Doing Data Science: A Kaggle Walkthrough Part 6 – Creating a Model
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In the final part of this 6 part series on the process of data science, and applying it to a Kaggle competition, building the predictive models is covered, and multiple algorithms are discussed.
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Creating the Model
Back to the modelling – now that we know what algorithm we are using (XGBoost algorithm for those skipping ahead), let talk about the approach.
Cross Validation
As mentioned in regards to decision trees, one of the keys risks when creating models of any type is the risk of overfitting. One of the primary ways data scientists will guard against overfitting is to estimate the accuracy of their models on data that was not used to train the model. To do this they typically use a method called cross validation. There are different methods for doing cross validation, but the method we will employ is called k-fold cross validation.
k-fold cross validation involves splitting the training data into k subsets (where k is greater than or equal to 2), training the model using k – 1 of those subsets, then running the model on the subset that was not used in the training process. Because all of the data used in the cross validation process is training data, the correct classification for each record is known and so the predicted category can be compared to the actual category. Once all folds have been completed, the average score across all folds is taken as an estimate of how the model will perform on other data. An example of a 3-fold cross validation is shown below:
Parameter Tuning
As you may have realized from the earlier description of the XGBoost algorithm – there are quite a few options (parameters) that we need to define to build the model. How many trees to build? How deep should each tree be? How much extra weight will be attached to each misclassified record? Tuning these parameters to get the best results from the model is often one of the most time consuming things that data scientists do. Fortunately, the process can be automated to a large degree so that we do not have to sit there rerunning the model repeatedly and noting down the results. Even better, using the Scikit-Learn package, we can merge the parameter tuning and cross validation steps into one, allowing us to search for the best combination of parameters while using k-fold cross validation to verify the results.
Training the Model
In order to train the model (using cross validation and parameter tuning as outlined above), we first need to define our training dataset – remembering that we previously combined the training and test data to simplify the cleaning and transforming process. To feed these into the model, we also need to split the training data into the three main components – the user IDs (we don’t want to use these for training as they are randomly generated), the features to use for training (X), and the categories we are trying to predict (y).
# Prepare training data for modelling df_train.set_index('id', inplace=True) df_train = pd.concat([df_train['country_destination'], df_all], axis=1, join='inner') id_train = df_train.index.values labels = df_train['country_destination'] le = LabelEncoder() y = le.fit_transform(labels) X = df_train.drop('country_destination', axis=1, inplace=False)
Now that we have our training data ready, we can use GridSearchCV to run the algorithm with a range of parameters, then select the model that has the highest cross validated score based on the chosen measure of a performance (in this case accuracy, but there are a range of metrics we could use based on our needs).
# Grid Search - Used to find best combination of parameters XGB_model = xgb.XGBClassifier(objective='multi:softprob', subsample=0.5, colsample_bytree=0.5, seed=0) param_grid = {'max_depth': [3, 4, 5], 'learning_rate': [0.1, 0.3], 'n_estimators': [25, 50]} model = grid_search.GridSearchCV(estimator=XGB_model, param_grid=param_grid, scoring='accuracy', verbose=10, n_jobs=1, iid=True, refit=True, cv=3) model.fit(X, y) print("Best score: %0.3f" % model.best_score_) print("Best parameters set:") best_parameters = model.best_estimator_.get_params() for param_name in sorted(param_grid.keys()): print("\t%s: %r" % (param_name, best_parameters[param_name]))
Please note that running this step can take a significant amount of time. Running the algorithm with 25 trees takes around 2.5 minutes for each cross validation on my Macbook Pro. Running the script above with all the options specified will likely take well over an hour.
Making the Predictions
Now that we have trained a model based on the best parameters, the next step is to use the model to make predictions for the records in the testing dataset. Again we need to extract the testing data out of the combined dataset we created for the cleaning and transformation steps, and again we need to separate the main components for the model. After these steps, we use the model created in the previous step to make the predictions.
# Prepare test data for prediction df_test.set_index('id', inplace=True) df_test = pd.merge(df_test.loc[:,['date_first_booking']], df_all, how='left', left_index=True, right_index=True, sort=False) X_test = df_test.drop('date_first_booking', axis=1, inplace=False) X_test = X_test.fillna(-1) id_test = df_test.index.values # Make predictions y_pred = model.predict_proba(X_test)
As you may have noted from the code above, we have used the predict_proba method instead of the usual predict method. This is done because of the way Kaggle will assess the results for this particular competition. Rather than just assessing one prediction for each user, Kaggle will assess up to 5 predictions for each user. In order to maximize the score, we will use the predicted probabilities that predict_proba produces to select the 5 best predictions. Finally, we will write these results to a file that will be created in the same folder as the script.
#Taking the 5 classes with highest probabilities ids = [] #list of ids cts = [] #list of countries for i in range(len(id_test)): idx = id_test[i] ids += [idx] * 5 cts += le.inverse_transform(np.argsort(y_pred[i])[::-1])[:5].tolist() #Generate submission print("Outputting final results...") sub = pd.DataFrame(np.column_stack((ids, cts)), columns=['id', 'country']) sub.to_csv('./submission.csv', index=False)
For those that wish to, you should be able to submit the file produced from this script on Kaggle. The competition is now finished and you will not receive an official position on the leaderboard, but your results will be processed and you will be told where you would have finished.
Wrapping Up
Those that are more experienced with data science may realize this series, as lengthy as it is, does not even scratch the surface of a lot of topics related to data science. Unsupervised learning, association rules mining, text analytics and deep learning are all topics that have not been covered at all. Unfortunately, the full scope of data science and machine learning are not something that can be covered in a blog. That said, I did have two goals for those reading these blog articles.
Firstly, I hope that this series demystifies some aspects of data science for those that currently see it as a black box. Although one can spend their career working in data science and still not master all aspects, even a cursory understanding of how machine learning algorithms work can help provide understanding as to what sort of questions machine learning can help to answer, and what sort of questions are problematic.
Secondly, I hope this series encourages some of you to dig deeper, to learn more about this topic. Machine learning is a rapidly growing field that is expanding to every aspect of life. This includes, recommendation engines on websites, astronomy – where it helps to identify stars and planets, the pharmaceutical industry – where it is being used to predict which molecular structures that are likely to produce useful drugs, and maybe most famously, in training self‑driving cars to drive in the real world. Whatever your primary interest, there is likely to be some machine learning applications being developed or being used already.
[1] There are a range of metrics that can be used to do this. For available metrics in the Scikit Learn package, see here.
Full script:
import pandas as pd import numpy as np import xgboost as xgb from sklearn import cross_validation, decomposition, grid_search from sklearn.preprocessing import LabelEncoder #################################################### # Functions # #################################################### # Remove outliers def remove_outliers(df, column, min_val, max_val): col_values = df[column].values df[column] = np.where(np.logical_or(col_values<=min_val, col_values>=max_val), np.NaN, col_values) return df # Home made One Hot Encoder def convert_to_binary(df, column_to_convert): categories = list(df[column_to_convert].drop_duplicates()) for category in categories: cat_name = str(category).replace(" ", "_").replace("(", "").replace(")", "").replace("/", "_").replace("-", "").lower() col_name = column_to_convert[:5] + '_' + cat_name[:10] df[col_name] = 0 df.loc[(df[column_to_convert] == category), col_name] = 1 return df # Count occurrences of value in a column def convert_to_counts(df, id_col, column_to_convert): id_list = df[id_col].drop_duplicates() df_counts = df.loc[:,[id_col, column_to_convert]] df_counts['count'] = 1 df_counts = df_counts.groupby(by=[id_col, column_to_convert], as_index=False, sort=False).sum() new_df = df_counts.pivot(index=id_col, columns=column_to_convert, values='count') new_df = new_df.fillna(0) # Rename Columns categories = list(df[column_to_convert].drop_duplicates()) for category in categories: cat_name = str(category).replace(" ", "_").replace("(", "").replace(")", "").replace("/", "_").replace("-", "").lower() col_name = column_to_convert + '_' + cat_name new_df.rename(columns = {category:col_name}, inplace=True) return new_df #################################################### # Cleaning # #################################################### # Import data print("Reading in data...") tr_filepath = "./train_users_2.csv" df_train = pd.read_csv(tr_filepath, header=0, index_col=None) te_filepath = "./test_users.csv" df_test = pd.read_csv(te_filepath, header=0, index_col=None) # Combine into one dataset df_all = pd.concat((df_train, df_test), axis=0, ignore_index=True) # Change Dates to consistent format print("Fixing timestamps...") df_all['date_account_created'] = pd.to_datetime(df_all['date_account_created'], format='%Y-%m-%d') df_all['timestamp_first_active'] = pd.to_datetime(df_all['timestamp_first_active'], format='%Y%m%d%H%M%S') df_all['date_account_created'].fillna(df_all.timestamp_first_active, inplace=True) # Remove date_first_booking column df_all.drop('date_first_booking', axis=1, inplace=True) # Fixing age column print("Fixing age column...") df_all = remove_outliers(df=df_all, column='age', min_val=15, max_val=90) df_all['age'].fillna(-1, inplace=True) # Fill first_affiliate_tracked column print("Filling first_affiliate_tracked column...") df_all['first_affiliate_tracked'].fillna(-1, inplace=True) #################################################### # Data Transformation # #################################################### # One Hot Encoding print("One Hot Encoding categorical data...") columns_to_convert = ['gender', 'signup_method', 'signup_flow', 'language', 'affiliate_channel', 'affiliate_provider', 'first_affiliate_tracked', 'signup_app', 'first_device_type', 'first_browser'] for column in columns_to_convert: df_all = convert_to_binary(df=df_all, column_to_convert=column) df_all.drop(column, axis=1, inplace=True) #################################################### # Feature Extraction # #################################################### # Add new date related fields print("Adding new fields...") df_all['day_account_created'] = df_all['date_account_created'].dt.weekday df_all['month_account_created'] = df_all['date_account_created'].dt.month df_all['quarter_account_created'] = df_all['date_account_created'].dt.quarter df_all['year_account_created'] = df_all['date_account_created'].dt.year df_all['hour_first_active'] = df_all['timestamp_first_active'].dt.hour df_all['day_first_active'] = df_all['timestamp_first_active'].dt.weekday df_all['month_first_active'] = df_all['timestamp_first_active'].dt.month df_all['quarter_first_active'] = df_all['timestamp_first_active'].dt.quarter df_all['year_first_active'] = df_all['timestamp_first_active'].dt.year df_all['created_less_active'] = (df_all['date_account_created'] - df_all['timestamp_first_active']).dt.days # Drop unnecessary columns columns_to_drop = ['date_account_created', 'timestamp_first_active', 'date_first_booking', 'country_destination'] for column in columns_to_drop: if column in df_all.columns: df_all.drop(column, axis=1, inplace=True) #################################################### # Add data from sessions.csv # #################################################### # Import sessions data s_filepath = "./sessions.csv" sessions = pd.read_csv(s_filepath, header=0, index_col=False) # Determine primary device print("Determing primary device...") sessions_device = sessions.loc[:, ['user_id', 'device_type', 'secs_elapsed']] aggregated_lvl1 = sessions_device.groupby(['user_id', 'device_type'], as_index=False, sort=False).aggregate(np.sum) idx = aggregated_lvl1.groupby(['user_id'], sort=False)['secs_elapsed'].transform(max) == aggregated_lvl1['secs_elapsed'] df_primary = pd.DataFrame(aggregated_lvl1.loc[idx , ['user_id', 'device_type', 'secs_elapsed']]) df_primary.rename(columns = {'device_type':'primary_device', 'secs_elapsed':'primary_secs'}, inplace=True) df_primary = convert_to_binary(df=df_primary, column_to_convert='primary_device') df_primary.drop('primary_device', axis=1, inplace=True) # Determine Secondary device print("Determing secondary device...") remaining = aggregated_lvl1.drop(aggregated_lvl1.index[idx]) idx = remaining.groupby(['user_id'], sort=False)['secs_elapsed'].transform(max) == remaining['secs_elapsed'] df_secondary = pd.DataFrame(remaining.loc[idx , ['user_id', 'device_type', 'secs_elapsed']]) df_secondary.rename(columns = {'device_type':'secondary_device', 'secs_elapsed':'secondary_secs'}, inplace=True) df_secondary = convert_to_binary(df=df_secondary, column_to_convert='secondary_device') df_secondary.drop('secondary_device', axis=1, inplace=True) # Aggregate and combine actions taken columns print("Aggregating actions taken...") session_actions = sessions.loc[:,['user_id', 'action', 'action_type', 'action_detail']] columns_to_convert = ['action', 'action_type', 'action_detail'] session_actions = session_actions.fillna('not provided') first = True for column in columns_to_convert: print("Converting " + column + " column...") current_data = convert_to_counts(df=session_actions, id_col='user_id', column_to_convert=column) # If first loop, current data becomes existing data, otherwise merge existing and current if first: first = False actions_data = current_data else: actions_data = pd.concat([actions_data, current_data], axis=1, join='inner') # Merge device datasets print("Combining results...") df_primary.set_index('user_id', inplace=True) df_secondary.set_index('user_id', inplace=True) device_data = pd.concat([df_primary, df_secondary], axis=1, join="outer") # Merge device and actions datasets combined_results = pd.concat([device_data, actions_data], axis=1, join='outer') df_sessions = combined_results.fillna(0) # Merge user and session datasets df_all.set_index('id', inplace=True) df_all = pd.concat([df_all, df_sessions], axis=1, join='inner') #################################################### # Building Model # #################################################### # Prepare training data for modelling df_train.set_index('id', inplace=True) df_train = pd.concat([df_train['country_destination'], df_all], axis=1, join='inner') id_train = df_train.index.values labels = df_train['country_destination'] le = LabelEncoder() y = le.fit_transform(labels) X = df_train.drop('country_destination', axis=1, inplace=False) # Training model print("Training model...") # Grid Search - Used to find best combination of parameters XGB_model = xgb.XGBClassifier(objective='multi:softprob', subsample=0.5, colsample_bytree=0.5, seed=0) param_grid = {'max_depth': [3, 4], 'learning_rate': [0.1, 0.3], 'n_estimators': [25, 50]} model = grid_search.GridSearchCV(estimator=XGB_model, param_grid=param_grid, scoring='accuracy', verbose=10, n_jobs=1, iid=True, refit=True, cv=3) model.fit(X, y) print("Best score: %0.3f" % model.best_score_) print("Best parameters set:") best_parameters = model.best_estimator_.get_params() for param_name in sorted(param_grid.keys()): print("\t%s: %r" % (param_name, best_parameters[param_name])) #################################################### # Make predictions # #################################################### print("Making predictions...") # Prepare test data for prediction df_test.set_index('id', inplace=True) df_test = pd.merge(df_test.loc[:,['date_first_booking']], df_all, how='left', left_index=True, right_index=True, sort=False) X_test = df_test.drop('date_first_booking', axis=1, inplace=False) X_test = X_test.fillna(-1) id_test = df_test.index.values # Make predictions y_pred = model.predict_proba(X_test) #Taking the 5 classes with highest probabilities ids = [] #list of ids cts = [] #list of countries for i in range(len(id_test)): idx = id_test[i] ids += [idx] * 5 cts += le.inverse_transform(np.argsort(y_pred[i])[::-1])[:5].tolist() #Generate submission print("Outputting final results...") sub = pd.DataFrame(np.column_stack((ids, cts)), columns=['id', 'country'])
Bio: Brett Romero is a data analyst with experience working in a number of countries and industries, including government, management consulting and finance. Currently based in Pristina, Kosovo, he is working as a data consultant with development agencies such as UNDP and Open Data Kosovo.
Original. Reposted with permission.
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