In [1]:
%load_ext autoreload
%autoreload 2
%matplotlib inline
In [2]:
from fastai.imports import *
from fastai.structured import *
from pandas_summary import DataFrameSummary
from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
from IPython.display import display
import xgboost as xgb
from sklearn import metrics
In [3]:
PATH = os.getcwd();
PATH
Out[3]:
'D:\\Github\\fastai\\courses\\ml1'
In [4]:
df_raw = pd.read_csv(f'{PATH}\\AV_Mckin\\train_encoded.csv', low_memory= False)
df_test = pd.read_csv(f'{PATH}\\AV_Mckin\\test_encoded.csv', low_memory=False)
In [15]:
df_raw = pd.read_csv(f'{PATH}\\AV_Mckin\\train_ajEneEa.csv', low_memory=False)
df_test = pd.read_csv(f'{PATH}\\AV_Mckin\\test_v2akXPA.csv', low_memory=False)
In [16]:
df_raw.shape, df_test.shape
Out[16]:
((43400, 12), (18601, 11))
In [17]:
df_raw.head(2)
Out[17]:
id
gender
age
hypertension
heart_disease
ever_married
work_type
Residence_type
avg_glucose_level
bmi
smoking_status
stroke
0
30669
Male
3.0
0
0
No
children
Rural
95.12
18.0
NaN
0
1
30468
Male
58.0
1
0
Yes
Private
Urban
87.96
39.2
never smoked
0
In [18]:
target = df_raw.stroke.values;
df_raw.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 43400 entries, 0 to 43399
Data columns (total 12 columns):
id 43400 non-null int64
gender 43400 non-null object
age 43400 non-null float64
hypertension 43400 non-null int64
heart_disease 43400 non-null int64
ever_married 43400 non-null object
work_type 43400 non-null object
Residence_type 43400 non-null object
avg_glucose_level 43400 non-null float64
bmi 41938 non-null float64
smoking_status 30108 non-null object
stroke 43400 non-null int64
dtypes: float64(3), int64(4), object(5)
memory usage: 4.0+ MB
In [19]:
# This way we have randomness and are able to reproduce the behaviour within this cell.
np.random.seed(13)
from sklearn.model_selection import KFold
def impact_coding(data, feature, target='y'):
'''
In this implementation we get the values and the dictionary as two different steps.
This is just because initially we were ignoring the dictionary as a result variable.
In this implementation the KFolds use shuffling. If you want reproducibility the cv
could be moved to a parameter.
'''
n_folds = 10
n_inner_folds = 5
impact_coded = pd.Series()
oof_default_mean = data[target].mean() # Gobal mean to use by default (you could further tune this)
kf = KFold(n_splits=n_folds, shuffle=True)
oof_mean_cv = pd.DataFrame()
split = 0
for infold, oof in kf.split(data[feature]):
impact_coded_cv = pd.Series()
kf_inner = KFold(n_splits=n_inner_folds, shuffle=True)
inner_split = 0
inner_oof_mean_cv = pd.DataFrame()
oof_default_inner_mean = data.iloc[infold][target].mean()
for infold_inner, oof_inner in kf_inner.split(data.iloc[infold]):
# The mean to apply to the inner oof split (a 1/n_folds % based on the rest)
oof_mean = data.iloc[infold_inner].groupby(by=feature)[target].mean()
impact_coded_cv = impact_coded_cv.append(data.iloc[infold].apply(
lambda x: oof_mean[x[feature]]
if x[feature] in oof_mean.index
else oof_default_inner_mean
, axis=1))
# Also populate mapping (this has all group -> mean for all inner CV folds)
inner_oof_mean_cv = inner_oof_mean_cv.join(pd.DataFrame(oof_mean), rsuffix=inner_split, how='outer')
inner_oof_mean_cv.fillna(value=oof_default_inner_mean, inplace=True)
inner_split += 1
# Also populate mapping
oof_mean_cv = oof_mean_cv.join(pd.DataFrame(inner_oof_mean_cv), rsuffix=split, how='outer')
oof_mean_cv.fillna(value=oof_default_mean, inplace=True)
split += 1
impact_coded = impact_coded.append(data.iloc[oof].apply(
lambda x: inner_oof_mean_cv.loc[x[feature]].mean()
if x[feature] in inner_oof_mean_cv.index
else oof_default_mean
, axis=1))
return impact_coded, oof_mean_cv.mean(axis=1), oof_default_mean
In [20]:
features = df_raw.columns
numeric_features = []
categorical_features = []
for dtype, feature in zip(df_raw.dtypes, df_raw.columns):
if dtype == object:
#print(column)
#print(train_data[column].describe())
categorical_features.append(feature)
else:
numeric_features.append(feature)
categorical_features
Out[20]:
['gender', 'ever_married', 'work_type', 'Residence_type', 'smoking_status']
In [22]:
%%time
# Apply the encoding to training and test data, and preserve the mapping
impact_coding_map = {}
for f in categorical_features:
print("Impact coding for {}".format(f))
df_raw["impact_encoded_{}".format(f)], impact_coding_mapping, default_coding = impact_coding(df_raw, f,'stroke')
impact_coding_map[f] = (impact_coding_mapping, default_coding)
mapping, default_mean = impact_coding_map[f]
df_test["impact_encoded_{}".format(f)] = df_test.apply(lambda x: mapping[x[f]]
if x[f] in mapping
else default_mean
, axis=1)
Impact coding for gender
Impact coding for ever_married
Impact coding for work_type
Impact coding for Residence_type
Impact coding for smoking_status
Wall time: 5min 5s
In [167]:
df_raw.to_csv(f'{PATH}\\AV_Mckin\\train_encoded.csv',index = False)
df_test.to_csv(f'{PATH}\\AV_Mckin\\test_encoded.csv',index = False)
In [175]:
categorical_features_indices1 = np.where(df_raw.dtypes == 'category')[0]
In [176]:
categorical_features_indices1
Out[176]:
array([ 1, 5, 6, 7, 10], dtype=int64)
In [33]:
df_raw.fillna(method='bfill',inplace=True)
In [41]:
df_raw.drop('stroke',axis=1,inplace=True)
In [179]:
from sklearn.model_selection import train_test_split
X_train, X_validation, y_train, y_validation = train_test_split(df_raw, target, train_size=0.8, random_state=1234)
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_split.py:2026: FutureWarning: From version 0.21, test_size will always complement train_size unless both are specified.
FutureWarning)
In [188]:
#importing library and building model
from catboost import CatBoostClassifier
model=CatBoostClassifier(iterations=1000, depth=12, learning_rate=0.01, loss_function='CrossEntropy',\
)
model.fit(X_train, y_train,cat_features=categorical_features_indices,eval_set=(X_validation, y_validation))
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bestTest = 0.08433424656
bestIteration = 623
Out[188]:
<catboost.core.CatBoostClassifier at 0x17c464626a0>
In [189]:
df_test.fillna(method='ffill', inplace=True)
In [190]:
prediction_proba = model.predict_proba(df_test)
In [191]:
prediction_proba[:,1]
Out[191]:
array([ 0.0757 , 0.12079, 0.00022, ..., 0.08743, 0.00652, 0.00109])
In [33]:
def make_submission(probs):
sample = pd.read_csv(f'{PATH}\\AV_Mckin\\sample_submission_1.csv')
submit = sample.copy()
submit['stroke'] = probs
return submit
In [34]:
sample = pd.read_csv(f'{PATH}\\AV_Mckin\\sample_submission_1.csv')
In [ ]:
submit = make_submission(preds_xgb)
In [ ]:
submit.head(2)
In [ ]:
submit.to_csv(f'{PATH}\\AV_Mckin\\xgb.csv', index=False)
In [66]:
sns.set()
feature_list = ['hypertension','heart_disease','ever_married','work_type','Residence_type','smoking_status']
for column_name in feature_list:
fig , (ax1,ax2) = plt.subplots(1,2,figsize = ( 15 , 6 ))
fig.suptitle(column_name,fontsize=16)
sns.countplot(df_raw[column_name],ax=ax1)
ax1.set_title("Train distribution")
for tick in ax1.get_xticklabels():
tick.set_rotation(45)
sns.countplot(df_test[column_name],ax=ax2,)
ax2.set_title("Test distribution")
for tick in ax2.get_xticklabels():
tick.set_rotation(45)
In [88]:
df_raw['y'] = y
In [150]:
df_raw['is_age>40'] = np.where(df_raw['age']>=40,1,0)
df_test['is_age>40'] = np.where(df_test['age']>=40,1,0)
df_raw['healthy'] = np.zeros(df_raw.shape[0])
my_query = df_raw.query('bmi>=18.5 & bmi<=25').index
df_raw.iloc[my_query, -1] = 1
df_raw['under_weight'] = np.zeros(df_raw.shape[0])
my_query = df_raw.query('bmi<=18.5').index
df_raw.iloc[my_query, -1] = 1
df_raw['over_weight'] = np.zeros(df_raw.shape[0])
my_query = df_raw.query('bmi>25. & bmi<=29.9').index
df_raw.iloc[my_query, -1] = 1
df_raw['obsese'] = np.zeros(df_raw.shape[0])
my_query = df_raw.query('bmi>=30').index
df_raw.iloc[my_query, -1] = 1
df_raw['normal_glucose'] = np.zeros(df_raw.shape[0])
my_query = df_raw.query('avg_glucose_level<=140').index
df_raw.iloc[my_query, -1] = 1
df_raw['pre_diabetes'] = np.zeros(df_raw.shape[0])
my_query = df_raw.query('avg_glucose_level>140 & avg_glucose_level<=199.').index
df_raw.iloc[my_query, -1] = 1
df_raw['diabetes'] = np.zeros(df_raw.shape[0])
my_query = df_raw.query('avg_glucose_level>=200').index
df_raw.iloc[my_query, -1] = 1
df_test['normal_glucose'] = np.zeros(df_test.shape[0])
my_query = df_test.query('avg_glucose_level<=140').index
df_test.iloc[my_query, -1] = 1
df_test['pre_diabetes'] = np.zeros(df_test.shape[0])
my_query = df_test.query('avg_glucose_level>140 & avg_glucose_level<=199.').index
df_test.iloc[my_query, -1] = 1
df_test['diabetes'] = np.zeros(df_test.shape[0])
my_query = df_test.query('avg_glucose_level>=200').index
df_test.iloc[my_query, -1] = 1
df_test['healthy'] = np.zeros(df_test.shape[0])
my_query = df_test.query('bmi>=18.5 & bmi<=25').index
df_test.iloc[my_query, -1] = 1
df_test['under_weight'] = np.zeros(df_test.shape[0])
my_query = df_test.query('bmi<=18.5').index
df_test.iloc[my_query, -1] = 1
df_test['over_weight'] = np.zeros(df_test.shape[0])
my_query = df_test.query('bmi>25. & bmi<=29.9').index
df_test.iloc[my_query, -1] = 1
df_test['obsese'] = np.zeros(df_test.shape[0])
my_query = df_test.query('bmi>=30').index
df_test.iloc[my_query, -1] = 1
In [115]:
df_train['y'] = y
corr = df_train.corr()
# Create a mask to hide the upper triangle of the correlation matrix (which is symmetric)
mask = np.zeros_like(corr, dtype=np.bool)
mask[np.triu_indices_from(mask)] = True
f, ax = plt.subplots(figsize=(20, 20))
sns.heatmap(corr, mask=mask, vmax=1, center=0, annot=True, fmt='.1f',
square=True, linewidths=.5, cbar_kws={"shrink": .5});
df_train.drop('y',axis=1,inplace=True)
In [119]:
df_uniques = pd.melt(frame=df_raw, value_vars=['gender','ever_married',
'work_type', 'Residence_type', 'smoking_status'])
df_uniques = pd.DataFrame(df_uniques.groupby(['variable',
'value'])['value'].count()) \
.sort_index(level=[0, 1]) \
.rename(columns={'value': 'count'}) \
.reset_index()
sns.factorplot(x='variable', y='count', hue='value',
data=df_uniques, kind='bar',size=8);
In [121]:
df_uniques = pd.melt(frame=df_raw, value_vars=['gender','ever_married',
'work_type', 'Residence_type', 'smoking_status'], id_vars=['y'])
df_uniques = pd.DataFrame(df_uniques.groupby(['variable',
'value', 'y'])['value'].count()) \
.sort_index(level=[0, 1]) \
.rename(columns={'value': 'count'}) \
.reset_index()
sns.factorplot(x='variable', y='count', hue='value',
col= 'y', data=df_uniques, kind='bar',size=12);
In [109]:
plt.figure(figsize=(15,15))
sns.lmplot(x='age', y='bmi', hue='work_type', data=df_raw);
<matplotlib.figure.Figure at 0x17c400395f8>
In [111]:
plt.figure(figsize=(20,20))
sns.lmplot(x='age', y='avg_glucose_level', hue='work_type', data=df_raw);
<matplotlib.figure.Figure at 0x17c400a3978>
In [89]:
sns.lmplot(x='age', y='bmi', hue='y', data=df_raw,)
Out[89]:
<seaborn.axisgrid.FacetGrid at 0x17c19979400>
In [102]:
sns.lmplot(x='age', y='avg_glucose_level', hue='y', data=df_raw,)
Out[102]:
<seaborn.axisgrid.FacetGrid at 0x17c3fd8cda0>
In [67]:
from sklearn.ensemble import GradientBoostingClassifier #GBM algorithm
from sklearn import cross_validation, metrics #Additional scklearn functions
from sklearn.grid_search import GridSearchCV #Perforing grid search
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.
"This module will be removed in 0.20.", DeprecationWarning)
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\grid_search.py:42: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.
DeprecationWarning)
In [77]:
train_cats(df_raw)
train_cats(df_test)
df_raw['y'] = target
df_test['y'] = target[:18601]
df_raw1, y, nas = proc_df(df_raw, 'y', max_n_cat=10,)
df_test1, _, _ = proc_df(df_test, 'y', na_dict=nas)
df_raw.drop('y', axis=1, inplace=True)
df_test.drop('y', axis=1, inplace=True)
In [79]:
def modelfit(alg, dtrain, predictors, performCV=True, printFeatureImportance=True, cv_folds=5):
#Fit the algorithm on the data
alg.fit(dtrain[predictors], target)
#Predict training set:
dtrain_predictions = alg.predict(dtrain[predictors])
dtrain_predprob = alg.predict_proba(dtrain[predictors])[:,1]
#Perform cross-validation:
if performCV:
cv_score = cross_validation.cross_val_score(alg, dtrain[predictors], target, cv=cv_folds, scoring='roc_auc')
#Print model report:
print ("\nModel Report")
print ("Accuracy : %.4g" % metrics.accuracy_score(target , dtrain_predictions))
print ("AUC Score (Train): %f" % metrics.roc_auc_score(target , dtrain_predprob))
if performCV:
print ("CV Score : Mean - %.7g | Std - %.7g | Min - %.7g | Max - %.7g" % (np.mean(cv_score),np.std(cv_score),np.min(cv_score),np.max(cv_score)))
#Print Feature Importance:
if printFeatureImportance:
feat_imp = pd.Series(alg.feature_importances_, predictors).sort_values(ascending=False)
plt.figure(figsize=(20,20))
feat_imp.plot(kind='bar', title='Feature Importances')
plt.ylabel('Feature Importance Score')
In [80]:
#Choose all predictors except target & IDcols
predictors = df_raw1.columns
gbm0 = GradientBoostingClassifier(random_state=10)
modelfit(gbm0, df_raw1, predictors)
Model Report
Accuracy : 0.9825
AUC Score (Train): 0.888989
CV Score : Mean - 0.8425214 | Std - 0.01152719 | Min - 0.8319186 | Max - 0.8631636