#!/usr/bin/env python
# coding: utf-8

# ## Outlook
# ### Approaching a machine learning problem
# ### Humans in the loop

# ### From prototype to production

# ### Testing production systems

# ### Building your own estimator

# In[1]:


from sklearn.base import BaseEstimator, TransformerMixin

class MyTransformer(BaseEstimator, TransformerMixin):
    def __init__(self, first_paramter=1, second_parameter=2):
        # all parameters must be specified in the __init__ function
        self.first_paramter = 1
        self.second_parameter = 2
        
    def fit(self, X, y=None):
        # fit should only take X and y as parameters
        # even if your model is unsupervised, you need to accept a y argument!
        
        # Model fitting code goes here
        print("fitting the model right here")
        # fit returns self
        return self
    
    def transform(self, X):
        # transform takes as parameter only X
        
        # apply some transformation to X:
        X_transformed = X + 1
        return X_transformed


# ### Where to go from here
# #### Theory
# #### Other machine learning frameworks and packages
# #### Ranking, recommender systems, time series, and other kinds of learning
# #### Probabilistic modeling, inference and probabilistic programming

# #### Neural Networks

# #### Scaling to larger datasets

# #### Honing your skills

# #### Conclusion