def fitAndScore(features):
data = ChurnData(features)
model = LogisticRegression()
model.fit(**data.split_train)
scores = data.getScores(model, 'split_val')
return {'model': model, 'scores': scores, 'features': features}
def printTestSetResultsRandFor():
grid = pickle.load(open(_RESULT_PATH + 'grid_search_result.pkl', 'rb'))
model = RandomForestClassifier(**grid.best_params_)
data = ChurnData()
model.fit(**data.train)
return data.printScores(model)
def printTestSetResultsLogReg():
gridL2 = pickle.load(open(_RESULT_PATH + 'logRegL2_grid.pkl', 'rb'))
accL2 = gridL2['accuracy']
params = accL2.best_params_
model = LogisticRegression(**params)
data = ChurnData()
model.fit(**data.train)
return data.printScores(model)
def runGridSearch():
param_grid = {
'n_estimators': [100],
'max_features': range(10, 30),
'max_depth': range(1, 20),
'min_samples_leaf': range(5, 25)
}
data = ChurnData()
model = RandomForestClassifier()
# fixed random state for cross validation
cv = StratifiedKFold(n_splits=10, shuffle=True, random_state=42)
# default scoring is accuracy
grid = GridSearchCV(estimator=model,
param_grid=param_grid,
verbose=1,
n_jobs=64,
cv=cv)
grid.fit(**data.train)
with open(_RESULT_PATH + 'grid_search_result.pkl', 'wb') as handle:
pickle.dump(grid, handle, protocol=pickle.HIGHEST_PROTOCOL)
return grid
def getFeatureImportances():
model = getBestModel()
data = ChurnData()
importances = pd.DataFrame(
list(zip(data.features, model.feature_importances_)))
importances.columns = ['feature', 'importance']
return importances.sort_values(by='importance', ascending=False)
def _runFeatureElimination(numFeatures, features, train=None, test=None):
model = LogisticRegression()
data = ChurnData(features)
rfe = RFE(model, numFeatures)
fit = rfe.fit(data.train['X'][train], data.train['y'][train])
features = data.features[fit.support_]
data = ChurnData(features)
model.fit(data.train['X'][train], data.train['y'][train])
scores = data.getScores(model,
X=data.train['X'][test],
y=data.train['y'][test])
return {
'features': features,
'accuracy': scores['accuracy'],
'roc_auc': scores['auc']
}
def bestModelAuc():
# auc = 0.8026
params = getResultGrid().best_params_
data = ChurnData()
# fixed random state for cross validation
cv = StratifiedKFold(n_splits=10, shuffle=True, random_state=42)
aucs = []
for i, (trainIndex, testIndex) in enumerate(cv.split(**data.train)):
print('Split{} out of 10'.format(i + 1))
model = RandomForestClassifier(**params)
model.fit(data.train['X'][trainIndex], data.train['y'][trainIndex])
aucs.append(
data.getScores(model,
X=data.train['X'][testIndex],
y=data.train['y'][testIndex])['auc'])
return np.mean(aucs)
def storeModel(model, **model_params):
data = ChurnData(predict='deltaNextHours')
m = model(data, **model_params)
m.fit(data.split_train_df)
with open(model.RESULT_PATH + 'model.pkl', 'wb') as handle:
pickle.dump(m, handle, protocol=pickle.HIGHEST_PROTOCOL)
pred_val = m.cf.predict_expectation(data.split_val_df).values.reshape(-1)
with open(model.RESULT_PATH + 'pred_val.pkl', 'wb') as handle:
pickle.dump(pred_val, handle, protocol=pickle.HIGHEST_PROTOCOL)
def runFeatureElimination(includeFeat='all'):
"""
Performs feature elimination
Run RFE for each fold, find average scores for AUC and accuracy for each step
:includeFeat: 'avg' or 'wght' -- include wght avg or avg only
"""
# load data
pool = Pool(64)
# all features
data = ChurnData()
features = data.features
if includeFeat == 'avg':
# only avg deltaPrev
features = list(
set(features) -
set(['logDeltaPrev_wght_avg', 'deltaPrev_wght_avg']))
elif includeFeat == 'wght':
# only weighted deltaPrev
features = list(
set(features) - set(['logDeltaPrev_avg', 'deltaPrev_avg']))
cv = StratifiedKFold(n_splits=10, shuffle=True, random_state=42)
scores = [0] * 10
for i, (train_ind, test_ind) in enumerate(cv.split(**data.train)):
print('Fold: {} out of 10'.format(i + 1))
scores[i] = pool.map(
partial(_runFeatureElimination,
features=features,
train=train_ind,
test=test_ind), range(1,
len(features) + 1))
pool.close()
features = [[s['features'] for s in ss] for ss in scores]
accuracy = np.array([[s['accuracy'] for s in ss] for ss in scores]).mean(0)
roc_auc = np.array([[s['roc_auc'] for s in ss] for ss in scores]).mean(0)
# accuracy = np.array([s['accuracy'] for s in scores]).mean(0)
# roc_auc = np.array([s['roc_auc '] for s in scores]).mean(0)
res = {'features': features, 'accuracy': accuracy, 'roc_auc': roc_auc}
with open('{}logReg_rfe_{}.pkl'.format(_RESULT_PATH, includeFeat),
'wb') as handle:
pickle.dump(res, handle, protocol=pickle.HIGHEST_PROTOCOL)
return res
def findPearsonCor():
data = ChurnData()
df = pd.DataFrame(data.X_split_train)
df.columns = data.features
df['churned'] = data.y_split_train
corr = df.corr().churned
keys = corr.keys()
feat_noWght = ['numSessions', 'recency']
feat = [
'deltaPrev', 'dayOfMonth', 'dayOfWeek', 'hourOfDay', 'sessionLen',
'price', 'numDivisions', 'numInteractions', 'numItemsViewed'
]
feat_dev = ['Desktop', 'Mobile', 'Ios', 'Android', 'Unknown']
corrs_feat_noWght = pd.DataFrame(columns=['feature', 'plain', 'log'])
corrs_feat_noWght.feature = feat_noWght
corrs_feat_noWght.plain = [corr[f] for f in feat_noWght]
corrs_feat_noWght.log = [corr['log' + upperfirst(f)] for f in feat_noWght]
corrs_feat = pd.DataFrame(
columns=['feature', 'avg', 'log_avg', 'wght_avg', 'log_wght_avg'])
corrs_feat.feature = feat
corrs_feat.avg = [corr[f + '_avg'] for f in feat]
corrs_feat.log_avg = [
corr['log' + upperfirst(f) + '_avg'] if 'log' + upperfirst(f) +
'_avg' in keys else np.nan for f in feat
]
corrs_feat.wght_avg = [corr[f + '_wght_avg'] for f in feat]
corrs_feat.log_wght_avg = [
corr['log' + upperfirst(f) + '_wght_avg'] if 'log' + upperfirst(f) +
'_wght_avg' in keys else np.nan for f in feat
]
corrs_dev = pd.DataFrame(columns=['feature', 'plain', 'wght'])
corrs_dev.feature = feat_dev
corrs_dev.plain = [corr['device' + f] for f in feat_dev]
corrs_dev.wght = [
corr['device' + upperfirst(f) + '_wght'] for f in feat_dev
]
return corrs_feat_noWght, corrs_feat, corrs_dev
def runBayesOpt(model,
include_recency=False,
error='concordance',
maximise=True):
"""
Cross-validated search for parameters
"""
nFolds = 10
nPools = 10
bounds = {'penalizer': (1000, 5000)}
n_iter = 20
print(model.RESULT_PATH)
# load churn data for splitting fold stratas
churnData = ChurnData()
cv = StratifiedKFold(n_splits=nFolds, shuffle=True, random_state=42)
splits = np.array(list(cv.split(**churnData.train)))
f = partial(_evaluatePenalizer,
model=model,
splits=splits,
nPools=nPools,
include_recency=include_recency,
error=error,
maximise=maximise)
bOpt = BayesianOptimization(f, bounds)
bOpt.maximize(init_points=2,
n_iter=n_iter,
acq='ucb',
kappa=5,
kernel=Matern())
with open(
model.RESULT_PATH + 'bayes_opt_{}{}.pkl'.format(
error, '_rec' if include_recency else ''), 'wb') as handle:
pickle.dump(bOpt, handle, protocol=pickle.HIGHEST_PROTOCOL)
return bOpt
def runGridSearch(model, include_recency=False):
"""
Cross-validated search for parameters
"""
nFolds = 10
nPools = 10
bounds = (2000, 3000)
n_iter = 21
space = np.linspace(bounds[0], bounds[1], n_iter)
print(model.RESULT_PATH)
# load churn data for splitting fold stratas
churnData = ChurnData()
cv = StratifiedKFold(n_splits=nFolds, shuffle=True, random_state=42)
splits = np.array(list(cv.split(**churnData.train)))
scores = []
for p in space:
print(p)
scores.append(
_evaluatePenalizer(p,
model=model,
splits=splits,
nPools=nPools,
include_recency=include_recency,
error=None))
res = {
'penalties': space,
'scores': {k: [d[k] for d in scores]
for k in scores[0]}
}
with open(
model.RESULT_PATH + 'grid_search{}_{}.pkl'.format(
'_rec' if include_recency else '', n_iter), 'wb') as handle:
pickle.dump(res, handle, protocol=pickle.HIGHEST_PROTOCOL)
return res
def crossValidate(model, penalizer=2045, include_recency=False, nFolds=10):
churnData = ChurnData()
cv = StratifiedKFold(n_splits=nFolds, shuffle=True, random_state=42)
splits = np.array(list(cv.split(**churnData.train)))
pool = Pool(nFolds)
scores = pool.map(
partial(_scoreModel,
model=model,
penalizer=penalizer,
include_recency=include_recency), splits)
res = {
key: np.mean([score[key] for score in scores])
for key in scores[0].keys()
}
pool.close()
pool.join()
return res
def runL2GridSearch():
"""
Runs grid search logistic regression model with L2 penalty
Uses 10-fold cross validation
"""
param_grid = {'penalty': ['l2'], 'C': np.logspace(-6, 0, 800)}
data = ChurnData()
model = LogisticRegression(penalty='l2')
# fixed random state for cross validation
cv = StratifiedKFold(n_splits=10, shuffle=True, random_state=42)
# default scoring is accuracy
grid_acc = GridSearchCV(estimator=model,
param_grid=param_grid,
verbose=1,
n_jobs=64,
cv=cv,
scoring='accuracy')
grid_acc.fit(**data.train)
grid_auc = GridSearchCV(estimator=model,
param_grid=param_grid,
verbose=1,
n_jobs=64,
cv=cv,
scoring='roc_auc')
grid_auc.fit(**data.train)
res = {'accuracy': grid_acc, 'roc_auc': grid_auc}
with open('{}logRegL2_grid.pkl'.format(_RESULT_PATH), 'wb') as handle:
pickle.dump(res, handle, protocol=pickle.HIGHEST_PROTOCOL)
return res
import numpy as np
from lifelines import CoxPHFitter
import sys
sys.path.insert(0, '../utils')
predPeriod = {
'start': pd.Timestamp('2016-02-01'),
'end': pd.Timestamp('2016-06-01')
}
predPeriodHours = (predPeriod['end'] - predPeriod['start']) / np.timedelta64(1, 'h')
class CoxChurnModel:
def __init__(self):
self.cf = CoxPHFitter()
def fit(self, dataset, pred_col='deltaNextHours', event_col='observed'):
self.cf.fit(dataset, pred_col, event_col=event_col)
def predict(self, df):
pred = self.cf.predict_expectation(df)
churned = (pred - df.recency.values.reshape((-1,1))) > predPeriodHours
return churned.values.reshape(-1)
def predict_proba(self, df):
return np.zeros(len(df))
model = CoxChurnModel()
data = ChurnData(dataset='cox')
def main():
data = ChurnData()
model = MajorityPredictor()
model.fit(**data.train)
data.getScore(model)
def __init__(self, include_recency=False):
self.data = ChurnData(predict='deltaNextHours'
) #, features=['recency', 'logNumSessions'])
self.include_recency = include_recency
