%matplotlib inline
import matplotlib.pylab as plt
import seaborn as sns
from tsfresh.examples.robot_execution_failures import download_robot_execution_failures, load_robot_execution_failures
from tsfresh import extract_features, extract_relevant_features, select_features
from tsfresh.utilities.dataframe_functions import impute
from tsfresh.feature_extraction import ComprehensiveFCParameters
from sklearn.tree import DecisionTreeClassifier
from sklearn.cross_validation import train_test_split
from sklearn.metrics import classification_report
#http://tsfresh.readthedocs.io/en/latest/text/quick_start.html
download_robot_execution_failures()
df, y = load_robot_execution_failures()
df.head()
df[df.id==3][['time','a','b','c','d','e','f']].plot(x='time', title='Success example (id 3)', figsize=(12,6));
df[df.id==20][['time','a','b','c','d','e','f']].plot(x='time', title='Failure example (id 20)', figsize=(12,6));
extraction_settings = ComprehensiveFCParameters()
#column_id (str) – The name of the id column to group by#column_sort (str) – The name of the sort column.
X = extract_features(df,
column_id='id', column_sort='time',
default_fc_parameters=extraction_settings,
impute_function= impute)
X_filtered = extract_relevant_features(df, y,
column_id='id', column_sort='time',
default_fc_parameters=extraction_settings)