scikit-learn - predict trained model on new input

Question

I have a dataset like below:

| "Consignor Code" | "Consignee Code" | "Origin" | "Destination" | "Carrier Code" | 
|------------------|------------------|----------|---------------|----------------| 
| "6402106844"     | "66903717"       | "DKCPH"  | "CNPVG"       | "6402746387"   | 
| "6402106844"     | "66903717"       | "DKCPH"  | "CNPVG"       | "6402746387"   | 
| "6402106844"     | "6404814143"     | "DKCPH"  | "CNPVG"       | "6402746387"   | 
| "6402107662"     | "66974631"       | "DKCPH"  | "VNSGN"       | "6402746393"   | 
| "6402107662"     | "6404518090"     | "DKCPH"  | "THBKK"       | "6402746393"   | 
| "6402107662"     | "6404518090"     | "DKBLL"  | "THBKK"       | "6402746393"   | 
| "6408507648"     | "6403601344"     | "DKCPH"  | "USTPA"       | "66565231"     | 

I am trying to build my very first ML model on it. For that, I am using scikit-learn. This is my code:

#Import the dependencies
from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import make_scorer, accuracy_score
from sklearn.model_selection import cross_val_score, train_test_split
from sklearn.externals import joblib
from sklearn import preprocessing
import pandas as pd

#Import the dataset (A CSV file)
dataset = pd.read_csv('shipments.csv', header=0, skip_blank_lines=True)
#Drop any rows containing NaN values
dataset.dropna(subset=['Consignor Code', 'Consignee Code',
                       'Origin', 'Destination', 'Carrier Code'], inplace=True)

#Convert the numeric only cells to strings
dataset['Consignor Code'] = dataset['Consignor Code'].astype('int64')
dataset['Consignee Code'] = dataset['Consignee Code'].astype('int64')
dataset['Carrier Code'] = dataset['Carrier Code'].astype('int64')

#Define our target (What we want to be able to predict)
target = dataset.pop('Destination')

#Convert all our data to numeric values, so we can use the .fit function.
#For that, we use LabelEncoder
le = preprocessing.LabelEncoder()
target = le.fit_transform(list(target))
dataset['Origin'] = le.fit_transform(list(dataset['Origin']))
dataset['Consignor Code'] = le.fit_transform(list(dataset['Consignor Code']))
dataset['Consignee Code'] = le.fit_transform(list(dataset['Consignee Code']))
dataset['Carrier Code'] = le.fit_transform(list(dataset['Carrier Code']))

#Prepare the dataset.
X_train, X_test, y_train, y_test = train_test_split(
    dataset, target, test_size=0.3, random_state=0)


#Prepare the model and .fit it.
model = RandomForestClassifier()
model.fit(X_train, y_train)

#Make a prediction on the test set.
predictions = model.predict(X_test)

#Print the accuracy score.
print("Accuracy score: {}".format(accuracy_score(y_test, predictions)))

Now above code returns:

Accuracy score: 0.7172413793103448

Now my question might be stupid - but how can I use my model to actually show me what it predicts on new data?

Consider below new input , and I want it to predict the Destination :

"6408507648","6403601344","DKCPH","","66565231"

How can query my model with this data and get the predicted Destination out?

Answer 1

Here you have a full working example with the prediction included. The most important part is to define the different label encoders for each feature, so you can fit the new data with the same encoding, otherwise you will run into errors (which might now show, but you will notice when you compute accuracy):

dataset = pd.DataFrame({'Consignor Code':["6402106844","6402106844","6402106844","6402107662","6402107662","6402107662","6408507648"],
                   'Consignee Code': ["66903717","66903717","6404814143","66974631","6404518090","6404518090","6403601344"],
                   'Origin':["DKCPH","DKCPH","DKCPH","DKCPH","DKCPH","DKBLL","DKCPH"],
                   'Destination':["CNPVG","CNPVG","CNPVG","VNSGN","THBKK","THBKK","USTPA"],
                   'Carrier Code':["6402746387","6402746387","6402746387","6402746393","6402746393","6402746393","66565231"]})

from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import make_scorer, accuracy_score
from sklearn.model_selection import cross_val_score, train_test_split
from sklearn.externals import joblib
from sklearn import preprocessing
import pandas as pd

#Import the dataset (A CSV file)
#Drop any rows containing NaN values
dataset.dropna(subset=['Consignor Code', 'Consignee Code',
                       'Origin', 'Destination', 'Carrier Code'], inplace=True)


#Define our target (What we want to be able to predict)
target = dataset.pop('Destination')

#Convert all our data to numeric values, so we can use the .fit function.
#For that, we use LabelEncoder
le_origin = preprocessing.LabelEncoder()
le_consignor = preprocessing.LabelEncoder()
le_consignee = preprocessing.LabelEncoder()
le_carrier = preprocessing.LabelEncoder()
le_target = preprocessing.LabelEncoder()
target = le_target.fit_transform(list(target))
dataset['Origin'] = le_origin.fit_transform(list(dataset['Origin']))
dataset['Consignor Code'] = le_consignor.fit_transform(list(dataset['Consignor Code']))
dataset['Consignee Code'] = le_consignee.fit_transform(list(dataset['Consignee Code']))
dataset['Carrier Code'] = le_carrier.fit_transform(list(dataset['Carrier Code']))

#Prepare the dataset.
X_train, X_test, y_train, y_test = train_test_split(
    dataset, target, test_size=0.3, random_state=42)


#Prepare the model and .fit it.
model = RandomForestClassifier(random_state=42)
model.fit(X_train, y_train)

#Make a prediction on the test set.
predictions = model.predict(X_test)

#Print the accuracy score.
print("Accuracy score: {}".format(accuracy_score(y_test, predictions)))

new_input = ["6408507648","6403601344","DKCPH","66565231"]
fitted_new_input = np.array([le_consignor.transform([new_input[0]])[0],
                                le_consignee.transform([new_input[1]])[0],
                                le_origin.transform([new_input[2]])[0],
                                le_carrier.transform([new_input[3]])[0]])
new_predictions = model.predict(fitted_new_input.reshape(1,-1))

print(le_target.inverse_transform(new_predictions))

Finally, your tree predicts:

['THBKK']

Answer 2

Here's something quick to illustrate the point. I wouldn't do it this way in practice and there are probably some errors. For example, I think this will fail if there are unseen classes in the test set.

#Prepare the dataset.
X_train, X_test, y_train, y_test = train_test_split(
    dataset, target, test_size=0.3, random_state=0)

#Convert all our data to numeric values, so we can use the .fit function.
#For that, we use LabelEncoder
le_target = preprocessing.LabelEncoder()
y_train = le_target.fit_transform(y_train)
y_test = le_target.transform(y_test)

# Now create a separate encoder for each of your features:
encoders = {}
for feature in ["Origin", "Consignor Code", "Consignee Code", "Carrier Code"]:
# NOTE: The LabelEncoder docs state clearly at the start that you shouldn't be using it on your inputs. I'm not going to get into that here though but just be aware that it's not a good encoding.
    encoders[feature] = preprocessing.LabelEncoder()
    X_train[feature] = encoders[feature].fit_transform(X_train[feature])
    X_test[feature] = encoders[feature].transform(X_test[feature])    

#Prepare the model and .fit it.
model = RandomForestClassifier()
model.fit(X_train, y_train)

#Make a prediction on the test set.
predictions = model.predict(X_test)

le_target.inverse_transform(predictions)

The key concepts for you here are to use separate encoders for your features because these encoder objects remember how to encode that feature. This is done in the fit stage. You then need to call transform on any new data to encode that correctly.