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In [3]:
import pandas as pd
import numpy as np

from scipy.stats import skew

import seaborn as sns

import plotly.express as ex

from sklearn.model_selection import train_test_split, KFold, StratifiedKFold
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import f1_score

from xgboost import XGBClassifier
from catboost import CatBoostClassifier

import matplotlib.pyplot as plt
%matplotlib inline

import warnings
warnings.filterwarnings('ignore')

from IPython.core.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


In [4]:
org_train_data = pd.read_csv('train.csv')
org_test_data = pd.read_csv('test.csv')
sample = pd.read_csv('sample_submission.csv')
In [5]:
train_data = org_train_data.drop(['Accident_ID'],axis=1)
train_data.Severity = train_data.Severity.astype('category')
# train_data.Severity = train_data.Severity.str.replace('Minor_Damage_And_Injuries','0')
# train_data.Severity = train_data.Severity.str.replace('Significant_Damage_And_Fatalities','1')
# train_data.Severity = train_data.Severity.str.replace('Significant_Damage_And_Serious_Injuries','2')
# train_data.Severity = train_data.Severity.str.replace('Highly_Fatal_And_Damaging','3')

# train_data.Severity= train_data.Severity.astype('int64')

train_data.Severity= train_data.Severity.cat.rename_categories({'Minor_Damage_And_Injuries':0, 
                                                                'Significant_Damage_And_Fatalities':1,
                                                               'Significant_Damage_And_Serious_Injuries':2,
                                                               'Highly_Fatal_And_Damaging':3})
train_data.Severity= train_data.Severity.astype('int64')

test_data = org_test_data.drop('Accident_ID',axis=1)

In [6]:
train_data.head()
Out[6]:
In [7]:
# print(org_train_data.info())
# print(org_test_data.info())
In [8]:
org_train_data.Severity.value_counts(dropna=False)
Out[8]:
Highly_Fatal_And_Damaging                  3049
Significant_Damage_And_Serious_Injuries    2729
Minor_Damage_And_Injuries                  2527
Significant_Damage_And_Fatalities          1695
Name: Severity, dtype: int64

Preprocessing

In [9]:
for x in list(train_data.columns):
    print(str(x) + ' ' + str(skew(train_data[x])))
Severity -0.2164887512187439 Safety_Score 0.23626606610551856 Days_Since_Inspection -0.07389854243316596 Total_Safety_Complaints 1.887797217861932 Control_Metric -0.20145151175126474 Turbulence_In_gforces 0.8166235967447771 Cabin_Temperature 0.8543090965616399 Accident_Type_Code 0.25232893533959505 Max_Elevation -0.03818926639634463 Violations 0.09675795224197366 Adverse_Weather_Metric 2.073774710171132
In [10]:
train_data[train_data.Total_Safety_Complaints>20].groupby('Severity').size()
Out[10]:
Severity
0    153
1     70
2    141
3    138
dtype: int64
In [11]:
train_data.Severity.value_counts()
Out[11]:
3    3049
2    2729
0    2527
1    1695
Name: Severity, dtype: int64
In [12]:
train_data.Adverse_Weather_Metric.max()
Out[12]:
2.36537839986842
In [13]:
## Safety Score
df = ex.data.tips()
fig = ex.box(df, y=train_data["Safety_Score"])
fig.show()

train_data.Safety_Score.loc[train_data.Safety_Score>84]=84
skew(train_data.Safety_Score)
Out[13]:
0.18786983295491047
In [14]:
## Days_Since_Inspection
df = ex.data.tips()
fig = ex.box(df, y=train_data["Days_Since_Inspection"])
fig.show()

train_data.Days_Since_Inspection.loc[train_data['Days_Since_Inspection']>21]=21
train_data.Days_Since_Inspection.loc[train_data['Days_Since_Inspection']<5]=5

skew(train_data.Days_Since_Inspection)
Out[14]:
-0.03170659526256455
In [15]:
## Total_Safety_Complaints
df = ex.data.tips()
fig = ex.box(df, y=train_data["Total_Safety_Complaints"])
fig.show()

train_data.Total_Safety_Complaints.loc[train_data['Total_Safety_Complaints']>19]=19

skew(train_data.Total_Safety_Complaints)
Out[15]:
0.9399225301506126
In [16]:
## Control_Metric
df = ex.data.tips()
fig = ex.box(df, y=train_data["Control_Metric"])
fig.show()

train_data.Control_Metric.loc[train_data['Control_Metric']>97]=97
train_data.Control_Metric.loc[train_data['Control_Metric']<32]=32

skew(train_data.Control_Metric)
Out[16]:
-0.133368939833902
In [17]:
## Turbulence_In_gforces
df = ex.data.tips()
fig = ex.box(df, y=train_data["Turbulence_In_gforces"])
fig.show()

train_data.Turbulence_In_gforces.loc[train_data['Turbulence_In_gforces']>0.68]=0.68

skew(train_data.Turbulence_In_gforces)
Out[17]:
0.6050047570516877
In [18]:
## Cabin_Temperature
df = ex.data.tips()
fig = ex.box(df, y=train_data["Cabin_Temperature"])
fig.show()

train_data.Cabin_Temperature.loc[train_data['Cabin_Temperature']>86]=86

skew(train_data.Cabin_Temperature)
Out[18]:
0.5454927518055006
In [19]:
## Accident_Type_Code - No Outliers
df = ex.data.tips()
fig = ex.box(df, y=train_data["Accident_Type_Code"])
fig.show()

# train_data.Accident_Type_Code.loc[train_data['Accident_Type_Code']>86]=86

# skew(train_data.Accident_Type_Code)
In [20]:
## Max_Elevation
df = ex.data.tips()
fig = ex.box(df, y=train_data["Max_Elevation"])
fig.show()

train_data.Max_Elevation.loc[train_data['Max_Elevation']>56900]=56900
train_data.Max_Elevation.loc[train_data['Max_Elevation']<7000]=7000


skew(train_data.Max_Elevation)
Out[20]:
-0.03685144166071417
In [21]:
## Violations - No Outliers
df = ex.data.tips()
fig = ex.box(df, y=train_data["Violations"])
fig.show()

# train_data.Violations.loc[train_data['Violations']>56900]=56900
# train_data.Violations.loc[train_data['Violations']<7000]=7000


# skew(train_data.Violations)
In [22]:
## Adverse_Weather_Metric
df = ex.data.tips()
fig = ex.box(df, y=train_data["Adverse_Weather_Metric"])
fig.show()

train_data.Adverse_Weather_Metric.loc[train_data['Adverse_Weather_Metric']>0.86]=0.86


skew(train_data.Adverse_Weather_Metric)
Out[22]:
1.3183856703791363
In [ ]:
 
In [146]:


# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state = 9)

model = RandomForestClassifier(n_jobs=-1, random_state=9, n_estimators=1000, )
model.fit(X_train,y_train)
pred = model.predict(X_test)
score = f1_score(y_test, pred, average='weighted')
print(score)
final_pred = model.predict(test_data)
0.9271995658595585
In [182]:
# def train_and_predict(model,X_train,y_train,X_val,y_val,test_data):
#     model.fit(X_train, y_train)
#     y_pred = model.predict(X_val)
#     val_score = f1_score(y_val, y_pred, average='weighted')
#     print(val_score)
#     pred = model.predict(test_data)
#     return val_score, pred

# y = train_data.Severity
# X = train_data.drop(['Severity'],axis=1)

# kf = KFold(n_splits=5, shuffle=True, random_state=3)
# pred_arr = []
# val_arr = []
# # model = RandomForestClassifier(n_jobs=-1, n_estimators=500)
# # for train_index, test_index in kf.split(X):    
# #     pred =train_and_predict(model,X.iloc[train_index],y.iloc[train_index],X.iloc[test_index],y.iloc[test_index],test_data)
# #     pred_arr.append(pred)
    
# model = XGBClassifier(n_jobs=-1, n_estimators=1000)
# for train_index, test_index in kf.split(X):    
#     val_score, pred =train_and_predict(model,X.iloc[train_index],y.iloc[train_index],X.iloc[test_index],y.iloc[test_index],test_data)
#     pred_arr.append(pred)
#     val_arr.append(val_score)
    

In [206]:
def train_and_predict(model,X_train,y_train,X_val,y_val,test_data):
    model.fit(X_train, y_train, eval_set=(X_val,y_val))
    y_pred = model.predict(X_val)
    val_score = f1_score(y_val, y_pred, average='weighted')
    print(val_score)
    pred = model.predict(test_data)
    return val_score, pred

y = train_data.Severity
X = train_data.drop(['Severity'],axis=1)

kf = StratifiedKFold(n_splits=5, random_state=3, )
pred_arr = []
val_arr = []
# model = RandomForestClassifier(n_jobs=-1, n_estimators=500)
# for train_index, test_index in kf.split(X):    
#     pred =train_and_predict(model,X.iloc[train_index],y.iloc[train_index],X.iloc[test_index],y.iloc[test_index],test_data)
#     pred_arr.append(pred)
    
model = CatBoostClassifier(eval_metric='Accuracy',use_best_model=True,random_seed=42, learning_rate=0.05)

for train_index, test_index in kf.split(X,y):    
    val_score, pred =train_and_predict(model,X.iloc[train_index],y.iloc[train_index],X.iloc[test_index],y.iloc[test_index],test_data)
    pred_arr.append(pred)
    val_arr.append(val_score)
    

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In [207]:
final_pred = (pred_arr[0]+pred_arr[1]+pred_arr[2]+pred_arr[3]+pred_arr[4])/5
print(sum(val_arr)/5) #95.31 #95.5
0.9565882480522916
In [208]:
from IPython.display import HTML
import base64

output = pd.DataFrame({'Accident_ID':org_test_data.Accident_ID,'Severity':pd.Series(final_pred.flatten())})
output.Severity = output.Severity.astype('category')

output.Severity= output.Severity.cat.rename_categories({0:'Minor_Damage_And_Injuries', 
                                                                1:'Significant_Damage_And_Fatalities',
                                                               2:'Significant_Damage_And_Serious_Injuries',
                                                               3:'Highly_Fatal_And_Damaging'})

def create_download_link(df, title = "Download CSV file", filename = "submission9.csv"):  
    csv = df.to_csv(index=False)
    b64 = base64.b64encode(csv.encode())
    payload = b64.decode()
    html = '<a download="{filename}" href="data:text/csv;base64,{payload}" target="_blank">{title}</a>'
    html = html.format(payload=payload,title=title,filename=filename)
    return HTML(html)

# create_download_link(test_df[['UID','Handle','Name','Description','Location','Followers_count','Following_count','HCP_Flag']])
create_download_link(output)
Out[208]:
In [198]:
pd.Series(final_pred.flatten())
Out[198]:
0       3.0
1       1.0
2       2.0
3       3.0
4       1.0
       ... 
2495    3.0
2496    2.0
2497    2.0
2498    2.0
2499    3.0
Length: 2500, dtype: float64
In [197]:
final_pred.flatten()
Out[197]:
numpy.ndarray
In [23]:
import jovian
In [ ]:
jovian.commit()
[jovian] Saving notebook..
In [ ]: