Flights-Copy1



In [1]:
%load_ext autoreload
%autoreload 2

%matplotlib inline

In [2]:
from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
from fastai.imports import *
from fastai.structured import *

from pandas_summary import DataFrameSummary
from IPython.display import display

from sklearn import metrics
from sklearn import metrics
import warnings
warnings.filterwarnings('ignore')
import seaborn as sns
sns.set()
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from xgboost import XGBRegressor
from sklearn.metrics import roc_auc_score

In [3]:
import os
PATH = os.getcwd();
PATH = PATH+'\\AV_Mckin\\ods\\';
PATH


Out[3]:
'D:\\Github\\fastai\\courses\\ml1\\AV_Mckin\\ods\\'

In [57]:
df_train_org = pd.read_csv(f'{PATH}\\flight_delays_train.csv', low_memory=False)
df_test_org = pd.read_csv(f'{PATH}\\flight_delays_test.csv', low_memory=False)
df_raw = pd.read_csv(f'{PATH}\\impact_encoded_train.csv', low_memory=False)
df_test = pd.read_csv(f'{PATH}\\impact_encoded_test.csv', low_memory=False)

In [58]:
df_raw.head(2)


Out[58]:
Month DayofMonth DayOfWeek DepTime UniqueCarrier Origin Dest Distance target impact_encoded_Month impact_encoded_DayofMonth impact_encoded_DayOfWeek impact_encoded_UniqueCarrier impact_encoded_Origin impact_encoded_Dest
0 c-8 c-21 c-7 1934 AA ATL DFW 732 0 0.202561 0.202928 0.191829 0.186803 0.258108 0.148708
1 c-4 c-20 c-3 1548 US PIT MCO 834 0 0.155567 0.213503 0.176398 0.167930 0.178860 0.177618

In [8]:
from sklearn.model_selection import train_test_split

In [65]:
def preprocess(X):
    X["Flight"] = X["Origin"] + "-" + X["Dest"]
    X["Hour"] = X["DepTime"] // 100
    X['Mins'] = X['DepTime'] % 100
    X["Month"] = X["Month"].apply(lambda x: int(x.split('-')[1]))
    X["DayOfMonth"] = X["DayofMonth"].apply(lambda x: int(x.split('-')[1]))
    X = X.drop(["DayofMonth"], axis=1)
    X['DayOfWeek'] = X['DayOfWeek'].apply(lambda x: int(x.split('-')[1]))
    X['weekend'] = np.where(X['DayOfWeek']>=6,1,0)
    return X

In [66]:
df_raw  = preprocess(df_raw.copy())
df_test = preprocess(df_test.copy())

In [67]:
y = df_raw.target.values
df_raw.drop('target',axis=1,inplace=True)
categorical_features_indices = np.where(df_raw.dtypes == object)[0]
categorical_features_indices


Out[67]:
array([ 3,  4,  5, 13], dtype=int64)

In [68]:
from sklearn.model_selection import train_test_split
X_train, X_validation, y_train, y_validation = train_test_split(df_raw, y, train_size=0.8, random_state=1234);

In [69]:
#importing library and building model
from catboost import CatBoostClassifier
model=CatBoostClassifier(iterations=1000, depth=10, 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 = 0.4208686696
bestIteration = 999

Out[69]:
<catboost.core.CatBoostClassifier at 0x2048192cda0>

In [70]:
prediction_proba = model.predict_proba(df_test)[:,1]

In [71]:
model.get_feature_importance(X_train,y_train,cat_features=categorical_features_indices)


Out[71]:
[2.4904738937880913,
 0.9023635736328831,
 26.784972512305057,
 3.474099618979113,
 2.489420620267185,
 4.015760514421802,
 3.6905698914829443,
 6.242727532021042,
 3.7215939749033424,
 4.357634070441675,
 3.5198557625898284,
 6.269741184129969,
 5.607910307964413,
 1.9890194408451036,
 18.474678710283758,
 2.991705877361596,
 2.528727859987967,
 0.4487446545942221]

In [72]:
pd.Series(prediction_proba, 
          name='dep_delayed_15min').to_csv(f'{PATH}cat_encoded_flights_ods.csv', 
                                           index_label='id', header=True)

In [77]:
# 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 [81]:
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[81]:
['Month', 'DayofMonth', 'DayOfWeek', 'UniqueCarrier', 'Origin', 'Dest']

In [88]:
df_raw['target'] = y.map({'Y': 1, 'N': 0}).values

In [90]:
%%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,'target')
    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)

df_raw.to_csv(PATH + '\\impact_encoded_train.csv',index = False)
df_test.to_csv(PATH + '\\impact_encoded_test.csv',index = False)


Impact coding for Month
Impact coding for DayofMonth
Impact coding for DayOfWeek
Impact coding for UniqueCarrier
Impact coding for Origin
Impact coding for Dest
Wall time: 13min 44s

Cheating Kaggle


In [6]:
!dir {PATH}


 Volume in drive D is Local Disk
 Volume Serial Number is B408-A348

 Directory of D:\Github\fastai\courses\ml1\AV_Mckin\ods

21-Apr-18  10:54 AM    <DIR>          .
21-Apr-18  10:54 AM    <DIR>          ..
18-Apr-18  08:21 AM    <DIR>          .ipynb_checkpoints
09-Apr-18  02:55 PM        30,203,258 add_test_dummies.csv
09-Apr-18  02:58 PM        35,412,316 add_test_dummiesv2.csv
09-Apr-18  02:55 PM        92,576,888 add_train_dummies.csv
09-Apr-18  02:58 PM       109,333,024 add_train_dummiesv2.csv
13-Apr-18  12:43 PM         2,659,732 cat_encoded_flights_ods.csv
07-Feb-18  08:21 PM         1,800,682 site_dic.pkl
21-Apr-18  10:09 AM         2,407,429 sub-210418.csv
21-Apr-18  10:09 AM         2,407,429 sub1.csv
17-Mar-18  11:25 PM        19,450,729 websites_test_sessions.csv
17-Mar-18  11:30 PM        60,526,801 websites_train_sessions.csv
              10 File(s)    356,778,288 bytes
               3 Dir(s)  183,789,621,248 bytes free

In [32]:
sub_df1 = pd.read_csv(f'{PATH}cat_encoded_flights_ods.csv');
sub_df2 = pd.read_csv(f'{PATH}cat_encoded_flights_ods.csv');

In [61]:
preds = (sub_df1.dep_delayed_15min * 1.3 + sub_df2.dep_delayed_15min*2.5)/4

In [62]:
max(preds)


Out[62]:
0.90939239921497128

In [63]:
preds1 = np.where(preds>.80, .84, preds)

In [64]:
sub_df['dep_delayed_15min'] = preds

In [65]:
sub_df.to_csv(f'{PATH}modified_flights.csv',index=None,)

In [ ]: