{
"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": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>pressure</th>\n",
" <th>temperature</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>a</td>\n",
" <td>-1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>a</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>b</td>\n",
" <td>-1</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>b</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"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": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
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" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>variable</th>\n",
" <th>pressure__length</th>\n",
" <th>pressure__maximum</th>\n",
" <th>pressure__mean</th>\n",
" <th>pressure__median</th>\n",
" <th>pressure__minimum</th>\n",
" <th>pressure__standard_deviation</th>\n",
" <th>pressure__sum_values</th>\n",
" <th>pressure__variance</th>\n",
" <th>temperature__length</th>\n",
" <th>temperature__maximum</th>\n",
" <th>temperature__mean</th>\n",
" <th>temperature__median</th>\n",
" <th>temperature__minimum</th>\n",
" <th>temperature__standard_deviation</th>\n",
" <th>temperature__sum_values</th>\n",
" <th>temperature__variance</th>\n",
" </tr>\n",
" <tr>\n",
" <th>id</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
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" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
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" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>a</th>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>0.5</td>\n",
" <td>0.5</td>\n",
" <td>-1.0</td>\n",
" <td>1.5</td>\n",
" <td>1.0</td>\n",
" <td>2.25</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>1.5</td>\n",
" <td>1.5</td>\n",
" <td>1.0</td>\n",
" <td>0.5</td>\n",
" <td>3.0</td>\n",
" <td>0.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b</th>\n",
" <td>2.0</td>\n",
" <td>7.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>-1.0</td>\n",
" <td>4.0</td>\n",
" <td>6.0</td>\n",
" <td>16.00</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>4.0</td>\n",
" <td>1.00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"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": [
"<div>\n",
"<style>\n",
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"\n",
" .dataframe tbody tr th {\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>variable</th>\n",
" <th>pressure__maximum</th>\n",
" <th>pressure__mean</th>\n",
" <th>pressure__median</th>\n",
" <th>pressure__minimum</th>\n",
" <th>pressure__standard_deviation</th>\n",
" <th>pressure__sum_values</th>\n",
" <th>pressure__variance</th>\n",
" <th>temperature__maximum</th>\n",
" <th>temperature__mean</th>\n",
" <th>temperature__median</th>\n",
" <th>temperature__minimum</th>\n",
" <th>temperature__standard_deviation</th>\n",
" <th>temperature__sum_values</th>\n",
" <th>temperature__variance</th>\n",
" </tr>\n",
" <tr>\n",
" <th>id</th>\n",
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" <th></th>\n",
" <th></th>\n",
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" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>a</th>\n",
" <td>2.0</td>\n",
" <td>0.5</td>\n",
" <td>0.5</td>\n",
" <td>-1.0</td>\n",
" <td>1.5</td>\n",
" <td>1.0</td>\n",
" <td>2.25</td>\n",
" <td>2.0</td>\n",
" <td>1.5</td>\n",
" <td>1.5</td>\n",
" <td>1.0</td>\n",
" <td>0.5</td>\n",
" <td>3.0</td>\n",
" <td>0.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b</th>\n",
" <td>7.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>-1.0</td>\n",
" <td>4.0</td>\n",
" <td>6.0</td>\n",
" <td>16.00</td>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>4.0</td>\n",
" <td>1.00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"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": [
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" <tr style=\"text-align: right;\">\n",
" <th>variable</th>\n",
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" <th>pressure__sum_values</th>\n",
" <th>temperature__maximum</th>\n",
" <th>temperature__minimum</th>\n",
" </tr>\n",
" <tr>\n",
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" <th>a</th>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b</th>\n",
" <td>2.0</td>\n",
" <td>6.0</td>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"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": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>variable</th>\n",
" <th>pressure__sum_values</th>\n",
" <th>temperature__maximum</th>\n",
" <th>temperature__minimum</th>\n",
" </tr>\n",
" <tr>\n",
" <th>id</th>\n",
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" <td>1.0</td>\n",
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" <tr>\n",
" <th>b</th>\n",
" <td>6.0</td>\n",
" <td>3.0</td>\n",
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"</table>\n",
"</div>"
],
"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",
" {'f_agg': 'var', 'isabs': False, 'qh': 0.2, 'ql': 0.0},\n",
" {'f_agg': 'mean', 'isabs': True, 'qh': 0.2, 'ql': 0.0},\n",
" {'f_agg': 'var', 'isabs': True, 'qh': 0.2, 'ql': 0.0},\n",
" {'f_agg': 'mean', 'isabs': False, 'qh': 0.4, 'ql': 0.0},\n",
" {'f_agg': 'var', 'isabs': False, 'qh': 0.4, 'ql': 0.0},\n",
" {'f_agg': 'mean', 'isabs': True, 'qh': 0.4, 'ql': 0.0},\n",
" {'f_agg': 'var', 'isabs': True, 'qh': 0.4, 'ql': 0.0},\n",
" {'f_agg': 'mean', 'isabs': False, 'qh': 0.6, 'ql': 0.0},\n",
" {'f_agg': 'var', 'isabs': False, 'qh': 0.6, 'ql': 0.0},\n",
" {'f_agg': 'mean', 'isabs': True, 'qh': 0.6, 'ql': 0.0},\n",
" {'f_agg': 'var', 'isabs': True, 'qh': 0.6, 'ql': 0.0},\n",
" {'f_agg': 'mean', 'isabs': False, 'qh': 0.8, 'ql': 0.0},\n",
" {'f_agg': 'var', 'isabs': False, 'qh': 0.8, 'ql': 0.0},\n",
" {'f_agg': 'mean', 'isabs': True, 'qh': 0.8, 'ql': 0.0},\n",
" {'f_agg': 'var', 'isabs': True, 'qh': 0.8, 'ql': 0.0},\n",
" {'f_agg': 'mean', 'isabs': False, 'qh': 1.0, 'ql': 0.0},\n",
" {'f_agg': 'var', 'isabs': False, 'qh': 1.0, 'ql': 0.0},\n",
" {'f_agg': 'mean', 'isabs': True, 'qh': 1.0, 'ql': 0.0},\n",
" {'f_agg': 'var', 'isabs': True, 'qh': 1.0, 'ql': 0.0},\n",
" {'f_agg': 'mean', 'isabs': False, 'qh': 0.2, 'ql': 0.2},\n",
" {'f_agg': 'var', 'isabs': False, 'qh': 0.2, 'ql': 0.2},\n",
" {'f_agg': 'mean', 'isabs': True, 'qh': 0.2, 'ql': 0.2},\n",
" {'f_agg': 'var', 'isabs': True, 'qh': 0.2, 'ql': 0.2},\n",
" {'f_agg': 'mean', 'isabs': False, 'qh': 0.4, 'ql': 0.2},\n",
" {'f_agg': 'var', 'isabs': False, 'qh': 0.4, 'ql': 0.2},\n",
" {'f_agg': 'mean', 'isabs': True, 'qh': 0.4, 'ql': 0.2},\n",
" {'f_agg': 'var', 'isabs': True, 'qh': 0.4, 'ql': 0.2},\n",
" {'f_agg': 'mean', 'isabs': False, 'qh': 0.6, 'ql': 0.2},\n",
" {'f_agg': 'var', 'isabs': False, 'qh': 0.6, 'ql': 0.2},\n",
" {'f_agg': 'mean', 'isabs': True, 'qh': 0.6, 'ql': 0.2},\n",
" {'f_agg': 'var', 'isabs': True, 'qh': 0.6, 'ql': 0.2},\n",
" {'f_agg': 'mean', 'isabs': False, 'qh': 0.8, 'ql': 0.2},\n",
" {'f_agg': 'var', 'isabs': False, 'qh': 0.8, 'ql': 0.2},\n",
" {'f_agg': 'mean', 'isabs': True, 'qh': 0.8, 'ql': 0.2},\n",
" {'f_agg': 'var', 'isabs': True, 'qh': 0.8, 'ql': 0.2},\n",
" {'f_agg': 'mean', 'isabs': False, 'qh': 1.0, 'ql': 0.2},\n",
" {'f_agg': 'var', 'isabs': False, 'qh': 1.0, 'ql': 0.2},\n",
" {'f_agg': 'mean', 'isabs': True, 'qh': 1.0, 'ql': 0.2},\n",
" {'f_agg': 'var', 'isabs': True, 'qh': 1.0, 'ql': 0.2},\n",
" {'f_agg': 'mean', 'isabs': False, 'qh': 0.2, 'ql': 0.4},\n",
" {'f_agg': 'var', 'isabs': False, 'qh': 0.2, 'ql': 0.4},\n",
" {'f_agg': 'mean', 'isabs': True, 'qh': 0.2, 'ql': 0.4},\n",
" {'f_agg': 'var', 'isabs': True, 'qh': 0.2, 'ql': 0.4},\n",
" {'f_agg': 'mean', 'isabs': False, 'qh': 0.4, 'ql': 0.4},\n",
" {'f_agg': 'var', 'isabs': False, 'qh': 0.4, 'ql': 0.4},\n",
" {'f_agg': 'mean', 'isabs': True, 'qh': 0.4, 'ql': 0.4},\n",
" {'f_agg': 'var', 'isabs': True, 'qh': 0.4, 'ql': 0.4},\n",
" {'f_agg': 'mean', 'isabs': False, 'qh': 0.6, 'ql': 0.4},\n",
" {'f_agg': 'var', 'isabs': False, 'qh': 0.6, 'ql': 0.4},\n",
" {'f_agg': 'mean', 'isabs': True, 'qh': 0.6, 'ql': 0.4},\n",
" {'f_agg': 'var', 'isabs': True, 'qh': 0.6, 'ql': 0.4},\n",
" {'f_agg': 'mean', 'isabs': False, 'qh': 0.8, 'ql': 0.4},\n",
" {'f_agg': 'var', 'isabs': False, 'qh': 0.8, 'ql': 0.4},\n",
" {'f_agg': 'mean', 'isabs': True, 'qh': 0.8, 'ql': 0.4},\n",
" {'f_agg': 'var', 'isabs': True, 'qh': 0.8, 'ql': 0.4},\n",
" {'f_agg': 'mean', 'isabs': False, 'qh': 1.0, 'ql': 0.4},\n",
" {'f_agg': 'var', 'isabs': False, 'qh': 1.0, 'ql': 0.4},\n",
" {'f_agg': 'mean', 'isabs': True, 'qh': 1.0, 'ql': 0.4},\n",
" {'f_agg': 'var', 'isabs': True, 'qh': 1.0, 'ql': 0.4},\n",
" {'f_agg': 'mean', 'isabs': False, 'qh': 0.2, 'ql': 0.6},\n",
" {'f_agg': 'var', 'isabs': False, 'qh': 0.2, 'ql': 0.6},\n",
" {'f_agg': 'mean', 'isabs': True, 'qh': 0.2, 'ql': 0.6},\n",
" {'f_agg': 'var', 'isabs': True, 'qh': 0.2, 'ql': 0.6},\n",
" {'f_agg': 'mean', 'isabs': False, 'qh': 0.4, 'ql': 0.6},\n",
" {'f_agg': 'var', 'isabs': False, 'qh': 0.4, 'ql': 0.6},\n",
" {'f_agg': 'mean', 'isabs': True, 'qh': 0.4, 'ql': 0.6},\n",
" {'f_agg': 'var', 'isabs': True, 'qh': 0.4, 'ql': 0.6},\n",
" {'f_agg': 'mean', 'isabs': False, 'qh': 0.6, 'ql': 0.6},\n",
" {'f_agg': 'var', 'isabs': False, 'qh': 0.6, 'ql': 0.6},\n",
" {'f_agg': 'mean', 'isabs': True, 'qh': 0.6, 'ql': 0.6},\n",
" {'f_agg': 'var', 'isabs': True, 'qh': 0.6, 'ql': 0.6},\n",
" {'f_agg': 'mean', 'isabs': False, 'qh': 0.8, 'ql': 0.6},\n",
" {'f_agg': 'var', 'isabs': False, 'qh': 0.8, 'ql': 0.6},\n",
" {'f_agg': 'mean', 'isabs': True, 'qh': 0.8, 'ql': 0.6},\n",
" {'f_agg': 'var', 'isabs': True, 'qh': 0.8, 'ql': 0.6},\n",
" {'f_agg': 'mean', 'isabs': False, 'qh': 1.0, 'ql': 0.6},\n",
" {'f_agg': 'var', 'isabs': False, 'qh': 1.0, 'ql': 0.6},\n",
" {'f_agg': 'mean', 'isabs': True, 'qh': 1.0, 'ql': 0.6},\n",
" {'f_agg': 'var', 'isabs': True, 'qh': 1.0, 'ql': 0.6},\n",
" {'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': 90},\n",
" {'attr': 'real', 'coeff': 91},\n",
" {'attr': 'real', 'coeff': 92},\n",
" {'attr': 'real', 'coeff': 93},\n",
" {'attr': 'real', 'coeff': 94},\n",
" {'attr': 'real', 'coeff': 95},\n",
" {'attr': 'real', 'coeff': 96},\n",
" {'attr': 'real', 'coeff': 97},\n",
" {'attr': 'real', 'coeff': 98},\n",
" {'attr': 'real', 'coeff': 99},\n",
" {'attr': 'imag', 'coeff': 0},\n",
" {'attr': 'imag', 'coeff': 1},\n",
" {'attr': 'imag', 'coeff': 2},\n",
" {'attr': 'imag', 'coeff': 3},\n",
" {'attr': 'imag', 'coeff': 4},\n",
" {'attr': 'imag', 'coeff': 5},\n",
" {'attr': 'imag', 'coeff': 6},\n",
" {'attr': 'imag', 'coeff': 7},\n",
" {'attr': 'imag', 'coeff': 8},\n",
" {'attr': 'imag', 'coeff': 9},\n",
" {'attr': 'imag', 'coeff': 10},\n",
" {'attr': 'imag', 'coeff': 11},\n",
" {'attr': 'imag', 'coeff': 12},\n",
" {'attr': 'imag', 'coeff': 13},\n",
" {'attr': 'imag', 'coeff': 14},\n",
" {'attr': 'imag', 'coeff': 15},\n",
" {'attr': 'imag', 'coeff': 16},\n",
" {'attr': 'imag', 'coeff': 17},\n",
" {'attr': 'imag', 'coeff': 18},\n",
" {'attr': 'imag', 'coeff': 19},\n",
" {'attr': 'imag', 'coeff': 20},\n",
" {'attr': 'imag', 'coeff': 21},\n",
" {'attr': 'imag', 'coeff': 22},\n",
" {'attr': 'imag', 'coeff': 23},\n",
" {'attr': 'imag', 'coeff': 24},\n",
" {'attr': 'imag', 'coeff': 25},\n",
" {'attr': 'imag', 'coeff': 26},\n",
" {'attr': 'imag', 'coeff': 27},\n",
" {'attr': 'imag', 'coeff': 28},\n",
" {'attr': 'imag', 'coeff': 29},\n",
" {'attr': 'imag', 'coeff': 30},\n",
" {'attr': 'imag', 'coeff': 31},\n",
" {'attr': 'imag', 'coeff': 32},\n",
" {'attr': 'imag', 'coeff': 33},\n",
" {'attr': 'imag', 'coeff': 34},\n",
" {'attr': 'imag', 'coeff': 35},\n",
" {'attr': 'imag', 'coeff': 36},\n",
" {'attr': 'imag', 'coeff': 37},\n",
" {'attr': 'imag', 'coeff': 38},\n",
" {'attr': 'imag', 'coeff': 39},\n",
" {'attr': 'imag', 'coeff': 40},\n",
" {'attr': 'imag', 'coeff': 41},\n",
" {'attr': 'imag', 'coeff': 42},\n",
" {'attr': 'imag', 'coeff': 43},\n",
" {'attr': 'imag', 'coeff': 44},\n",
" {'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",
" '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': [{'f_agg': 'mean', 'isabs': False, 'qh': 0.2, 'ql': 0.0},\n",
" {'f_agg': 'var', 'isabs': False, 'qh': 0.2, 'ql': 0.0},\n",
" {'f_agg': 'mean', 'isabs': True, 'qh': 0.2, 'ql': 0.0},\n",
" {'f_agg': 'var', 'isabs': True, 'qh': 0.2, 'ql': 0.0},\n",
" {'f_agg': 'mean', 'isabs': False, 'qh': 0.4, 'ql': 0.0},\n",
" {'f_agg': 'var', 'isabs': False, 'qh': 0.4, 'ql': 0.0},\n",
" {'f_agg': 'mean', 'isabs': True, 'qh': 0.4, 'ql': 0.0},\n",
" {'f_agg': 'var', 'isabs': True, 'qh': 0.4, 'ql': 0.0},\n",
" {'f_agg': 'mean', 'isabs': False, 'qh': 0.6, 'ql': 0.0},\n",
" {'f_agg': 'var', 'isabs': False, 'qh': 0.6, 'ql': 0.0},\n",
" {'f_agg': 'mean', 'isabs': True, 'qh': 0.6, 'ql': 0.0},\n",
" {'f_agg': 'var', 'isabs': True, 'qh': 0.6, 'ql': 0.0},\n",
" {'f_agg': 'mean', 'isabs': False, 'qh': 0.8, 'ql': 0.0},\n",
" {'f_agg': 'var', 'isabs': False, 'qh': 0.8, 'ql': 0.0},\n",
" {'f_agg': 'mean', 'isabs': True, 'qh': 0.8, 'ql': 0.0},\n",
" {'f_agg': 'var', 'isabs': True, 'qh': 0.8, 'ql': 0.0},\n",
" {'f_agg': 'mean', 'isabs': False, 'qh': 1.0, 'ql': 0.0},\n",
" {'f_agg': 'var', 'isabs': False, 'qh': 1.0, 'ql': 0.0},\n",
" {'f_agg': 'mean', 'isabs': True, 'qh': 1.0, 'ql': 0.0},\n",
" {'f_agg': 'var', 'isabs': True, 'qh': 1.0, 'ql': 0.0},\n",
" {'f_agg': 'mean', 'isabs': False, 'qh': 0.2, 'ql': 0.2},\n",
" {'f_agg': 'var', 'isabs': False, 'qh': 0.2, 'ql': 0.2},\n",
" {'f_agg': 'mean', 'isabs': True, 'qh': 0.2, 'ql': 0.2},\n",
" {'f_agg': 'var', 'isabs': True, 'qh': 0.2, 'ql': 0.2},\n",
" {'f_agg': 'mean', 'isabs': False, 'qh': 0.4, 'ql': 0.2},\n",
" {'f_agg': 'var', 'isabs': False, 'qh': 0.4, 'ql': 0.2},\n",
" {'f_agg': 'mean', 'isabs': True, 'qh': 0.4, 'ql': 0.2},\n",
" {'f_agg': 'var', 'isabs': True, 'qh': 0.4, 'ql': 0.2},\n",
" {'f_agg': 'mean', 'isabs': False, 'qh': 0.6, 'ql': 0.2},\n",
" {'f_agg': 'var', 'isabs': False, 'qh': 0.6, 'ql': 0.2},\n",
" {'f_agg': 'mean', 'isabs': True, 'qh': 0.6, 'ql': 0.2},\n",
" {'f_agg': 'var', 'isabs': True, 'qh': 0.6, 'ql': 0.2},\n",
" {'f_agg': 'mean', 'isabs': False, 'qh': 0.8, 'ql': 0.2},\n",
" {'f_agg': 'var', 'isabs': False, 'qh': 0.8, 'ql': 0.2},\n",
" {'f_agg': 'mean', 'isabs': True, 'qh': 0.8, 'ql': 0.2},\n",
" {'f_agg': 'var', 'isabs': True, 'qh': 0.8, 'ql': 0.2},\n",
" {'f_agg': 'mean', 'isabs': False, 'qh': 1.0, 'ql': 0.2},\n",
" {'f_agg': 'var', 'isabs': False, 'qh': 1.0, 'ql': 0.2},\n",
" {'f_agg': 'mean', 'isabs': True, 'qh': 1.0, 'ql': 0.2},\n",
" {'f_agg': 'var', 'isabs': True, 'qh': 1.0, 'ql': 0.2},\n",
" {'f_agg': 'mean', 'isabs': False, 'qh': 0.2, 'ql': 0.4},\n",
" {'f_agg': 'var', 'isabs': False, 'qh': 0.2, 'ql': 0.4},\n",
" {'f_agg': 'mean', 'isabs': True, 'qh': 0.2, 'ql': 0.4},\n",
" {'f_agg': 'var', 'isabs': True, 'qh': 0.2, 'ql': 0.4},\n",
" {'f_agg': 'mean', 'isabs': False, 'qh': 0.4, 'ql': 0.4},\n",
" {'f_agg': 'var', 'isabs': False, 'qh': 0.4, 'ql': 0.4},\n",
" {'f_agg': 'mean', 'isabs': True, 'qh': 0.4, 'ql': 0.4},\n",
" {'f_agg': 'var', 'isabs': True, 'qh': 0.4, 'ql': 0.4},\n",
" {'f_agg': 'mean', 'isabs': False, 'qh': 0.6, 'ql': 0.4},\n",
" {'f_agg': 'var', 'isabs': False, 'qh': 0.6, 'ql': 0.4},\n",
" {'f_agg': 'mean', 'isabs': True, 'qh': 0.6, 'ql': 0.4},\n",
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" {'f_agg': 'mean', 'isabs': False, 'qh': 0.8, 'ql': 0.4},\n",
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" '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",
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" {'coeff': 4, 'w': 10, 'widths': (2, 5, 10, 20)},\n",
" {'coeff': 4, 'w': 20, 'widths': (2, 5, 10, 20)},\n",
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" {'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",
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" {'coeff': 6, 'w': 10, 'widths': (2, 5, 10, 20)},\n",
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" {'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",
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" {'coeff': 8, 'w': 10, 'widths': (2, 5, 10, 20)},\n",
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" {'coeff': 9, 'w': 20, 'widths': (2, 5, 10, 20)},\n",
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" {'coeff': 10, 'w': 10, 'widths': (2, 5, 10, 20)},\n",
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" {'coeff': 11, 'w': 10, 'widths': (2, 5, 10, 20)},\n",
" {'coeff': 11, 'w': 20, 'widths': (2, 5, 10, 20)},\n",
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" {'coeff': 12, 'w': 10, 'widths': (2, 5, 10, 20)},\n",
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" {'coeff': 13, 'w': 10, 'widths': (2, 5, 10, 20)},\n",
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" {'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",
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" {'attr': 'real', 'coeff': 7},\n",
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" {'attr': 'real', 'coeff': 10},\n",
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" {'attr': 'real', 'coeff': 14},\n",
" {'attr': 'real', 'coeff': 15},\n",
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" {'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': 90},\n",
" {'attr': 'real', 'coeff': 91},\n",
" {'attr': 'real', 'coeff': 92},\n",
" {'attr': 'real', 'coeff': 93},\n",
" {'attr': 'real', 'coeff': 94},\n",
" {'attr': 'real', 'coeff': 95},\n",
" {'attr': 'real', 'coeff': 96},\n",
" {'attr': 'real', 'coeff': 97},\n",
" {'attr': 'real', 'coeff': 98},\n",
" {'attr': 'real', 'coeff': 99},\n",
" {'attr': 'imag', 'coeff': 0},\n",
" {'attr': 'imag', 'coeff': 1},\n",
" {'attr': 'imag', 'coeff': 2},\n",
" {'attr': 'imag', 'coeff': 3},\n",
" {'attr': 'imag', 'coeff': 4},\n",
" {'attr': 'imag', 'coeff': 5},\n",
" {'attr': 'imag', 'coeff': 6},\n",
" {'attr': 'imag', 'coeff': 7},\n",
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" {'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",
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" 'has_duplicate': None,\n",
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" {'q': 0.2},\n",
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" {'q': 0.6},\n",
" {'q': 0.7},\n",
" {'q': 0.8},\n",
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" 'kurtosis': None,\n",
" 'large_standard_deviation': [{'r': 0.05},\n",
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" '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",
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" '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": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
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" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>variable</th>\n",
" <th>pressure__large_standard_deviation__r_0.05</th>\n",
" <th>pressure__large_standard_deviation__r_0.1</th>\n",
" <th>pressure__large_standard_deviation__r_0.15</th>\n",
" <th>pressure__large_standard_deviation__r_0.2</th>\n",
" <th>pressure__large_standard_deviation__r_0.25</th>\n",
" <th>pressure__large_standard_deviation__r_0.3</th>\n",
" <th>pressure__large_standard_deviation__r_0.35</th>\n",
" <th>pressure__large_standard_deviation__r_0.4</th>\n",
" <th>pressure__large_standard_deviation__r_0.45</th>\n",
" <th>pressure__large_standard_deviation__r_0.5</th>\n",
" <th>...</th>\n",
" <th>temperature__large_standard_deviation__r_0.5</th>\n",
" <th>temperature__large_standard_deviation__r_0.55</th>\n",
" <th>temperature__large_standard_deviation__r_0.6</th>\n",
" <th>temperature__large_standard_deviation__r_0.65</th>\n",
" <th>temperature__large_standard_deviation__r_0.7</th>\n",
" <th>temperature__large_standard_deviation__r_0.75</th>\n",
" <th>temperature__large_standard_deviation__r_0.8</th>\n",
" <th>temperature__large_standard_deviation__r_0.85</th>\n",
" <th>temperature__large_standard_deviation__r_0.9</th>\n",
" <th>temperature__large_standard_deviation__r_0.95</th>\n",
" </tr>\n",
" <tr>\n",
" <th>id</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>a</th>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b</th>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2 rows × 38 columns</p>\n",
"</div>"
],
"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
}