Baseline for DOTAW

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Baseline for the challenge DOTAW¶

Author - Pulkit Gera¶

In [ ]:

!pip install numpy
!pip install pandas
!pip install sklearn

In [2]:

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split 
from sklearn.linear_model import LogisticRegression
from sklearn import metrics

Download data¶

The first step is to download out train test data. We will be training a classifier on the train data and make predictions on test data. We submit our predictions

In [ ]:

!wget https://s3.eu-central-1.wasabisys.com/aicrowd-public-datasets/aicrowd_educational_dotaw/data/public/test.zip
!wget https://s3.eu-central-1.wasabisys.com/aicrowd-public-datasets/aicrowd_educational_dotaw/data/public/train.zip
!unzip train.zip
!unzip test.zip

Load Data¶

We use pandas library to load our data. Pandas loads them into dataframes which helps us analyze our data easily. Learn more about it here

In [3]:

train_data = pd.read_csv('train.csv')

Analyse Data¶

In [4]:

train_data.head()

Out[4]:

	winner	cluster_id	game_mode	game_type	hero_0	hero_1	hero_2	hero_3	hero_4	hero_5	...	hero_103	hero_104	hero_105	hero_106	hero_107	hero_108	hero_109	hero_110	hero_111	hero_112
0	-1	223	2	2	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
1	1	152	2	2	0	0	0	1	0	-1	...	0	0	0	0	0	0	0	0	0	0
2	1	131	2	2	0	0	0	1	0	-1	...	0	0	0	0	0	0	0	0	0	0
3	1	154	2	2	0	0	0	0	0	0	...	-1	0	0	0	0	0	0	0	0	0
4	-1	171	2	3	0	0	0	0	0	-1	...	0	0	0	0	0	0	0	0	0	0

5 rows × 117 columns

Here we use the describe function to get an understanding of the data. It shows us the distribution for all the columns. You can use more functions like info() to get useful info.

In [5]:

train_data.describe()
#train_data.info()

Out[5]:

	winner	cluster_id	game_mode	game_type	hero_0	hero_1	hero_2	hero_3	hero_4	hero_5	...	hero_103	hero_104	hero_105	hero_106	hero_107	hero_108	hero_109	hero_110	hero_111	hero_112
count	92650.000000	92650.000000	92650.000000	92650.000000	92650.000000	92650.000000	92650.000000	92650.000000	92650.000000	92650.000000	...	92650.000000	92650.000000	92650.000000	92650.000000	92650.0	92650.000000	92650.000000	92650.000000	92650.000000	92650.000000
mean	0.053038	175.864145	3.317572	2.384587	-0.001630	-0.000971	0.000691	-0.000799	-0.002008	0.003173	...	-0.001371	-0.000950	0.000885	0.000594	0.0	0.001025	0.000648	-0.000227	-0.000043	0.000896
std	0.998598	35.658214	2.633070	0.486833	0.402004	0.467672	0.165052	0.355393	0.329348	0.483950	...	0.535024	0.206112	0.283985	0.155940	0.0	0.220703	0.204166	0.168707	0.189868	0.139033
min	-1.000000	111.000000	1.000000	1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000	...	-1.000000	-1.000000	-1.000000	-1.000000	0.0	-1.000000	-1.000000	-1.000000	-1.000000	-1.000000
25%	-1.000000	152.000000	2.000000	2.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000
50%	1.000000	156.000000	2.000000	2.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000
75%	1.000000	223.000000	2.000000	3.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000
max	1.000000	261.000000	9.000000	3.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	...	1.000000	1.000000	1.000000	1.000000	0.0	1.000000	1.000000	1.000000	1.000000	1.000000

8 rows × 117 columns

Split Data into Train and Validation¶

Now we want to see how well our classifier is performing, but we dont have the test data labels with us to check. What do we do ? So we split our dataset into train and validation. The idea is that we test our classifier on validation set in order to get an idea of how well our classifier works. This way we can also ensure that we dont overfit on the train dataset. There are many ways to do validation like k-fold,leave one out, etc

In [6]:

X = train_data.drop('winner',1)
y = train_data['winner']
# Validation testing
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)

Here we have selected the size of the validation data to be 20% of the total data. You can change it and see what effect it has on the accuracies. To learn more about the train_test_split function click here.

Define the Classifier and Train¶

Now we come to the juicy part. We have fixed our data and now we train a classifier. The classifier will learn the function by looking at the inputs and corresponding outputs. There are a ton of classifiers to choose from some being Logistic Regression, SVM, Random Forests, Decision Trees, etc.
Tip: A good model doesnt depend solely on the classifier but on the features(columns) you choose. So make sure to play with your data and keep only whats important.

In [2]:

classifier = LogisticRegression()
classifier.fit(X_train,y_train)

# from sklearn.svm import SVC
# clf = SVC(gamma='auto')
# clf.fit(X_train, y_train)

# from sklearn import tree
# clf = tree.DecisionTreeClassifier()
# clf = clf.fit(X_train, y_train)

We have used Logistic Regression as a classifier here and set few of the parameteres. But one can set more parameters and increase the performance. To see the list of parameters visit here.
Also given are SVM and Decision Tree examples. Check out SVM's parameters here and Decision Tree's here

got a warning! Dont worry, its just beacuse the number of iteration is very less(defined in the classifier in the above cell).Increase the number of iterations and see if the warning vanishes.Do remember increasing iterations also increases the running time.( Hint: max_iter=500)

Predict on Validation¶

Now we predict our trained classifier on the validation set and evaluate our model

In [9]:

y_pred = classifier.predict(X_val)

In [10]:

df = pd.DataFrame({'Actual': y_val, 'Predicted': y_pred})
df1 = df.head(25)
df1

Out[10]:

	Actual	Predicted
26389	1	-1
55196	-1	1
51250	-1	1
25508	1	-1
24128	1	-1
2442	-1	-1
5638	-1	-1
3714	-1	1
36579	-1	1
10399	-1	-1
13464	-1	-1
71600	-1	1
80162	1	-1
7077	1	1
63431	-1	1
78584	1	-1
31413	1	1
13393	1	1
90845	1	1
23339	-1	-1
13756	-1	1
63563	-1	-1
81880	-1	1
77591	-1	-1
23311	1	1

Evaluate the Performance¶

We use the same metrics as that will be used for the test set.
F1 score and ROC AUC are the metrics for this challenge

In [11]:

print('F1 score Score:', metrics.f1_score(y_val, y_pred))  
print('ROC AUC Score:', metrics.roc_auc_score(y_val, y_pred))

F1 score Error: 0.638888888888889
ROC AUC Error: 0.5928579002999843

Load Test Set¶

Load the test data now

In [12]:

test_data = pd.read_csv('test.csv')

Predict Test Set¶

Time for the moment of truth! Predict on test set and time to make the submission.

In [13]:

y_test = classifier.predict(test_data)

In [15]:

df = pd.DataFrame(y_test,columns=['winner'])
df.to_csv('submission.csv',index=False)

To download the generated csv in collab run the below command¶

In [ ]:

from google.colab import files
files.download('submission.csv')

To participate in the challenge click here