#$HIDE$
import pandas as pd
from sklearn.model_selection import train_test_split

# Read the data
data = pd.read_csv('../input/melbourne-housing-snapshot/melb_data.csv')

# Separate target from predictors
y = data.Price
X = data.drop(['Price'], axis=1)

# Divide data into training and validation subsets
X_train_full, X_valid_full, y_train, y_valid = train_test_split(X, y, train_size=0.8, test_size=0.2,
                                                                random_state=0)

# "Cardinality" means the number of unique values in a column
# Select categorical columns with relatively low cardinality (convenient but arbitrary)
categorical_cols = [cname for cname in X_train_full.columns if X_train_full[cname].nunique() < 10 and 
                        X_train_full[cname].dtype == "object"]

# Select numerical columns
numerical_cols = [cname for cname in X_train_full.columns if X_train_full[cname].dtype in ['int64', 'float64']]

# Keep selected columns only
my_cols = categorical_cols + numerical_cols
X_train = X_train_full[my_cols].copy()
X_valid = X_valid_full[my_cols].copy()

"""
Explanation: In this tutorial, you will learn how to use pipelines to clean up your modeling code.
Introduction
Pipelines are a simple way to keep your data preprocessing and modeling code organized.  Specifically, a pipeline bundles preprocessing and modeling steps so you can use the whole bundle as if it were a single step.
Many data scientists hack together models without pipelines, but pipelines have some important benefits. Those include:
1. Cleaner Code: Accounting for data at each step of preprocessing can get messy.  With a pipeline, you won't need to manually keep track of your training and validation data at each step.
2. Fewer Bugs: There are fewer opportunities to misapply a step or forget a preprocessing step.
3. Easier to Productionize: It can be surprisingly hard to transition a model from a prototype to something deployable at scale.  We won't go into the many related concerns here, but pipelines can help.
4. More Options for Model Validation: You will see an example in the next tutorial, which covers cross-validation.
Example
As in the previous tutorial, we will work with the Melbourne Housing dataset.  
We won't focus on the data loading step. Instead, you can imagine you are at a point where you already have the training and validation data in X_train, X_valid, y_train, and y_valid.
End of explanation
"""


X_train.head()

"""
Explanation: We take a peek at the training data with the head() method below.  Notice that the data contains both categorical data and columns with missing values.  With a pipeline, it's easy to deal with both!
End of explanation
"""


from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder

# Preprocessing for numerical data
numerical_transformer = SimpleImputer(strategy='constant')

# Preprocessing for categorical data
categorical_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='most_frequent')),
    ('onehot', OneHotEncoder(handle_unknown='ignore'))
])

# Bundle preprocessing for numerical and categorical data
preprocessor = ColumnTransformer(
    transformers=[
        ('num', numerical_transformer, numerical_cols),
        ('cat', categorical_transformer, categorical_cols)
    ])

"""
Explanation: We construct the full pipeline in three steps.
Step 1: Define Preprocessing Steps
Similar to how a pipeline bundles together preprocessing and modeling steps, we use the ColumnTransformer class to bundle together different preprocessing steps.  The code below:
- imputes missing values in numerical data, and
- imputes missing values and applies a one-hot encoding to categorical data.
End of explanation
"""


from sklearn.ensemble import RandomForestRegressor

model = RandomForestRegressor(n_estimators=100, random_state=0)

"""
Explanation: Step 2: Define the Model
Next, we define a random forest model with the familiar RandomForestRegressor class.
End of explanation
"""


from sklearn.metrics import mean_absolute_error

# Bundle preprocessing and modeling code in a pipeline
my_pipeline = Pipeline(steps=[('preprocessor', preprocessor),
                              ('model', model)
                             ])

# Preprocessing of training data, fit model 
my_pipeline.fit(X_train, y_train)

# Preprocessing of validation data, get predictions
preds = my_pipeline.predict(X_valid)

# Evaluate the model
score = mean_absolute_error(y_valid, preds)
print('MAE:', score)

"""
Explanation: Step 3: Create and Evaluate the Pipeline
Finally, we use the Pipeline class to define a pipeline that bundles the preprocessing and modeling steps.  There are a few important things to notice:
- With the pipeline, we preprocess the training data and fit the model in a single line of code.  (In contrast, without a pipeline, we have to do imputation, one-hot encoding, and model training in separate steps.  This becomes especially messy if we have to deal with both numerical and categorical variables!)
- With the pipeline, we supply the unprocessed features in X_valid to the predict() command, and the pipeline automatically preprocesses the features before generating predictions.  (However, without a pipeline, we have to remember to preprocess the validation data before making predictions.)
End of explanation
"""


