Class Dynamic Feature -> Ts_extraction

transplant/features/dynamic_features.py

@@ -0,0 +1,55 @@
+import pandas as pd
+import json
+import os
+import pandas as pd
+import seaborn as sns
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+class ts_extraction:
+	"""
+	Compute features on TS for the dynamic dataset
+
+	Input :
+		- df, dynamic Dataset
+	Output :
+		- df, with  new features (prefix _tresh)
+
+	"""
+	def __init__(self, df):
+		self.df = df
+
+	def f_treshold(self, treshold_json):
+
+		for i in treshold_json.keys():
+
+ 			tjson = treshold_json[i]
+ 			name_new_feature = i
+ 			serie = self.df[i]
+ 			cond1 = (serie >= tjson["etendue"][0] ) & (serie < tjson["normalite"][0])
+ 			cond2 = (serie <= tjson["etendue"][1] ) & (serie > tjson["normalite"][1])
+
+ 			self.df[i + "_abnormal_tresh"] = np.where(cond1 | cond2,  1, 0)
+ 			self.df[i + "_clean_tresh"] = np.where((serie >= tjson["etendue"][0]) & (serie <= tjson["etendue"][1] ),  1, 0)
+ 			self.df[i + "_normal_tresh"] = np.where((serie >= tjson["normalite"][0]) & (serie <= tjson["normalite"][1] ),  1, 0)
+ 			self.df[i + "_dirty_tresh"] = np.where((serie <= tjson["etendue"][0]) & ( serie >= tjson["etendue"][1] ),  1, 0)
+
+	def plot_tresh(self, treshold_json ,id_patient = 100, var = "Pmean", title = '') :
+
+
+		serie = self.df[self.df.id_patient == id_patient ][var]
+
+		y = serie.values
+		x = serie.reset_index().index
+
+		fig, ax, = plt.subplots(1, 1, sharex=True)
+		ax.plot(x, y , color='black')
+
+		cond1 =  (y <= treshold_json[var]["normalite"][0])
+		cond2 = (y >= treshold_json[var]["normalite"][1])
+
+		ax.fill_between(x, y, treshold_json[var]["normalite"][0],  where = cond1, facecolor='red', interpolate=True)
+		ax.fill_between(x, y, treshold_json[var]["normalite"][1],  where = cond2, facecolor='red', interpolate=True)
+		ax.set_title(title)
+		plt.show()