Same form of dataset has 2 different shapes

I am quite new to Machine Learning and am just grasping the techniques. As such, I am trying to train a model on the following classifiers using a dataset that has 4 features and the target feature/class (the truth value 1 or 0).

Classifiers

SGD Classifier
Random Forest Classifier
Linear Support Vector Classifier
Gaussian Process Classifier

I am training the model on the following dataset [Part of the dataset is shown below].

Training set : train_sop_truth.csv

Subject,Predicate,Object,Computed,Truth
concept:sportsteam:hawks,concept:teamplaysincity,concept:city:atlanta,0.4255912602,1
concept:stadiumoreventvenue:honda+AF8-center,concept:stadiumlocatedincity,concept:city:anaheim,0.4276425838,1
concept:sportsteam:ducks,concept:teamplaysincity,concept:city:anaheim,0.4762486517,1
concept:sportsteam:n1985+AF8-chicago+AF8-bears,concept:teamplaysincity,concept:city:chicago,0.4106097221,1
concept:stadiumoreventvenue:philips+AF8-arena,concept:stadiumlocatedincity,concept:city:atlanta,0.4190083146,1
concept:stadiumoreventvenue:united+AF8-center,concept:stadiumlocatedincity,concept:city:chicago,0.4211134315,1

The test dataset is in another .csv file as test_sop_truth.csv.

Testing set : test_sop_truth.csv

Subject,Predicate,Object,Computed,Truth
Nigel_Cole,isMarriedTo,Kate_Isitt,0.9350595474,1
Véra_Clouzot,isMarriedTo,Henri-Georges_Clouzot,0.4773990512,1
Norodom_Sihanouk,produced,The_Last_Days_of_Colonel_Savath,0.3942225575,1
Farouk_of_Egypt,isMarriedTo,Farida_of_Egypt,0.4276426733,1

Then I wanted to check the shape of the features for each and expected to see the same number of features as I am applying the same transformations to both datasets. But they differed.

Python Code

import pandas as pd
import numpy as np
from termcolor import colored

features = pd.read_csv('../Data/train_sop_truth.csv')
testFeatures = pd.read_csv('../Data/test_sop_truth.csv')
print(features.head(5))

print(colored('\nThe shape of our features is:','green'), features.shape)
print(colored('\nThe shape of our Test features is:','green'), testFeatures.shape)

print()
print(colored('\n     DESCRIPTIVE STATISTICS\n','yellow'))
print(colored(features.describe(),'cyan'))
print()
print(colored(testFeatures.describe(),'cyan'))


features = pd.get_dummies(features)
testFeatures = pd.get_dummies(testFeatures)

features.iloc[:,5:].head(5)
testFeatures.iloc[:,5].head(5)

labels = np.array(features['Truth'])
testlabels = np.array(testFeatures['Truth'])


features= features.drop('Truth', axis = 1)
testFeatures = testFeatures.drop('Truth', axis = 1)

feature_list = list(features.columns)
testFeature_list = list(testFeatures.columns)

features = np.array(features)
testFeatures = np.array(testFeatures)

train_samples = 100


testX_train, textX_test, testy_train, testy_test = model_selection.train_test_split(testFeatures, testlabels, test_size=0.25, random_state = 42)

X_train, X_test, y_train, y_test = model_selection.train_test_split(features, labels, test_size = 0.25, random_state = 42)

print(colored('\n    TRAINING & TESTING SETS','yellow'))
print(colored('\nTraining Features Shape:','magenta'), X_train.shape)
print(colored('Training Labels Shape:','magenta'), X_test.shape)
print(colored('Testing Features Shape:','magenta'), y_train.shape)
print(colored('Testing Labels Shape:','magenta'), y_test.shape)

print()

print(colored('\n    TRAINING & TESTING SETS','yellow'))
print(colored('\nTraining Features Shape:','magenta'), testX_train.shape)
print(colored('Training Labels Shape:','magenta'), textX_test.shape)
print(colored('Testing Features Shape:','magenta'), testy_train.shape)
print(colored('Testing Labels Shape:','magenta'), testy_test.shape)

Output

The shape of our features is: (1860, 5)

The shape of our Test features is: (1386, 5)


     DESCRIPTIVE STATISTICS

          Computed        Truth
count  1860.000000  1860.000000
mean      0.443222     0.913441
std       0.110788     0.281264
min       0.000000     0.000000
25%       0.418164     1.000000
50%       0.427643     1.000000
75%       0.450023     1.000000
max       1.000000     1.000000

          Computed        Truth
count  1386.000000  1386.000000
mean      0.511809     0.992063
std       0.197954     0.088765
min       0.009042     0.000000
25%       0.418649     1.000000
50%       0.429140     1.000000
75%       0.515809     1.000000
max       1.702856     1.000000

    TRAINING & TESTING SETS

Training Features Shape: (1395, 1045)
Training Labels Shape: (465, 1045)
Testing Features Shape: (1395,)
Testing Labels Shape: (465,)


    TRAINING & TESTING SETS

Training Features Shape: (1039, 1790)
Training Labels Shape: (347, 1790)
Testing Features Shape: (1039,)
Testing Labels Shape: (347,)

What I do not understand here is how the feature shape can be different as 1045 for the features(training set) and 1790 for the testFeatures (testing set), despite undergoing the same transformations and having the same number of features and form of features in the csv files.

Any suggestions or clarifications in this regard will be much appreciated.

when you apply get_dummies for the test dataset you might have got few columns added or deleted based on the data values of the categorical variables.

def add_missing_dummy_columns( d, columns ):
        missing_cols = set( columns ) - set( d.columns )
        for c in missing_cols:
            d[c] = 0

def fix_columns( d, columns ):  

    add_missing_dummy_columns( d, columns )

    # make sure we have all the columns we need
    assert( set( columns ) - set( d.columns ) == set())

    extra_cols = set( d.columns ) - set( columns )
    if extra_cols: print ("extra columns:", extra_cols)

    d = d[ columns ]
    return d

testFeatures= fix_columns( testFeatures, features.columns )