000001},\n", " {'r': 0.9},\n", " {'r': 0.9500000000000001}]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "settings_comprehensive['large_standard_deviation']" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "would trigger the calculation of 20 different 'large_standard_deviation' features, one for r=0.05, for n=0.10 up to r=0.95. Lets just take them and extract some features" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "{'large_standard_deviation': [{'r': 0.05},\n", " {'r': 0.1},\n", " {'r': 0.15000000000000002},\n", " {'r': 0.2},\n", " {'r': 0.25},\n", " {'r': 0.30000000000000004},\n", " {'r': 0.35000000000000003},\n", " {'r': 0.4},\n", " {'r': 0.45},\n", " {'r': 0.5},\n", " {'r': 0.55},\n", " {'r': 0.6000000000000001},\n", " {'r': 0.65},\n", " {'r': 0.7000000000000001},\n", " {'r': 0.75},\n", " {'r': 0.8},\n", " {'r': 0.8500000000000001},\n", " {'r': 0.9},\n", " {'r': 0.9500000000000001}]}" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "settings_value_count = {'large_standard_deviation': settings_comprehensive['large_standard_deviation']}\n", "settings_value_count" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Feature Extraction: 100%|██████████| 4/4 [00:00<00:00, 772.36it/s]\n" ] }, { "data": { "text/html": [ "
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2 rows × 38 columns

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" ], "text/plain": [ "variable pressure__large_standard_deviation__r_0.05 \\\n", "id \n", "a 1.0 \n", "b 1.0 \n", "\n", "variable pressure__large_standard_deviation__r_0.1 \\\n", "id \n", "a 1.0 \n", "b 1.0 \n", "\n", "variable pressure__large_standard_deviation__r_0.15 \\\n", "id \n", "a 1.0 \n", "b 1.0 \n", "\n", "variable pressure__large_standard_deviation__r_0.2 \\\n", "id \n", "a 1.0 \n", "b 1.0 \n", "\n", "variable pressure__large_standard_deviation__r_0.25 \\\n", "id \n", "a 1.0 \n", "b 1.0 \n", "\n", "variable pressure__large_standard_deviation__r_0.3 \\\n", "id \n", "a 1.0 \n", "b 1.0 \n", "\n", "variable pressure__large_standard_deviation__r_0.35 \\\n", "id \n", "a 1.0 \n", "b 1.0 \n", "\n", "variable pressure__large_standard_deviation__r_0.4 \\\n", "id \n", "a 1.0 \n", "b 1.0 \n", "\n", "variable pressure__large_standard_deviation__r_0.45 \\\n", "id \n", "a 1.0 \n", "b 1.0 \n", "\n", "variable pressure__large_standard_deviation__r_0.5 \\\n", "id \n", "a 0.0 \n", "b 0.0 \n", "\n", "variable ... \\\n", "id ... \n", "a ... \n", "b ... \n", "\n", "variable temperature__large_standard_deviation__r_0.5 \\\n", "id \n", "a 0.0 \n", "b 0.0 \n", "\n", "variable temperature__large_standard_deviation__r_0.55 \\\n", "id \n", "a 0.0 \n", "b 0.0 \n", "\n", "variable temperature__large_standard_deviation__r_0.6 \\\n", "id \n", "a 0.0 \n", "b 0.0 \n", "\n", "variable temperature__large_standard_deviation__r_0.65 \\\n", "id \n", "a 0.0 \n", "b 0.0 \n", "\n", "variable temperature__large_standard_deviation__r_0.7 \\\n", "id \n", "a 0.0 \n", "b 0.0 \n", "\n", "variable temperature__large_standard_deviation__r_0.75 \\\n", "id \n", "a 0.0 \n", "b 0.0 \n", "\n", "variable temperature__large_standard_deviation__r_0.8 \\\n", "id \n", "a 0.0 \n", "b 0.0 \n", "\n", "variable temperature__large_standard_deviation__r_0.85 \\\n", "id \n", "a 0.0 \n", "b 0.0 \n", "\n", "variable temperature__large_standard_deviation__r_0.9 \\\n", "id \n", "a 0.0 \n", "b 0.0 \n", "\n", "variable temperature__large_standard_deviation__r_0.95 \n", "id \n", "a 0.0 \n", "b 0.0 \n", "\n", "[2 rows x 38 columns]" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_tsfresh = extract_features(df, column_id=\"id\", default_fc_parameters=settings_value_count)\n", "X_tsfresh.head()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "The nice thing is, we actually contain the parameters in the feature name, so it is possible to reconstruct \n", "how the feature was calculated." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "{'pressure': {'large_standard_deviation': [{'r': 0.05},\n", " {'r': 0.1},\n", " {'r': 0.15},\n", " {'r': 0.2},\n", " {'r': 0.25},\n", " {'r': 0.3},\n", " {'r': 0.35},\n", " {'r': 0.4},\n", " {'r': 0.45},\n", " {'r': 0.5},\n", " {'r': 0.55},\n", " {'r': 0.6},\n", " {'r': 0.65},\n", " {'r': 0.7},\n", " {'r': 0.75},\n", " {'r': 0.8},\n", " {'r': 0.85},\n", " {'r': 0.9},\n", " {'r': 0.95}]},\n", " 'temperature': {'large_standard_deviation': [{'r': 0.05},\n", " {'r': 0.1},\n", " {'r': 0.15},\n", " {'r': 0.2},\n", " {'r': 0.25},\n", " {'r': 0.3},\n", " {'r': 0.35},\n", " {'r': 0.4},\n", " {'r': 0.45},\n", " {'r': 0.5},\n", " {'r': 0.55},\n", " {'r': 0.6},\n", " {'r': 0.65},\n", " {'r': 0.7},\n", " {'r': 0.75},\n", " {'r': 0.8},\n", " {'r': 0.85},\n", " {'r': 0.9},\n", " {'r': 0.95}]}}" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from_columns(X_tsfresh)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "This means that you should never change a column name. Otherwise the information how it was calculated can get lost." ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 2 }