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Making the Explainer work with both classification and regression models #2

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165 changes: 110 additions & 55 deletions pyBreakDown/explainer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,6 +2,8 @@
from collections import deque
from blist import blist
from pyBreakDown import explanation as e
from tqdm import tqdm


class Explainer:
"""
Expand All @@ -17,17 +19,19 @@ class Explainer:
Dataset feature names.
"""
def __init__(self, clf, data, colnames):
assert len(colnames) == data.shape[1] #otherwise it wouldnt make any sense
assert len(colnames) == data.shape[1] # otherwise it wouldn't make any sense
self.clf = clf
self.data = data
self.colnames = colnames

def _transform_observation (self, observation):
@staticmethod
def _transform_observation (observation):
if observation.ndim < 2:
observation = np.expand_dims(observation, axis=0)
return observation

def _get_initial_dataset(self, observation, data):
@staticmethod
def _get_initial_dataset(observation, data):
assert observation.ndim == 2 and observation.shape[0] == 1
return np.repeat(observation,repeats=data.shape[0], axis=0)

Expand Down Expand Up @@ -55,16 +59,19 @@ def explain (self, observation, direction, useIntercept = False, baseline=0):

"""
data = np.copy(self.data)
assert direction in ["up","down"]
observation = self._transform_observation(observation) #expand dims from 1D to 2D if necessary
assert direction in ["up", "down"]
observation = self._transform_observation(observation) # expand dims from 1D to 2D if necessary
assert len(self.colnames) == observation.shape[1]

if direction=="up":
exp = self._explain_up(observation, baseline, data)
if direction=="down":
exp = self._explain_down(observation, baseline, data)
if direction == "up":
exp = self._explain_up(observation, data)
else:
exp = self._explain_down(observation, data)

mean_prediction = np.mean(self.clf.predict(data))
if hasattr(self.clf, 'predict_proba'):
mean_prediction = np.mean(self.clf.predict_proba(data)[:, 0])
else:
mean_prediction = np.mean(self.clf.predict(data))

if useIntercept:
baseline = mean_prediction
Expand All @@ -77,63 +84,110 @@ def explain (self, observation, direction, useIntercept = False, baseline=0):
exp.make_final_prediction()
return exp


def _explain_up (self, observation, baseline, data):
def _explain_up(self, observation, data):
new_data = self._get_initial_dataset(observation, data)

baseline_yhat = np.mean(self.clf.predict(data))

open_variables = blist(range(0,data.shape[1]))
important_variables = deque()
important_yhats = {}

for i in range(0, data.shape[1]):
yhats = {}
yhats_diff = np.repeat(-float('inf'), data.shape[1])

for variable in open_variables:
tmp_data = np.copy(data)
tmp_data[:,variable] = new_data[:,variable]
yhats[variable] = self.clf.predict(tmp_data)
yhats_diff[variable] = abs(baseline_yhat - np.mean(yhats[variable]))

amax = np.argmax(yhats_diff)
important_variables.append(amax)
important_yhats[i] = yhats[amax]
data[:,amax] = new_data[:,amax]
open_variables.remove(amax)
if hasattr(self.clf, 'predict_proba'):
baseline_yhat = np.mean(self.clf.predict_proba(data)[:, 0])
open_variables = blist(range(0, data.shape[1]))
important_variables = deque()
important_yhats = {}
temp = np.copy(data)

for i in tqdm(range(0, data.shape[1])):
yhats = {}
yhats_diff = np.repeat([-float('inf')], data.shape[1])

for variable in open_variables:
data[:, variable] = new_data[:, variable]
yhats[variable] = self.clf.predict_proba(data)[:, 0]
yhats_diff[variable] = abs(baseline_yhat - np.mean(yhats[variable]))
data[:, variable]=temp[:, variable]

amax = np.argmax(yhats_diff)
important_variables.append(amax)
important_yhats[i] = yhats[amax]
data[:, amax] = new_data[:, amax]
open_variables.remove(amax)
else:
baseline_yhat = np.mean(self.clf.predict(data))
open_variables = blist(range(0, data.shape[1]))
important_variables = deque()
important_yhats = {}
temp = np.copy(data)

for i in tqdm(range(0, data.shape[1])):
yhats = {}
yhats_diff = np.repeat([-float('inf')], data.shape[1])

for variable in open_variables:
data[:,variable] = data[:,variable]
yhats[variable] = self.clf.predict(data)[:, 0]
yhats_diff[variable] = abs(baseline_yhat - np.mean(yhats[variable]))
data[:, variable] = temp[:, variable]

amax = np.argmax(yhats_diff)
important_variables.append(amax)
important_yhats[i] = yhats[amax]
data[:,amax] = new_data[:,amax]
open_variables.remove(amax)

var_names = np.array(self.colnames)[important_variables]
var_values = observation[0,important_variables]
var_values = observation[0, important_variables]
means = self._get_means_from_yhats(important_yhats)
means.appendleft(baseline_yhat)
contributions = np.diff(means)
return e.Explanation(var_names, var_values, contributions, e.ExplainerDirection.Up)

def _explain_down (self, observation, baseline, data):
def _explain_down (self, observation, data):
new_data = self._get_initial_dataset(observation, data)

target_yhat = self.clf.predict(observation)
if hasattr(self.clf, 'predict_proba'):
target_yhat = self.clf.predict_proba(observation)[:, 0]

open_variables = blist(range(0, data.shape[1]))
important_variables = deque()
important_yhats = {}
temp = np.copy(new_data)

for i in range(0, data.shape[1]):
yhats = {}
yhats_diff = np.repeat([float('inf')], data.shape[1])

for variable in open_variables:
new_data[:,variable] = data[:,variable]
yhats[variable] = self.clf.predict_proba(new_data)[:, 0]
yhats_diff[variable] = abs(target_yhat - np.mean(yhats[variable]))
new_data[:, variable] = temp[:, variable]

amin = np.argmin(yhats_diff)
important_variables.append(amin)
important_yhats[i] = yhats[amin]
new_data[:,amin] = data[:,amin]
open_variables.remove(amin)
else:
target_yhat = self.clf.predict(observation)[:, 0]

open_variables = blist(range(0, data.shape[1]))
important_variables = deque()
important_yhats = {}
temp = np.copy(new_data)

open_variables = blist(range(0,data.shape[1]))
important_variables = deque()
important_yhats = {}
for i in range(0, data.shape[1]):
yhats = {}
yhats_diff = np.repeat([float('inf')], data.shape[1])

for i in range(0, data.shape[1]):
yhats = {}
yhats_diff = np.repeat(float('inf'), data.shape[1])

for variable in open_variables:
tmp_data = np.copy(new_data)
tmp_data[:,variable] = data[:,variable]
yhats[variable] = self.clf.predict(tmp_data)
yhats_diff[variable] = abs(target_yhat - np.mean(yhats[variable]))
for variable in open_variables:
new_data[:, variable] = data[:, variable]
yhats[variable] = self.clf.predict(new_data)[:, 0]
yhats_diff[variable] = abs(target_yhat - np.mean(yhats[variable]))
new_data[:, variable] = temp[:, variable]

amin = np.argmin(yhats_diff)
important_variables.append(amin)
important_yhats[i] = yhats[amin]
new_data[:,amin] = data[:,amin]
open_variables.remove(amin)
amin = np.argmin(yhats_diff)
important_variables.append(amin)
important_yhats[i] = yhats[amin]
new_data[:, amin] = data[:, amin]
open_variables.remove(amin)

important_variables.reverse()
var_names = np.array(self.colnames)[important_variables]
Expand All @@ -145,5 +199,6 @@ def _explain_down (self, observation, baseline, data):

return e.Explanation(var_names, var_values, contributions, e.ExplainerDirection.Down)

def _get_means_from_yhats (self, important_yhats):
return deque([np.array(v).mean() for k,v in important_yhats.items()])
@staticmethod
def _get_means_from_yhats (important_yhats):
return deque([np.array(v).mean() for k, v in important_yhats.items()])
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