# Baseline for CRDSM Challenge

A getting started code with a simple SVM model for the challenge.

# Getting Started Code for CRDSM Educational Challenge¶

#### Author : Ayush Shivani¶

In [2]:
!pip install aicrowd-cli

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

In [3]:
API_KEY = "cf6330dec358de63587e9c9a3e7201e1" #Please enter your API Key from [https://www.aicrowd.com/participants/me]

API Key valid
Saved API Key successfully!

In [4]:
!aicrowd dataset download --challenge crdsm

train.csv: 100% 2.54M/2.54M [00:00<00:00, 5.49MB/s]
sample_submission.csv: 100% 606/606 [00:00<00:00, 70.6kB/s]
test.csv: 100% 72.1k/72.1k [00:00<00:00, 595kB/s]

In [5]:
!rm -rf data
!mkdir data
!mv train.csv data/train.csv
!mv test.csv data/test.csv


## Import packages¶

In [6]:
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.neural_network import MLPClassifier
from sklearn.svm import SVC
from sklearn.metrics import f1_score,precision_score,recall_score,accuracy_score


• We use pandas 🐼 library to load our data.
• Pandas loads the data into dataframes and facilitates us to analyse the data.
In [7]:
all_data_path = "data/train.csv" #path where data is stored

In [8]:
all_data = pd.read_csv(all_data_path) #load data in dataframe using pandas


## Visualize the data 👀¶

In [9]:
all_data.head()

Out[9]:
max_ndvi 20150720_N 20150602_N 20150517_N 20150501_N 20150415_N 20150330_N 20150314_N 20150226_N 20150210_N 20150125_N 20150109_N 20141117_N 20141101_N 20141016_N 20140930_N 20140813_N 20140626_N 20140610_N 20140525_N 20140509_N 20140423_N 20140407_N 20140322_N 20140218_N 20140202_N 20140117_N 20140101_N class
0 997.904 637.5950 658.668 -1882.030 -1924.36 997.904 -1739.990 630.087 -1628.240 -1325.64 -944.084 277.107 -206.7990 536.441 749.348 -482.993 492.001 655.770 -921.193 -1043.160 -1942.490 267.138 366.608 452.238 211.328 -2203.02 -1180.190 433.906 4
1 914.198 634.2400 593.705 -1625.790 -1672.32 914.198 -692.386 707.626 -1670.590 -1408.64 -989.285 214.200 -75.5979 893.439 401.281 -389.933 394.053 666.603 -954.719 -933.934 -625.385 120.059 364.858 476.972 220.878 -2250.00 -1360.560 524.075 4
2 3800.810 1671.3400 1206.880 449.735 1071.21 546.371 1077.840 214.564 849.599 1283.63 1304.910 542.100 922.6190 889.774 836.292 1824.160 1670.270 2307.220 1562.210 1566.160 2208.440 1056.600 385.203 300.560 293.730 2762.57 150.931 3800.810 4
3 952.178 58.0174 -1599.160 210.714 -1052.63 578.807 -1564.630 -858.390 729.790 -3162.14 -1521.680 433.396 228.1530 555.359 530.936 952.178 -1074.760 545.761 -1025.880 368.622 -1786.950 -1227.800 304.621 291.336 369.214 -2202.12 600.359 -1343.550 4
4 1232.120 72.5180 -1220.880 380.436 -1256.93 515.805 -1413.180 -802.942 683.254 -2829.40 -1267.540 461.025 317.5210 404.898 563.716 1232.120 -117.779 682.559 -1813.950 155.624 -1189.710 -924.073 432.150 282.833 298.320 -2197.36 626.379 -826.727 4

## Split Data into Train and Validation 🔪¶

• The next step is to think of a way to test how well our model is performing. we cannot use the test data given as it does not contain the data labels for us to verify.
• The workaround this is to split the given training data into training and validation. Typically validation sets give us an idea of how our model will perform on unforeseen data. it is like holding back a chunk of data while training our model and then using it to for the purpose of testing. it is a standard way to fine-tune hyperparameters in a model.
• There are multiple ways to split a dataset into validation and training sets. following are two popular ways to go about it, k-fold, leave one out. 🧐
• Validation sets are also used to avoid your model from overfitting on the train dataset.
In [10]:
X_train, X_val= train_test_split(all_data, test_size=0.2, random_state=42)

• We have decided to split the data with 20 % as validation and 80 % as training.
• This is of course the simplest way to validate your model by simply taking a random chunk of the train set and setting it aside solely for the purpose of testing our train model on unseen data. as mentioned in the previous block, you can experiment 🔬 with and choose more sophisticated techniques and make your model better.
• Now, since we have our data splitted into train and validation sets, we need to get the corresponding labels separated from the data.
• with this step we are all set move to the next step with a prepared dataset.
In [11]:
X_train,y_train = X_train.iloc[:,:-1],X_train.iloc[:,-1]
X_val,y_val = X_val.iloc[:,:-1],X_val.iloc[:,-1]


# TRAINING PHASE 🏋️¶

## Define the Model¶

• We have fixed our data and now we are ready to train our model.

• There are a ton of classifiers to choose from some being Logistic Regression, SVM, Random Forests, Decision Trees, etc.🧐

• Remember that there are no hard-laid rules here. you can mix and match classifiers, it is advisable to read up on the numerous techniques and choose the best fit for your solution , experimentation is the key.

• A good model does not depend solely on the classifier but also on the features you choose. So make sure to analyse and understand your data well and move forward with a clear view of the problem at hand. you can gain important insight from here.🧐

In [12]:
classifier = SVC(gamma='auto')

#from sklearn.linear_model import LogisticRegression
# classifier = LogisticRegression()

• To start you off, We have used a basic Support Vector Machines classifier here.
• But you can tune parameters and increase the performance. To see the list of parameters visit here.
• Do keep in mind there exist sophisticated techniques for everything, the key as quoted earlier is to search them and experiment to fit your implementation.

To read more about other sklearn classifiers visit here 🧐. Try and use other classifiers to see how the performance of your model changes. Try using Logistic Regression or MLP and compare how the performance changes.

## Train the Model¶

In [13]:
classifier.fit(X_train, y_train)

Out[13]:
SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,
decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',
max_iter=-1, probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False)

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 and also see how the performance changes.Do remember increasing iterations also increases the running time.( Hint: max_iter=500)

# Validation Phase 🤔¶

Wonder how well your model learned! Lets check it.

## Predict on Validation¶

Now we predict using our trained model on the validation set we created and evaluate our model on unforeseen data.

In [14]:
y_pred = classifier.predict(X_val)


## Evaluate the Performance¶

• We have used basic metrics to quantify the performance of our model.
• This is a crucial step, you should reason out the metrics and take hints to improve aspects of your model.
• Do read up on the meaning and use of different metrics. there exist more metrics and measures, you should learn to use them correctly with respect to the solution,dataset and other factors.
• F1 score and Log Loss are the metrics for this challenge
In [15]:
precision = precision_score(y_val,y_pred,average='micro')
recall = recall_score(y_val,y_pred,average='micro')
accuracy = accuracy_score(y_val,y_pred)
f1 = f1_score(y_val,y_pred,average='macro')

In [16]:
print("Accuracy of the model is :" ,accuracy)
print("Recall of the model is :" ,recall)
print("Precision of the model is :" ,precision)
print("F1 score of the model is :" ,f1)

Accuracy of the model is : 0.7140825035561877
Recall of the model is : 0.7140825035561877
Precision of the model is : 0.7140825035561877
F1 score of the model is : 0.138865836791148


# Testing Phase 😅¶

We are almost done. We trained and validated on the training data. Now its the time to predict on test set and make a submission.

Load the test data on which final submission is to be made.

In [17]:
final_test_path = "data/test.csv"


## Predict Test Set¶

Predict on the test set and you are all set to make the submission !

In [18]:
submission = classifier.predict(final_test)


## Save the prediction to csv¶

In [21]:
submission = pd.DataFrame(submission)


### Making Direct Submission thought Aicrowd CLI¶

In [22]:
!aicrowd submission create -c crdsm -f submission.csv

submission.csv ━━━━━━━━━━━━━━━━━━━━━━━━━━ 100.0% • 2,251/606 bytes • ? • 0:00:00
╭─────────────────────────╮
│ Successfully submitted! │
╰─────────────────────────╯
┌──────────────────┬──────────────────────────────────────────────────────────────────────────┐
│  This submission │ https://www.aicrowd.com/challenges/crdsm/submissions/128432              │
│                  │                                                                          │
│  All submissions │ https://www.aicrowd.com/challenges/crdsm/submissions?my_submissions=true │
│                  │                                                                          │
│                  │                                                                          │
│ Discussion forum │ https://discourse.aicrowd.com/c/crdsm                                    │
│                  │                                                                          │
│   Challenge page │ https://www.aicrowd.com/challenges/crdsm                                 │
└──────────────────┴──────────────────────────────────────────────────────────────────────────┘
{'submission_id': 128432, 'created_at': '2021-04-06T10:18:18.737Z'}

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